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import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase_ = 16
lowerCAmelCase_ = 32
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = 16 ) -> Optional[int]:
lowercase__ : Tuple = AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowercase__ : List[Any] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : Dict = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=lowerCamelCase_ , max_length=lowerCamelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase__ : str = datasets.map(
lowerCamelCase_ , batched=lowerCamelCase_ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : Dict = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ : str = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase__ : str = 16
elif accelerator.mixed_precision != "no":
lowercase__ : List[Any] = 8
else:
lowercase__ : Tuple = None
return tokenizer.pad(
lowerCamelCase_ , padding='''longest''' , max_length=lowerCamelCase_ , pad_to_multiple_of=lowerCamelCase_ , return_tensors='''pt''' , )
# Instantiate dataloaders.
lowercase__ : Any = DataLoader(
tokenized_datasets['''train'''] , shuffle=lowerCamelCase_ , collate_fn=lowerCamelCase_ , batch_size=lowerCamelCase_ )
lowercase__ : Optional[int] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=lowerCamelCase_ , collate_fn=lowerCamelCase_ , batch_size=lowerCamelCase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowerCAmelCase_ = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Dict:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , lowerCamelCase_ ) == "1":
lowercase__ : List[Any] = 2
# Initialize accelerator
lowercase__ : Any = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : Optional[Any] = config["""lr"""]
lowercase__ : List[str] = int(config['''num_epochs'''] )
lowercase__ : Optional[Any] = int(config['''seed'''] )
lowercase__ : int = int(config['''batch_size'''] )
lowercase__ : Union[str, Any] = evaluate.load('''glue''' , '''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowerCamelCase_ )
def inner_training_loop(__lowerCamelCase ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowerCamelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=lowerCamelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase__ : List[Any] = model.to(accelerator.device )
# Instantiate optimizer
lowercase__ : int = AdamW(params=model.parameters() , lr=lowerCamelCase_ )
lowercase__ : Union[str, Any] = get_dataloaders(lowerCamelCase_ , lowerCamelCase_ )
# Instantiate scheduler
lowercase__ : int = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase_ , num_warmup_steps=1_00 , num_training_steps=(len(lowerCamelCase_ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase__ : List[Any] = accelerator.prepare(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# Now we train the model
for epoch in range(lowerCamelCase_ ):
model.train()
for step, batch in enumerate(lowerCamelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase__ : List[Any] = model(**lowerCamelCase_ )
lowercase__ : Dict = outputs.loss
accelerator.backward(lowerCamelCase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCamelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ : str = model(**lowerCamelCase_ )
lowercase__ : int = outputs.logits.argmax(dim=-1 )
lowercase__ : Dict = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=lowerCamelCase_ , references=lowerCamelCase_ , )
lowercase__ : Dict = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , lowerCamelCase_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( ) -> Dict:
lowercase__ : Tuple = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=lowerCamelCase_ , default=lowerCamelCase_ , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
lowercase__ : Optional[Any] = parser.parse_args()
lowercase__ : int = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowerCamelCase_ , lowerCamelCase_ )
if __name__ == "__main__":
main()
| 358
|
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAmelCase_ = _symbol_database.Default()
lowerCAmelCase_ = _descriptor_pool.Default().AddSerializedFile(
B'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
lowerCAmelCase_ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
lowerCAmelCase_ = None
lowerCAmelCase_ = B'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
lowerCAmelCase_ = 45
lowerCAmelCase_ = 1_581
lowerCAmelCase_ = 1_517
lowerCAmelCase_ = 1_570
lowerCAmelCase_ = 1_584
lowerCAmelCase_ = 1_793
lowerCAmelCase_ = 1_795
lowerCAmelCase_ = 1_916
lowerCAmelCase_ = 1_864
lowerCAmelCase_ = 1_905
lowerCAmelCase_ = 1_919
lowerCAmelCase_ = 2_429
lowerCAmelCase_ = 2_208
lowerCAmelCase_ = 2_418
lowerCAmelCase_ = 2_323
lowerCAmelCase_ = 2_407
# @@protoc_insertion_point(module_scope)
| 302
| 0
|
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Any:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
lowercase__ : Tuple = str(bin(__lowerCamelCase ) )[2:] # remove the leading "0b"
lowercase__ : int = str(bin(__lowerCamelCase ) )[2:] # remove the leading "0b"
lowercase__ : Optional[Any] = max(len(__lowerCamelCase ) , len(__lowerCamelCase ) )
return "0b" + "".join(
str(int(char_a == '''1''' and char_b == '''1''' ) )
for char_a, char_b in zip(a_binary.zfill(__lowerCamelCase ) , b_binary.zfill(__lowerCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Pix2StructConfig',
'Pix2StructTextConfig',
'Pix2StructVisionConfig',
],
'processing_pix2struct': ['Pix2StructProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['Pix2StructImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Pix2StructPreTrainedModel',
'Pix2StructForConditionalGeneration',
'Pix2StructVisionModel',
'Pix2StructTextModel',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 302
| 0
|
"""simple docstring"""
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]:
lowercase__ : Dict = tmp_path / "file.csv"
lowercase__ : List[str] = textwrap.dedent(
'''\\n header1,header2\n 1,2\n 10,20\n ''' )
with open(__lowerCamelCase , '''w''' ) as f:
f.write(__lowerCamelCase )
return str(__lowerCamelCase )
@pytest.fixture
def __UpperCAmelCase ( __lowerCamelCase ) -> Tuple:
lowercase__ : Union[str, Any] = tmp_path / "malformed_file.csv"
lowercase__ : Union[str, Any] = textwrap.dedent(
'''\\n header1,header2\n 1,2\n 10,20,\n ''' )
with open(__lowerCamelCase , '''w''' ) as f:
f.write(__lowerCamelCase )
return str(__lowerCamelCase )
@pytest.fixture
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> int:
lowercase__ : Optional[Any] = tmp_path / "csv_with_image.csv"
lowercase__ : List[Any] = textwrap.dedent(
f"""\
image
{image_file}
""" )
with open(__lowerCamelCase , '''w''' ) as f:
f.write(__lowerCamelCase )
return str(__lowerCamelCase )
@pytest.fixture
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
lowercase__ : Union[str, Any] = tmp_path / "csv_with_label.csv"
lowercase__ : Any = textwrap.dedent(
'''\\n label\n good\n bad\n good\n ''' )
with open(__lowerCamelCase , '''w''' ) as f:
f.write(__lowerCamelCase )
return str(__lowerCamelCase )
@pytest.fixture
def __UpperCAmelCase ( __lowerCamelCase ) -> Dict:
lowercase__ : Any = tmp_path / "csv_with_int_list.csv"
lowercase__ : Any = textwrap.dedent(
'''\\n int_list\n 1 2 3\n 4 5 6\n 7 8 9\n ''' )
with open(__lowerCamelCase , '''w''' ) as f:
f.write(__lowerCamelCase )
return str(__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> str:
lowercase__ : Dict = Csv()
lowercase__ : List[str] = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(__lowerCamelCase , match='''Error tokenizing data''' ):
for _ in generator:
pass
assert any(
record.levelname == '''ERROR'''
and '''Failed to read file''' in record.message
and os.path.basename(__lowerCamelCase ) in record.message
for record in caplog.records )
@require_pil
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
with open(__lowerCamelCase , encoding='''utf-8''' ) as f:
lowercase__ : int = f.read().splitlines()[1]
lowercase__ : List[str] = Csv(encoding='''utf-8''' , features=Features({'''image''': Image()} ) )
lowercase__ : str = csv._generate_tables([[csv_file_with_image]] )
lowercase__ : Any = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''image''' ).type == Image()()
lowercase__ : Tuple = pa_table.to_pydict()["image"]
assert generated_content == [{"path": image_file, "bytes": None}]
def __UpperCAmelCase ( __lowerCamelCase ) -> List[str]:
with open(__lowerCamelCase , encoding='''utf-8''' ) as f:
lowercase__ : List[str] = f.read().splitlines()[1:]
lowercase__ : Optional[int] = Csv(encoding='''utf-8''' , features=Features({'''label''': ClassLabel(names=['''good''', '''bad'''] )} ) )
lowercase__ : Union[str, Any] = csv._generate_tables([[csv_file_with_label]] )
lowercase__ : Any = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''label''' ).type == ClassLabel(names=['''good''', '''bad'''] )()
lowercase__ : Union[str, Any] = pa_table.to_pydict()["label"]
assert generated_content == [ClassLabel(names=['''good''', '''bad'''] ).straint(__lowerCamelCase ) for label in labels]
def __UpperCAmelCase ( __lowerCamelCase ) -> List[str]:
lowercase__ : Optional[Any] = Csv(encoding='''utf-8''' , sep=''',''' , converters={'''int_list''': lambda __lowerCamelCase : [int(__lowerCamelCase ) for i in x.split()]} )
lowercase__ : Optional[Any] = csv._generate_tables([[csv_file_with_int_list]] )
lowercase__ : Union[str, Any] = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field('''int_list''' ).type )
lowercase__ : int = pa_table.to_pydict()["int_list"]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 360
|
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
lowercase__ : List[str] = FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
lowercase__ : List[str] = AutoTokenizer.from_pretrained('''xlm-roberta-base''' )
lowercase__ : List[str] = '''The dog is cute and lives in the garden house'''
lowercase__ : int = jnp.array([tokenizer.encode(_snake_case )] )
lowercase__ : Any = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
lowercase__ : Tuple = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
lowercase__ : Optional[Any] = model(_snake_case )['''last_hidden_state''']
self.assertEqual(output.shape ,_snake_case )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] ,_snake_case ,atol=1e-3 ) )
| 302
| 0
|
"""simple docstring"""
from collections import namedtuple
lowerCAmelCase_ = namedtuple('from_to', 'from_ to')
lowerCAmelCase_ = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.0_0_1, 1_000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_0_4_5_4, 2_6_4.1_7_2),
'cubicyard': from_to(0.7_6_4_5_5, 1.3_0_7_9_5),
'cubicfoot': from_to(0.0_2_8, 3_5.3_1_4_7),
'cup': from_to(0.0_0_0_2_3_6_5_8_8, 4_2_2_6.7_5),
}
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> float:
if from_type not in METRIC_CONVERSION:
raise ValueError(
f"""Invalid \'from_type\' value: {from_type!r} Supported values are:\n"""
+ ''', '''.join(lowerCAmelCase__ ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
f"""Invalid \'to_type\' value: {to_type!r}. Supported values are:\n"""
+ ''', '''.join(lowerCAmelCase__ ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 361
|
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = '#'
class __A :
'''simple docstring'''
def __init__( self : str ) -> None:
"""simple docstring"""
lowercase__ : dict = {}
def UpperCAmelCase ( self : List[str] ,_snake_case : str ) -> None:
"""simple docstring"""
lowercase__ : str = self._trie
for char in text:
if char not in trie:
lowercase__ : Union[str, Any] = {}
lowercase__ : Optional[Any] = trie[char]
lowercase__ : Dict = True
def UpperCAmelCase ( self : Tuple ,_snake_case : str ) -> tuple | list:
"""simple docstring"""
lowercase__ : Optional[Any] = self._trie
for char in prefix:
if char in trie:
lowercase__ : Union[str, Any] = trie[char]
else:
return []
return self._elements(_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : dict ) -> tuple:
"""simple docstring"""
lowercase__ : str = []
for c, v in d.items():
lowercase__ : List[Any] = [''' '''] if c == END else [(c + s) for s in self._elements(_snake_case )]
result.extend(_snake_case )
return tuple(_snake_case )
lowerCAmelCase_ = Trie()
lowerCAmelCase_ = ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
trie.insert_word(word)
def __UpperCAmelCase ( __lowerCamelCase ) -> tuple:
lowercase__ : List[Any] = trie.find_word(__lowerCamelCase )
return tuple(string + word for word in suffixes )
def __UpperCAmelCase ( ) -> None:
print(autocomplete_using_trie('''de''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 302
| 0
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
lowercase__ : str = DPTConfig(embedding_type='''hybrid''' )
if "large" in checkpoint_url:
lowercase__ : Tuple = 10_24
lowercase__ : int = 40_96
lowercase__ : List[str] = 24
lowercase__ : Tuple = 16
lowercase__ : Union[str, Any] = [5, 11, 17, 23]
lowercase__ : str = [2_56, 5_12, 10_24, 10_24]
lowercase__ : str = (1, 3_84, 3_84)
if "nyu" or "midas" in checkpoint_url:
lowercase__ : Tuple = 7_68
lowercase__ : Optional[int] = [1, 1, 1, 0.5]
lowercase__ : List[Any] = [2_56, 5_12, 7_68, 7_68]
lowercase__ : Union[str, Any] = 1_50
lowercase__ : int = 16
lowercase__ : Optional[Any] = (1, 3_84, 3_84)
lowercase__ : Optional[int] = False
lowercase__ : Optional[int] = 'project'
if "ade" in checkpoint_url:
lowercase__ : List[str] = True
lowercase__ : Dict = 7_68
lowercase__ : Optional[int] = [1, 1, 1, 0.5]
lowercase__ : Tuple = 1_50
lowercase__ : str = 16
lowercase__ : Dict = 'huggingface/label-files'
lowercase__ : int = 'ade20k-id2label.json'
lowercase__ : Dict = json.load(open(cached_download(hf_hub_url(lowercase__ , lowercase__ , repo_type='''dataset''' ) ) , '''r''' ) )
lowercase__ : Dict = {int(lowercase__ ): v for k, v in idalabel.items()}
lowercase__ : Tuple = idalabel
lowercase__ : List[str] = {v: k for k, v in idalabel.items()}
lowercase__ : Any = [1, 1_50, 4_80, 4_80]
return config, expected_shape
def __UpperCAmelCase ( __lowerCamelCase ) -> Tuple:
lowercase__ : int = ['pretrained.model.head.weight', 'pretrained.model.head.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
def __UpperCAmelCase ( __lowerCamelCase ) -> List[Any]:
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
lowercase__ : str = name.replace('''pretrained.model''' , '''dpt.encoder''' )
if "pretrained.model" in name:
lowercase__ : Optional[int] = name.replace('''pretrained.model''' , '''dpt.embeddings''' )
if "patch_embed" in name:
lowercase__ : str = name.replace('''patch_embed''' , '''''' )
if "pos_embed" in name:
lowercase__ : Any = name.replace('''pos_embed''' , '''position_embeddings''' )
if "attn.proj" in name:
lowercase__ : List[Any] = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "proj" in name and "project" not in name:
lowercase__ : Union[str, Any] = name.replace('''proj''' , '''projection''' )
if "blocks" in name:
lowercase__ : Any = name.replace('''blocks''' , '''layer''' )
if "mlp.fc1" in name:
lowercase__ : Tuple = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
lowercase__ : Optional[Any] = name.replace('''mlp.fc2''' , '''output.dense''' )
if "norm1" in name and "backbone" not in name:
lowercase__ : List[Any] = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name and "backbone" not in name:
lowercase__ : int = name.replace('''norm2''' , '''layernorm_after''' )
if "scratch.output_conv" in name:
lowercase__ : Dict = name.replace('''scratch.output_conv''' , '''head''' )
if "scratch" in name:
lowercase__ : Union[str, Any] = name.replace('''scratch''' , '''neck''' )
if "layer1_rn" in name:
lowercase__ : Dict = name.replace('''layer1_rn''' , '''convs.0''' )
if "layer2_rn" in name:
lowercase__ : Any = name.replace('''layer2_rn''' , '''convs.1''' )
if "layer3_rn" in name:
lowercase__ : Union[str, Any] = name.replace('''layer3_rn''' , '''convs.2''' )
if "layer4_rn" in name:
lowercase__ : Dict = name.replace('''layer4_rn''' , '''convs.3''' )
if "refinenet" in name:
lowercase__ : Union[str, Any] = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
lowercase__ : List[Any] = name.replace(f"""refinenet{layer_idx}""" , f"""fusion_stage.layers.{abs(layer_idx-4 )}""" )
if "out_conv" in name:
lowercase__ : int = name.replace('''out_conv''' , '''projection''' )
if "resConfUnit1" in name:
lowercase__ : str = name.replace('''resConfUnit1''' , '''residual_layer1''' )
if "resConfUnit2" in name:
lowercase__ : Optional[int] = name.replace('''resConfUnit2''' , '''residual_layer2''' )
if "conv1" in name:
lowercase__ : List[str] = name.replace('''conv1''' , '''convolution1''' )
if "conv2" in name:
lowercase__ : List[Any] = name.replace('''conv2''' , '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
lowercase__ : List[Any] = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
lowercase__ : Dict = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
lowercase__ : str = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
lowercase__ : int = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
lowercase__ : List[Any] = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
lowercase__ : List[Any] = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
lowercase__ : Union[str, Any] = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
lowercase__ : Any = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
lowercase__ : Optional[Any] = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
lowercase__ : List[str] = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
lowercase__ : Any = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
lowercase__ : List[Any] = name.replace('''pretrained''' , '''dpt''' )
if "bn" in name:
lowercase__ : Dict = name.replace('''bn''' , '''batch_norm''' )
if "head" in name:
lowercase__ : Any = name.replace('''head''' , '''head.head''' )
if "encoder.norm" in name:
lowercase__ : List[Any] = name.replace('''encoder.norm''' , '''layernorm''' )
if "auxlayer" in name:
lowercase__ : int = name.replace('''auxlayer''' , '''auxiliary_head.head''' )
if "backbone" in name:
lowercase__ : str = name.replace('''backbone''' , '''backbone.bit.encoder''' )
if ".." in name:
lowercase__ : Dict = name.replace('''..''' , '''.''' )
if "stem.conv" in name:
lowercase__ : Union[str, Any] = name.replace('''stem.conv''' , '''bit.embedder.convolution''' )
if "blocks" in name:
lowercase__ : Union[str, Any] = name.replace('''blocks''' , '''layers''' )
if "convolution" in name and "backbone" in name:
lowercase__ : Tuple = name.replace('''convolution''' , '''conv''' )
if "layer" in name and "backbone" in name:
lowercase__ : List[Any] = name.replace('''layer''' , '''layers''' )
if "backbone.bit.encoder.bit" in name:
lowercase__ : List[Any] = name.replace('''backbone.bit.encoder.bit''' , '''backbone.bit''' )
if "embedder.conv" in name:
lowercase__ : Optional[Any] = name.replace('''embedder.conv''' , '''embedder.convolution''' )
if "backbone.bit.encoder.stem.norm" in name:
lowercase__ : Any = name.replace('''backbone.bit.encoder.stem.norm''' , '''backbone.bit.embedder.norm''' )
return name
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> str:
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase__ : Dict = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" )
lowercase__ : Optional[Any] = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
lowercase__ : List[Any] = in_proj_weight[: config.hidden_size, :]
lowercase__ : List[str] = in_proj_bias[: config.hidden_size]
lowercase__ : Dict = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase__ : Dict = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase__ : Optional[int] = in_proj_weight[
-config.hidden_size :, :
]
lowercase__ : Any = in_proj_bias[-config.hidden_size :]
def __UpperCAmelCase ( ) -> List[Any]:
lowercase__ : int = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowercase__ : str = Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw )
return im
@torch.no_grad()
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> List[str]:
lowercase__ : Any = get_dpt_config(lowercase__ )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
lowercase__ : str = torch.load(lowercase__ , map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(lowercase__ )
# rename keys
for key in state_dict.copy().keys():
lowercase__ : Any = state_dict.pop(lowercase__ )
lowercase__ : str = val
# read in qkv matrices
read_in_q_k_v(lowercase__ , lowercase__ )
# load HuggingFace model
lowercase__ : List[Any] = DPTForSemanticSegmentation(lowercase__ ) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowercase__ )
model.load_state_dict(lowercase__ )
model.eval()
# Check outputs on an image
lowercase__ : Optional[int] = 4_80 if 'ade' in checkpoint_url else 3_84
lowercase__ : Union[str, Any] = DPTImageProcessor(size=lowercase__ )
lowercase__ : Optional[int] = prepare_img()
lowercase__ : str = image_processor(lowercase__ , return_tensors='''pt''' )
# forward pass
lowercase__ : Tuple = model(**lowercase__ ).logits if 'ade' in checkpoint_url else model(**lowercase__ ).predicted_depth
if show_prediction:
lowercase__ : Optional[int] = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode='''bicubic''' , align_corners=lowercase__ , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 2_55 ).show()
if pytorch_dump_folder_path is not None:
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase__ )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowercase__ )
if push_to_hub:
model.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
type=str,
help='URL of the original DPT checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=False,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
parser.add_argument(
'--model_name',
default='dpt-large',
type=str,
help='Name of the model, in case you\'re pushing to the hub.',
)
parser.add_argument(
'--show_prediction',
action='store_true',
)
lowerCAmelCase_ = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 362
|
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
lowerCAmelCase_ = logging.get_logger(__name__)
# General docstring
lowerCAmelCase_ = 'RegNetConfig'
# Base docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = [1, 1_088, 7, 7]
# Image classification docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = 'tabby, tabby cat'
lowerCAmelCase_ = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : int ,_snake_case : int ,_snake_case : int ,_snake_case : int = 3 ,_snake_case : int = 1 ,_snake_case : int = 1 ,_snake_case : Optional[str] = "relu" ,) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = nn.Convad(
_snake_case ,_snake_case ,kernel_size=_snake_case ,stride=_snake_case ,padding=kernel_size // 2 ,groups=_snake_case ,bias=_snake_case ,)
lowercase__ : List[Any] = nn.BatchNormad(_snake_case )
lowercase__ : Optional[int] = ACTaFN[activation] if activation is not None else nn.Identity()
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.convolution(_snake_case )
lowercase__ : Tuple = self.normalization(_snake_case )
lowercase__ : Tuple = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] ,_snake_case : RegNetConfig ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = RegNetConvLayer(
config.num_channels ,config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act )
lowercase__ : str = config.num_channels
def UpperCAmelCase ( self : int ,_snake_case : Dict ) -> str:
"""simple docstring"""
lowercase__ : Union[str, Any] = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
lowercase__ : Optional[int] = self.embedder(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : str ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ) -> Any:
"""simple docstring"""
super().__init__()
lowercase__ : List[str] = nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ,stride=_snake_case ,bias=_snake_case )
lowercase__ : Any = nn.BatchNormad(_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ) -> Tensor:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.convolution(_snake_case )
lowercase__ : Optional[int] = self.normalization(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : int ,_snake_case : int ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Any = nn.AdaptiveAvgPoolad((1, 1) )
lowercase__ : Dict = nn.Sequential(
nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.ReLU() ,nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.Sigmoid() ,)
def UpperCAmelCase ( self : int ,_snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.pooler(_snake_case )
lowercase__ : Union[str, Any] = self.attention(_snake_case )
lowercase__ : List[str] = hidden_state * attention
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> List[str]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = in_channels != out_channels or stride != 1
lowercase__ : Optional[int] = max(1 ,out_channels // config.groups_width )
lowercase__ : str = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : Optional[int] = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : str = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ : Tuple = hidden_state
lowercase__ : Union[str, Any] = self.layer(_snake_case )
lowercase__ : List[Any] = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : Optional[int] = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> Optional[int]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = in_channels != out_channels or stride != 1
lowercase__ : List[str] = max(1 ,out_channels // config.groups_width )
lowercase__ : Tuple = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : str = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetSELayer(_snake_case ,reduced_channels=int(round(in_channels / 4 ) ) ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : Optional[Any] = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ : str = hidden_state
lowercase__ : Optional[Any] = self.layer(_snake_case )
lowercase__ : int = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : str = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ,_snake_case : int = 2 ,) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[Any] = RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer
lowercase__ : Optional[Any] = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
_snake_case ,_snake_case ,_snake_case ,stride=_snake_case ,) ,*[layer(_snake_case ,_snake_case ,_snake_case ) for _ in range(depth - 1 )] ,)
def UpperCAmelCase ( self : Tuple ,_snake_case : int ) -> List[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.layers(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : RegNetConfig ) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : str = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
_snake_case ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,) )
lowercase__ : str = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(_snake_case ,config.depths[1:] ):
self.stages.append(RegNetStage(_snake_case ,_snake_case ,_snake_case ,depth=_snake_case ) )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ,_snake_case : bool = False ,_snake_case : bool = True ) -> BaseModelOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Dict = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase__ : int = hidden_states + (hidden_state,)
lowercase__ : Any = stage_module(_snake_case )
if output_hidden_states:
lowercase__ : Optional[int] = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_snake_case ,hidden_states=_snake_case )
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : int = RegNetConfig
lowerCAmelCase : List[Any] = "regnet"
lowerCAmelCase : Optional[int] = "pixel_values"
lowerCAmelCase : Union[str, Any] = True
def UpperCAmelCase ( self : Any ,_snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
nn.init.kaiming_normal_(module.weight ,mode='''fan_out''' ,nonlinearity='''relu''' )
elif isinstance(_snake_case ,(nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight ,1 )
nn.init.constant_(module.bias ,0 )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Dict ,_snake_case : Any=False ) -> Optional[int]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
lowercase__ : str = value
lowerCAmelCase_ = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCAmelCase_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Any ) -> Tuple:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Any = config
lowercase__ : List[str] = RegNetEmbeddings(_snake_case )
lowercase__ : Any = RegNetEncoder(_snake_case )
lowercase__ : Dict = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,modality='''vision''' ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCAmelCase ( self : Dict ,_snake_case : Tensor ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
"""simple docstring"""
lowercase__ : List[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ : Dict = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : Union[str, Any] = self.embedder(_snake_case )
lowercase__ : List[Any] = self.encoder(
_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : str = encoder_outputs[0]
lowercase__ : Optional[int] = self.pooler(_snake_case )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_snake_case ,pooler_output=_snake_case ,hidden_states=encoder_outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : int ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Optional[Any] = config.num_labels
lowercase__ : int = RegNetModel(_snake_case )
# classification head
lowercase__ : str = nn.Sequential(
nn.Flatten() ,nn.Linear(config.hidden_sizes[-1] ,config.num_labels ) if config.num_labels > 0 else nn.Identity() ,)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCAmelCase ( self : List[Any] ,_snake_case : Optional[torch.FloatTensor] = None ,_snake_case : Optional[torch.LongTensor] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ,) -> ImageClassifierOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Any = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : List[Any] = self.regnet(_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : List[str] = outputs.pooler_output if return_dict else outputs[1]
lowercase__ : Union[str, Any] = self.classifier(_snake_case )
lowercase__ : Optional[int] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase__ : List[Any] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase__ : Dict = '''single_label_classification'''
else:
lowercase__ : Optional[int] = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase__ : Union[str, Any] = MSELoss()
if self.num_labels == 1:
lowercase__ : List[Any] = loss_fct(logits.squeeze() ,labels.squeeze() )
else:
lowercase__ : Tuple = loss_fct(_snake_case ,_snake_case )
elif self.config.problem_type == "single_label_classification":
lowercase__ : Tuple = CrossEntropyLoss()
lowercase__ : str = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase__ : Any = BCEWithLogitsLoss()
lowercase__ : Union[str, Any] = loss_fct(_snake_case ,_snake_case )
if not return_dict:
lowercase__ : Tuple = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_snake_case ,logits=_snake_case ,hidden_states=outputs.hidden_states )
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|
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ : Tuple = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
lowercase__ : Optional[Any] = get_activation('''gelu''' )
self.assertTrue(torch.allclose(gelu_python(_snake_case ) ,torch_builtin(_snake_case ) ) )
self.assertFalse(torch.allclose(gelu_python(_snake_case ) ,gelu_new(_snake_case ) ) )
def UpperCAmelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Any = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
lowercase__ : Optional[Any] = get_activation('''gelu''' )
lowercase__ : List[Any] = get_activation('''gelu_10''' )
lowercase__ : List[str] = torch_builtin(_snake_case )
lowercase__ : List[str] = geluaa(_snake_case )
lowercase__ : Optional[Any] = torch.where(y_gelu_aa < 10.0 ,1 ,0 )
self.assertTrue(torch.max(_snake_case ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask ,y_gelu_aa * clipped_mask ) )
def UpperCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
get_activation('''gelu''' )
get_activation('''gelu_10''' )
get_activation('''gelu_fast''' )
get_activation('''gelu_new''' )
get_activation('''gelu_python''' )
get_activation('''gelu_pytorch_tanh''' )
get_activation('''linear''' )
get_activation('''mish''' )
get_activation('''quick_gelu''' )
get_activation('''relu''' )
get_activation('''sigmoid''' )
get_activation('''silu''' )
get_activation('''swish''' )
get_activation('''tanh''' )
with self.assertRaises(_snake_case ):
get_activation('''bogus''' )
with self.assertRaises(_snake_case ):
get_activation(_snake_case )
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
lowercase__ : Dict = get_activation('''gelu''' )
lowercase__ : Optional[Any] = 1
lowercase__ : str = get_activation('''gelu''' )
self.assertEqual(acta.a ,1 )
with self.assertRaises(_snake_case ):
lowercase__ : Tuple = acta.a
| 363
|
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = 1.6021E-19 # units = C
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 302
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
'processing_speech_to_text': ['Speech2TextProcessor'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['Speech2TextTokenizer']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['Speech2TextFeatureExtractor']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFSpeech2TextForConditionalGeneration',
'TFSpeech2TextModel',
'TFSpeech2TextPreTrainedModel',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Speech2TextForConditionalGeneration',
'Speech2TextModel',
'Speech2TextPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 364
|
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : str = ["pixel_values"]
def __init__( self : Tuple ,_snake_case : bool = True ,_snake_case : Optional[Dict[str, int]] = None ,_snake_case : PILImageResampling = PILImageResampling.BICUBIC ,_snake_case : bool = True ,_snake_case : bool = True ,_snake_case : Union[int, float] = 1 / 255 ,_snake_case : Dict[str, int] = None ,_snake_case : bool = True ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,**_snake_case : Optional[Any] ,) -> None:
"""simple docstring"""
super().__init__(**_snake_case )
lowercase__ : str = size if size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[int] = get_size_dict(_snake_case )
lowercase__ : List[Any] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[int] = get_size_dict(_snake_case ,default_to_square=_snake_case ,param_name='''crop_size''' )
lowercase__ : Tuple = do_resize
lowercase__ : List[Any] = do_rescale
lowercase__ : Any = do_normalize
lowercase__ : List[str] = do_center_crop
lowercase__ : Optional[Any] = crop_size
lowercase__ : Union[str, Any] = size
lowercase__ : Any = resample
lowercase__ : int = rescale_factor
lowercase__ : Tuple = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
lowercase__ : str = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase ( self : str ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : PILImageResampling = PILImageResampling.BILINEAR ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Dict ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : List[str] = get_size_dict(_snake_case )
if "shortest_edge" in size:
lowercase__ : str = get_resize_output_image_size(_snake_case ,size=size['''shortest_edge'''] ,default_to_square=_snake_case )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
lowercase__ : int = (size['''height'''], size['''width'''])
else:
raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}""" )
return resize(_snake_case ,size=_snake_case ,resample=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : List[Any] ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Tuple ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : Optional[Any] = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(_snake_case ,size=(size['''height'''], size['''width''']) ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : np.ndarray ,_snake_case : float ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Optional[int] ) -> np.ndarray:
"""simple docstring"""
return rescale(_snake_case ,scale=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : np.ndarray ,_snake_case : Union[float, List[float]] ,_snake_case : Union[float, List[float]] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Dict ,) -> np.ndarray:
"""simple docstring"""
return normalize(_snake_case ,mean=_snake_case ,std=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : ImageInput ,_snake_case : Optional[bool] = None ,_snake_case : Dict[str, int] = None ,_snake_case : PILImageResampling = None ,_snake_case : bool = None ,_snake_case : int = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[float] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[str, TensorType]] = None ,_snake_case : Union[str, ChannelDimension] = ChannelDimension.FIRST ,**_snake_case : List[str] ,) -> BatchFeature:
"""simple docstring"""
lowercase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
lowercase__ : int = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ : int = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ : Optional[int] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ : Optional[Any] = crop_size if crop_size is not None else self.crop_size
lowercase__ : Tuple = get_size_dict(_snake_case ,param_name='''crop_size''' ,default_to_square=_snake_case )
lowercase__ : Tuple = resample if resample is not None else self.resample
lowercase__ : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ : Union[str, Any] = image_mean if image_mean is not None else self.image_mean
lowercase__ : List[str] = image_std if image_std is not None else self.image_std
lowercase__ : Optional[int] = size if size is not None else self.size
lowercase__ : int = get_size_dict(_snake_case )
if not is_batched(_snake_case ):
lowercase__ : Optional[Any] = [images]
if not valid_images(_snake_case ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
# All transformations expect numpy arrays.
lowercase__ : str = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
lowercase__ : int = [self.resize(image=_snake_case ,size=_snake_case ,resample=_snake_case ) for image in images]
if do_center_crop:
lowercase__ : str = [self.center_crop(image=_snake_case ,size=_snake_case ) for image in images]
if do_rescale:
lowercase__ : Optional[Any] = [self.rescale(image=_snake_case ,scale=_snake_case ) for image in images]
if do_normalize:
lowercase__ : List[str] = [self.normalize(image=_snake_case ,mean=_snake_case ,std=_snake_case ) for image in images]
lowercase__ : Union[str, Any] = [to_channel_dimension_format(_snake_case ,_snake_case ) for image in images]
lowercase__ : Any = {'''pixel_values''': images}
return BatchFeature(data=_snake_case ,tensor_type=_snake_case )
| 302
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|
"""simple docstring"""
import datasets
from .evaluate import evaluate
lowerCAmelCase_ = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
lowerCAmelCase_ = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
lowerCAmelCase_ = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': {
'''id''': datasets.Value('''string''' ),
'''prediction_text''': datasets.features.Sequence(datasets.Value('''string''' ) ),
},
'''references''': {
'''id''': datasets.Value('''string''' ),
'''answers''': datasets.features.Sequence(
{
'''text''': datasets.Value('''string''' ),
'''answer_start''': datasets.Value('''int32''' ),
} ),
},
} ) ,codebase_urls=['''https://www.atticusprojectai.org/cuad'''] ,reference_urls=['''https://www.atticusprojectai.org/cuad'''] ,)
def UpperCAmelCase ( self : Any ,_snake_case : Optional[Any] ,_snake_case : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Optional[int] = {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
lowercase__ : Dict = [
{
'''paragraphs''': [
{
'''qas''': [
{
'''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
'''id''': ref['''id'''],
}
for ref in references
]
}
]
}
]
lowercase__ : Union[str, Any] = evaluate(dataset=_a ,predictions=_a )
return score
| 365
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_efficientnet': [
'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EfficientNetConfig',
'EfficientNetOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['EfficientNetImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'EfficientNetForImageClassification',
'EfficientNetModel',
'EfficientNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 302
| 0
|
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __A ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCAmelCase : Dict = ["image_processor", "tokenizer"]
lowerCAmelCase : List[str] = "FlavaImageProcessor"
lowerCAmelCase : Dict = ("BertTokenizer", "BertTokenizerFast")
def __init__( self : str ,_snake_case : Tuple=None ,_snake_case : Dict=None ,**_snake_case : int ) -> Tuple:
"""simple docstring"""
lowercase__ : Dict = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' ,__UpperCAmelCase ,)
lowercase__ : List[Any] = kwargs.pop('''feature_extractor''' )
lowercase__ : List[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(__UpperCAmelCase ,__UpperCAmelCase )
lowercase__ : Dict = self.image_processor
def __call__( self : Optional[int] ,_snake_case : str = None ,_snake_case : List[str] = None ,_snake_case : int = True ,_snake_case : Optional[int] = False ,_snake_case : str = False ,_snake_case : List[str] = None ,_snake_case : str = 0 ,_snake_case : str = None ,_snake_case : Tuple = None ,_snake_case : Any = None ,_snake_case : Dict = None ,_snake_case : Any = None ,_snake_case : List[Any] = False ,_snake_case : Any = False ,_snake_case : List[Any] = False ,_snake_case : Tuple = False ,_snake_case : Any = True ,_snake_case : int = None ,**_snake_case : Tuple ,) -> Optional[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
lowercase__ : Optional[Any] = self.tokenizer(
text=__UpperCAmelCase ,add_special_tokens=__UpperCAmelCase ,padding=__UpperCAmelCase ,truncation=__UpperCAmelCase ,max_length=__UpperCAmelCase ,stride=__UpperCAmelCase ,pad_to_multiple_of=__UpperCAmelCase ,return_token_type_ids=__UpperCAmelCase ,return_attention_mask=__UpperCAmelCase ,return_overflowing_tokens=__UpperCAmelCase ,return_special_tokens_mask=__UpperCAmelCase ,return_offsets_mapping=__UpperCAmelCase ,return_length=__UpperCAmelCase ,verbose=__UpperCAmelCase ,return_tensors=__UpperCAmelCase ,**__UpperCAmelCase ,)
if images is not None:
lowercase__ : List[Any] = self.image_processor(
__UpperCAmelCase ,return_image_mask=__UpperCAmelCase ,return_codebook_pixels=__UpperCAmelCase ,return_tensors=__UpperCAmelCase ,**__UpperCAmelCase ,)
if text is not None and images is not None:
encoding.update(__UpperCAmelCase )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCAmelCase ) ,tensor_type=__UpperCAmelCase )
def UpperCAmelCase ( self : str ,*_snake_case : Dict ,**_snake_case : Union[str, Any] ) -> int:
"""simple docstring"""
return self.tokenizer.batch_decode(*__UpperCAmelCase ,**__UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ,*_snake_case : int ,**_snake_case : Any ) -> Tuple:
"""simple docstring"""
return self.tokenizer.decode(*__UpperCAmelCase ,**__UpperCAmelCase )
@property
def UpperCAmelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Tuple = self.tokenizer.model_input_names
lowercase__ : Union[str, Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase ( self : str ) -> List[str]:
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,__UpperCAmelCase ,)
return self.image_processor_class
@property
def UpperCAmelCase ( self : int ) -> int:
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,__UpperCAmelCase ,)
return self.image_processor
| 366
|
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = "cpu" , __lowerCamelCase = None ) -> None:
lowercase__ : List[str] = torch.load(__lowerCamelCase , map_location=__lowerCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__lowerCamelCase , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
lowercase__ : List[Any] = v.half()
if save_path is None: # overwrite src_path
lowercase__ : Any = src_path
torch.save(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 302
| 0
|
from __future__ import annotations
class __A :
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : list[list[int]] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Dict = TypeError(
'''Matrices must be formed from a list of zero or more lists containing at '''
'''least one and the same number of values, each of which must be of type '''
'''int or float.''' )
if len(lowerCAmelCase__ ) != 0:
lowercase__ : List[Any] = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(lowerCAmelCase__ ) != cols:
raise error
for value in row:
if not isinstance(lowerCAmelCase__ ,(int, float) ):
raise error
lowercase__ : int = rows
else:
lowercase__ : List[str] = []
def UpperCAmelCase ( self : Tuple ) -> list[list[int]]:
"""simple docstring"""
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def UpperCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
return len(self.rows )
@property
def UpperCAmelCase ( self : Any ) -> int:
"""simple docstring"""
return len(self.rows[0] )
@property
def UpperCAmelCase ( self : Optional[int] ) -> tuple[int, int]:
"""simple docstring"""
return (self.num_rows, self.num_columns)
@property
def UpperCAmelCase ( self : Optional[int] ) -> bool:
"""simple docstring"""
return self.order[0] == self.order[1]
def UpperCAmelCase ( self : Union[str, Any] ) -> Matrix:
"""simple docstring"""
lowercase__ : Any = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(lowerCAmelCase__ )
def UpperCAmelCase ( self : Any ) -> int:
"""simple docstring"""
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def UpperCAmelCase ( self : Tuple ) -> bool:
"""simple docstring"""
return bool(self.determinant() )
def UpperCAmelCase ( self : List[str] ,_snake_case : int ,_snake_case : int ) -> int:
"""simple docstring"""
lowercase__ : Dict = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(lowerCAmelCase__ ).determinant()
def UpperCAmelCase ( self : Dict ,_snake_case : int ,_snake_case : int ) -> int:
"""simple docstring"""
if (row + column) % 2 == 0:
return self.get_minor(lowerCAmelCase__ ,lowerCAmelCase__ )
return -1 * self.get_minor(lowerCAmelCase__ ,lowerCAmelCase__ )
def UpperCAmelCase ( self : Any ) -> Matrix:
"""simple docstring"""
return Matrix(
[
[self.get_minor(lowerCAmelCase__ ,lowerCAmelCase__ ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def UpperCAmelCase ( self : Union[str, Any] ) -> Matrix:
"""simple docstring"""
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def UpperCAmelCase ( self : str ) -> Matrix:
"""simple docstring"""
lowercase__ : Union[str, Any] = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(lowerCAmelCase__ )
def UpperCAmelCase ( self : Optional[int] ) -> Matrix:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.determinant()
if not determinant:
raise TypeError('''Only matrices with a non-zero determinant have an inverse''' )
return self.adjugate() * (1 / determinant)
def __repr__( self : Tuple ) -> str:
"""simple docstring"""
return str(self.rows )
def __str__( self : Tuple ) -> str:
"""simple docstring"""
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
'''[''' + '''. '''.join([str(lowerCAmelCase__ ) for value in row] ) + '''.]'''
for row in self.rows
] )
+ "]"
)
def UpperCAmelCase ( self : List[Any] ,_snake_case : list[int] ,_snake_case : int | None = None ) -> None:
"""simple docstring"""
lowercase__ : Dict = TypeError('''Row must be a list containing all ints and/or floats''' )
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
raise type_error
for value in row:
if not isinstance(lowerCAmelCase__ ,(int, float) ):
raise type_error
if len(lowerCAmelCase__ ) != self.num_columns:
raise ValueError(
'''Row must be equal in length to the other rows in the matrix''' )
if position is None:
self.rows.append(lowerCAmelCase__ )
else:
lowercase__ : List[Any] = self.rows[0:position] + [row] + self.rows[position:]
def UpperCAmelCase ( self : List[str] ,_snake_case : list[int] ,_snake_case : int | None = None ) -> None:
"""simple docstring"""
lowercase__ : Union[str, Any] = TypeError(
'''Column must be a list containing all ints and/or floats''' )
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
raise type_error
for value in column:
if not isinstance(lowerCAmelCase__ ,(int, float) ):
raise type_error
if len(lowerCAmelCase__ ) != self.num_rows:
raise ValueError(
'''Column must be equal in length to the other columns in the matrix''' )
if position is None:
lowercase__ : int = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
lowercase__ : Any = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self : List[Any] ,_snake_case : object ) -> bool:
"""simple docstring"""
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
return NotImplemented
return self.rows == other.rows
def __ne__( self : List[str] ,_snake_case : object ) -> bool:
"""simple docstring"""
return not self == other
def __neg__( self : Dict ) -> Matrix:
"""simple docstring"""
return self * -1
def __add__( self : List[Any] ,_snake_case : Matrix ) -> Matrix:
"""simple docstring"""
if self.order != other.order:
raise ValueError('''Addition requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self : Tuple ,_snake_case : Matrix ) -> Matrix:
"""simple docstring"""
if self.order != other.order:
raise ValueError('''Subtraction requires matrices of the same order''' )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self : Optional[Any] ,_snake_case : Matrix | int | float ) -> Matrix:
"""simple docstring"""
if isinstance(lowerCAmelCase__ ,(int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
if self.num_columns != other.num_rows:
raise ValueError(
'''The number of columns in the first matrix must '''
'''be equal to the number of rows in the second''' )
return Matrix(
[
[Matrix.dot_product(lowerCAmelCase__ ,lowerCAmelCase__ ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
'''A Matrix can only be multiplied by an int, float, or another matrix''' )
def __pow__( self : List[Any] ,_snake_case : int ) -> Matrix:
"""simple docstring"""
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
raise TypeError('''A Matrix can only be raised to the power of an int''' )
if not self.is_square:
raise ValueError('''Only square matrices can be raised to a power''' )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'''Only invertable matrices can be raised to a negative power''' )
lowercase__ : List[Any] = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def UpperCAmelCase ( cls : Dict ,_snake_case : list[int] ,_snake_case : list[int] ) -> int:
"""simple docstring"""
return sum(row[i] * column[i] for i in range(len(lowerCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : UNetaDModel
lowerCAmelCase : ScoreSdeVeScheduler
def __init__( self : Optional[Any] ,_snake_case : UNetaDModel ,_snake_case : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=_snake_case ,scheduler=_snake_case )
@torch.no_grad()
def __call__( self : Any ,_snake_case : int = 1 ,_snake_case : int = 2_000 ,_snake_case : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,_snake_case : Optional[str] = "pil" ,_snake_case : bool = True ,**_snake_case : Any ,) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.unet.config.sample_size
lowercase__ : Dict = (batch_size, 3, img_size, img_size)
lowercase__ : Tuple = self.unet
lowercase__ : Any = randn_tensor(_snake_case ,generator=_snake_case ) * self.scheduler.init_noise_sigma
lowercase__ : Union[str, Any] = sample.to(self.device )
self.scheduler.set_timesteps(_snake_case )
self.scheduler.set_sigmas(_snake_case )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
lowercase__ : Tuple = self.scheduler.sigmas[i] * torch.ones(shape[0] ,device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
lowercase__ : List[str] = self.unet(_snake_case ,_snake_case ).sample
lowercase__ : Optional[Any] = self.scheduler.step_correct(_snake_case ,_snake_case ,generator=_snake_case ).prev_sample
# prediction step
lowercase__ : str = model(_snake_case ,_snake_case ).sample
lowercase__ : List[Any] = self.scheduler.step_pred(_snake_case ,_snake_case ,_snake_case ,generator=_snake_case )
lowercase__ , lowercase__ : Optional[int] = output.prev_sample, output.prev_sample_mean
lowercase__ : Union[str, Any] = sample_mean.clamp(0 ,1 )
lowercase__ : int = sample.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
lowercase__ : Any = self.numpy_to_pil(_snake_case )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=_snake_case )
| 302
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-encoder': (
'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-scorer': (
'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
),
'google/realm-cc-news-pretrained-openqa': (
'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
),
'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
# See all REALM models at https://huggingface.co/models?filter=realm
}
class lowerCAmelCase__ ( _UpperCamelCase ):
'''simple docstring'''
lowerCAmelCase : int = 'realm'
def __init__( self : Tuple ,_snake_case : List[str]=30_522 ,_snake_case : Union[str, Any]=768 ,_snake_case : str=128 ,_snake_case : Any=12 ,_snake_case : Dict=12 ,_snake_case : Tuple=8 ,_snake_case : Union[str, Any]=3_072 ,_snake_case : List[Any]="gelu_new" ,_snake_case : List[Any]=0.1 ,_snake_case : Optional[Any]=0.1 ,_snake_case : Dict=512 ,_snake_case : str=2 ,_snake_case : Optional[Any]=0.02 ,_snake_case : Tuple=1e-12 ,_snake_case : Any=256 ,_snake_case : List[str]=10 ,_snake_case : int=1e-3 ,_snake_case : Tuple=5 ,_snake_case : List[Any]=320 ,_snake_case : List[str]=13_353_718 ,_snake_case : Optional[Any]=5_000 ,_snake_case : int=1 ,_snake_case : int=0 ,_snake_case : Union[str, Any]=2 ,**_snake_case : Optional[Any] ,) -> Optional[Any]:
"""simple docstring"""
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,bos_token_id=_SCREAMING_SNAKE_CASE ,eos_token_id=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
# Common config
lowercase__ : Any = vocab_size
lowercase__ : Tuple = max_position_embeddings
lowercase__ : Optional[int] = hidden_size
lowercase__ : Dict = retriever_proj_size
lowercase__ : str = num_hidden_layers
lowercase__ : Tuple = num_attention_heads
lowercase__ : List[str] = num_candidates
lowercase__ : Optional[int] = intermediate_size
lowercase__ : int = hidden_act
lowercase__ : Any = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : str = initializer_range
lowercase__ : str = type_vocab_size
lowercase__ : Union[str, Any] = layer_norm_eps
# Reader config
lowercase__ : Any = span_hidden_size
lowercase__ : int = max_span_width
lowercase__ : Tuple = reader_layer_norm_eps
lowercase__ : Union[str, Any] = reader_beam_size
lowercase__ : str = reader_seq_len
# Retrieval config
lowercase__ : List[Any] = num_block_records
lowercase__ : Optional[Any] = searcher_beam_size
| 368
|
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCAmelCase_ = {
'facebook/maskformer-swin-base-ade': (
'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = "maskformer"
lowerCAmelCase : Any = {"hidden_size": "mask_feature_size"}
lowerCAmelCase : Optional[int] = ["resnet", "swin"]
lowerCAmelCase : str = ["detr"]
def __init__( self : int ,_snake_case : int = 256 ,_snake_case : int = 256 ,_snake_case : float = 0.1 ,_snake_case : bool = False ,_snake_case : Optional[Dict] = None ,_snake_case : Optional[Dict] = None ,_snake_case : float = 0.02 ,_snake_case : float = 1.0 ,_snake_case : float = 1.0 ,_snake_case : float = 1.0 ,_snake_case : float = 20.0 ,_snake_case : Optional[bool] = None ,**_snake_case : Optional[Any] ,) -> Dict:
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowercase__ : Any = SwinConfig(
image_size=384 ,in_channels=3 ,patch_size=4 ,embed_dim=128 ,depths=[2, 2, 18, 2] ,num_heads=[4, 8, 16, 32] ,window_size=12 ,drop_path_rate=0.3 ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] ,)
if isinstance(_snake_case ,_snake_case ):
lowercase__ : List[str] = backbone_config.pop('''model_type''' )
lowercase__ : List[Any] = CONFIG_MAPPING[backbone_model_type]
lowercase__ : str = config_class.from_dict(_snake_case )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
f"""Supported model types: {",".join(self.backbones_supported )}""" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowercase__ : Union[str, Any] = DetrConfig()
else:
# verify that the decoder is supported
lowercase__ : Tuple = (
decoder_config.pop('''model_type''' ) if isinstance(_snake_case ,_snake_case ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f"""Transformer Decoder {decoder_type} not supported, please use one of"""
f""" {",".join(self.decoders_supported )}""" )
if isinstance(_snake_case ,_snake_case ):
lowercase__ : Optional[int] = CONFIG_MAPPING[decoder_type]
lowercase__ : Optional[Any] = config_class.from_dict(_snake_case )
lowercase__ : List[Any] = backbone_config
lowercase__ : List[Any] = decoder_config
# main feature dimension for the model
lowercase__ : List[str] = fpn_feature_size
lowercase__ : int = mask_feature_size
# initializer
lowercase__ : str = init_std
lowercase__ : str = init_xavier_std
# Hungarian matcher && loss
lowercase__ : Optional[int] = cross_entropy_weight
lowercase__ : List[Any] = dice_weight
lowercase__ : List[str] = mask_weight
lowercase__ : str = use_auxiliary_loss
lowercase__ : Optional[int] = no_object_weight
lowercase__ : Optional[Any] = output_auxiliary_logits
lowercase__ : Optional[Any] = self.decoder_config.encoder_attention_heads
lowercase__ : Optional[Any] = self.decoder_config.num_hidden_layers
super().__init__(**_snake_case )
@classmethod
def UpperCAmelCase ( cls : Any ,_snake_case : PretrainedConfig ,_snake_case : PretrainedConfig ,**_snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return cls(
backbone_config=_snake_case ,decoder_config=_snake_case ,**_snake_case ,)
def UpperCAmelCase ( self : str ) -> Dict[str, any]:
"""simple docstring"""
lowercase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase__ : int = self.backbone_config.to_dict()
lowercase__ : List[Any] = self.decoder_config.to_dict()
lowercase__ : List[str] = self.__class__.model_type
return output
| 302
| 0
|
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase_ = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
lowerCAmelCase_ = 256_047
lowerCAmelCase_ = 256_145
@require_sentencepiece
@require_tokenizers
class __A ( a__ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Optional[Any] = NllbTokenizer
lowerCAmelCase : Tuple = NllbTokenizerFast
lowerCAmelCase : Any = True
lowerCAmelCase : Optional[int] = True
lowerCAmelCase : Any = {}
def UpperCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
lowercase__ : List[Any] = NllbTokenizer(_lowerCamelCase ,keep_accents=_lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
lowercase__ : int = NllbTokenizer(_lowerCamelCase ,keep_accents=_lowerCamelCase )
lowercase__ : Optional[int] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_lowerCamelCase ,['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowerCamelCase ) ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,)
lowercase__ : int = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_lowerCamelCase ,[
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] ,)
lowercase__ : Any = tokenizer.convert_tokens_to_ids(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase ,[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] ,)
lowercase__ : int = tokenizer.convert_ids_to_tokens(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase ,[
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] ,)
def UpperCAmelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
lowercase__ : List[str] = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-nllb''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowercase__ : Any = self.rust_tokenizer_class.from_pretrained(_lowerCamelCase ,**_lowerCamelCase )
lowercase__ : List[str] = self.tokenizer_class.from_pretrained(_lowerCamelCase ,**_lowerCamelCase )
lowercase__ : Optional[Any] = tempfile.mkdtemp()
lowercase__ : Any = tokenizer_r.save_pretrained(_lowerCamelCase )
lowercase__ : List[str] = tokenizer_p.save_pretrained(_lowerCamelCase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
lowercase__ : int = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(_lowerCamelCase ,_lowerCamelCase )
# Checks everything loads correctly in the same way
lowercase__ : Tuple = tokenizer_r.from_pretrained(_lowerCamelCase )
lowercase__ : List[Any] = tokenizer_p.from_pretrained(_lowerCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_lowerCamelCase ,_lowerCamelCase ) )
shutil.rmtree(_lowerCamelCase )
# Save tokenizer rust, legacy_format=True
lowercase__ : List[Any] = tempfile.mkdtemp()
lowercase__ : Optional[Any] = tokenizer_r.save_pretrained(_lowerCamelCase ,legacy_format=_lowerCamelCase )
lowercase__ : Dict = tokenizer_p.save_pretrained(_lowerCamelCase )
# Checks it save with the same files
self.assertSequenceEqual(_lowerCamelCase ,_lowerCamelCase )
# Checks everything loads correctly in the same way
lowercase__ : List[Any] = tokenizer_r.from_pretrained(_lowerCamelCase )
lowercase__ : Optional[Any] = tokenizer_p.from_pretrained(_lowerCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_lowerCamelCase ,_lowerCamelCase ) )
shutil.rmtree(_lowerCamelCase )
# Save tokenizer rust, legacy_format=False
lowercase__ : Dict = tempfile.mkdtemp()
lowercase__ : List[Any] = tokenizer_r.save_pretrained(_lowerCamelCase ,legacy_format=_lowerCamelCase )
lowercase__ : Dict = tokenizer_p.save_pretrained(_lowerCamelCase )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowercase__ : Any = tokenizer_r.from_pretrained(_lowerCamelCase )
lowercase__ : int = tokenizer_p.from_pretrained(_lowerCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_lowerCamelCase ,_lowerCamelCase ) )
shutil.rmtree(_lowerCamelCase )
@require_torch
def UpperCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
if not self.test_seqaseq:
return
lowercase__ : Dict = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Longer text that will definitely require truncation.
lowercase__ : Dict = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'''
''' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'''
''' will only worsen the violence and misery for millions of people.''',
]
lowercase__ : Optional[Any] = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'''
''' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'''
''' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
try:
lowercase__ : Optional[Any] = tokenizer.prepare_seqaseq_batch(
src_texts=_lowerCamelCase ,tgt_texts=_lowerCamelCase ,max_length=3 ,max_target_length=10 ,return_tensors='''pt''' ,src_lang='''eng_Latn''' ,tgt_lang='''ron_Latn''' ,)
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] ,3 )
self.assertEqual(batch.labels.shape[1] ,10 )
# max_target_length will default to max_length if not specified
lowercase__ : List[str] = tokenizer.prepare_seqaseq_batch(
_lowerCamelCase ,tgt_texts=_lowerCamelCase ,max_length=3 ,return_tensors='''pt''' )
self.assertEqual(batch.input_ids.shape[1] ,3 )
self.assertEqual(batch.labels.shape[1] ,3 )
lowercase__ : Optional[int] = tokenizer.prepare_seqaseq_batch(
src_texts=_lowerCamelCase ,max_length=3 ,max_target_length=10 ,return_tensors='''pt''' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] ,3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] ,3 )
self.assertNotIn('''decoder_input_ids''' ,_lowerCamelCase )
@unittest.skip('''Unfortunately way too slow to build a BPE with SentencePiece.''' )
def UpperCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
pass
def UpperCAmelCase ( self : Dict ) -> str:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowercase__ : List[Any] = [AddedToken('''<special>''' ,lstrip=_lowerCamelCase )]
lowercase__ : List[Any] = self.rust_tokenizer_class.from_pretrained(
_lowerCamelCase ,additional_special_tokens=_lowerCamelCase ,**_lowerCamelCase )
lowercase__ : int = tokenizer_r.encode('''Hey this is a <special> token''' )
lowercase__ : Tuple = tokenizer_r.encode('''<special>''' ,add_special_tokens=_lowerCamelCase )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
lowercase__ : Dict = self.rust_tokenizer_class.from_pretrained(
_lowerCamelCase ,additional_special_tokens=_lowerCamelCase ,**_lowerCamelCase ,)
lowercase__ : int = self.tokenizer_class.from_pretrained(
_lowerCamelCase ,additional_special_tokens=_lowerCamelCase ,**_lowerCamelCase )
lowercase__ : Union[str, Any] = tokenizer_p.encode('''Hey this is a <special> token''' )
lowercase__ : Optional[Any] = tokenizer_cr.encode('''Hey this is a <special> token''' )
self.assertEqual(_lowerCamelCase ,_lowerCamelCase )
self.assertEqual(_lowerCamelCase ,_lowerCamelCase )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class __A ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : int = 'facebook/nllb-200-distilled-600M'
lowerCAmelCase : List[str] = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
lowerCAmelCase : int = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
lowerCAmelCase : Dict = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def UpperCAmelCase ( cls : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name ,src_lang='''eng_Latn''' ,tgt_lang='''ron_Latn''' )
lowercase__ : str = 1
return cls
def UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Arab'''] ,256_001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Latn'''] ,256_002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''fra_Latn'''] ,256_057 )
def UpperCAmelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens ,_lowerCamelCase )
def UpperCAmelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
self.assertIn(_lowerCamelCase ,self.tokenizer.all_special_ids )
# fmt: off
lowercase__ : Optional[int] = [RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
# fmt: on
lowercase__ : List[str] = self.tokenizer.decode(_lowerCamelCase ,skip_special_tokens=_lowerCamelCase )
lowercase__ : List[Any] = self.tokenizer.decode(generated_ids[1:] ,skip_special_tokens=_lowerCamelCase )
self.assertEqual(_lowerCamelCase ,_lowerCamelCase )
self.assertNotIn(self.tokenizer.eos_token ,_lowerCamelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ : Any = ['''this is gunna be a long sentence ''' * 20]
assert isinstance(src_text[0] ,_lowerCamelCase )
lowercase__ : Tuple = 10
lowercase__ : Any = self.tokenizer(_lowerCamelCase ,max_length=_lowerCamelCase ,truncation=_lowerCamelCase ).input_ids[0]
self.assertEqual(ids[-1] ,2 )
self.assertEqual(ids[0] ,_lowerCamelCase )
self.assertEqual(len(_lowerCamelCase ) ,_lowerCamelCase )
def UpperCAmelCase ( self : int ) -> List[Any]:
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) ,[256_203, 3] )
def UpperCAmelCase ( self : int ) -> str:
"""simple docstring"""
lowercase__ : Optional[Any] = tempfile.mkdtemp()
lowercase__ : Tuple = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(_lowerCamelCase )
lowercase__ : Any = NllbTokenizer.from_pretrained(_lowerCamelCase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,_lowerCamelCase )
@require_torch
def UpperCAmelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
lowercase__ : Optional[int] = self.tokenizer(
self.src_text ,text_target=self.tgt_text ,padding=_lowerCamelCase ,truncation=_lowerCamelCase ,max_length=len(self.expected_src_tokens ) ,return_tensors='''pt''' ,)
lowercase__ : List[Any] = shift_tokens_right(
batch['''labels'''] ,self.tokenizer.pad_token_id ,self.tokenizer.lang_code_to_id['''ron_Latn'''] )
self.assertIsInstance(_lowerCamelCase ,_lowerCamelCase )
self.assertEqual((2, 15) ,batch.input_ids.shape )
self.assertEqual((2, 15) ,batch.attention_mask.shape )
lowercase__ : List[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens ,_lowerCamelCase )
self.assertEqual(_lowerCamelCase ,batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens ,[EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens ,[self.tokenizer.eos_token_id] )
def UpperCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
lowercase__ : Optional[int] = self.tokenizer(self.src_text ,padding=_lowerCamelCase ,truncation=_lowerCamelCase ,max_length=3 ,return_tensors='''pt''' )
lowercase__ : int = self.tokenizer(
text_target=self.tgt_text ,padding=_lowerCamelCase ,truncation=_lowerCamelCase ,max_length=10 ,return_tensors='''pt''' )
lowercase__ : Union[str, Any] = targets['''input_ids''']
lowercase__ : Union[str, Any] = shift_tokens_right(
_lowerCamelCase ,self.tokenizer.pad_token_id ,decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] ,)
self.assertEqual(batch.input_ids.shape[1] ,3 )
self.assertEqual(batch.decoder_input_ids.shape[1] ,10 )
@require_torch
def UpperCAmelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
lowercase__ : int = self.tokenizer._build_translation_inputs(
'''A test''' ,return_tensors='''pt''' ,src_lang='''eng_Latn''' ,tgt_lang='''fra_Latn''' )
self.assertEqual(
nested_simplify(_lowerCamelCase ) ,{
# A, test, EOS, en_XX
'''input_ids''': [[256_047, 70, 7_356, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 256_057,
} ,)
@require_torch
def UpperCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
lowercase__ : Dict = True
lowercase__ : List[Any] = self.tokenizer(
'''UN Chief says there is no military solution in Syria''' ,src_lang='''eng_Latn''' ,tgt_lang='''fra_Latn''' )
self.assertEqual(
inputs.input_ids ,[16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047] )
lowercase__ : Any = False
lowercase__ : List[Any] = self.tokenizer(
'''UN Chief says there is no military solution in Syria''' ,src_lang='''eng_Latn''' ,tgt_lang='''fra_Latn''' )
self.assertEqual(
inputs.input_ids ,[256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2] )
| 369
|
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]:
lowercase__ : int = [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
lowercase__ : Dict = True if '''large''' in model_name or '''huge''' in model_name else False
lowercase__ : Optional[int] = True if '''large''' in model_name or '''huge''' in model_name else False
lowercase__ : List[Any] = True if '''large''' in model_name or '''huge''' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
lowercase__ : Dict = [3, 3, 3, 3]
lowercase__ : str = [5, 5, 5, 5]
elif "fl4" in model_name:
lowercase__ : List[str] = [4, 4, 4, 4]
lowercase__ : Any = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
lowercase__ : List[str] = [3, 3, 3, 3]
if "lrf" in model_name:
lowercase__ : List[str] = [3, 3, 3, 3]
else:
lowercase__ : Optional[Any] = [2, 2, 2, 2]
if "tiny" in model_name:
lowercase__ : Optional[int] = 96
elif "small" in model_name:
lowercase__ : Union[str, Any] = 96
elif "base" in model_name:
lowercase__ : Tuple = 1_28
elif "large" in model_name:
lowercase__ : Any = 1_92
elif "xlarge" in model_name:
lowercase__ : Any = 2_56
elif "huge" in model_name:
lowercase__ : Union[str, Any] = 3_52
# set label information
lowercase__ : List[Any] = '''huggingface/label-files'''
if "large" in model_name or "huge" in model_name:
lowercase__ : Optional[int] = '''imagenet-22k-id2label.json'''
else:
lowercase__ : Optional[Any] = '''imagenet-1k-id2label.json'''
lowercase__ : Dict = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ : Union[str, Any] = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
lowercase__ : Optional[Any] = {v: k for k, v in idalabel.items()}
lowercase__ : int = FocalNetConfig(
embed_dim=__lowerCamelCase , depths=__lowerCamelCase , focal_levels=__lowerCamelCase , focal_windows=__lowerCamelCase , use_conv_embed=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase , use_post_layernorm=__lowerCamelCase , use_layerscale=__lowerCamelCase , )
return config
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
if "patch_embed.proj" in name:
lowercase__ : Any = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowercase__ : Tuple = name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if "layers" in name:
lowercase__ : Dict = '''encoder.''' + name
if "encoder.layers" in name:
lowercase__ : Tuple = name.replace('''encoder.layers''' , '''encoder.stages''' )
if "downsample.proj" in name:
lowercase__ : Union[str, Any] = name.replace('''downsample.proj''' , '''downsample.projection''' )
if "blocks" in name:
lowercase__ : Optional[Any] = name.replace('''blocks''' , '''layers''' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
lowercase__ : Dict = name.replace('''modulation.f''' , '''modulation.projection_in''' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
lowercase__ : Dict = name.replace('''modulation.h''' , '''modulation.projection_context''' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
lowercase__ : Optional[Any] = name.replace('''modulation.proj''' , '''modulation.projection_out''' )
if name == "norm.weight":
lowercase__ : Dict = '''layernorm.weight'''
if name == "norm.bias":
lowercase__ : Dict = '''layernorm.bias'''
if "head" in name:
lowercase__ : Dict = name.replace('''head''' , '''classifier''' )
else:
lowercase__ : List[Any] = '''focalnet.''' + name
return name
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> List[str]:
# fmt: off
lowercase__ : Any = {
'''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
'''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
'''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
'''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
'''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
'''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
'''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
'''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
'''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
'''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
}
# fmt: on
lowercase__ : Optional[int] = model_name_to_url[model_name]
print('''Checkpoint URL: ''' , __lowerCamelCase )
lowercase__ : str = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location='''cpu''' )['''model''']
# rename keys
for key in state_dict.copy().keys():
lowercase__ : int = state_dict.pop(__lowerCamelCase )
lowercase__ : Any = val
lowercase__ : List[Any] = get_focalnet_config(__lowerCamelCase )
lowercase__ : Optional[int] = FocalNetForImageClassification(__lowerCamelCase )
model.eval()
# load state dict
model.load_state_dict(__lowerCamelCase )
# verify conversion
lowercase__ : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase__ : int = BitImageProcessor(
do_resize=__lowerCamelCase , size={'''shortest_edge''': 2_56} , resample=PILImageResampling.BILINEAR , do_center_crop=__lowerCamelCase , crop_size=2_24 , do_normalize=__lowerCamelCase , image_mean=__lowerCamelCase , image_std=__lowerCamelCase , )
lowercase__ : str = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw )
lowercase__ : List[str] = processor(images=__lowerCamelCase , return_tensors='''pt''' )
lowercase__ : List[str] = transforms.Compose(
[
transforms.Resize(2_56 ),
transforms.CenterCrop(2_24 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
lowercase__ : Optional[Any] = image_transforms(__lowerCamelCase ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , __lowerCamelCase , atol=1E-4 )
lowercase__ : Optional[Any] = model(**__lowerCamelCase )
lowercase__ : Optional[int] = outputs.logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
print('''First values of logits:''' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
lowercase__ : Dict = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] )
elif model_name == "focalnet-tiny-lrf":
lowercase__ : Union[str, Any] = torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5] )
elif model_name == "focalnet-small":
lowercase__ : Optional[int] = torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1] )
elif model_name == "focalnet-small-lrf":
lowercase__ : Dict = torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1] )
elif model_name == "focalnet-base":
lowercase__ : List[str] = torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0] )
elif model_name == "focalnet-base-lrf":
lowercase__ : List[str] = torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8] )
assert torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
print(f"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(f"""{model_name}""" )
processor.push_to_hub(f"""{model_name}""" )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='focalnet-tiny',
type=str,
help='Name of the FocalNet model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub.',
)
lowerCAmelCase_ = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
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"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = '▁'
lowerCAmelCase_ = {'vocab_file': 'sentencepiece.bpe.model'}
lowerCAmelCase_ = {
'vocab_file': {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
}
}
lowerCAmelCase_ = {
'facebook/xglm-564M': 2_048,
}
class __A ( __UpperCamelCase ):
'''simple docstring'''
lowerCAmelCase : Tuple = VOCAB_FILES_NAMES
lowerCAmelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase : str = ["input_ids", "attention_mask"]
def __init__( self : str ,_snake_case : Tuple ,_snake_case : Optional[int]="<s>" ,_snake_case : int="</s>" ,_snake_case : Any="</s>" ,_snake_case : str="<s>" ,_snake_case : List[Any]="<unk>" ,_snake_case : Union[str, Any]="<pad>" ,_snake_case : Optional[Dict[str, Any]] = None ,**_snake_case : Dict ,) -> Dict:
"""simple docstring"""
lowercase__ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
lowercase__ : Dict = 7
lowercase__ : List[Any] = [f"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
lowercase__ : int = kwargs.get('''additional_special_tokens''' ,[] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=_lowerCAmelCase ,eos_token=_lowerCAmelCase ,unk_token=_lowerCAmelCase ,sep_token=_lowerCAmelCase ,cls_token=_lowerCAmelCase ,pad_token=_lowerCAmelCase ,sp_model_kwargs=self.sp_model_kwargs ,**_lowerCAmelCase ,)
lowercase__ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_lowerCAmelCase ) )
lowercase__ : List[Any] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
lowercase__ : Optional[Any] = 1
# Mimic fairseq token-to-id alignment for the first 4 token
lowercase__ : Optional[int] = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
lowercase__ : Optional[int] = len(self.sp_model )
lowercase__ : Optional[Any] = {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(_lowerCAmelCase )
lowercase__ : Tuple = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : str ) -> int:
"""simple docstring"""
lowercase__ : int = self.__dict__.copy()
lowercase__ : List[Any] = None
lowercase__ : int = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Optional[Any] ,_snake_case : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Tuple = d
# for backward compatibility
if not hasattr(self ,'''sp_model_kwargs''' ):
lowercase__ : Optional[int] = {}
lowercase__ : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def UpperCAmelCase ( self : Any ,_snake_case : List[int] ,_snake_case : Optional[List[int]] = None ) -> Optional[Any]:
"""simple docstring"""
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
lowercase__ : Optional[Any] = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def UpperCAmelCase ( self : Dict ,_snake_case : List[int] ,_snake_case : Optional[List[int]] = None ,_snake_case : bool = False ) -> str:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase ,token_ids_a=_lowerCAmelCase ,already_has_special_tokens=_lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(_lowerCAmelCase ))
return [1] + ([0] * len(_lowerCAmelCase )) + [1, 1] + ([0] * len(_lowerCAmelCase ))
def UpperCAmelCase ( self : List[Any] ,_snake_case : List[int] ,_snake_case : Optional[List[int]] = None ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : str = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def UpperCAmelCase ( self : str ) -> Any:
"""simple docstring"""
lowercase__ : Tuple = {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCAmelCase ( self : List[Any] ,_snake_case : str ) -> List[Any]:
"""simple docstring"""
return self.sp_model.encode(_lowerCAmelCase ,out_type=_lowerCAmelCase )
def UpperCAmelCase ( self : Any ,_snake_case : Tuple ) -> int:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowercase__ : Dict = self.sp_model.PieceToId(_lowerCAmelCase )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def UpperCAmelCase ( self : Optional[int] ,_snake_case : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def UpperCAmelCase ( self : str ,_snake_case : Tuple ) -> Dict:
"""simple docstring"""
lowercase__ : str = """""".join(_lowerCAmelCase ).replace(_lowerCAmelCase ,''' ''' ).strip()
return out_string
def UpperCAmelCase ( self : List[str] ,_snake_case : str ,_snake_case : Optional[str] = None ) -> int:
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase__ : List[Any] = os.path.join(
_lowerCAmelCase ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,_lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCAmelCase ,'''wb''' ) as fi:
lowercase__ : Dict = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
return (out_vocab_file,)
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"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : List[Any] = ["image_processor", "tokenizer"]
lowerCAmelCase : int = "ChineseCLIPImageProcessor"
lowerCAmelCase : str = ("BertTokenizer", "BertTokenizerFast")
def __init__( self : Tuple ,_snake_case : str=None ,_snake_case : Union[str, Any]=None ,**_snake_case : str ) -> Any:
"""simple docstring"""
lowercase__ : Any = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' ,_snake_case ,)
lowercase__ : Tuple = kwargs.pop('''feature_extractor''' )
lowercase__ : Any = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(_snake_case ,_snake_case )
lowercase__ : List[Any] = self.image_processor
def __call__( self : List[Any] ,_snake_case : Optional[int]=None ,_snake_case : Dict=None ,_snake_case : List[Any]=None ,**_snake_case : List[str] ) -> List[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
lowercase__ : str = self.tokenizer(_snake_case ,return_tensors=_snake_case ,**_snake_case )
if images is not None:
lowercase__ : str = self.image_processor(_snake_case ,return_tensors=_snake_case ,**_snake_case )
if text is not None and images is not None:
lowercase__ : Any = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_snake_case ) ,tensor_type=_snake_case )
def UpperCAmelCase ( self : Any ,*_snake_case : List[Any] ,**_snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
return self.tokenizer.batch_decode(*_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Union[str, Any] ,*_snake_case : Tuple ,**_snake_case : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.decode(*_snake_case ,**_snake_case )
@property
def UpperCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.tokenizer.model_input_names
lowercase__ : int = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,_snake_case ,)
return self.image_processor_class
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"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = "▁"
lowerCAmelCase_ = {"vocab_file": "sentencepiece.bpe.model"}
lowerCAmelCase_ = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
lowerCAmelCase_ = {
"facebook/xglm-564M": 2_048,
}
class __A ( _UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase : List[str] = VOCAB_FILES_NAMES
lowerCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase : List[str] = ["input_ids", "attention_mask"]
def __init__( self : str ,_snake_case : Optional[int] ,_snake_case : List[Any]="<s>" ,_snake_case : Tuple="</s>" ,_snake_case : List[Any]="</s>" ,_snake_case : List[Any]="<s>" ,_snake_case : Tuple="<unk>" ,_snake_case : Any="<pad>" ,_snake_case : Optional[Dict[str, Any]] = None ,**_snake_case : Union[str, Any] ,) -> Any:
"""simple docstring"""
lowercase__ : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
lowercase__ : List[str] = 7
lowercase__ : Union[str, Any] = [f"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
lowercase__ : Optional[Any] = kwargs.get('''additional_special_tokens''' ,[] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=lowercase_ ,eos_token=lowercase_ ,unk_token=lowercase_ ,sep_token=lowercase_ ,cls_token=lowercase_ ,pad_token=lowercase_ ,sp_model_kwargs=self.sp_model_kwargs ,**lowercase_ ,)
lowercase__ : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowercase_ ) )
lowercase__ : str = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
lowercase__ : Optional[Any] = 1
# Mimic fairseq token-to-id alignment for the first 4 token
lowercase__ : int = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
lowercase__ : Dict = len(self.sp_model )
lowercase__ : Dict = {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(lowercase_ )
lowercase__ : Union[str, Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ : Optional[Any] = self.__dict__.copy()
lowercase__ : int = None
lowercase__ : Optional[Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Optional[Any] ,_snake_case : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : int = d
# for backward compatibility
if not hasattr(self ,'''sp_model_kwargs''' ):
lowercase__ : Union[str, Any] = {}
lowercase__ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def UpperCAmelCase ( self : Any ,_snake_case : List[int] ,_snake_case : Optional[List[int]] = None ) -> Tuple:
"""simple docstring"""
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
lowercase__ : str = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def UpperCAmelCase ( self : Tuple ,_snake_case : List[int] ,_snake_case : Optional[List[int]] = None ,_snake_case : bool = False ) -> Optional[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase_ ,token_ids_a=lowercase_ ,already_has_special_tokens=lowercase_ )
if token_ids_a is None:
return [1] + ([0] * len(lowercase_ ))
return [1] + ([0] * len(lowercase_ )) + [1, 1] + ([0] * len(lowercase_ ))
def UpperCAmelCase ( self : List[Any] ,_snake_case : List[int] ,_snake_case : Optional[List[int]] = None ) -> List[Any]:
"""simple docstring"""
lowercase__ : Tuple = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def UpperCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def UpperCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
lowercase__ : str = {self.convert_ids_to_tokens(lowercase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCAmelCase ( self : List[str] ,_snake_case : str ) -> Optional[int]:
"""simple docstring"""
return self.sp_model.encode(lowercase_ ,out_type=lowercase_ )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowercase__ : int = self.sp_model.PieceToId(lowercase_ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def UpperCAmelCase ( self : Any ,_snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def UpperCAmelCase ( self : str ,_snake_case : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : str = """""".join(lowercase_ ).replace(lowercase_ ,''' ''' ).strip()
return out_string
def UpperCAmelCase ( self : Tuple ,_snake_case : str ,_snake_case : Optional[str] = None ) -> Any:
"""simple docstring"""
if not os.path.isdir(lowercase_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase__ : Optional[int] = os.path.join(
lowercase_ ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,lowercase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase_ ,'''wb''' ) as fi:
lowercase__ : List[Any] = self.sp_model.serialized_model_proto()
fi.write(lowercase_ )
return (out_vocab_file,)
| 371
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
'tokenization_roberta': ['RobertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['RobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'RobertaForCausalLM',
'RobertaForMaskedLM',
'RobertaForMultipleChoice',
'RobertaForQuestionAnswering',
'RobertaForSequenceClassification',
'RobertaForTokenClassification',
'RobertaModel',
'RobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRobertaForCausalLM',
'TFRobertaForMaskedLM',
'TFRobertaForMultipleChoice',
'TFRobertaForQuestionAnswering',
'TFRobertaForSequenceClassification',
'TFRobertaForTokenClassification',
'TFRobertaMainLayer',
'TFRobertaModel',
'TFRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'FlaxRobertaForCausalLM',
'FlaxRobertaForMaskedLM',
'FlaxRobertaForMultipleChoice',
'FlaxRobertaForQuestionAnswering',
'FlaxRobertaForSequenceClassification',
'FlaxRobertaForTokenClassification',
'FlaxRobertaModel',
'FlaxRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 302
| 0
|
"""simple docstring"""
import string
from math import logaa
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> int:
lowercase__ : int = document.translate(
str.maketrans('''''' , '''''' , string.punctuation ) ).replace('''\n''' , '''''' )
lowercase__ : Tuple = document_without_punctuation.split(''' ''' ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> int:
lowercase__ : str = corpus.lower().translate(
str.maketrans('''''' , '''''' , string.punctuation ) ) # strip all punctuation and replace it with ''
lowercase__ : List[str] = corpus_without_punctuation.split('''\n''' )
lowercase__ : List[str] = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__a ))
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> Any:
if smoothing:
if n == 0:
raise ValueError('''log10(0) is undefined.''' )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError('''df must be > 0''' )
elif n == 0:
raise ValueError('''log10(0) is undefined.''' )
return round(logaa(n / df ) , 3 )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
return round(tf * idf , 3 )
| 350
|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowerCAmelCase_ = logging.getLogger(__name__)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowerCAmelCase : Optional[str] = field(
default="NER" ,metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
lowerCAmelCase : bool = field(default=A_ ,metadata={"help": "Set this flag to use fast tokenization."} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : str = field(
metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} ,)
lowerCAmelCase : int = field(
default=1_2_8 ,metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} ,)
lowerCAmelCase : bool = field(
default=A_ ,metadata={"help": "Overwrite the cached training and evaluation sets"} )
def __UpperCAmelCase ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase__ : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase__ , lowercase__ , lowercase__ : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase__ , lowercase__ , lowercase__ : List[str] = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
lowercase__ : str = import_module('''tasks''' )
try:
lowercase__ : List[str] = getattr(__lowerCamelCase , model_args.task_type )
lowercase__ : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __lowerCamelCase )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
lowercase__ : Union[str, Any] = token_classification_task.get_labels(data_args.labels )
lowercase__ : Dict[int, str] = dict(enumerate(__lowerCamelCase ) )
lowercase__ : Optional[int] = len(__lowerCamelCase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase__ : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid={label: i for i, label in enumerate(__lowerCamelCase )} , cache_dir=model_args.cache_dir , )
lowercase__ : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
lowercase__ : str = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__lowerCamelCase , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase__ : str = (
TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowercase__ : str = (
TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(__lowerCamelCase , __lowerCamelCase ) -> Tuple[List[int], List[int]]:
lowercase__ : Tuple = np.argmax(__lowerCamelCase , axis=2 )
lowercase__ , lowercase__ : Tuple = preds.shape
lowercase__ : List[str] = [[] for _ in range(__lowerCamelCase )]
lowercase__ : Tuple = [[] for _ in range(__lowerCamelCase )]
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(__lowerCamelCase ) -> Dict:
lowercase__ , lowercase__ : List[Any] = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(__lowerCamelCase , __lowerCamelCase ),
"precision": precision_score(__lowerCamelCase , __lowerCamelCase ),
"recall": recall_score(__lowerCamelCase , __lowerCamelCase ),
"f1": fa_score(__lowerCamelCase , __lowerCamelCase ),
}
# Data collator
lowercase__ : Tuple = DataCollatorWithPadding(__lowerCamelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase__ : str = Trainer(
model=__lowerCamelCase , args=__lowerCamelCase , train_dataset=__lowerCamelCase , eval_dataset=__lowerCamelCase , compute_metrics=__lowerCamelCase , data_collator=__lowerCamelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase__ : int = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase__ : Optional[int] = trainer.evaluate()
lowercase__ : Union[str, Any] = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __lowerCamelCase , __lowerCamelCase )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__lowerCamelCase )
# Predict
if training_args.do_predict:
lowercase__ : Optional[int] = TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = trainer.predict(__lowerCamelCase )
lowercase__ , lowercase__ : Tuple = align_predictions(__lowerCamelCase , __lowerCamelCase )
lowercase__ : Dict = os.path.join(training_args.output_dir , '''test_results.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
for key, value in metrics.items():
logger.info(''' %s = %s''' , __lowerCamelCase , __lowerCamelCase )
writer.write('''%s = %s\n''' % (key, value) )
# Save predictions
lowercase__ : Dict = os.path.join(training_args.output_dir , '''test_predictions.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
with open(os.path.join(data_args.data_dir , '''test.txt''' ) , '''r''' ) as f:
token_classification_task.write_predictions_to_file(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return results
def __UpperCAmelCase ( __lowerCamelCase ) -> List[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 302
| 0
|
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
lowerCAmelCase_ = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n year={2020},\n booktitle={Findings of EMNLP},\n}\n'
lowerCAmelCase_ = '\\n IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n'
lowerCAmelCase_ = '\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n predictions: list of predictions to score (as int64),\n except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).\n references: list of ground truth labels corresponding to the predictions (as int64),\n except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n "accuracy": Accuracy\n "f1": F1 score\n "precision": Precision@10\nExamples:\n\n >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\') # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0, \'f1\': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')\n >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'precision@10\': 1.0}\n\n'
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Tuple:
return float((preds == labels).mean() )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> List[str]:
lowercase__ : Tuple = simple_accuracy(__lowerCamelCase , __lowerCamelCase )
lowercase__ : List[str] = float(fa_score(y_true=__lowerCamelCase , y_pred=__lowerCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> List[str]:
lowercase__ : int = np.array(__lowerCamelCase )
lowercase__ : List[Any] = np.array(__lowerCamelCase )
lowercase__ : Optional[Any] = en_sentvecs.shape[0]
# mean centering
lowercase__ : Optional[int] = en_sentvecs - np.mean(__lowerCamelCase , axis=0 )
lowercase__ : int = in_sentvecs - np.mean(__lowerCamelCase , axis=0 )
lowercase__ : List[str] = cdist(__lowerCamelCase , __lowerCamelCase , '''cosine''' )
lowercase__ : Any = np.array(range(__lowerCamelCase ) )
lowercase__ : Dict = sim.argsort(axis=1 )[:, :10]
lowercase__ : Any = np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase ( self : Any ) -> List[Any]:
"""simple docstring"""
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
'''You should supply a configuration name selected in '''
'''[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", '''
'''\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", '''
'''\"wiki-ner\"]''' )
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': datasets.Value('''int64''' )
if self.config_name != '''cvit-mkb-clsr'''
else datasets.Sequence(datasets.Value('''float32''' ) ),
'''references''': datasets.Value('''int64''' )
if self.config_name != '''cvit-mkb-clsr'''
else datasets.Sequence(datasets.Value('''float32''' ) ),
} ) ,codebase_urls=[] ,reference_urls=[] ,format='''numpy''' if self.config_name != '''cvit-mkb-clsr''' else None ,)
def UpperCAmelCase ( self : Tuple ,_snake_case : Any ,_snake_case : int ) -> Dict:
"""simple docstring"""
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(lowercase_ ,lowercase_ )}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(lowercase_ ,lowercase_ )
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(lowercase_ ,lowercase_ )}
else:
raise KeyError(
'''You should supply a configuration name selected in '''
'''[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", '''
'''\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", '''
'''\"wiki-ner\"]''' )
| 351
|
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase_ = 16
lowerCAmelCase_ = 32
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = 16 ) -> Optional[int]:
lowercase__ : Optional[int] = AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowercase__ : List[str] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : List[str] = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=__lowerCamelCase , max_length=__lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase__ : Dict = datasets.map(
__lowerCamelCase , batched=__lowerCamelCase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : int = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ : List[str] = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase__ : List[str] = 16
elif accelerator.mixed_precision != "no":
lowercase__ : List[Any] = 8
else:
lowercase__ : Optional[int] = None
return tokenizer.pad(
__lowerCamelCase , padding='''longest''' , max_length=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_tensors='''pt''' , )
# Instantiate dataloaders.
lowercase__ : Dict = DataLoader(
tokenized_datasets['''train'''] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
lowercase__ : Union[str, Any] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowerCAmelCase_ = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Tuple:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , __lowerCamelCase ) == "1":
lowercase__ : Any = 2
# Initialize accelerator
lowercase__ : str = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : List[Any] = config['''lr''']
lowercase__ : Union[str, Any] = int(config['''num_epochs'''] )
lowercase__ : List[str] = int(config['''seed'''] )
lowercase__ : Any = int(config['''batch_size'''] )
lowercase__ : int = evaluate.load('''glue''' , '''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__lowerCamelCase )
def inner_training_loop(__lowerCamelCase ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__lowerCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : Optional[Any] = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=__lowerCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase__ : str = model.to(accelerator.device )
# Instantiate optimizer
lowercase__ : Optional[int] = AdamW(params=model.parameters() , lr=__lowerCamelCase )
lowercase__ , lowercase__ : List[str] = get_dataloaders(__lowerCamelCase , __lowerCamelCase )
# Instantiate scheduler
lowercase__ : Optional[Any] = get_linear_schedule_with_warmup(
optimizer=__lowerCamelCase , num_warmup_steps=1_00 , num_training_steps=(len(__lowerCamelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = accelerator.prepare(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# Now we train the model
for epoch in range(__lowerCamelCase ):
model.train()
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase__ : int = model(**__lowerCamelCase )
lowercase__ : Optional[int] = outputs.loss
accelerator.backward(__lowerCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ : Tuple = model(**__lowerCamelCase )
lowercase__ : Dict = outputs.logits.argmax(dim=-1 )
lowercase__ , lowercase__ : Any = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=__lowerCamelCase , references=__lowerCamelCase , )
lowercase__ : Optional[int] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , __lowerCamelCase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( ) -> Tuple:
lowercase__ : List[str] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=__lowerCamelCase , default=__lowerCamelCase , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
lowercase__ : Union[str, Any] = parser.parse_args()
lowercase__ : Union[str, Any] = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
main()
| 302
| 0
|
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __UpperCAmelCase ( ) -> Dict:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
lowercase__ : int = '''__test_patch_submodule_mock__'''
with patch_submodule(_test_patching , '''os.path.join''' , _snake_case ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def __UpperCAmelCase ( ) -> Optional[int]:
assert _test_patching.open is open
lowercase__ : str = '''__test_patch_submodule_builtin_mock__'''
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , '''open''' , _snake_case ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def __UpperCAmelCase ( ) -> List[str]:
lowercase__ : Tuple = '''__test_patch_submodule_missing_mock__'''
with patch_submodule(_test_patching , '''pandas.read_csv''' , _snake_case ):
pass
def __UpperCAmelCase ( ) -> Optional[int]:
lowercase__ : Union[str, Any] = '''__test_patch_submodule_missing_builtin_mock__'''
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , '''len''' , _snake_case ) is None
with patch_submodule(_test_patching , '''len''' , _snake_case ):
assert _test_patching.len is mock
assert _test_patching.len is len
def __UpperCAmelCase ( ) -> Optional[int]:
lowercase__ : Optional[Any] = '''__test_patch_submodule_start_and_stop_mock__'''
lowercase__ : Optional[int] = patch_submodule(_test_patching , '''open''' , _snake_case )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def __UpperCAmelCase ( ) -> Any:
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
lowercase__ : Dict = '''__test_patch_submodule_successive_join__'''
lowercase__ : Optional[Any] = '''__test_patch_submodule_successive_dirname__'''
lowercase__ : Dict = '''__test_patch_submodule_successive_rename__'''
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , '''os.path.join''' , _snake_case ):
with patch_submodule(_test_patching , '''os.rename''' , _snake_case ):
with patch_submodule(_test_patching , '''os.path.dirname''' , _snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , '''os.rename''' , _snake_case ):
with patch_submodule(_test_patching , '''os.path.join''' , _snake_case ):
with patch_submodule(_test_patching , '''os.path.dirname''' , _snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def __UpperCAmelCase ( ) -> Optional[Any]:
lowercase__ : str = '''__test_patch_submodule_doesnt_exist_mock__'''
with patch_submodule(_test_patching , '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''' , _snake_case ):
pass
with patch_submodule(_test_patching , '''os.__attribute_that_doesn_exist__''' , _snake_case ):
pass
| 352
|
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( self : int ) -> str:
"""simple docstring"""
lowercase__ : List[Any] = '''hf-internal-testing/tiny-random-t5'''
lowercase__ : List[Any] = AutoTokenizer.from_pretrained(_snake_case )
lowercase__ : int = AutoModelForSeqaSeqLM.from_pretrained(_snake_case )
lowercase__ : str = tokenizer('''This is me''' ,return_tensors='''pt''' )
lowercase__ : Tuple = model.to_bettertransformer()
self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
lowercase__ : Optional[int] = model.generate(**_snake_case )
lowercase__ : List[Any] = model.reverse_bettertransformer()
self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_snake_case )
lowercase__ : Tuple = AutoModelForSeqaSeqLM.from_pretrained(_snake_case )
self.assertFalse(
any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
lowercase__ : int = model_reloaded.generate(**_snake_case )
self.assertTrue(torch.allclose(_snake_case ,_snake_case ) )
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : List[str] = '''hf-internal-testing/tiny-random-t5'''
lowercase__ : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_snake_case )
lowercase__ : Union[str, Any] = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(_snake_case ):
model.save_pretrained(_snake_case )
lowercase__ : int = model.reverse_bettertransformer()
model.save_pretrained(_snake_case )
| 302
| 0
|
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : Union[str, Any] ,_snake_case : List[str] ,_snake_case : Tuple ,_snake_case : Optional[Any]=0.0 ,_snake_case : Dict = None ,_snake_case : int = "geglu" ,_snake_case : List[Any] = None ,_snake_case : Optional[int] = False ,_snake_case : Tuple = False ,_snake_case : str = False ,_snake_case : Optional[Any] = False ,_snake_case : List[str] = True ,_snake_case : List[str] = "layer_norm" ,_snake_case : Optional[int] = False ,) -> str:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[int] = only_cross_attention
lowercase__ : Tuple = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
lowercase__ : List[str] = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
f""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""" )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
lowercase__ : List[Any] = AdaLayerNorm(snake_case__ ,snake_case__ )
elif self.use_ada_layer_norm_zero:
lowercase__ : List[Any] = AdaLayerNormZero(snake_case__ ,snake_case__ )
else:
lowercase__ : Optional[int] = nn.LayerNorm(snake_case__ ,elementwise_affine=snake_case__ )
lowercase__ : Optional[Any] = Attention(
query_dim=snake_case__ ,heads=snake_case__ ,dim_head=snake_case__ ,dropout=snake_case__ ,bias=snake_case__ ,cross_attention_dim=cross_attention_dim if only_cross_attention else None ,upcast_attention=snake_case__ ,)
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
lowercase__ : Optional[Any] = (
AdaLayerNorm(snake_case__ ,snake_case__ )
if self.use_ada_layer_norm
else nn.LayerNorm(snake_case__ ,elementwise_affine=snake_case__ )
)
lowercase__ : List[Any] = Attention(
query_dim=snake_case__ ,cross_attention_dim=cross_attention_dim if not double_self_attention else None ,heads=snake_case__ ,dim_head=snake_case__ ,dropout=snake_case__ ,bias=snake_case__ ,upcast_attention=snake_case__ ,) # is self-attn if encoder_hidden_states is none
else:
lowercase__ : Union[str, Any] = None
lowercase__ : int = None
# 3. Feed-forward
lowercase__ : Optional[int] = nn.LayerNorm(snake_case__ ,elementwise_affine=snake_case__ )
lowercase__ : Union[str, Any] = FeedForward(snake_case__ ,dropout=snake_case__ ,activation_fn=snake_case__ ,final_dropout=snake_case__ )
# let chunk size default to None
lowercase__ : Optional[int] = None
lowercase__ : Union[str, Any] = 0
def UpperCAmelCase ( self : Tuple ,_snake_case : List[str] ,_snake_case : List[str] ) -> str:
"""simple docstring"""
lowercase__ : Optional[int] = chunk_size
lowercase__ : Tuple = dim
def UpperCAmelCase ( self : int ,_snake_case : Optional[Any] ,_snake_case : str = None ,_snake_case : Optional[Any] = None ,_snake_case : List[Any] = None ,_snake_case : List[str] = None ,_snake_case : List[str] = None ,_snake_case : Union[str, Any] = None ,) -> Optional[Any]:
"""simple docstring"""
if self.use_ada_layer_norm:
lowercase__ : Union[str, Any] = self.norma(snake_case__ ,snake_case__ )
elif self.use_ada_layer_norm_zero:
lowercase__ : str = self.norma(
snake_case__ ,snake_case__ ,snake_case__ ,hidden_dtype=hidden_states.dtype )
else:
lowercase__ : Union[str, Any] = self.norma(snake_case__ )
lowercase__ : str = cross_attention_kwargs if cross_attention_kwargs is not None else {}
lowercase__ : Union[str, Any] = self.attna(
snake_case__ ,encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None ,attention_mask=snake_case__ ,**snake_case__ ,)
if self.use_ada_layer_norm_zero:
lowercase__ : int = gate_msa.unsqueeze(1 ) * attn_output
lowercase__ : str = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
lowercase__ : List[str] = (
self.norma(snake_case__ ,snake_case__ ) if self.use_ada_layer_norm else self.norma(snake_case__ )
)
lowercase__ : Optional[int] = self.attna(
snake_case__ ,encoder_hidden_states=snake_case__ ,attention_mask=snake_case__ ,**snake_case__ ,)
lowercase__ : Optional[int] = attn_output + hidden_states
# 3. Feed-forward
lowercase__ : Optional[Any] = self.norma(snake_case__ )
if self.use_ada_layer_norm_zero:
lowercase__ : Any = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
f"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""" )
lowercase__ : Optional[int] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
lowercase__ : str = torch.cat(
[self.ff(snake_case__ ) for hid_slice in norm_hidden_states.chunk(snake_case__ ,dim=self._chunk_dim )] ,dim=self._chunk_dim ,)
else:
lowercase__ : Tuple = self.ff(snake_case__ )
if self.use_ada_layer_norm_zero:
lowercase__ : int = gate_mlp.unsqueeze(1 ) * ff_output
lowercase__ : Dict = ff_output + hidden_states
return hidden_states
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] ,_snake_case : Union[str, Any] ,_snake_case : List[str] = None ,_snake_case : Optional[Any] = 4 ,_snake_case : Union[str, Any] = 0.0 ,_snake_case : Any = "geglu" ,_snake_case : Union[str, Any] = False ,) -> int:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[Any] = int(dim * mult )
lowercase__ : Optional[Any] = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
lowercase__ : Tuple = GELU(snake_case__ ,snake_case__ )
if activation_fn == "gelu-approximate":
lowercase__ : Any = GELU(snake_case__ ,snake_case__ ,approximate='''tanh''' )
elif activation_fn == "geglu":
lowercase__ : Tuple = GEGLU(snake_case__ ,snake_case__ )
elif activation_fn == "geglu-approximate":
lowercase__ : Any = ApproximateGELU(snake_case__ ,snake_case__ )
lowercase__ : Union[str, Any] = nn.ModuleList([] )
# project in
self.net.append(snake_case__ )
# project dropout
self.net.append(nn.Dropout(snake_case__ ) )
# project out
self.net.append(nn.Linear(snake_case__ ,snake_case__ ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(snake_case__ ) )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : int ) -> Optional[Any]:
"""simple docstring"""
for module in self.net:
lowercase__ : List[Any] = module(snake_case__ )
return hidden_states
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : int ,_snake_case : str ,_snake_case : Any ,_snake_case : int = "none" ) -> Tuple:
"""simple docstring"""
super().__init__()
lowercase__ : str = nn.Linear(snake_case__ ,snake_case__ )
lowercase__ : List[Any] = approximate
def UpperCAmelCase ( self : List[str] ,_snake_case : int ) -> Dict:
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(snake_case__ ,approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ,approximate=self.approximate ).to(dtype=gate.dtype )
def UpperCAmelCase ( self : Optional[int] ,_snake_case : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.proj(snake_case__ )
lowercase__ : str = self.gelu(snake_case__ )
return hidden_states
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : str ,_snake_case : List[str] ,_snake_case : Dict ) -> int:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = nn.Linear(snake_case__ ,dim_out * 2 )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : int ) -> Any:
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(snake_case__ )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def UpperCAmelCase ( self : List[str] ,_snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Dict = self.proj(snake_case__ ).chunk(2 ,dim=-1 )
return hidden_states * self.gelu(snake_case__ )
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : List[str] ,_snake_case : List[str] ) -> Tuple:
"""simple docstring"""
super().__init__()
lowercase__ : List[str] = nn.Linear(snake_case__ ,snake_case__ )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : str ) -> int:
"""simple docstring"""
lowercase__ : Dict = self.proj(snake_case__ )
return x * torch.sigmoid(1.702 * x )
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : Optional[int] ,_snake_case : Optional[int] ) -> List[str]:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[Any] = nn.Embedding(snake_case__ ,snake_case__ )
lowercase__ : List[Any] = nn.SiLU()
lowercase__ : Optional[int] = nn.Linear(snake_case__ ,embedding_dim * 2 )
lowercase__ : Optional[int] = nn.LayerNorm(snake_case__ ,elementwise_affine=snake_case__ )
def UpperCAmelCase ( self : Optional[int] ,_snake_case : Dict ,_snake_case : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.linear(self.silu(self.emb(snake_case__ ) ) )
lowercase__ : List[Any] = torch.chunk(snake_case__ ,2 )
lowercase__ : Optional[Any] = self.norm(snake_case__ ) * (1 + scale) + shift
return x
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : str ,_snake_case : Any ,_snake_case : Dict ) -> Any:
"""simple docstring"""
super().__init__()
lowercase__ : Union[str, Any] = CombinedTimestepLabelEmbeddings(snake_case__ ,snake_case__ )
lowercase__ : Dict = nn.SiLU()
lowercase__ : Tuple = nn.Linear(snake_case__ ,6 * embedding_dim ,bias=snake_case__ )
lowercase__ : Optional[Any] = nn.LayerNorm(snake_case__ ,elementwise_affine=snake_case__ ,eps=1e-6 )
def UpperCAmelCase ( self : Tuple ,_snake_case : Optional[Any] ,_snake_case : Union[str, Any] ,_snake_case : Dict ,_snake_case : List[str]=None ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.linear(self.silu(self.emb(snake_case__ ,snake_case__ ,hidden_dtype=snake_case__ ) ) )
lowercase__ : Any = emb.chunk(6 ,dim=1 )
lowercase__ : Any = self.norm(snake_case__ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : List[str] ,_snake_case : Union[str, Any] ,_snake_case : Any ,_snake_case : Any = None ,_snake_case : Dict = 1e-5 ) -> Optional[int]:
"""simple docstring"""
super().__init__()
lowercase__ : Dict = num_groups
lowercase__ : Dict = eps
if act_fn is None:
lowercase__ : Union[str, Any] = None
else:
lowercase__ : List[Any] = get_activation(snake_case__ )
lowercase__ : int = nn.Linear(snake_case__ ,out_dim * 2 )
def UpperCAmelCase ( self : List[str] ,_snake_case : str ,_snake_case : Optional[int] ) -> Optional[int]:
"""simple docstring"""
if self.act:
lowercase__ : List[Any] = self.act(snake_case__ )
lowercase__ : Optional[int] = self.linear(snake_case__ )
lowercase__ : str = emb[:, :, None, None]
lowercase__ : Any = emb.chunk(2 ,dim=1 )
lowercase__ : int = F.group_norm(snake_case__ ,self.num_groups ,eps=self.eps )
lowercase__ : List[Any] = x * (1 + scale) + shift
return x
| 353
|
"""simple docstring"""
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> Any:
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
if not is_sharded:
lowercase__ : List[str] = os.path.abspath(__lowerCamelCase )
logger.info(f"""Loading PyTorch weights from {pt_path}""" )
lowercase__ : List[Any] = torch.load(__lowerCamelCase , map_location='''cpu''' )
logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.""" )
lowercase__ : int = convert_pytorch_state_dict_to_flax(__lowerCamelCase , __lowerCamelCase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
lowercase__ : Dict = convert_pytorch_sharded_state_dict_to_flax(__lowerCamelCase , __lowerCamelCase )
return flax_state_dict
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> (Tuple[str], np.ndarray):
def is_key_or_prefix_key_in_dict(__lowerCamelCase ) -> bool:
return len(set(__lowerCamelCase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
lowercase__ : int = pt_tuple_key[:-1] + ('''scale''',)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
lowercase__ : Union[str, Any] = pt_tuple_key[:-1] + ('''mean''',)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
lowercase__ : Any = pt_tuple_key[:-1] + ('''var''',)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
lowercase__ : Tuple = pt_tuple_key[:-1] + ('''embedding''',)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowercase__ : str = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
lowercase__ : str = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowercase__ : Union[str, Any] = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
lowercase__ : Optional[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowercase__ : Optional[int] = pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowercase__ : List[Any] = pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
lowercase__ : List[str] = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
lowercase__ : List[str] = pt_tuple_key[-2] + '''_g'''
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
lowercase__ : List[str] = pt_tuple_key[-2] + '''_v'''
if name is not None:
lowercase__ : Optional[Any] = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
# convert pytorch tensor to numpy
lowercase__ : Optional[Any] = {k: v.numpy() for k, v in pt_state_dict.items()}
lowercase__ : List[Any] = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
lowercase__ : str = flax_model.params['''params''']
else:
lowercase__ : Optional[int] = flax_model.params
lowercase__ : Optional[Any] = flatten_dict(__lowerCamelCase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowercase__ : Tuple = flatten_dict(flax_model.params['''batch_stats'''] )
random_flax_state_dict.update(__lowerCamelCase )
lowercase__ : int = {}
lowercase__ : List[str] = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
lowercase__ : Union[str, Any] = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase__ : Optional[Any] = tuple(pt_key.split('''.''' ) )
# remove base model prefix if necessary
lowercase__ : Union[str, Any] = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase__ : Union[str, Any] = pt_tuple_key[1:]
# Correctly rename weight parameters
lowercase__ , lowercase__ : List[str] = rename_key_and_reshape_tensor(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# add model prefix if necessary
lowercase__ : Union[str, Any] = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowercase__ : Dict = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
lowercase__ : int = jnp.asarray(__lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__lowerCamelCase , __lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
lowercase__ : Tuple = jnp.asarray(__lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
lowercase__ : Any = jnp.asarray(__lowerCamelCase )
return unflatten_dict(__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Dict:
import torch
# Load the index
lowercase__ : Dict = {}
for shard_file in shard_filenames:
# load using msgpack utils
lowercase__ : Optional[int] = torch.load(__lowerCamelCase )
lowercase__ : str = {k: v.numpy() for k, v in pt_state_dict.items()}
lowercase__ : Dict = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowercase__ : Optional[Any] = flax_model.params['''params''']
lowercase__ : List[Any] = flatten_dict(__lowerCamelCase )
random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats'''] ) )
else:
lowercase__ : Union[str, Any] = flax_model.params
lowercase__ : Tuple = flatten_dict(__lowerCamelCase )
lowercase__ : Tuple = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
lowercase__ : int = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase__ : List[str] = tuple(pt_key.split('''.''' ) )
# remove base model prefix if necessary
lowercase__ : Tuple = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase__ : List[str] = pt_tuple_key[1:]
# Correctly rename weight parameters
lowercase__ , lowercase__ : str = rename_key_and_reshape_tensor(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# add model prefix if necessary
lowercase__ : Union[str, Any] = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowercase__ : Dict = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
lowercase__ : Union[str, Any] = jnp.asarray(__lowerCamelCase )
continue
if "var" in flax_key[-1]:
lowercase__ : str = jnp.asarray(__lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__lowerCamelCase , __lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
lowercase__ : List[str] = jnp.asarray(__lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
lowercase__ : Union[str, Any] = jnp.asarray(__lowerCamelCase )
return unflatten_dict(__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
lowercase__ : List[str] = os.path.abspath(__lowerCamelCase )
logger.info(f"""Loading Flax weights from {flax_checkpoint_path}""" )
# import correct flax class
lowercase__ : Optional[int] = getattr(__lowerCamelCase , '''Flax''' + model.__class__.__name__ )
# load flax weight dict
with open(__lowerCamelCase , '''rb''' ) as state_f:
try:
lowercase__ : str = from_bytes(__lowerCamelCase , state_f.read() )
except UnpicklingError:
raise EnvironmentError(f"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """ )
return load_flax_weights_in_pytorch_model(__lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> List[str]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
# check if we have bf16 weights
lowercase__ : Any = flatten_dict(jax.tree_util.tree_map(lambda __lowerCamelCase : x.dtype == jnp.bfloataa , __lowerCamelCase ) ).values()
if any(__lowerCamelCase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
'''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
'''before loading those in PyTorch model.''' )
lowercase__ : Union[str, Any] = jax.tree_util.tree_map(
lambda __lowerCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , __lowerCamelCase )
lowercase__ : Tuple = flatten_dict(__lowerCamelCase )
lowercase__ : List[str] = pt_model.state_dict()
lowercase__ : int = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split('''.''' )[0] for k in pt_model_dict.keys()}
)
lowercase__ : int = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split('''.''' )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
lowercase__ : List[str] = []
lowercase__ : Tuple = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
lowercase__ : List[Any] = flax_key_tuple[0] == pt_model.base_model_prefix
lowercase__ : Optional[int] = '''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase__ : Tuple = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
lowercase__ : Optional[Any] = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowerCamelCase ) not in pt_model_dict:
# conv layer
lowercase__ : Dict = flax_key_tuple[:-1] + ('''weight''',)
lowercase__ : List[str] = jnp.transpose(__lowerCamelCase , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCamelCase ) not in pt_model_dict:
# linear layer
lowercase__ : Optional[int] = flax_key_tuple[:-1] + ('''weight''',)
lowercase__ : str = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
lowercase__ : Dict = flax_key_tuple[:-1] + ('''weight''',)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
lowercase__ : Any = flax_key_tuple[:-1] + ('''running_mean''',)
elif "var" in flax_key_tuple[-1]:
lowercase__ : Dict = flax_key_tuple[:-1] + ('''running_var''',)
if "batch_stats" in flax_state:
lowercase__ : Union[str, Any] = '''.'''.join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
lowercase__ : Dict = '''.'''.join(__lowerCamelCase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
lowercase__ : Optional[int] = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
lowercase__ : str = key.split('''.''' )
lowercase__ : Optional[Any] = None
if key_components[-3::2] == ["parametrizations", "original0"]:
lowercase__ : List[str] = key_components[-2] + '''_g'''
elif key_components[-3::2] == ["parametrizations", "original1"]:
lowercase__ : str = key_components[-2] + '''_v'''
if name is not None:
lowercase__ : Optional[int] = key_components[:-3] + [name]
lowercase__ : List[str] = '''.'''.join(__lowerCamelCase )
lowercase__ : List[Any] = key
if flax_key in special_pt_names:
lowercase__ : Any = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
else:
# add weight to pytorch dict
lowercase__ : List[str] = np.asarray(__lowerCamelCase ) if not isinstance(__lowerCamelCase , np.ndarray ) else flax_tensor
lowercase__ : List[str] = torch.from_numpy(__lowerCamelCase )
# remove from missing keys
missing_keys.remove(__lowerCamelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(__lowerCamelCase )
pt_model.load_state_dict(__lowerCamelCase )
# re-transform missing_keys to list
lowercase__ : Optional[Any] = list(__lowerCamelCase )
if len(__lowerCamelCase ) > 0:
logger.warning(
'''Some weights of the Flax model were not used when initializing the PyTorch model'''
f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
''' FlaxBertForSequenceClassification model).''' )
else:
logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n""" )
if len(__lowerCamelCase ) > 0:
logger.warning(
f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
''' use it for predictions and inference.''' )
else:
logger.warning(
f"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
'''If your task is similar to the task the model of the checkpoint was trained on, '''
f"""you can already use {pt_model.__class__.__name__} for predictions without further training.""" )
return pt_model
| 302
| 0
|
"""simple docstring"""
import torch
def __UpperCAmelCase ( ) -> str:
if torch.cuda.is_available():
lowercase__ : Optional[int] = torch.cuda.device_count()
else:
lowercase__ : Any = 0
print(f"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__main__":
main()
| 354
|
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Any ,_snake_case : UNetaDModel ,_snake_case : UNetaDModel ,_snake_case : DDPMScheduler ,_snake_case : Any ,) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[int] = value_function
lowercase__ : Optional[int] = unet
lowercase__ : Tuple = scheduler
lowercase__ : Dict = env
lowercase__ : int = env.get_dataset()
lowercase__ : Dict = {}
for key in self.data.keys():
try:
lowercase__ : Optional[Any] = self.data[key].mean()
except: # noqa: E722
pass
lowercase__ : List[Any] = {}
for key in self.data.keys():
try:
lowercase__ : str = self.data[key].std()
except: # noqa: E722
pass
lowercase__ : Tuple = env.observation_space.shape[0]
lowercase__ : Optional[int] = env.action_space.shape[0]
def UpperCAmelCase ( self : str ,_snake_case : Any ,_snake_case : int ) -> Optional[Any]:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def UpperCAmelCase ( self : Dict ,_snake_case : int ,_snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Dict ) -> Optional[int]:
"""simple docstring"""
if type(_snake_case ) is dict:
return {k: self.to_torch(_snake_case ) for k, v in x_in.items()}
elif torch.is_tensor(_snake_case ):
return x_in.to(self.unet.device )
return torch.tensor(_snake_case ,device=self.unet.device )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Any ,_snake_case : int ,_snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
for key, val in cond.items():
lowercase__ : List[Any] = val.clone()
return x_in
def UpperCAmelCase ( self : int ,_snake_case : Optional[int] ,_snake_case : List[Any] ,_snake_case : int ,_snake_case : int ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Any = x.shape[0]
lowercase__ : Dict = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
lowercase__ : Dict = torch.full((batch_size,) ,_snake_case ,device=self.unet.device ,dtype=torch.long )
for _ in range(_snake_case ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
lowercase__ : int = self.value_function(x.permute(0 ,2 ,1 ) ,_snake_case ).sample
lowercase__ : Optional[Any] = torch.autograd.grad([y.sum()] ,[x] )[0]
lowercase__ : List[str] = self.scheduler._get_variance(_snake_case )
lowercase__ : Union[str, Any] = torch.exp(0.5 * posterior_variance )
lowercase__ : Optional[int] = model_std * grad
lowercase__ : Optional[Any] = 0
lowercase__ : str = x.detach()
lowercase__ : Dict = x + scale * grad
lowercase__ : str = self.reset_xa(_snake_case ,_snake_case ,self.action_dim )
lowercase__ : Union[str, Any] = self.unet(x.permute(0 ,2 ,1 ) ,_snake_case ).sample.permute(0 ,2 ,1 )
# TODO: verify deprecation of this kwarg
lowercase__ : Dict = self.scheduler.step(_snake_case ,_snake_case ,_snake_case ,predict_epsilon=_snake_case )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
lowercase__ : Dict = self.reset_xa(_snake_case ,_snake_case ,self.action_dim )
lowercase__ : Union[str, Any] = self.to_torch(_snake_case )
return x, y
def __call__( self : Union[str, Any] ,_snake_case : Any ,_snake_case : Tuple=64 ,_snake_case : Any=32 ,_snake_case : Optional[Any]=2 ,_snake_case : str=0.1 ) -> List[Any]:
"""simple docstring"""
lowercase__ : Any = self.normalize(_snake_case ,'''observations''' )
lowercase__ : Tuple = obs[None].repeat(_snake_case ,axis=0 )
lowercase__ : Dict = {0: self.to_torch(_snake_case )}
lowercase__ : int = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
lowercase__ : Optional[int] = randn_tensor(_snake_case ,device=self.unet.device )
lowercase__ : Tuple = self.reset_xa(_snake_case ,_snake_case ,self.action_dim )
lowercase__ : str = self.to_torch(_snake_case )
# run the diffusion process
lowercase__ , lowercase__ : int = self.run_diffusion(_snake_case ,_snake_case ,_snake_case ,_snake_case )
# sort output trajectories by value
lowercase__ : Optional[Any] = y.argsort(0 ,descending=_snake_case ).squeeze()
lowercase__ : str = x[sorted_idx]
lowercase__ : str = sorted_values[:, :, : self.action_dim]
lowercase__ : Optional[int] = actions.detach().cpu().numpy()
lowercase__ : List[str] = self.de_normalize(_snake_case ,key='''actions''' )
# select the action with the highest value
if y is not None:
lowercase__ : str = 0
else:
# if we didn't run value guiding, select a random action
lowercase__ : str = np.random.randint(0 ,_snake_case )
lowercase__ : int = denorm_actions[selected_index, 0]
return denorm_actions
| 302
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|
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( _UpperCAmelCase ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : List[Any] ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[int] = nn.ModuleList(SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Union[str, Any] ,_snake_case : Dict ,_snake_case : Optional[Any] ,_snake_case : Optional[Any] ,_snake_case : Tuple ,_snake_case : List[Any] = None ,_snake_case : List[Any] = None ,_snake_case : Optional[Any] = None ,_snake_case : Tuple = None ,_snake_case : int = False ,_snake_case : Dict = True ,) -> int:
"""simple docstring"""
for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,self.nets ) ):
lowercase__ : str = controlnet(
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,)
# merge samples
if i == 0:
lowercase__ : List[str] = down_samples, mid_sample
else:
lowercase__ : int = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def UpperCAmelCase ( self : int ,_snake_case : List[str] ,_snake_case : Any = True ,_snake_case : Optional[int] = None ,_snake_case : List[Any] = False ,_snake_case : Any = None ,) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Dict = 0
lowercase__ : Any = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
SCREAMING_SNAKE_CASE_ ,is_main_process=SCREAMING_SNAKE_CASE_ ,save_function=SCREAMING_SNAKE_CASE_ ,safe_serialization=SCREAMING_SNAKE_CASE_ ,variant=SCREAMING_SNAKE_CASE_ ,)
idx += 1
lowercase__ : str = model_path_to_save + f"""_{idx}"""
@classmethod
def UpperCAmelCase ( cls : Optional[Any] ,_snake_case : Dict ,**_snake_case : Any ) -> Any:
"""simple docstring"""
lowercase__ : Dict = 0
lowercase__ : Optional[int] = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
lowercase__ : Tuple = pretrained_model_path
while os.path.isdir(SCREAMING_SNAKE_CASE_ ):
lowercase__ : Any = ControlNetModel.from_pretrained(SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
controlnets.append(SCREAMING_SNAKE_CASE_ )
idx += 1
lowercase__ : Tuple = pretrained_model_path + f"""_{idx}"""
logger.info(f"""{len(SCREAMING_SNAKE_CASE_ )} controlnets loaded from {pretrained_model_path}.""" )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
raise ValueError(
f"""No ControlNets found under {os.path.dirname(SCREAMING_SNAKE_CASE_ )}. Expected at least {pretrained_model_path + "_0"}.""" )
return cls(SCREAMING_SNAKE_CASE_ )
| 355
|
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
lowerCAmelCase_ = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n'
lowerCAmelCase_ = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'
lowerCAmelCase_ = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,homepage='''https://github.com/krishnap25/mauve''' ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' ,id='''sequence''' ),
'''references''': datasets.Value('''string''' ,id='''sequence''' ),
} ) ,codebase_urls=['''https://github.com/krishnap25/mauve'''] ,reference_urls=[
'''https://arxiv.org/abs/2102.01454''',
'''https://github.com/krishnap25/mauve''',
] ,)
def UpperCAmelCase ( self : Dict ,_snake_case : Optional[Any] ,_snake_case : Any ,_snake_case : List[str]=None ,_snake_case : Tuple=None ,_snake_case : List[Any]=None ,_snake_case : Any=None ,_snake_case : Optional[int]="auto" ,_snake_case : Optional[int]=-1 ,_snake_case : Optional[int]=0.9 ,_snake_case : Any=5 ,_snake_case : Dict=500 ,_snake_case : Optional[int]="gpt2-large" ,_snake_case : Optional[Any]=-1 ,_snake_case : Tuple=1_024 ,_snake_case : Optional[int]=25 ,_snake_case : Dict=5 ,_snake_case : int=True ,_snake_case : Union[str, Any]=25 ,) -> Any:
"""simple docstring"""
lowercase__ : Any = compute_mauve(
p_text=_snake_case ,q_text=_snake_case ,p_features=_snake_case ,q_features=_snake_case ,p_tokens=_snake_case ,q_tokens=_snake_case ,num_buckets=_snake_case ,pca_max_data=_snake_case ,kmeans_explained_var=_snake_case ,kmeans_num_redo=_snake_case ,kmeans_max_iter=_snake_case ,featurize_model_name=_snake_case ,device_id=_snake_case ,max_text_length=_snake_case ,divergence_curve_discretization_size=_snake_case ,mauve_scaling_factor=_snake_case ,verbose=_snake_case ,seed=_snake_case ,)
return out
| 302
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|
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
lowerCAmelCase_ : List[Any] = logging.get_logger(__name__)
class __A ( __UpperCamelCase ):
'''simple docstring'''
def __init__( self : Optional[int] ,_snake_case : int ,_snake_case : int ,_snake_case : float ,**_snake_case : int ) -> int:
"""simple docstring"""
lowercase__ : List[Any] = feature_size
lowercase__ : Dict = sampling_rate
lowercase__ : str = padding_value
lowercase__ : Optional[Any] = kwargs.pop('''padding_side''' ,'''right''' )
lowercase__ : Any = kwargs.pop('''return_attention_mask''' ,_snake_case )
super().__init__(**_snake_case )
def UpperCAmelCase ( self : int ,_snake_case : Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] ,_snake_case : Union[bool, str, PaddingStrategy] = True ,_snake_case : Optional[int] = None ,_snake_case : bool = False ,_snake_case : Optional[int] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[Union[str, TensorType]] = None ,) -> Union[str, Any]:
"""simple docstring"""
if isinstance(_snake_case ,(list, tuple) ) and isinstance(processed_features[0] ,(dict, BatchFeature) ):
lowercase__ : List[Any] = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
'''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
f""" to this method that includes {self.model_input_names[0]}, but you provided"""
f""" {list(processed_features.keys() )}""" )
lowercase__ : List[Any] = processed_features[self.model_input_names[0]]
lowercase__ : Union[str, Any] = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(_snake_case ) == 0:
if return_attention_mask:
lowercase__ : Any = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
lowercase__ : Dict = required_input[0]
if isinstance(_snake_case ,(list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
lowercase__ : str = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(_snake_case ):
lowercase__ : Union[str, Any] = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(_snake_case ):
lowercase__ : List[str] = """tf"""
elif is_torch_tensor(_snake_case ):
lowercase__ : Tuple = """pt"""
elif isinstance(_snake_case ,(int, float, list, tuple, np.ndarray) ):
lowercase__ : Optional[Any] = """np"""
else:
raise ValueError(
f"""type of {first_element} unknown: {type(_snake_case )}. """
'''Should be one of a python, numpy, pytorch or tensorflow object.''' )
for key, value in processed_features.items():
if isinstance(value[0] ,(int, float) ):
lowercase__ : Union[str, Any] = to_numpy(_snake_case )
else:
lowercase__ : Union[str, Any] = [to_numpy(_snake_case ) for v in value]
# Convert padding_strategy in PaddingStrategy
lowercase__ : str = self._get_padding_strategies(padding=_snake_case ,max_length=_snake_case )
lowercase__ : str = processed_features[self.model_input_names[0]]
lowercase__ : List[Any] = len(_snake_case )
if not all(len(_snake_case ) == batch_size for v in processed_features.values() ):
raise ValueError('''Some items in the output dictionary have a different batch size than others.''' )
lowercase__ : int = []
for i in range(_snake_case ):
lowercase__ : Any = {k: v[i] for k, v in processed_features.items()}
# truncation
lowercase__ : str = self._truncate(
_snake_case ,max_length=_snake_case ,pad_to_multiple_of=_snake_case ,truncation=_snake_case ,)
truncated_inputs.append(_snake_case )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
lowercase__ : Optional[Any] = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
lowercase__ : str = PaddingStrategy.MAX_LENGTH
lowercase__ : Optional[int] = {}
for i in range(_snake_case ):
# padding
lowercase__ : List[Any] = self._pad(
truncated_inputs[i] ,max_length=_snake_case ,padding_strategy=_snake_case ,pad_to_multiple_of=_snake_case ,return_attention_mask=_snake_case ,)
for key, value in outputs.items():
if key not in batch_outputs:
lowercase__ : List[Any] = []
if value.dtype is np.dtype(np.floataa ):
lowercase__ : List[str] = value.astype(np.floataa )
batch_outputs[key].append(_snake_case )
return BatchFeature(_snake_case ,tensor_type=_snake_case )
def UpperCAmelCase ( self : Any ,_snake_case : Union[Dict[str, np.ndarray], BatchFeature] ,_snake_case : Optional[int] = None ,_snake_case : PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,_snake_case : Optional[int] = None ,_snake_case : Optional[bool] = None ,) -> List[Any]:
"""simple docstring"""
lowercase__ : Any = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
lowercase__ : Optional[int] = len(_snake_case )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
lowercase__ : Any = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
lowercase__ : Tuple = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_snake_case ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
lowercase__ : List[str] = np.ones(len(_snake_case ) ,dtype=np.intaa )
if needs_to_be_padded:
lowercase__ : Optional[int] = max_length - len(_snake_case )
if self.padding_side == "right":
if return_attention_mask:
lowercase__ : List[str] = np.pad(
processed_features['''attention_mask'''] ,(0, difference) )
lowercase__ : int = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
lowercase__ : Any = np.pad(
_snake_case ,_snake_case ,'''constant''' ,constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
lowercase__ : Dict = np.pad(
processed_features['''attention_mask'''] ,(difference, 0) )
lowercase__ : Union[str, Any] = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
lowercase__ : Dict = np.pad(
_snake_case ,_snake_case ,'''constant''' ,constant_values=self.padding_value )
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return processed_features
def UpperCAmelCase ( self : List[Any] ,_snake_case : Union[Dict[str, np.ndarray], BatchFeature] ,_snake_case : Optional[int] = None ,_snake_case : Optional[int] = None ,_snake_case : Optional[bool] = None ,) -> Any:
"""simple docstring"""
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.''' )
lowercase__ : Any = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
lowercase__ : int = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
lowercase__ : Optional[Any] = len(_snake_case ) > max_length
if needs_to_be_truncated:
lowercase__ : str = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
lowercase__ : Optional[Any] = processed_features["""attention_mask"""][:max_length]
return processed_features
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : str=False ,_snake_case : str=None ) -> List[str]:
"""simple docstring"""
if padding is not False:
if padding is True:
lowercase__ : str = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(_snake_case ,_snake_case ):
lowercase__ : Any = PaddingStrategy(_snake_case )
elif isinstance(_snake_case ,_snake_case ):
lowercase__ : str = padding
else:
lowercase__ : Any = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined""" )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
'''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.''' )
return padding_strategy
| 356
|
"""simple docstring"""
import math
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
lowercase__ : Tuple = 0
lowercase__ : Tuple = 0
while num > 0:
lowercase__ : int = num % 8
lowercase__ : Tuple = octal + (remainder * math.floor(math.pow(10 , __lowerCamelCase ) ))
counter += 1
lowercase__ : Optional[Any] = math.floor(num / 8 ) # basically /= 8 without remainder if any
# This formatting removes trailing '.0' from `octal`.
return f"""0o{int(__lowerCamelCase )}"""
def __UpperCAmelCase ( ) -> None:
print('''\n2 in octal is:''' )
print(decimal_to_octal(2 ) ) # = 2
print('''\n8 in octal is:''' )
print(decimal_to_octal(8 ) ) # = 10
print('''\n65 in octal is:''' )
print(decimal_to_octal(65 ) ) # = 101
print('''\n216 in octal is:''' )
print(decimal_to_octal(2_16 ) ) # = 330
print('''\n512 in octal is:''' )
print(decimal_to_octal(5_12 ) ) # = 1000
print('''\n''' )
if __name__ == "__main__":
main()
| 302
| 0
|
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( __lowerCamelCase ) -> List[str]: # picklable for multiprocessing
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __UpperCAmelCase ( ) -> List[Any]:
with parallel_backend('''spark''' ):
assert ParallelBackendConfig.backend_name == "spark"
lowercase__ : str = [1, 2, 3]
with pytest.raises(A__ ):
with parallel_backend('''unsupported backend''' ):
map_nested(A__ , A__ , num_proc=2 )
with pytest.raises(A__ ):
with parallel_backend('''unsupported backend''' ):
map_nested(A__ , A__ , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('''num_proc''' , [2, -1] )
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
lowercase__ : List[str] = [1, 2]
lowercase__ : Optional[int] = {'''a''': 1, '''b''': 2}
lowercase__ : List[Any] = {'''a''': [1, 2], '''b''': [3, 4]}
lowercase__ : Any = {'''a''': {'''1''': 1}, '''b''': 2}
lowercase__ : Union[str, Any] = {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
lowercase__ : Optional[Any] = [2, 3]
lowercase__ : Any = {'''a''': 2, '''b''': 3}
lowercase__ : int = {'''a''': [2, 3], '''b''': [4, 5]}
lowercase__ : Dict = {'''a''': {'''1''': 2}, '''b''': 3}
lowercase__ : str = {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
with parallel_backend('''spark''' ):
assert map_nested(A__ , A__ , num_proc=A__ ) == expected_map_nested_sa
assert map_nested(A__ , A__ , num_proc=A__ ) == expected_map_nested_sa
assert map_nested(A__ , A__ , num_proc=A__ ) == expected_map_nested_sa
assert map_nested(A__ , A__ , num_proc=A__ ) == expected_map_nested_sa
assert map_nested(A__ , A__ , num_proc=A__ ) == expected_map_nested_sa
| 357
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
lowerCAmelCase_ = [
'openmmlab/upernet-convnext-tiny',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
lowerCAmelCase_ = 'UperNetConfig'
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : int ,_snake_case : int ,_snake_case : Union[int, Tuple[int, int]] ,_snake_case : Union[int, Tuple[int, int], str] = 0 ,_snake_case : bool = False ,_snake_case : Union[int, Tuple[int, int]] = 1 ,) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[int] = nn.Convad(
in_channels=_snake_case ,out_channels=_snake_case ,kernel_size=_snake_case ,padding=_snake_case ,bias=_snake_case ,dilation=_snake_case ,)
lowercase__ : Tuple = nn.BatchNormad(_snake_case )
lowercase__ : List[str] = nn.ReLU()
def UpperCAmelCase ( self : str ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.conv(_snake_case )
lowercase__ : List[str] = self.batch_norm(_snake_case )
lowercase__ : Tuple = self.activation(_snake_case )
return output
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : int ,_snake_case : int ,_snake_case : int ) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = [
nn.AdaptiveAvgPoolad(_snake_case ),
UperNetConvModule(_snake_case ,_snake_case ,kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(_snake_case ) ,_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : Any = input
for layer in self.layers:
lowercase__ : int = layer(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : Tuple[int, ...] ,_snake_case : int ,_snake_case : int ,_snake_case : bool ) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : int = pool_scales
lowercase__ : Dict = align_corners
lowercase__ : Optional[Any] = in_channels
lowercase__ : Optional[Any] = channels
lowercase__ : int = []
for i, pool_scale in enumerate(_snake_case ):
lowercase__ : Optional[Any] = UperNetPyramidPoolingBlock(pool_scale=_snake_case ,in_channels=_snake_case ,channels=_snake_case )
self.blocks.append(_snake_case )
self.add_module(str(_snake_case ) ,_snake_case )
def UpperCAmelCase ( self : Any ,_snake_case : torch.Tensor ) -> List[torch.Tensor]:
"""simple docstring"""
lowercase__ : int = []
for ppm in self.blocks:
lowercase__ : Any = ppm(_snake_case )
lowercase__ : int = nn.functional.interpolate(
_snake_case ,size=x.size()[2:] ,mode='''bilinear''' ,align_corners=self.align_corners )
ppm_outs.append(_snake_case )
return ppm_outs
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : List[str] ,_snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
super().__init__()
lowercase__ : str = config
lowercase__ : Optional[Any] = config.pool_scales # e.g. (1, 2, 3, 6)
lowercase__ : Optional[Any] = in_channels
lowercase__ : Any = config.hidden_size
lowercase__ : Optional[Any] = False
lowercase__ : Optional[int] = nn.Convad(self.channels ,config.num_labels ,kernel_size=1 )
# PSP Module
lowercase__ : Dict = UperNetPyramidPoolingModule(
self.pool_scales ,self.in_channels[-1] ,self.channels ,align_corners=self.align_corners ,)
lowercase__ : str = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels ,self.channels ,kernel_size=3 ,padding=1 ,)
# FPN Module
lowercase__ : Any = nn.ModuleList()
lowercase__ : Union[str, Any] = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
lowercase__ : List[Any] = UperNetConvModule(_snake_case ,self.channels ,kernel_size=1 )
lowercase__ : Optional[int] = UperNetConvModule(self.channels ,self.channels ,kernel_size=3 ,padding=1 )
self.lateral_convs.append(_snake_case )
self.fpn_convs.append(_snake_case )
lowercase__ : int = UperNetConvModule(
len(self.in_channels ) * self.channels ,self.channels ,kernel_size=3 ,padding=1 ,)
def UpperCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
self.apply(self._init_weights )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[Any] ) -> List[str]:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
module.weight.data.normal_(mean=0.0 ,std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Optional[Any] ) -> str:
"""simple docstring"""
lowercase__ : Dict = inputs[-1]
lowercase__ : Optional[int] = [x]
psp_outs.extend(self.psp_modules(_snake_case ) )
lowercase__ : Optional[Any] = torch.cat(_snake_case ,dim=1 )
lowercase__ : List[str] = self.bottleneck(_snake_case )
return output
def UpperCAmelCase ( self : List[str] ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : Tuple = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(_snake_case ) )
# build top-down path
lowercase__ : List[Any] = len(_snake_case )
for i in range(used_backbone_levels - 1 ,0 ,-1 ):
lowercase__ : Union[str, Any] = laterals[i - 1].shape[2:]
lowercase__ : int = laterals[i - 1] + nn.functional.interpolate(
laterals[i] ,size=_snake_case ,mode='''bilinear''' ,align_corners=self.align_corners )
# build outputs
lowercase__ : List[str] = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 ,0 ,-1 ):
lowercase__ : Any = nn.functional.interpolate(
fpn_outs[i] ,size=fpn_outs[0].shape[2:] ,mode='''bilinear''' ,align_corners=self.align_corners )
lowercase__ : Any = torch.cat(_snake_case ,dim=1 )
lowercase__ : Any = self.fpn_bottleneck(_snake_case )
lowercase__ : str = self.classifier(_snake_case )
return output
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : List[Any] ,_snake_case : int = 2 ,_snake_case : int = 3 ,_snake_case : Union[int, Tuple[int, int]] = 1 ) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : int = config
lowercase__ : Dict = config.auxiliary_in_channels
lowercase__ : Optional[int] = config.auxiliary_channels
lowercase__ : List[Any] = config.auxiliary_num_convs
lowercase__ : List[Any] = config.auxiliary_concat_input
lowercase__ : str = in_index
lowercase__ : Any = (kernel_size // 2) * dilation
lowercase__ : Optional[Any] = []
convs.append(
UperNetConvModule(
self.in_channels ,self.channels ,kernel_size=_snake_case ,padding=_snake_case ,dilation=_snake_case ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels ,self.channels ,kernel_size=_snake_case ,padding=_snake_case ,dilation=_snake_case ) )
if self.num_convs == 0:
lowercase__ : List[str] = nn.Identity()
else:
lowercase__ : Dict = nn.Sequential(*_snake_case )
if self.concat_input:
lowercase__ : int = UperNetConvModule(
self.in_channels + self.channels ,self.channels ,kernel_size=_snake_case ,padding=kernel_size // 2 )
lowercase__ : List[str] = nn.Convad(self.channels ,config.num_labels ,kernel_size=1 )
def UpperCAmelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
self.apply(self._init_weights )
def UpperCAmelCase ( self : List[Any] ,_snake_case : List[Any] ) -> Dict:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
module.weight.data.normal_(mean=0.0 ,std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase ( self : List[str] ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : str = encoder_hidden_states[self.in_index]
lowercase__ : List[str] = self.convs(_snake_case )
if self.concat_input:
lowercase__ : Any = self.conv_cat(torch.cat([hidden_states, output] ,dim=1 ) )
lowercase__ : Dict = self.classifier(_snake_case )
return output
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Any = UperNetConfig
lowerCAmelCase : str = "pixel_values"
lowerCAmelCase : Dict = True
def UpperCAmelCase ( self : int ,_snake_case : str ) -> Optional[int]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def UpperCAmelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def UpperCAmelCase ( self : int ,_snake_case : str ,_snake_case : str=False ) -> List[str]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
lowercase__ : List[Any] = value
lowerCAmelCase_ = R'\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCAmelCase_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes." ,A_ ,)
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Tuple ) -> int:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : int = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
lowercase__ : Any = UperNetHead(_snake_case ,in_channels=self.backbone.channels )
lowercase__ : str = UperNetFCNHead(_snake_case ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length''' ) )
@replace_return_docstrings(output_type=_snake_case ,config_class=_CONFIG_FOR_DOC )
def UpperCAmelCase ( self : Dict ,_snake_case : Optional[torch.Tensor] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[torch.Tensor] = None ,_snake_case : Optional[bool] = None ,) -> Union[tuple, SemanticSegmenterOutput]:
"""simple docstring"""
lowercase__ : int = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : Any = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ : Any = output_attentions if output_attentions is not None else self.config.output_attentions
lowercase__ : Optional[Any] = self.backbone.forward_with_filtered_kwargs(
_snake_case ,output_hidden_states=_snake_case ,output_attentions=_snake_case )
lowercase__ : Optional[int] = outputs.feature_maps
lowercase__ : Tuple = self.decode_head(_snake_case )
lowercase__ : Optional[int] = nn.functional.interpolate(_snake_case ,size=pixel_values.shape[2:] ,mode='''bilinear''' ,align_corners=_snake_case )
lowercase__ : List[str] = None
if self.auxiliary_head is not None:
lowercase__ : str = self.auxiliary_head(_snake_case )
lowercase__ : Dict = nn.functional.interpolate(
_snake_case ,size=pixel_values.shape[2:] ,mode='''bilinear''' ,align_corners=_snake_case )
lowercase__ : Any = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''' )
else:
# compute weighted loss
lowercase__ : Union[str, Any] = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
lowercase__ : List[str] = loss_fct(_snake_case ,_snake_case )
lowercase__ : List[str] = loss_fct(_snake_case ,_snake_case )
lowercase__ : Optional[Any] = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
lowercase__ : Tuple = (logits,) + outputs[1:]
else:
lowercase__ : int = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=_snake_case ,logits=_snake_case ,hidden_states=outputs.hidden_states ,attentions=outputs.attentions ,)
| 302
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase_ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['ConvNextFeatureExtractor']
lowerCAmelCase_ = ['ConvNextImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConvNextForImageClassification',
'ConvNextModel',
'ConvNextPreTrainedModel',
'ConvNextBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'TFConvNextForImageClassification',
'TFConvNextModel',
'TFConvNextPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 358
|
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAmelCase_ = _symbol_database.Default()
lowerCAmelCase_ = _descriptor_pool.Default().AddSerializedFile(
B'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
lowerCAmelCase_ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
lowerCAmelCase_ = None
lowerCAmelCase_ = B'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
lowerCAmelCase_ = 45
lowerCAmelCase_ = 1_581
lowerCAmelCase_ = 1_517
lowerCAmelCase_ = 1_570
lowerCAmelCase_ = 1_584
lowerCAmelCase_ = 1_793
lowerCAmelCase_ = 1_795
lowerCAmelCase_ = 1_916
lowerCAmelCase_ = 1_864
lowerCAmelCase_ = 1_905
lowerCAmelCase_ = 1_919
lowerCAmelCase_ = 2_429
lowerCAmelCase_ = 2_208
lowerCAmelCase_ = 2_418
lowerCAmelCase_ = 2_323
lowerCAmelCase_ = 2_407
# @@protoc_insertion_point(module_scope)
| 302
| 0
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self : List[Any] ,*_snake_case : Optional[Any] ,**_snake_case : Tuple ) -> int:
"""simple docstring"""
warnings.warn(
'''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use BeitImageProcessor instead.''' ,a_ ,)
super().__init__(*a_ ,**a_ )
| 359
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Pix2StructConfig',
'Pix2StructTextConfig',
'Pix2StructVisionConfig',
],
'processing_pix2struct': ['Pix2StructProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['Pix2StructImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Pix2StructPreTrainedModel',
'Pix2StructForConditionalGeneration',
'Pix2StructVisionModel',
'Pix2StructTextModel',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 302
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConfig""",
"""XCLIPTextConfig""",
"""XCLIPVisionConfig""",
],
"""processing_x_clip""": ["""XCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
"""XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XCLIPModel""",
"""XCLIPPreTrainedModel""",
"""XCLIPTextModel""",
"""XCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 360
|
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
lowercase__ : List[str] = FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
lowercase__ : List[str] = AutoTokenizer.from_pretrained('''xlm-roberta-base''' )
lowercase__ : List[str] = '''The dog is cute and lives in the garden house'''
lowercase__ : int = jnp.array([tokenizer.encode(_snake_case )] )
lowercase__ : Any = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
lowercase__ : Tuple = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
lowercase__ : Optional[Any] = model(_snake_case )['''last_hidden_state''']
self.assertEqual(output.shape ,_snake_case )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] ,_snake_case ,atol=1e-3 ) )
| 302
| 0
|
"""simple docstring"""
import numpy as np
from PIL import Image
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> np.ndarray:
lowercase__ : Tuple = np.array(a__ )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input array is not a square matrix''' )
lowercase__ : Dict = 0
lowercase__ : int = 0
lowercase__ : Any = 0
lowercase__ : str = 0
# compute the shape of the output matrix
lowercase__ : str = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
lowercase__ : List[Any] = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
lowercase__ : Optional[Any] = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
lowercase__ : str = 0
lowercase__ : Any = 0
return updated_arr
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> np.ndarray:
lowercase__ : int = np.array(a__ )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input array is not a square matrix''' )
lowercase__ : List[Any] = 0
lowercase__ : Optional[int] = 0
lowercase__ : Optional[Any] = 0
lowercase__ : int = 0
# compute the shape of the output matrix
lowercase__ : List[str] = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
lowercase__ : Optional[Any] = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
lowercase__ : Any = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
lowercase__ : Dict = 0
lowercase__ : List[str] = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name='avgpooling', verbose=True)
# Loading the image
lowerCAmelCase_ = Image.open('path_to_image')
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 361
|
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = '#'
class __A :
'''simple docstring'''
def __init__( self : str ) -> None:
"""simple docstring"""
lowercase__ : dict = {}
def UpperCAmelCase ( self : List[str] ,_snake_case : str ) -> None:
"""simple docstring"""
lowercase__ : str = self._trie
for char in text:
if char not in trie:
lowercase__ : Union[str, Any] = {}
lowercase__ : Optional[Any] = trie[char]
lowercase__ : Dict = True
def UpperCAmelCase ( self : Tuple ,_snake_case : str ) -> tuple | list:
"""simple docstring"""
lowercase__ : Optional[Any] = self._trie
for char in prefix:
if char in trie:
lowercase__ : Union[str, Any] = trie[char]
else:
return []
return self._elements(_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : dict ) -> tuple:
"""simple docstring"""
lowercase__ : str = []
for c, v in d.items():
lowercase__ : List[Any] = [''' '''] if c == END else [(c + s) for s in self._elements(_snake_case )]
result.extend(_snake_case )
return tuple(_snake_case )
lowerCAmelCase_ = Trie()
lowerCAmelCase_ = ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
trie.insert_word(word)
def __UpperCAmelCase ( __lowerCamelCase ) -> tuple:
lowercase__ : List[Any] = trie.find_word(__lowerCamelCase )
return tuple(string + word for word in suffixes )
def __UpperCAmelCase ( ) -> None:
print(autocomplete_using_trie('''de''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 302
| 0
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
def UpperCAmelCase ( self : int ) -> List[Any]:
"""simple docstring"""
lowercase__ : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
lowercase__ : List[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
lowercase__ : Union[str, Any] = '''xvjiarui/stable-diffusion-2-inpainting'''
lowercase__ , lowercase__ : int = FlaxStableDiffusionInpaintPipeline.from_pretrained(_snake_case ,safety_checker=_snake_case )
lowercase__ : List[str] = '''Face of a yellow cat, high resolution, sitting on a park bench'''
lowercase__ : Optional[int] = jax.random.PRNGKey(0 )
lowercase__ : Optional[int] = 50
lowercase__ : Optional[int] = jax.device_count()
lowercase__ : int = num_samples * [prompt]
lowercase__ : Dict = num_samples * [init_image]
lowercase__ : Union[str, Any] = num_samples * [mask_image]
lowercase__ , lowercase__ , lowercase__ : List[str] = pipeline.prepare_inputs(_snake_case ,_snake_case ,_snake_case )
# shard inputs and rng
lowercase__ : str = replicate(_snake_case )
lowercase__ : Union[str, Any] = jax.random.split(_snake_case ,jax.device_count() )
lowercase__ : List[Any] = shard(_snake_case )
lowercase__ : Tuple = shard(_snake_case )
lowercase__ : Optional[Any] = shard(_snake_case )
lowercase__ : Union[str, Any] = pipeline(
_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,jit=_snake_case )
lowercase__ : Optional[Any] = output.images.reshape(_snake_case ,512 ,512 ,3 )
lowercase__ : List[str] = images[0, 253:256, 253:256, -1]
lowercase__ : Dict = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowercase__ : str = jnp.array(
[0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 362
|
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
lowerCAmelCase_ = logging.get_logger(__name__)
# General docstring
lowerCAmelCase_ = 'RegNetConfig'
# Base docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = [1, 1_088, 7, 7]
# Image classification docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = 'tabby, tabby cat'
lowerCAmelCase_ = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : int ,_snake_case : int ,_snake_case : int ,_snake_case : int = 3 ,_snake_case : int = 1 ,_snake_case : int = 1 ,_snake_case : Optional[str] = "relu" ,) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = nn.Convad(
_snake_case ,_snake_case ,kernel_size=_snake_case ,stride=_snake_case ,padding=kernel_size // 2 ,groups=_snake_case ,bias=_snake_case ,)
lowercase__ : List[Any] = nn.BatchNormad(_snake_case )
lowercase__ : Optional[int] = ACTaFN[activation] if activation is not None else nn.Identity()
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.convolution(_snake_case )
lowercase__ : Tuple = self.normalization(_snake_case )
lowercase__ : Tuple = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] ,_snake_case : RegNetConfig ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = RegNetConvLayer(
config.num_channels ,config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act )
lowercase__ : str = config.num_channels
def UpperCAmelCase ( self : int ,_snake_case : Dict ) -> str:
"""simple docstring"""
lowercase__ : Union[str, Any] = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
lowercase__ : Optional[int] = self.embedder(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : str ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ) -> Any:
"""simple docstring"""
super().__init__()
lowercase__ : List[str] = nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ,stride=_snake_case ,bias=_snake_case )
lowercase__ : Any = nn.BatchNormad(_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ) -> Tensor:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.convolution(_snake_case )
lowercase__ : Optional[int] = self.normalization(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : int ,_snake_case : int ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Any = nn.AdaptiveAvgPoolad((1, 1) )
lowercase__ : Dict = nn.Sequential(
nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.ReLU() ,nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.Sigmoid() ,)
def UpperCAmelCase ( self : int ,_snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.pooler(_snake_case )
lowercase__ : Union[str, Any] = self.attention(_snake_case )
lowercase__ : List[str] = hidden_state * attention
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> List[str]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = in_channels != out_channels or stride != 1
lowercase__ : Optional[int] = max(1 ,out_channels // config.groups_width )
lowercase__ : str = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : Optional[int] = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : str = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ : Tuple = hidden_state
lowercase__ : Union[str, Any] = self.layer(_snake_case )
lowercase__ : List[Any] = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : Optional[int] = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> Optional[int]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = in_channels != out_channels or stride != 1
lowercase__ : List[str] = max(1 ,out_channels // config.groups_width )
lowercase__ : Tuple = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : str = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetSELayer(_snake_case ,reduced_channels=int(round(in_channels / 4 ) ) ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : Optional[Any] = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ : str = hidden_state
lowercase__ : Optional[Any] = self.layer(_snake_case )
lowercase__ : int = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : str = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ,_snake_case : int = 2 ,) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[Any] = RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer
lowercase__ : Optional[Any] = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
_snake_case ,_snake_case ,_snake_case ,stride=_snake_case ,) ,*[layer(_snake_case ,_snake_case ,_snake_case ) for _ in range(depth - 1 )] ,)
def UpperCAmelCase ( self : Tuple ,_snake_case : int ) -> List[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.layers(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : RegNetConfig ) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : str = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
_snake_case ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,) )
lowercase__ : str = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(_snake_case ,config.depths[1:] ):
self.stages.append(RegNetStage(_snake_case ,_snake_case ,_snake_case ,depth=_snake_case ) )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ,_snake_case : bool = False ,_snake_case : bool = True ) -> BaseModelOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Dict = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase__ : int = hidden_states + (hidden_state,)
lowercase__ : Any = stage_module(_snake_case )
if output_hidden_states:
lowercase__ : Optional[int] = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_snake_case ,hidden_states=_snake_case )
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : int = RegNetConfig
lowerCAmelCase : List[Any] = "regnet"
lowerCAmelCase : Optional[int] = "pixel_values"
lowerCAmelCase : Union[str, Any] = True
def UpperCAmelCase ( self : Any ,_snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
nn.init.kaiming_normal_(module.weight ,mode='''fan_out''' ,nonlinearity='''relu''' )
elif isinstance(_snake_case ,(nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight ,1 )
nn.init.constant_(module.bias ,0 )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Dict ,_snake_case : Any=False ) -> Optional[int]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
lowercase__ : str = value
lowerCAmelCase_ = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCAmelCase_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Any ) -> Tuple:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Any = config
lowercase__ : List[str] = RegNetEmbeddings(_snake_case )
lowercase__ : Any = RegNetEncoder(_snake_case )
lowercase__ : Dict = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,modality='''vision''' ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCAmelCase ( self : Dict ,_snake_case : Tensor ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
"""simple docstring"""
lowercase__ : List[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ : Dict = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : Union[str, Any] = self.embedder(_snake_case )
lowercase__ : List[Any] = self.encoder(
_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : str = encoder_outputs[0]
lowercase__ : Optional[int] = self.pooler(_snake_case )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_snake_case ,pooler_output=_snake_case ,hidden_states=encoder_outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : int ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Optional[Any] = config.num_labels
lowercase__ : int = RegNetModel(_snake_case )
# classification head
lowercase__ : str = nn.Sequential(
nn.Flatten() ,nn.Linear(config.hidden_sizes[-1] ,config.num_labels ) if config.num_labels > 0 else nn.Identity() ,)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCAmelCase ( self : List[Any] ,_snake_case : Optional[torch.FloatTensor] = None ,_snake_case : Optional[torch.LongTensor] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ,) -> ImageClassifierOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Any = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : List[Any] = self.regnet(_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : List[str] = outputs.pooler_output if return_dict else outputs[1]
lowercase__ : Union[str, Any] = self.classifier(_snake_case )
lowercase__ : Optional[int] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase__ : List[Any] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase__ : Dict = '''single_label_classification'''
else:
lowercase__ : Optional[int] = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase__ : Union[str, Any] = MSELoss()
if self.num_labels == 1:
lowercase__ : List[Any] = loss_fct(logits.squeeze() ,labels.squeeze() )
else:
lowercase__ : Tuple = loss_fct(_snake_case ,_snake_case )
elif self.config.problem_type == "single_label_classification":
lowercase__ : Tuple = CrossEntropyLoss()
lowercase__ : str = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase__ : Any = BCEWithLogitsLoss()
lowercase__ : Union[str, Any] = loss_fct(_snake_case ,_snake_case )
if not return_dict:
lowercase__ : Tuple = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_snake_case ,logits=_snake_case ,hidden_states=outputs.hidden_states )
| 302
| 0
|
"""simple docstring"""
from __future__ import annotations
class __A :
'''simple docstring'''
def __init__( self : List[Any] ,_snake_case : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ : str = data
lowercase__ : Optional[int] = None
lowercase__ : Optional[int] = None
def __UpperCAmelCase ( __lowerCamelCase ) -> Union[str, Any]: # In Order traversal of the tree
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def __UpperCAmelCase ( __lowerCamelCase ) -> Dict:
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def __UpperCAmelCase ( __lowerCamelCase ) -> Union[str, Any]:
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def __UpperCAmelCase ( ) -> Dict: # Main function for testing.
lowercase__ : Any = Node(1 )
lowercase__ : Any = Node(2 )
lowercase__ : List[Any] = Node(3 )
lowercase__ : Any = Node(4 )
lowercase__ : Optional[Any] = Node(5 )
lowercase__ : List[str] = Node(6 )
lowercase__ : Any = Node(7 )
lowercase__ : Union[str, Any] = Node(8 )
lowercase__ : Union[str, Any] = Node(9 )
print(is_full_binary_tree(UpperCamelCase__ ) )
print(depth_of_tree(UpperCamelCase__ ) )
print('''Tree is: ''' )
display(UpperCamelCase__ )
if __name__ == "__main__":
main()
| 363
|
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = 1.6021E-19 # units = C
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 302
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}
class __A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
lowerCAmelCase : List[Any] = 'mgp-str'
def __init__( self : List[str] ,_snake_case : Dict=[32, 128] ,_snake_case : List[str]=4 ,_snake_case : Union[str, Any]=3 ,_snake_case : Optional[int]=27 ,_snake_case : Union[str, Any]=38 ,_snake_case : int=50_257 ,_snake_case : int=30_522 ,_snake_case : Dict=768 ,_snake_case : Tuple=12 ,_snake_case : List[str]=12 ,_snake_case : Dict=4.0 ,_snake_case : int=True ,_snake_case : Optional[int]=False ,_snake_case : int=1e-5 ,_snake_case : List[str]=0.0 ,_snake_case : str=0.0 ,_snake_case : Dict=0.0 ,_snake_case : List[str]=False ,_snake_case : Dict=0.02 ,**_snake_case : Optional[int] ,) -> int:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
lowercase__ : List[Any] = image_size
lowercase__ : str = patch_size
lowercase__ : List[Any] = num_channels
lowercase__ : str = max_token_length
lowercase__ : str = num_character_labels
lowercase__ : Optional[int] = num_bpe_labels
lowercase__ : Union[str, Any] = num_wordpiece_labels
lowercase__ : List[str] = hidden_size
lowercase__ : Any = num_hidden_layers
lowercase__ : Dict = num_attention_heads
lowercase__ : str = mlp_ratio
lowercase__ : List[Any] = distilled
lowercase__ : int = layer_norm_eps
lowercase__ : str = drop_rate
lowercase__ : Dict = qkv_bias
lowercase__ : int = attn_drop_rate
lowercase__ : Union[str, Any] = drop_path_rate
lowercase__ : Union[str, Any] = output_aa_attentions
lowercase__ : Tuple = initializer_range
| 364
|
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : str = ["pixel_values"]
def __init__( self : Tuple ,_snake_case : bool = True ,_snake_case : Optional[Dict[str, int]] = None ,_snake_case : PILImageResampling = PILImageResampling.BICUBIC ,_snake_case : bool = True ,_snake_case : bool = True ,_snake_case : Union[int, float] = 1 / 255 ,_snake_case : Dict[str, int] = None ,_snake_case : bool = True ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,**_snake_case : Optional[Any] ,) -> None:
"""simple docstring"""
super().__init__(**_snake_case )
lowercase__ : str = size if size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[int] = get_size_dict(_snake_case )
lowercase__ : List[Any] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[int] = get_size_dict(_snake_case ,default_to_square=_snake_case ,param_name='''crop_size''' )
lowercase__ : Tuple = do_resize
lowercase__ : List[Any] = do_rescale
lowercase__ : Any = do_normalize
lowercase__ : List[str] = do_center_crop
lowercase__ : Optional[Any] = crop_size
lowercase__ : Union[str, Any] = size
lowercase__ : Any = resample
lowercase__ : int = rescale_factor
lowercase__ : Tuple = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
lowercase__ : str = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase ( self : str ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : PILImageResampling = PILImageResampling.BILINEAR ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Dict ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : List[str] = get_size_dict(_snake_case )
if "shortest_edge" in size:
lowercase__ : str = get_resize_output_image_size(_snake_case ,size=size['''shortest_edge'''] ,default_to_square=_snake_case )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
lowercase__ : int = (size['''height'''], size['''width'''])
else:
raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}""" )
return resize(_snake_case ,size=_snake_case ,resample=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : List[Any] ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Tuple ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : Optional[Any] = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(_snake_case ,size=(size['''height'''], size['''width''']) ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : np.ndarray ,_snake_case : float ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Optional[int] ) -> np.ndarray:
"""simple docstring"""
return rescale(_snake_case ,scale=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : np.ndarray ,_snake_case : Union[float, List[float]] ,_snake_case : Union[float, List[float]] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Dict ,) -> np.ndarray:
"""simple docstring"""
return normalize(_snake_case ,mean=_snake_case ,std=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : ImageInput ,_snake_case : Optional[bool] = None ,_snake_case : Dict[str, int] = None ,_snake_case : PILImageResampling = None ,_snake_case : bool = None ,_snake_case : int = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[float] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[str, TensorType]] = None ,_snake_case : Union[str, ChannelDimension] = ChannelDimension.FIRST ,**_snake_case : List[str] ,) -> BatchFeature:
"""simple docstring"""
lowercase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
lowercase__ : int = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ : int = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ : Optional[int] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ : Optional[Any] = crop_size if crop_size is not None else self.crop_size
lowercase__ : Tuple = get_size_dict(_snake_case ,param_name='''crop_size''' ,default_to_square=_snake_case )
lowercase__ : Tuple = resample if resample is not None else self.resample
lowercase__ : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ : Union[str, Any] = image_mean if image_mean is not None else self.image_mean
lowercase__ : List[str] = image_std if image_std is not None else self.image_std
lowercase__ : Optional[int] = size if size is not None else self.size
lowercase__ : int = get_size_dict(_snake_case )
if not is_batched(_snake_case ):
lowercase__ : Optional[Any] = [images]
if not valid_images(_snake_case ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
# All transformations expect numpy arrays.
lowercase__ : str = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
lowercase__ : int = [self.resize(image=_snake_case ,size=_snake_case ,resample=_snake_case ) for image in images]
if do_center_crop:
lowercase__ : str = [self.center_crop(image=_snake_case ,size=_snake_case ) for image in images]
if do_rescale:
lowercase__ : Optional[Any] = [self.rescale(image=_snake_case ,scale=_snake_case ) for image in images]
if do_normalize:
lowercase__ : List[str] = [self.normalize(image=_snake_case ,mean=_snake_case ,std=_snake_case ) for image in images]
lowercase__ : Union[str, Any] = [to_channel_dimension_format(_snake_case ,_snake_case ) for image in images]
lowercase__ : Any = {'''pixel_values''': images}
return BatchFeature(data=_snake_case ,tensor_type=_snake_case )
| 302
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|
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : Tuple = 4_2
lowerCAmelCase : Tuple = None
lowerCAmelCase : Optional[int] = None
lowerCAmelCase_ = namedtuple('CoinsDistribResult', 'moves excess')
def __UpperCAmelCase ( __lowerCamelCase ) -> Tuple:
if root is None:
return 0
# Validation
def count_nodes(__lowerCamelCase ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(__lowerCamelCase ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(_UpperCAmelCase ) != count_coins(_UpperCAmelCase ):
raise ValueError('''The nodes number should be same as the number of coins''' )
# Main calculation
def get_distrib(__lowerCamelCase ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
lowercase__ : Any = get_distrib(node.left )
lowercase__ : str = get_distrib(node.right )
lowercase__ : List[str] = 1 - left_distrib_excess
lowercase__ : Optional[int] = 1 - right_distrib_excess
lowercase__ : Union[str, Any] = (
left_distrib_moves
+ right_distrib_moves
+ abs(_UpperCAmelCase )
+ abs(_UpperCAmelCase )
)
lowercase__ : List[Any] = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(_UpperCAmelCase , _UpperCAmelCase )
return get_distrib(_UpperCAmelCase )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 365
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_efficientnet': [
'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EfficientNetConfig',
'EfficientNetOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['EfficientNetImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'EfficientNetForImageClassification',
'EfficientNetModel',
'EfficientNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 302
| 0
|
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
lowercase__ : str = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowercase__ : Tuple = ''''''
lowercase__ : int = ''''''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(SCREAMING_SNAKE_CASE_ ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
lowercase__ , lowercase__ : Tuple = 0, 0
# length[i] shows the length of palindromic substring with center i
lowercase__ : str = [1 for i in range(len(SCREAMING_SNAKE_CASE_ ) )]
# for each character in new_string find corresponding palindromic string
lowercase__ : Any = 0
for j in range(len(SCREAMING_SNAKE_CASE_ ) ):
lowercase__ : List[str] = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(SCREAMING_SNAKE_CASE_ )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
lowercase__ : Tuple = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
lowercase__ : Union[str, Any] = j - k + 1 # noqa: E741
lowercase__ : List[str] = j + k - 1
# update max_length and start position
if max_length < length[j]:
lowercase__ : int = length[j]
lowercase__ : Any = j
# create that string
lowercase__ : int = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366
|
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = "cpu" , __lowerCamelCase = None ) -> None:
lowercase__ : List[str] = torch.load(__lowerCamelCase , map_location=__lowerCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__lowerCamelCase , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
lowercase__ : List[Any] = v.half()
if save_path is None: # overwrite src_path
lowercase__ : Any = src_path
torch.save(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 302
| 0
|
def __UpperCAmelCase ( __lowerCamelCase ) -> bool:
lowercase__ : List[str] = [int(UpperCamelCase__ ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(UpperCamelCase__ ) == 4 and all(0 <= int(UpperCamelCase__ ) <= 2_54 for octet in octets )
if __name__ == "__main__":
lowerCAmelCase_ = input().strip()
lowerCAmelCase_ = 'valid' if is_ip_va_address_valid(ip) else 'invalid'
print(F'''{ip} is a {valid_or_invalid} IP v4 address.''')
| 367
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : UNetaDModel
lowerCAmelCase : ScoreSdeVeScheduler
def __init__( self : Optional[Any] ,_snake_case : UNetaDModel ,_snake_case : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=_snake_case ,scheduler=_snake_case )
@torch.no_grad()
def __call__( self : Any ,_snake_case : int = 1 ,_snake_case : int = 2_000 ,_snake_case : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,_snake_case : Optional[str] = "pil" ,_snake_case : bool = True ,**_snake_case : Any ,) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.unet.config.sample_size
lowercase__ : Dict = (batch_size, 3, img_size, img_size)
lowercase__ : Tuple = self.unet
lowercase__ : Any = randn_tensor(_snake_case ,generator=_snake_case ) * self.scheduler.init_noise_sigma
lowercase__ : Union[str, Any] = sample.to(self.device )
self.scheduler.set_timesteps(_snake_case )
self.scheduler.set_sigmas(_snake_case )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
lowercase__ : Tuple = self.scheduler.sigmas[i] * torch.ones(shape[0] ,device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
lowercase__ : List[str] = self.unet(_snake_case ,_snake_case ).sample
lowercase__ : Optional[Any] = self.scheduler.step_correct(_snake_case ,_snake_case ,generator=_snake_case ).prev_sample
# prediction step
lowercase__ : str = model(_snake_case ,_snake_case ).sample
lowercase__ : List[Any] = self.scheduler.step_pred(_snake_case ,_snake_case ,_snake_case ,generator=_snake_case )
lowercase__ , lowercase__ : Optional[int] = output.prev_sample, output.prev_sample_mean
lowercase__ : Union[str, Any] = sample_mean.clamp(0 ,1 )
lowercase__ : int = sample.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
lowercase__ : Any = self.numpy_to_pil(_snake_case )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=_snake_case )
| 302
| 0
|
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
lowerCAmelCase_ = 'sshleifer/bart-tiny-random'
lowerCAmelCase_ = 'patrickvonplaten/t5-tiny-random'
@require_torch
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
return AutoConfig.from_pretrained(_lowerCamelCase )
def UpperCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
lowercase__ , *lowercase__ : str = create_student_by_copying_alternating_layers(_lowerCamelCase ,tempfile.mkdtemp() ,e=1 ,d=1 )
self.assertEqual(student.config.num_hidden_layers ,1 )
def UpperCAmelCase ( self : List[str] ) -> str:
"""simple docstring"""
lowercase__ , *lowercase__ : Union[str, Any] = create_student_by_copying_alternating_layers(_lowerCamelCase ,tempfile.mkdtemp() ,e=1 ,d=_lowerCamelCase )
def UpperCAmelCase ( self : Any ) -> str:
"""simple docstring"""
lowercase__ , *lowercase__ : str = create_student_by_copying_alternating_layers(_lowerCamelCase ,tempfile.mkdtemp() ,e=1 ,d=_lowerCamelCase )
self.assertEqual(student.config.encoder_layers ,1 )
self.assertEqual(student.config.decoder_layers ,self.teacher_config.encoder_layers )
def UpperCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
lowercase__ , *lowercase__ : Tuple = create_student_by_copying_alternating_layers(_lowerCamelCase ,tempfile.mkdtemp() ,e=1 ,d=1 )
self.assertEqual(student.config.encoder_layers ,1 )
self.assertEqual(student.config.decoder_layers ,1 )
def UpperCAmelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
with self.assertRaises(_lowerCamelCase ):
create_student_by_copying_alternating_layers(_lowerCamelCase ,tempfile.mkdtemp() ,e=_lowerCamelCase ,d=_lowerCamelCase )
| 368
|
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCAmelCase_ = {
'facebook/maskformer-swin-base-ade': (
'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = "maskformer"
lowerCAmelCase : Any = {"hidden_size": "mask_feature_size"}
lowerCAmelCase : Optional[int] = ["resnet", "swin"]
lowerCAmelCase : str = ["detr"]
def __init__( self : int ,_snake_case : int = 256 ,_snake_case : int = 256 ,_snake_case : float = 0.1 ,_snake_case : bool = False ,_snake_case : Optional[Dict] = None ,_snake_case : Optional[Dict] = None ,_snake_case : float = 0.02 ,_snake_case : float = 1.0 ,_snake_case : float = 1.0 ,_snake_case : float = 1.0 ,_snake_case : float = 20.0 ,_snake_case : Optional[bool] = None ,**_snake_case : Optional[Any] ,) -> Dict:
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowercase__ : Any = SwinConfig(
image_size=384 ,in_channels=3 ,patch_size=4 ,embed_dim=128 ,depths=[2, 2, 18, 2] ,num_heads=[4, 8, 16, 32] ,window_size=12 ,drop_path_rate=0.3 ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] ,)
if isinstance(_snake_case ,_snake_case ):
lowercase__ : List[str] = backbone_config.pop('''model_type''' )
lowercase__ : List[Any] = CONFIG_MAPPING[backbone_model_type]
lowercase__ : str = config_class.from_dict(_snake_case )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
f"""Supported model types: {",".join(self.backbones_supported )}""" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowercase__ : Union[str, Any] = DetrConfig()
else:
# verify that the decoder is supported
lowercase__ : Tuple = (
decoder_config.pop('''model_type''' ) if isinstance(_snake_case ,_snake_case ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f"""Transformer Decoder {decoder_type} not supported, please use one of"""
f""" {",".join(self.decoders_supported )}""" )
if isinstance(_snake_case ,_snake_case ):
lowercase__ : Optional[int] = CONFIG_MAPPING[decoder_type]
lowercase__ : Optional[Any] = config_class.from_dict(_snake_case )
lowercase__ : List[Any] = backbone_config
lowercase__ : List[Any] = decoder_config
# main feature dimension for the model
lowercase__ : List[str] = fpn_feature_size
lowercase__ : int = mask_feature_size
# initializer
lowercase__ : str = init_std
lowercase__ : str = init_xavier_std
# Hungarian matcher && loss
lowercase__ : Optional[int] = cross_entropy_weight
lowercase__ : List[Any] = dice_weight
lowercase__ : List[str] = mask_weight
lowercase__ : str = use_auxiliary_loss
lowercase__ : Optional[int] = no_object_weight
lowercase__ : Optional[Any] = output_auxiliary_logits
lowercase__ : Optional[Any] = self.decoder_config.encoder_attention_heads
lowercase__ : Optional[Any] = self.decoder_config.num_hidden_layers
super().__init__(**_snake_case )
@classmethod
def UpperCAmelCase ( cls : Any ,_snake_case : PretrainedConfig ,_snake_case : PretrainedConfig ,**_snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return cls(
backbone_config=_snake_case ,decoder_config=_snake_case ,**_snake_case ,)
def UpperCAmelCase ( self : str ) -> Dict[str, any]:
"""simple docstring"""
lowercase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase__ : int = self.backbone_config.to_dict()
lowercase__ : List[Any] = self.decoder_config.to_dict()
lowercase__ : List[str] = self.__class__.model_type
return output
| 302
| 0
|
"""simple docstring"""
lowerCAmelCase_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def __UpperCAmelCase ( __lowerCamelCase ) -> bytes:
if not isinstance(_snake_case , _snake_case ):
lowercase__ : Any = f"""a bytes-like object is required, not \'{data.__class__.__name__}\'"""
raise TypeError(_snake_case )
lowercase__ : List[str] = "".join(bin(_snake_case )[2:].zfill(8 ) for byte in data )
lowercase__ : Any = len(_snake_case ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowercase__ : str = B"=" * ((6 - len(_snake_case ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(_snake_case ) % 6)
else:
lowercase__ : Any = B""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(_snake_case ) , 6 ) ).encode()
+ padding
)
def __UpperCAmelCase ( __lowerCamelCase ) -> bytes:
if not isinstance(_snake_case , _snake_case ) and not isinstance(_snake_case , _snake_case ):
lowercase__ : str = (
"argument should be a bytes-like object or ASCII string, "
f"""not \'{encoded_data.__class__.__name__}\'"""
)
raise TypeError(_snake_case )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(_snake_case , _snake_case ):
try:
lowercase__ : List[str] = encoded_data.decode('''utf-8''' )
except UnicodeDecodeError:
raise ValueError('''base64 encoded data should only contain ASCII characters''' )
lowercase__ : Dict = encoded_data.count('''=''' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(_snake_case ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowercase__ : Tuple = encoded_data[:-padding]
lowercase__ : Dict = "".join(
bin(B64_CHARSET.index(_snake_case ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowercase__ : int = "".join(
bin(B64_CHARSET.index(_snake_case ) )[2:].zfill(6 ) for char in encoded_data )
lowercase__ : int = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(_snake_case ) , 8 )
]
return bytes(_snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 369
|
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]:
lowercase__ : int = [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
lowercase__ : Dict = True if '''large''' in model_name or '''huge''' in model_name else False
lowercase__ : Optional[int] = True if '''large''' in model_name or '''huge''' in model_name else False
lowercase__ : List[Any] = True if '''large''' in model_name or '''huge''' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
lowercase__ : Dict = [3, 3, 3, 3]
lowercase__ : str = [5, 5, 5, 5]
elif "fl4" in model_name:
lowercase__ : List[str] = [4, 4, 4, 4]
lowercase__ : Any = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
lowercase__ : List[str] = [3, 3, 3, 3]
if "lrf" in model_name:
lowercase__ : List[str] = [3, 3, 3, 3]
else:
lowercase__ : Optional[Any] = [2, 2, 2, 2]
if "tiny" in model_name:
lowercase__ : Optional[int] = 96
elif "small" in model_name:
lowercase__ : Union[str, Any] = 96
elif "base" in model_name:
lowercase__ : Tuple = 1_28
elif "large" in model_name:
lowercase__ : Any = 1_92
elif "xlarge" in model_name:
lowercase__ : Any = 2_56
elif "huge" in model_name:
lowercase__ : Union[str, Any] = 3_52
# set label information
lowercase__ : List[Any] = '''huggingface/label-files'''
if "large" in model_name or "huge" in model_name:
lowercase__ : Optional[int] = '''imagenet-22k-id2label.json'''
else:
lowercase__ : Optional[Any] = '''imagenet-1k-id2label.json'''
lowercase__ : Dict = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ : Union[str, Any] = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
lowercase__ : Optional[Any] = {v: k for k, v in idalabel.items()}
lowercase__ : int = FocalNetConfig(
embed_dim=__lowerCamelCase , depths=__lowerCamelCase , focal_levels=__lowerCamelCase , focal_windows=__lowerCamelCase , use_conv_embed=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase , use_post_layernorm=__lowerCamelCase , use_layerscale=__lowerCamelCase , )
return config
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
if "patch_embed.proj" in name:
lowercase__ : Any = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowercase__ : Tuple = name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if "layers" in name:
lowercase__ : Dict = '''encoder.''' + name
if "encoder.layers" in name:
lowercase__ : Tuple = name.replace('''encoder.layers''' , '''encoder.stages''' )
if "downsample.proj" in name:
lowercase__ : Union[str, Any] = name.replace('''downsample.proj''' , '''downsample.projection''' )
if "blocks" in name:
lowercase__ : Optional[Any] = name.replace('''blocks''' , '''layers''' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
lowercase__ : Dict = name.replace('''modulation.f''' , '''modulation.projection_in''' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
lowercase__ : Dict = name.replace('''modulation.h''' , '''modulation.projection_context''' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
lowercase__ : Optional[Any] = name.replace('''modulation.proj''' , '''modulation.projection_out''' )
if name == "norm.weight":
lowercase__ : Dict = '''layernorm.weight'''
if name == "norm.bias":
lowercase__ : Dict = '''layernorm.bias'''
if "head" in name:
lowercase__ : Dict = name.replace('''head''' , '''classifier''' )
else:
lowercase__ : List[Any] = '''focalnet.''' + name
return name
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> List[str]:
# fmt: off
lowercase__ : Any = {
'''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
'''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
'''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
'''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
'''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
'''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
'''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
'''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
'''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
'''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
}
# fmt: on
lowercase__ : Optional[int] = model_name_to_url[model_name]
print('''Checkpoint URL: ''' , __lowerCamelCase )
lowercase__ : str = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location='''cpu''' )['''model''']
# rename keys
for key in state_dict.copy().keys():
lowercase__ : int = state_dict.pop(__lowerCamelCase )
lowercase__ : Any = val
lowercase__ : List[Any] = get_focalnet_config(__lowerCamelCase )
lowercase__ : Optional[int] = FocalNetForImageClassification(__lowerCamelCase )
model.eval()
# load state dict
model.load_state_dict(__lowerCamelCase )
# verify conversion
lowercase__ : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase__ : int = BitImageProcessor(
do_resize=__lowerCamelCase , size={'''shortest_edge''': 2_56} , resample=PILImageResampling.BILINEAR , do_center_crop=__lowerCamelCase , crop_size=2_24 , do_normalize=__lowerCamelCase , image_mean=__lowerCamelCase , image_std=__lowerCamelCase , )
lowercase__ : str = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw )
lowercase__ : List[str] = processor(images=__lowerCamelCase , return_tensors='''pt''' )
lowercase__ : List[str] = transforms.Compose(
[
transforms.Resize(2_56 ),
transforms.CenterCrop(2_24 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
lowercase__ : Optional[Any] = image_transforms(__lowerCamelCase ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , __lowerCamelCase , atol=1E-4 )
lowercase__ : Optional[Any] = model(**__lowerCamelCase )
lowercase__ : Optional[int] = outputs.logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
print('''First values of logits:''' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
lowercase__ : Dict = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] )
elif model_name == "focalnet-tiny-lrf":
lowercase__ : Union[str, Any] = torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5] )
elif model_name == "focalnet-small":
lowercase__ : Optional[int] = torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1] )
elif model_name == "focalnet-small-lrf":
lowercase__ : Dict = torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1] )
elif model_name == "focalnet-base":
lowercase__ : List[str] = torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0] )
elif model_name == "focalnet-base-lrf":
lowercase__ : List[str] = torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8] )
assert torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
print(f"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(f"""{model_name}""" )
processor.push_to_hub(f"""{model_name}""" )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='focalnet-tiny',
type=str,
help='Name of the FocalNet model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub.',
)
lowerCAmelCase_ = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 302
| 0
|
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]: # picklable for multiprocessing
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __UpperCAmelCase ( ) -> List[str]:
with parallel_backend('''spark''' ):
assert ParallelBackendConfig.backend_name == "spark"
lowercase__ : int = [1, 2, 3]
with pytest.raises(__lowerCamelCase ):
with parallel_backend('''unsupported backend''' ):
map_nested(__lowerCamelCase , __lowerCamelCase , num_proc=2 )
with pytest.raises(__lowerCamelCase ):
with parallel_backend('''unsupported backend''' ):
map_nested(__lowerCamelCase , __lowerCamelCase , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('''num_proc''' , [2, -1] )
def __UpperCAmelCase ( __lowerCamelCase ) -> Dict:
lowercase__ : List[str] = [1, 2]
lowercase__ : str = {"a": 1, "b": 2}
lowercase__ : int = {"a": [1, 2], "b": [3, 4]}
lowercase__ : Optional[int] = {"a": {"1": 1}, "b": 2}
lowercase__ : Optional[int] = {"a": 1, "b": 2, "c": 3, "d": 4}
lowercase__ : List[str] = [2, 3]
lowercase__ : Union[str, Any] = {"a": 2, "b": 3}
lowercase__ : Tuple = {"a": [2, 3], "b": [4, 5]}
lowercase__ : Union[str, Any] = {"a": {"1": 2}, "b": 3}
lowercase__ : Optional[int] = {"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend('''spark''' ):
assert map_nested(__lowerCamelCase , __lowerCamelCase , num_proc=__lowerCamelCase ) == expected_map_nested_sa
assert map_nested(__lowerCamelCase , __lowerCamelCase , num_proc=__lowerCamelCase ) == expected_map_nested_sa
assert map_nested(__lowerCamelCase , __lowerCamelCase , num_proc=__lowerCamelCase ) == expected_map_nested_sa
assert map_nested(__lowerCamelCase , __lowerCamelCase , num_proc=__lowerCamelCase ) == expected_map_nested_sa
assert map_nested(__lowerCamelCase , __lowerCamelCase , num_proc=__lowerCamelCase ) == expected_map_nested_sa
| 370
|
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : List[Any] = ["image_processor", "tokenizer"]
lowerCAmelCase : int = "ChineseCLIPImageProcessor"
lowerCAmelCase : str = ("BertTokenizer", "BertTokenizerFast")
def __init__( self : Tuple ,_snake_case : str=None ,_snake_case : Union[str, Any]=None ,**_snake_case : str ) -> Any:
"""simple docstring"""
lowercase__ : Any = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' ,_snake_case ,)
lowercase__ : Tuple = kwargs.pop('''feature_extractor''' )
lowercase__ : Any = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(_snake_case ,_snake_case )
lowercase__ : List[Any] = self.image_processor
def __call__( self : List[Any] ,_snake_case : Optional[int]=None ,_snake_case : Dict=None ,_snake_case : List[Any]=None ,**_snake_case : List[str] ) -> List[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
lowercase__ : str = self.tokenizer(_snake_case ,return_tensors=_snake_case ,**_snake_case )
if images is not None:
lowercase__ : str = self.image_processor(_snake_case ,return_tensors=_snake_case ,**_snake_case )
if text is not None and images is not None:
lowercase__ : Any = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_snake_case ) ,tensor_type=_snake_case )
def UpperCAmelCase ( self : Any ,*_snake_case : List[Any] ,**_snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
return self.tokenizer.batch_decode(*_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Union[str, Any] ,*_snake_case : Tuple ,**_snake_case : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.decode(*_snake_case ,**_snake_case )
@property
def UpperCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.tokenizer.model_input_names
lowercase__ : int = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,_snake_case ,)
return self.image_processor_class
| 302
| 0
|
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCAmelCase_ = logging.getLogger(__name__)
lowerCAmelCase_ = 50 # max width of layer names
lowerCAmelCase_ = 70 # max width of quantizer names
def __UpperCAmelCase ( __lowerCamelCase ) -> List[Any]:
lowercase__ : str = parser.add_argument_group('''quant_trainer arguments''' )
group.add_argument('''--wprec''' , type=_lowerCAmelCase , default=8 , help='''weight precision''' )
group.add_argument('''--aprec''' , type=_lowerCAmelCase , default=8 , help='''activation precision''' )
group.add_argument('''--quant-per-tensor''' , action='''store_true''' , help='''per tensor weight scaling''' )
group.add_argument('''--quant-disable''' , action='''store_true''' , help='''disable all quantizers''' )
group.add_argument('''--quant-disable-embeddings''' , action='''store_true''' , help='''disable all embeddings quantizers''' )
group.add_argument('''--quant-disable-keyword''' , type=_lowerCAmelCase , nargs='''+''' , help='''disable quantizers by keyword''' )
group.add_argument('''--quant-disable-layer-module''' , type=_lowerCAmelCase , help='''disable quantizers by keyword under layer.''' )
group.add_argument('''--quant-enable-layer-module''' , type=_lowerCAmelCase , help='''enable quantizers by keyword under layer''' )
group.add_argument('''--calibrator''' , default='''max''' , help='''which quantization range calibrator to use''' )
group.add_argument('''--percentile''' , default=_lowerCAmelCase , type=_lowerCAmelCase , help='''percentile for PercentileCalibrator''' )
group.add_argument('''--fuse-qkv''' , action='''store_true''' , help='''use the same scale factor for qkv''' )
group.add_argument('''--clip-gelu''' , metavar='''N''' , type=_lowerCAmelCase , help='''clip gelu output maximum value to N''' )
group.add_argument(
'''--recalibrate-weights''' , action='''store_true''' , help=(
'''recalibrate weight amaxes by taking the max of the weights.'''
''' amaxes will be computed with the current quantization granularity (axis).'''
) , )
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
if args.calibrator == "max":
lowercase__ : int = """max"""
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError('''Specify --percentile when using percentile calibrator''' )
lowercase__ : Union[str, Any] = """histogram"""
elif args.calibrator == "mse":
lowercase__ : Any = """histogram"""
else:
raise ValueError(f"""Invalid calibrator {args.calibrator}""" )
lowercase__ : List[str] = QuantDescriptor(num_bits=args.aprec , calib_method=_lowerCAmelCase )
lowercase__ : int = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(_lowerCAmelCase )
quant_nn.QuantLinear.set_default_quant_desc_weight(_lowerCAmelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False , __lowerCamelCase=False ) -> Any:
logger.info('''Configuring Model for Quantization''' )
logger.info(f"""using quantization package {pytorch_quantization.__file__}""" )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(_lowerCAmelCase , ['''embeddings'''] , which='''weight''' , _disabled=_lowerCAmelCase )
if args.quant_disable:
set_quantizer_by_name(_lowerCAmelCase , [''''''] , _disabled=_lowerCAmelCase )
if args.quant_disable_keyword:
set_quantizer_by_name(_lowerCAmelCase , args.quant_disable_keyword , _disabled=_lowerCAmelCase )
if args.quant_disable_layer_module:
set_quantizer_by_name(_lowerCAmelCase , [r'''layer.\d+.''' + args.quant_disable_layer_module] , _disabled=_lowerCAmelCase )
if args.quant_enable_layer_module:
set_quantizer_by_name(_lowerCAmelCase , [r'''layer.\d+.''' + args.quant_enable_layer_module] , _disabled=_lowerCAmelCase )
if args.recalibrate_weights:
recalibrate_weights(_lowerCAmelCase )
if args.fuse_qkv:
fuse_qkv(_lowerCAmelCase , _lowerCAmelCase )
if args.clip_gelu:
clip_gelu(_lowerCAmelCase , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(_lowerCAmelCase )
def __UpperCAmelCase ( __lowerCamelCase ) -> int:
logger.info('''Enabling Calibration''' )
for name, module in model.named_modules():
if name.endswith('''_quantizer''' ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(f"""{name:80}: {module}""" )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> str:
logger.info('''Loading calibrated amax''' )
for name, module in model.named_modules():
if name.endswith('''_quantizer''' ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax('''percentile''' , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(_lowerCAmelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
def fusea(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
for mod in [qq, qk, qv]:
if not hasattr(_lowerCAmelCase , '''_amax''' ):
print(''' WARNING: NO AMAX BUFFER''' )
return
lowercase__ : Dict = qq._amax.detach().item()
lowercase__ : Optional[int] = qk._amax.detach().item()
lowercase__ : Any = qv._amax.detach().item()
lowercase__ : Dict = max(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
qq._amax.fill_(_lowerCAmelCase )
qk._amax.fill_(_lowerCAmelCase )
qv._amax.fill_(_lowerCAmelCase )
logger.info(f""" q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}""" )
for name, mod in model.named_modules():
if name.endswith('''.attention.self''' ):
logger.info(f"""FUSE_QKV: {name:{name_width}}""" )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
for name, mod in model.named_modules():
if name.endswith('''.output.dense''' ) and not name.endswith('''attention.output.dense''' ):
lowercase__ : Dict = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=_lowerCAmelCase )
lowercase__ : Optional[int] = mod._input_quantizer._amax.data.detach().item()
logger.info(f"""CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}""" )
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
for name, mod in model.named_modules():
if hasattr(_lowerCAmelCase , '''_weight_quantizer''' ) and mod._weight_quantizer.axis is not None:
lowercase__ : Any = mod.weight.shape[0]
lowercase__ : Optional[int] = mod._weight_quantizer._amax.detach()
lowercase__ : Optional[int] = torch.ones(_lowerCAmelCase , dtype=amax.dtype , device=amax.device ) * amax
print(f"""expanding {name} {amax} -> {mod._weight_quantizer._amax}""" )
def __UpperCAmelCase ( __lowerCamelCase ) -> Dict:
for name, mod in model.named_modules():
if hasattr(_lowerCAmelCase , '''_weight_quantizer''' ):
if not hasattr(mod.weight_quantizer , '''_amax''' ):
print('''RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER''' )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
lowercase__ : List[Any] = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
lowercase__ : Optional[int] = set(range(len(mod.weight.size() ) ) ) - axis_set
lowercase__ : List[Any] = pytorch_quantization.utils.reduce_amax(mod.weight , axis=_lowerCAmelCase , keepdims=_lowerCAmelCase ).detach()
logger.info(f"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}""" )
lowercase__ : Any = amax
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase=25 , __lowerCamelCase=1_80 , __lowerCamelCase=None ) -> List[Any]:
if ignore is None:
lowercase__ : Dict = []
elif not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
lowercase__ : Optional[Any] = [ignore]
lowercase__ : Optional[Any] = 0
for name, mod in model.named_modules():
if not hasattr(_lowerCAmelCase , '''weight''' ):
continue
lowercase__ : Union[str, Any] = max(_lowerCAmelCase , len(_lowerCAmelCase ) )
for name, mod in model.named_modules():
lowercase__ : str = getattr(_lowerCAmelCase , '''_input_quantizer''' , _lowerCAmelCase )
lowercase__ : List[str] = getattr(_lowerCAmelCase , '''_weight_quantizer''' , _lowerCAmelCase )
if not hasattr(_lowerCAmelCase , '''weight''' ):
continue
if type(_lowerCAmelCase ) in ignore:
continue
if [True for s in ignore if type(_lowerCAmelCase ) is str and s in name]:
continue
lowercase__ : Optional[Any] = f"""Act:{input_q.extra_repr()}"""
lowercase__ : str = f"""Wgt:{weight_q.extra_repr()}"""
lowercase__ : Tuple = f"""{name:{name_width}} {act_str} {wgt_str}"""
if len(_lowerCAmelCase ) <= line_width:
logger.info(_lowerCAmelCase )
else:
logger.info(f"""{name:{name_width}} {act_str}""" )
logger.info(f"""{" ":{name_width}} {wgt_str}""" )
def __UpperCAmelCase ( __lowerCamelCase ) -> Union[str, Any]:
lowercase__ : List[Any] = 0
for name, mod in model.named_modules():
if isinstance(_lowerCAmelCase , pytorch_quantization.nn.TensorQuantizer ):
print(f"""{name:80} {mod}""" )
count += 1
print(f"""{count} TensorQuantizers found in model""" )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[int]:
lowercase__ : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if quantizer_mod is not None:
assert hasattr(_lowerCAmelCase , _lowerCAmelCase )
setattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
logger.warning(f"""{name} has no {quantizer}""" )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase="both" , **__lowerCamelCase ) -> Optional[int]:
lowercase__ : Optional[int] = f"""Warning: changing {which} quantizers of {name:{qname_width}}"""
for k, v in kwargs.items():
s += f""" {k}={v}"""
if which in ["input", "both"]:
set_quantizer(_lowerCAmelCase , _lowerCAmelCase , '''_input_quantizer''' , _lowerCAmelCase , _lowerCAmelCase )
if which in ["weight", "both"]:
set_quantizer(_lowerCAmelCase , _lowerCAmelCase , '''_weight_quantizer''' , _lowerCAmelCase , _lowerCAmelCase )
logger.info(_lowerCAmelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase ) -> Dict:
for name, mod in model.named_modules():
if hasattr(_lowerCAmelCase , '''_input_quantizer''' ) or hasattr(_lowerCAmelCase , '''_weight_quantizer''' ):
for n in names:
if re.search(_lowerCAmelCase , _lowerCAmelCase ):
set_quantizers(_lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase )
elif name.endswith('''_quantizer''' ):
for n in names:
if re.search(_lowerCAmelCase , _lowerCAmelCase ):
lowercase__ : Union[str, Any] = f"""Warning: changing {name:{name_width}}"""
for k, v in kwargs.items():
s += f""" {k}={v}"""
setattr(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
logger.info(_lowerCAmelCase )
| 371
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
'tokenization_roberta': ['RobertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['RobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'RobertaForCausalLM',
'RobertaForMaskedLM',
'RobertaForMultipleChoice',
'RobertaForQuestionAnswering',
'RobertaForSequenceClassification',
'RobertaForTokenClassification',
'RobertaModel',
'RobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRobertaForCausalLM',
'TFRobertaForMaskedLM',
'TFRobertaForMultipleChoice',
'TFRobertaForQuestionAnswering',
'TFRobertaForSequenceClassification',
'TFRobertaForTokenClassification',
'TFRobertaMainLayer',
'TFRobertaModel',
'TFRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'FlaxRobertaForCausalLM',
'FlaxRobertaForMaskedLM',
'FlaxRobertaForMultipleChoice',
'FlaxRobertaForQuestionAnswering',
'FlaxRobertaForSequenceClassification',
'FlaxRobertaForTokenClassification',
'FlaxRobertaModel',
'FlaxRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 302
| 0
|
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
def __UpperCAmelCase ( __lowerCamelCase=None , __lowerCamelCase=None ) -> Optional[int]:
return field(default_factory=lambda: default , metadata=__lowerCAmelCase )
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : List[str] = list_field(
default=[] ,metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
} ,)
lowerCAmelCase : List[int] = list_field(
default=[8] ,metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
lowerCAmelCase : List[int] = list_field(
default=[8, 3_2, 1_2_8, 5_1_2] ,metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"} ,)
lowerCAmelCase : bool = field(
default=__lowerCamelCase ,metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."} ,)
lowerCAmelCase : bool = field(
default=__lowerCamelCase ,metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."} ,)
lowerCAmelCase : bool = field(
default=__lowerCamelCase ,metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
lowerCAmelCase : bool = field(default=__lowerCamelCase ,metadata={"help": "Use FP16 to accelerate inference."} )
lowerCAmelCase : bool = field(default=__lowerCamelCase ,metadata={"help": "Benchmark training of model"} )
lowerCAmelCase : bool = field(default=__lowerCamelCase ,metadata={"help": "Verbose memory tracing"} )
lowerCAmelCase : bool = field(
default=__lowerCamelCase ,metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."} ,)
lowerCAmelCase : bool = field(
default=__lowerCamelCase ,metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
} ,)
lowerCAmelCase : bool = field(default=__lowerCamelCase ,metadata={"help": "Trace memory line by line"} )
lowerCAmelCase : bool = field(default=__lowerCamelCase ,metadata={"help": "Save result to a CSV file"} )
lowerCAmelCase : bool = field(default=__lowerCamelCase ,metadata={"help": "Save all print statements in a log file"} )
lowerCAmelCase : bool = field(default=__lowerCamelCase ,metadata={"help": "Whether to print environment information"} )
lowerCAmelCase : bool = field(
default=__lowerCamelCase ,metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
} ,)
lowerCAmelCase : str = field(
default=F"inference_time_{round(time() )}.csv" ,metadata={"help": "CSV filename used if saving time results to csv."} ,)
lowerCAmelCase : str = field(
default=F"inference_memory_{round(time() )}.csv" ,metadata={"help": "CSV filename used if saving memory results to csv."} ,)
lowerCAmelCase : str = field(
default=F"train_time_{round(time() )}.csv" ,metadata={"help": "CSV filename used if saving time results to csv for training."} ,)
lowerCAmelCase : str = field(
default=F"train_memory_{round(time() )}.csv" ,metadata={"help": "CSV filename used if saving memory results to csv for training."} ,)
lowerCAmelCase : str = field(
default=F"env_info_{round(time() )}.csv" ,metadata={"help": "CSV filename used if saving environment information."} ,)
lowerCAmelCase : str = field(
default=F"log_{round(time() )}.csv" ,metadata={"help": "Log filename used if print statements are saved in log."} ,)
lowerCAmelCase : int = field(default=3 ,metadata={"help": "Times an experiment will be run."} )
lowerCAmelCase : bool = field(
default=__lowerCamelCase ,metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
} ,)
def UpperCAmelCase ( self : List[str] ) -> str:
"""simple docstring"""
warnings.warn(
f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' ,__lowercase ,)
def UpperCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
return json.dumps(dataclasses.asdict(self ) ,indent=2 )
@property
def UpperCAmelCase ( self : Any ) -> str:
"""simple docstring"""
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 350
|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowerCAmelCase_ = logging.getLogger(__name__)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowerCAmelCase : Optional[str] = field(
default="NER" ,metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
lowerCAmelCase : bool = field(default=A_ ,metadata={"help": "Set this flag to use fast tokenization."} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : str = field(
metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} ,)
lowerCAmelCase : int = field(
default=1_2_8 ,metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} ,)
lowerCAmelCase : bool = field(
default=A_ ,metadata={"help": "Overwrite the cached training and evaluation sets"} )
def __UpperCAmelCase ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase__ : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase__ , lowercase__ , lowercase__ : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase__ , lowercase__ , lowercase__ : List[str] = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
lowercase__ : str = import_module('''tasks''' )
try:
lowercase__ : List[str] = getattr(__lowerCamelCase , model_args.task_type )
lowercase__ : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __lowerCamelCase )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
lowercase__ : Union[str, Any] = token_classification_task.get_labels(data_args.labels )
lowercase__ : Dict[int, str] = dict(enumerate(__lowerCamelCase ) )
lowercase__ : Optional[int] = len(__lowerCamelCase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase__ : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid={label: i for i, label in enumerate(__lowerCamelCase )} , cache_dir=model_args.cache_dir , )
lowercase__ : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
lowercase__ : str = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__lowerCamelCase , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase__ : str = (
TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowercase__ : str = (
TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(__lowerCamelCase , __lowerCamelCase ) -> Tuple[List[int], List[int]]:
lowercase__ : Tuple = np.argmax(__lowerCamelCase , axis=2 )
lowercase__ , lowercase__ : Tuple = preds.shape
lowercase__ : List[str] = [[] for _ in range(__lowerCamelCase )]
lowercase__ : Tuple = [[] for _ in range(__lowerCamelCase )]
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(__lowerCamelCase ) -> Dict:
lowercase__ , lowercase__ : List[Any] = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(__lowerCamelCase , __lowerCamelCase ),
"precision": precision_score(__lowerCamelCase , __lowerCamelCase ),
"recall": recall_score(__lowerCamelCase , __lowerCamelCase ),
"f1": fa_score(__lowerCamelCase , __lowerCamelCase ),
}
# Data collator
lowercase__ : Tuple = DataCollatorWithPadding(__lowerCamelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase__ : str = Trainer(
model=__lowerCamelCase , args=__lowerCamelCase , train_dataset=__lowerCamelCase , eval_dataset=__lowerCamelCase , compute_metrics=__lowerCamelCase , data_collator=__lowerCamelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase__ : int = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase__ : Optional[int] = trainer.evaluate()
lowercase__ : Union[str, Any] = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __lowerCamelCase , __lowerCamelCase )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__lowerCamelCase )
# Predict
if training_args.do_predict:
lowercase__ : Optional[int] = TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = trainer.predict(__lowerCamelCase )
lowercase__ , lowercase__ : Tuple = align_predictions(__lowerCamelCase , __lowerCamelCase )
lowercase__ : Dict = os.path.join(training_args.output_dir , '''test_results.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
for key, value in metrics.items():
logger.info(''' %s = %s''' , __lowerCamelCase , __lowerCamelCase )
writer.write('''%s = %s\n''' % (key, value) )
# Save predictions
lowercase__ : Dict = os.path.join(training_args.output_dir , '''test_predictions.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
with open(os.path.join(data_args.data_dir , '''test.txt''' ) , '''r''' ) as f:
token_classification_task.write_predictions_to_file(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return results
def __UpperCAmelCase ( __lowerCamelCase ) -> List[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 302
| 0
|
"""simple docstring"""
from manim import *
class __A ( __UpperCamelCase ):
'''simple docstring'''
def UpperCAmelCase ( self : int ) -> List[Any]:
"""simple docstring"""
lowercase__ : str = Rectangle(height=0.5 ,width=0.5 )
lowercase__ : List[Any] = Rectangle(height=0.25 ,width=0.25 )
lowercase__ : str = Rectangle(height=0.46 ,width=0.46 ).set_stroke(width=0 )
lowercase__ : Union[str, Any] = [mem.copy() for i in range(6 )]
lowercase__ : List[str] = [mem.copy() for i in range(6 )]
lowercase__ : int = VGroup(*_snake_case ).arrange(_snake_case ,buff=0 )
lowercase__ : List[str] = VGroup(*_snake_case ).arrange(_snake_case ,buff=0 )
lowercase__ : Optional[Any] = VGroup(_snake_case ,_snake_case ).arrange(_snake_case ,buff=0 )
lowercase__ : int = Text('''CPU''' ,font_size=24 )
lowercase__ : int = Group(_snake_case ,_snake_case ).arrange(_snake_case ,buff=0.5 ,aligned_edge=_snake_case )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_snake_case )
lowercase__ : str = [mem.copy() for i in range(4 )]
lowercase__ : int = VGroup(*_snake_case ).arrange(_snake_case ,buff=0 )
lowercase__ : Any = Text('''GPU''' ,font_size=24 )
lowercase__ : List[str] = Group(_snake_case ,_snake_case ).arrange(_snake_case ,buff=0.5 ,aligned_edge=_snake_case )
gpu.move_to([-1, -1, 0] )
self.add(_snake_case )
lowercase__ : List[str] = [mem.copy() for i in range(6 )]
lowercase__ : Tuple = VGroup(*_snake_case ).arrange(_snake_case ,buff=0 )
lowercase__ : str = Text('''Model''' ,font_size=24 )
lowercase__ : List[Any] = Group(_snake_case ,_snake_case ).arrange(_snake_case ,buff=0.5 ,aligned_edge=_snake_case )
model.move_to([3, -1.0, 0] )
self.add(_snake_case )
lowercase__ : List[Any] = []
lowercase__ : Tuple = []
lowercase__ : Dict = []
for i, rect in enumerate(_snake_case ):
rect.set_stroke(_snake_case )
lowercase__ : Union[str, Any] = Rectangle(height=0.46 / 4 ,width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(_snake_case ,opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) ,buff=0.02 ,direction=_snake_case )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] ,direction=_snake_case ,buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] ,direction=_snake_case ,buff=0.0 )
self.add(_snake_case )
model_cpu_arr.append(_snake_case )
self.add(*_snake_case ,*_snake_case ,*_snake_case )
lowercase__ : str = [mem.copy() for i in range(6 )]
lowercase__ : str = VGroup(*_snake_case ).arrange(_snake_case ,buff=0 )
lowercase__ : Dict = Text('''Loaded Checkpoint''' ,font_size=24 )
lowercase__ : str = Group(_snake_case ,_snake_case ).arrange(_snake_case ,buff=0.5 ,aligned_edge=_snake_case )
checkpoint.move_to([3, 0.5, 0] )
self.add(_snake_case )
lowercase__ : List[Any] = []
lowercase__ : List[str] = []
for i, rect in enumerate(_snake_case ):
lowercase__ : Union[str, Any] = fill.copy().set_fill(_snake_case ,opacity=0.7 )
target.move_to(_snake_case )
ckpt_arr.append(_snake_case )
lowercase__ : Optional[int] = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(_snake_case )
self.add(*_snake_case ,*_snake_case )
lowercase__ : Tuple = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowercase__ : Any = MarkupText(
f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" ,font_size=18 ,)
key_text.move_to([-5, 2.4, 0] )
self.add(_snake_case ,_snake_case )
lowercase__ : Tuple = MarkupText(
f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" ,font_size=18 ,)
blue_text.next_to(_snake_case ,DOWN * 2.4 ,aligned_edge=key_text.get_left() )
self.add(_snake_case )
lowercase__ : Tuple = MarkupText(
f"""Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.""" ,font_size=24 ,)
step_a.move_to([2, 2, 0] )
lowercase__ : str = [meta_mem.copy() for i in range(6 )]
lowercase__ : Tuple = [meta_mem.copy() for i in range(6 )]
lowercase__ : Tuple = VGroup(*_snake_case ).arrange(_snake_case ,buff=0 )
lowercase__ : int = VGroup(*_snake_case ).arrange(_snake_case ,buff=0 )
lowercase__ : Optional[Any] = VGroup(_snake_case ,_snake_case ).arrange(_snake_case ,buff=0 )
lowercase__ : Tuple = Text('''Disk''' ,font_size=24 )
lowercase__ : Tuple = Group(_snake_case ,_snake_case ).arrange(_snake_case ,buff=0.5 ,aligned_edge=_snake_case )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(_snake_case ,run_time=3 ) ,Write(_snake_case ,run_time=1 ) ,Create(_snake_case ,run_time=1 ) )
lowercase__ : List[Any] = []
for i, rect in enumerate(_snake_case ):
lowercase__ : List[str] = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(_snake_case ,run_time=1.5 ) )
self.play(*_snake_case )
self.play(FadeOut(_snake_case ) )
lowercase__ : Dict = MarkupText(f"""Then, the checkpoint is removed from memory\nthrough garbage collection.""" ,font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(_snake_case ,run_time=3 ) )
self.play(
FadeOut(_snake_case ,_snake_case ,*_snake_case ,*_snake_case ) ,)
self.wait()
| 351
|
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase_ = 16
lowerCAmelCase_ = 32
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = 16 ) -> Optional[int]:
lowercase__ : Optional[int] = AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowercase__ : List[str] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : List[str] = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=__lowerCamelCase , max_length=__lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase__ : Dict = datasets.map(
__lowerCamelCase , batched=__lowerCamelCase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : int = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ : List[str] = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase__ : List[str] = 16
elif accelerator.mixed_precision != "no":
lowercase__ : List[Any] = 8
else:
lowercase__ : Optional[int] = None
return tokenizer.pad(
__lowerCamelCase , padding='''longest''' , max_length=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_tensors='''pt''' , )
# Instantiate dataloaders.
lowercase__ : Dict = DataLoader(
tokenized_datasets['''train'''] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
lowercase__ : Union[str, Any] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowerCAmelCase_ = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Tuple:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , __lowerCamelCase ) == "1":
lowercase__ : Any = 2
# Initialize accelerator
lowercase__ : str = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : List[Any] = config['''lr''']
lowercase__ : Union[str, Any] = int(config['''num_epochs'''] )
lowercase__ : List[str] = int(config['''seed'''] )
lowercase__ : Any = int(config['''batch_size'''] )
lowercase__ : int = evaluate.load('''glue''' , '''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__lowerCamelCase )
def inner_training_loop(__lowerCamelCase ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__lowerCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : Optional[Any] = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=__lowerCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase__ : str = model.to(accelerator.device )
# Instantiate optimizer
lowercase__ : Optional[int] = AdamW(params=model.parameters() , lr=__lowerCamelCase )
lowercase__ , lowercase__ : List[str] = get_dataloaders(__lowerCamelCase , __lowerCamelCase )
# Instantiate scheduler
lowercase__ : Optional[Any] = get_linear_schedule_with_warmup(
optimizer=__lowerCamelCase , num_warmup_steps=1_00 , num_training_steps=(len(__lowerCamelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = accelerator.prepare(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# Now we train the model
for epoch in range(__lowerCamelCase ):
model.train()
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase__ : int = model(**__lowerCamelCase )
lowercase__ : Optional[int] = outputs.loss
accelerator.backward(__lowerCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ : Tuple = model(**__lowerCamelCase )
lowercase__ : Dict = outputs.logits.argmax(dim=-1 )
lowercase__ , lowercase__ : Any = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=__lowerCamelCase , references=__lowerCamelCase , )
lowercase__ : Optional[int] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , __lowerCamelCase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( ) -> Tuple:
lowercase__ : List[str] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=__lowerCamelCase , default=__lowerCamelCase , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
lowercase__ : Union[str, Any] = parser.parse_args()
lowercase__ : Union[str, Any] = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
main()
| 302
| 0
|
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __A ( lowerCAmelCase_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = LEDTokenizer
lowerCAmelCase : Dict = LEDTokenizerFast
lowerCAmelCase : Union[str, Any] = True
def UpperCAmelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
super().setUp()
lowercase__ : Optional[Any] = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
lowercase__ : Any = dict(zip(__SCREAMING_SNAKE_CASE ,range(len(__SCREAMING_SNAKE_CASE ) ) ) )
lowercase__ : int = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
lowercase__ : Tuple = {'''unk_token''': '''<unk>'''}
lowercase__ : Optional[int] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['''vocab_file'''] )
lowercase__ : str = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file ,'''w''' ,encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__SCREAMING_SNAKE_CASE ) + '''\n''' )
with open(self.merges_file ,'''w''' ,encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase ( self : List[Any] ,**_snake_case : Tuple ) -> List[str]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**__SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self : int ,**_snake_case : Tuple ) -> str:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**__SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self : Any ,_snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
return "lower newer", "lower newer"
@cached_property
def UpperCAmelCase ( self : int ) -> str:
"""simple docstring"""
return LEDTokenizer.from_pretrained('''allenai/led-base-16384''' )
@cached_property
def UpperCAmelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384''' )
@require_torch
def UpperCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ : List[str] = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowercase__ : List[Any] = [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowercase__ : Optional[Any] = tokenizer(__SCREAMING_SNAKE_CASE ,max_length=len(__SCREAMING_SNAKE_CASE ) ,padding=__SCREAMING_SNAKE_CASE ,return_tensors='''pt''' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
self.assertEqual((2, 9) ,batch.input_ids.shape )
self.assertEqual((2, 9) ,batch.attention_mask.shape )
lowercase__ : List[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
@require_torch
def UpperCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
lowercase__ : Dict = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowercase__ : str = tokenizer(__SCREAMING_SNAKE_CASE ,padding=__SCREAMING_SNAKE_CASE ,return_tensors='''pt''' )
self.assertIn('''input_ids''' ,__SCREAMING_SNAKE_CASE )
self.assertIn('''attention_mask''' ,__SCREAMING_SNAKE_CASE )
self.assertNotIn('''labels''' ,__SCREAMING_SNAKE_CASE )
self.assertNotIn('''decoder_attention_mask''' ,__SCREAMING_SNAKE_CASE )
@require_torch
def UpperCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ : Any = [
'''Summary of the text.''',
'''Another summary.''',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowercase__ : List[Any] = tokenizer(text_target=__SCREAMING_SNAKE_CASE ,max_length=32 ,padding='''max_length''' ,return_tensors='''pt''' )
self.assertEqual(32 ,targets['''input_ids'''].shape[1] )
@require_torch
def UpperCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowercase__ : List[Any] = tokenizer(
['''I am a small frog''' * 1_024, '''I am a small frog'''] ,padding=__SCREAMING_SNAKE_CASE ,truncation=__SCREAMING_SNAKE_CASE ,return_tensors='''pt''' )
self.assertIsInstance(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
self.assertEqual(batch.input_ids.shape ,(2, 5_122) )
@require_torch
def UpperCAmelCase ( self : str ) -> int:
"""simple docstring"""
lowercase__ : Tuple = ['''A long paragraph for summarization.''']
lowercase__ : Tuple = [
'''Summary of the text.''',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowercase__ : List[str] = tokenizer(__SCREAMING_SNAKE_CASE ,return_tensors='''pt''' )
lowercase__ : int = tokenizer(text_target=__SCREAMING_SNAKE_CASE ,return_tensors='''pt''' )
lowercase__ : Union[str, Any] = inputs['''input_ids''']
lowercase__ : Tuple = targets['''input_ids''']
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def UpperCAmelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowercase__ : List[str] = ['''Summary of the text.''', '''Another summary.''']
lowercase__ : Tuple = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
lowercase__ : Optional[int] = tokenizer(__SCREAMING_SNAKE_CASE ,padding=__SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = [[0] * len(__SCREAMING_SNAKE_CASE ) for x in encoded_output['''input_ids''']]
lowercase__ : Union[str, Any] = tokenizer.pad(__SCREAMING_SNAKE_CASE )
self.assertSequenceEqual(outputs['''global_attention_mask'''] ,__SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
def UpperCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowercase__ : Dict = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE ,**__SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE ,**__SCREAMING_SNAKE_CASE )
lowercase__ : List[str] = '''A, <mask> AllenNLP sentence.'''
lowercase__ : Dict = tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE ,add_special_tokens=__SCREAMING_SNAKE_CASE ,return_token_type_ids=__SCREAMING_SNAKE_CASE )
lowercase__ : Any = tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE ,add_special_tokens=__SCREAMING_SNAKE_CASE ,return_token_type_ids=__SCREAMING_SNAKE_CASE )
self.assertEqual(sum(tokens_r['''token_type_ids'''] ) ,sum(tokens_p['''token_type_ids'''] ) )
self.assertEqual(
sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] ) ,sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] ) ,)
lowercase__ : List[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
lowercase__ : List[str] = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )
self.assertSequenceEqual(tokens_p['''input_ids'''] ,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r['''input_ids'''] ,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
__SCREAMING_SNAKE_CASE ,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
self.assertSequenceEqual(
__SCREAMING_SNAKE_CASE ,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
| 352
|
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( self : int ) -> str:
"""simple docstring"""
lowercase__ : List[Any] = '''hf-internal-testing/tiny-random-t5'''
lowercase__ : List[Any] = AutoTokenizer.from_pretrained(_snake_case )
lowercase__ : int = AutoModelForSeqaSeqLM.from_pretrained(_snake_case )
lowercase__ : str = tokenizer('''This is me''' ,return_tensors='''pt''' )
lowercase__ : Tuple = model.to_bettertransformer()
self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
lowercase__ : Optional[int] = model.generate(**_snake_case )
lowercase__ : List[Any] = model.reverse_bettertransformer()
self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_snake_case )
lowercase__ : Tuple = AutoModelForSeqaSeqLM.from_pretrained(_snake_case )
self.assertFalse(
any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
lowercase__ : int = model_reloaded.generate(**_snake_case )
self.assertTrue(torch.allclose(_snake_case ,_snake_case ) )
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : List[str] = '''hf-internal-testing/tiny-random-t5'''
lowercase__ : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_snake_case )
lowercase__ : Union[str, Any] = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(_snake_case ):
model.save_pretrained(_snake_case )
lowercase__ : int = model.reverse_bettertransformer()
model.save_pretrained(_snake_case )
| 302
| 0
|
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def __UpperCAmelCase ( __lowerCamelCase ):
lowercase__ : str = model.config
lowercase__ : List[str] = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=1_28 , )
lowercase__ : List[str] = MBartConfig(
is_decoder=lowerCamelCase__ , is_encoder_decoder=lowerCamelCase__ , add_cross_attention=lowerCamelCase__ , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=lowerCamelCase__ , add_final_layer_norm=lowerCamelCase__ , )
return encoder_config, decoder_config
def __UpperCAmelCase ( __lowerCamelCase ):
if "encoder.model" in name:
lowercase__ : List[Any] = name.replace('''encoder.model''' , '''encoder''' )
if "decoder.model" in name:
lowercase__ : List[str] = name.replace('''decoder.model''' , '''decoder''' )
if "patch_embed.proj" in name:
lowercase__ : int = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowercase__ : Tuple = name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if name.startswith('''encoder''' ):
if "layers" in name:
lowercase__ : Union[str, Any] = '''encoder.''' + name
if "attn.proj" in name:
lowercase__ : int = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name and "mask" not in name:
lowercase__ : Union[str, Any] = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
lowercase__ : Dict = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
lowercase__ : Dict = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
lowercase__ : Optional[Any] = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
lowercase__ : Dict = name.replace('''mlp.fc2''' , '''output.dense''' )
if name == "encoder.norm.weight":
lowercase__ : Dict = '''encoder.layernorm.weight'''
if name == "encoder.norm.bias":
lowercase__ : Union[str, Any] = '''encoder.layernorm.bias'''
return name
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ):
for key in orig_state_dict.copy().keys():
lowercase__ : List[Any] = orig_state_dict.pop(lowerCamelCase__ )
if "qkv" in key:
lowercase__ : List[str] = key.split('''.''' )
lowercase__ : Any = int(key_split[3] )
lowercase__ : Any = int(key_split[5] )
lowercase__ : List[str] = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
lowercase__ : Any = val[:dim, :]
lowercase__ : Tuple = val[dim : dim * 2, :]
lowercase__ : str = val[-dim:, :]
else:
lowercase__ : Any = val[:dim]
lowercase__ : Dict = val[dim : dim * 2]
lowercase__ : Union[str, Any] = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
lowercase__ : Any = val
return orig_state_dict
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase=False ):
# load original model
lowercase__ : Union[str, Any] = DonutModel.from_pretrained(lowerCamelCase__ ).eval()
# load HuggingFace model
lowercase__ , lowercase__ : Tuple = get_configs(lowerCamelCase__ )
lowercase__ : Union[str, Any] = DonutSwinModel(lowerCamelCase__ )
lowercase__ : Any = MBartForCausalLM(lowerCamelCase__ )
lowercase__ : Optional[Any] = VisionEncoderDecoderModel(encoder=lowerCamelCase__ , decoder=lowerCamelCase__ )
model.eval()
lowercase__ : Dict = original_model.state_dict()
lowercase__ : Union[str, Any] = convert_state_dict(lowerCamelCase__ , lowerCamelCase__ )
model.load_state_dict(lowerCamelCase__ )
# verify results on scanned document
lowercase__ : Optional[Any] = load_dataset('''hf-internal-testing/example-documents''' )
lowercase__ : List[Any] = dataset['''test'''][0]['''image'''].convert('''RGB''' )
lowercase__ : int = XLMRobertaTokenizerFast.from_pretrained(lowerCamelCase__ , from_slow=lowerCamelCase__ )
lowercase__ : Union[str, Any] = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
lowercase__ : str = DonutProcessor(lowerCamelCase__ , lowerCamelCase__ )
lowercase__ : Tuple = processor(lowerCamelCase__ , return_tensors='''pt''' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
lowercase__ : List[Any] = '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
lowercase__ : List[Any] = '''When is the coffee break?'''
lowercase__ : List[Any] = task_prompt.replace('''{user_input}''' , lowerCamelCase__ )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
lowercase__ : Any = '''<s_rvlcdip>'''
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
lowercase__ : str = '''<s_cord>'''
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
lowercase__ : Dict = '''s_cord-v2>'''
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
lowercase__ : List[Any] = '''<s_zhtrainticket>'''
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
lowercase__ : List[str] = '''hello world'''
else:
raise ValueError('''Model name not supported''' )
lowercase__ : Dict = original_model.decoder.tokenizer(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ , return_tensors='''pt''' )[
'''input_ids'''
]
lowercase__ : List[str] = original_model.encoder.model.patch_embed(lowerCamelCase__ )
lowercase__ , lowercase__ : Optional[Any] = model.encoder.embeddings(lowerCamelCase__ )
assert torch.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1E-3 )
# verify encoder hidden states
lowercase__ : Union[str, Any] = original_model.encoder(lowerCamelCase__ )
lowercase__ : Tuple = model.encoder(lowerCamelCase__ ).last_hidden_state
assert torch.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1E-2 )
# verify decoder hidden states
lowercase__ : str = original_model(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ).logits
lowercase__ : List[Any] = model(lowerCamelCase__ , decoder_input_ids=lowerCamelCase__ ).logits
assert torch.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1E-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowerCamelCase__ )
processor.save_pretrained(lowerCamelCase__ )
if push_to_hub:
model.push_to_hub('''nielsr/''' + model_name.split('''/''' )[-1] , commit_message='''Update model''' )
processor.push_to_hub('''nielsr/''' + model_name.split('''/''' )[-1] , commit_message='''Update model''' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='naver-clova-ix/donut-base-finetuned-docvqa',
required=False,
type=str,
help='Name of the original model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
required=False,
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether or not to push the converted model and processor to the 🤗 hub.',
)
lowerCAmelCase_ = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 353
|
"""simple docstring"""
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> Any:
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
if not is_sharded:
lowercase__ : List[str] = os.path.abspath(__lowerCamelCase )
logger.info(f"""Loading PyTorch weights from {pt_path}""" )
lowercase__ : List[Any] = torch.load(__lowerCamelCase , map_location='''cpu''' )
logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.""" )
lowercase__ : int = convert_pytorch_state_dict_to_flax(__lowerCamelCase , __lowerCamelCase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
lowercase__ : Dict = convert_pytorch_sharded_state_dict_to_flax(__lowerCamelCase , __lowerCamelCase )
return flax_state_dict
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> (Tuple[str], np.ndarray):
def is_key_or_prefix_key_in_dict(__lowerCamelCase ) -> bool:
return len(set(__lowerCamelCase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
lowercase__ : int = pt_tuple_key[:-1] + ('''scale''',)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
lowercase__ : Union[str, Any] = pt_tuple_key[:-1] + ('''mean''',)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
lowercase__ : Any = pt_tuple_key[:-1] + ('''var''',)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
lowercase__ : Tuple = pt_tuple_key[:-1] + ('''embedding''',)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowercase__ : str = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
lowercase__ : str = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowercase__ : Union[str, Any] = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
lowercase__ : Optional[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowercase__ : Optional[int] = pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowercase__ : List[Any] = pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
lowercase__ : List[str] = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
lowercase__ : List[str] = pt_tuple_key[-2] + '''_g'''
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
lowercase__ : List[str] = pt_tuple_key[-2] + '''_v'''
if name is not None:
lowercase__ : Optional[Any] = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
# convert pytorch tensor to numpy
lowercase__ : Optional[Any] = {k: v.numpy() for k, v in pt_state_dict.items()}
lowercase__ : List[Any] = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
lowercase__ : str = flax_model.params['''params''']
else:
lowercase__ : Optional[int] = flax_model.params
lowercase__ : Optional[Any] = flatten_dict(__lowerCamelCase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowercase__ : Tuple = flatten_dict(flax_model.params['''batch_stats'''] )
random_flax_state_dict.update(__lowerCamelCase )
lowercase__ : int = {}
lowercase__ : List[str] = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
lowercase__ : Union[str, Any] = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase__ : Optional[Any] = tuple(pt_key.split('''.''' ) )
# remove base model prefix if necessary
lowercase__ : Union[str, Any] = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase__ : Union[str, Any] = pt_tuple_key[1:]
# Correctly rename weight parameters
lowercase__ , lowercase__ : List[str] = rename_key_and_reshape_tensor(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# add model prefix if necessary
lowercase__ : Union[str, Any] = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowercase__ : Dict = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
lowercase__ : int = jnp.asarray(__lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__lowerCamelCase , __lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
lowercase__ : Tuple = jnp.asarray(__lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
lowercase__ : Any = jnp.asarray(__lowerCamelCase )
return unflatten_dict(__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Dict:
import torch
# Load the index
lowercase__ : Dict = {}
for shard_file in shard_filenames:
# load using msgpack utils
lowercase__ : Optional[int] = torch.load(__lowerCamelCase )
lowercase__ : str = {k: v.numpy() for k, v in pt_state_dict.items()}
lowercase__ : Dict = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowercase__ : Optional[Any] = flax_model.params['''params''']
lowercase__ : List[Any] = flatten_dict(__lowerCamelCase )
random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats'''] ) )
else:
lowercase__ : Union[str, Any] = flax_model.params
lowercase__ : Tuple = flatten_dict(__lowerCamelCase )
lowercase__ : Tuple = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
lowercase__ : int = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase__ : List[str] = tuple(pt_key.split('''.''' ) )
# remove base model prefix if necessary
lowercase__ : Tuple = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase__ : List[str] = pt_tuple_key[1:]
# Correctly rename weight parameters
lowercase__ , lowercase__ : str = rename_key_and_reshape_tensor(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# add model prefix if necessary
lowercase__ : Union[str, Any] = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowercase__ : Dict = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
lowercase__ : Union[str, Any] = jnp.asarray(__lowerCamelCase )
continue
if "var" in flax_key[-1]:
lowercase__ : str = jnp.asarray(__lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__lowerCamelCase , __lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
lowercase__ : List[str] = jnp.asarray(__lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
lowercase__ : Union[str, Any] = jnp.asarray(__lowerCamelCase )
return unflatten_dict(__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
lowercase__ : List[str] = os.path.abspath(__lowerCamelCase )
logger.info(f"""Loading Flax weights from {flax_checkpoint_path}""" )
# import correct flax class
lowercase__ : Optional[int] = getattr(__lowerCamelCase , '''Flax''' + model.__class__.__name__ )
# load flax weight dict
with open(__lowerCamelCase , '''rb''' ) as state_f:
try:
lowercase__ : str = from_bytes(__lowerCamelCase , state_f.read() )
except UnpicklingError:
raise EnvironmentError(f"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """ )
return load_flax_weights_in_pytorch_model(__lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> List[str]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
# check if we have bf16 weights
lowercase__ : Any = flatten_dict(jax.tree_util.tree_map(lambda __lowerCamelCase : x.dtype == jnp.bfloataa , __lowerCamelCase ) ).values()
if any(__lowerCamelCase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
'''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
'''before loading those in PyTorch model.''' )
lowercase__ : Union[str, Any] = jax.tree_util.tree_map(
lambda __lowerCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , __lowerCamelCase )
lowercase__ : Tuple = flatten_dict(__lowerCamelCase )
lowercase__ : List[str] = pt_model.state_dict()
lowercase__ : int = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split('''.''' )[0] for k in pt_model_dict.keys()}
)
lowercase__ : int = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split('''.''' )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
lowercase__ : List[str] = []
lowercase__ : Tuple = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
lowercase__ : List[Any] = flax_key_tuple[0] == pt_model.base_model_prefix
lowercase__ : Optional[int] = '''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase__ : Tuple = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
lowercase__ : Optional[Any] = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowerCamelCase ) not in pt_model_dict:
# conv layer
lowercase__ : Dict = flax_key_tuple[:-1] + ('''weight''',)
lowercase__ : List[str] = jnp.transpose(__lowerCamelCase , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCamelCase ) not in pt_model_dict:
# linear layer
lowercase__ : Optional[int] = flax_key_tuple[:-1] + ('''weight''',)
lowercase__ : str = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
lowercase__ : Dict = flax_key_tuple[:-1] + ('''weight''',)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
lowercase__ : Any = flax_key_tuple[:-1] + ('''running_mean''',)
elif "var" in flax_key_tuple[-1]:
lowercase__ : Dict = flax_key_tuple[:-1] + ('''running_var''',)
if "batch_stats" in flax_state:
lowercase__ : Union[str, Any] = '''.'''.join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
lowercase__ : Dict = '''.'''.join(__lowerCamelCase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
lowercase__ : Optional[int] = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
lowercase__ : str = key.split('''.''' )
lowercase__ : Optional[Any] = None
if key_components[-3::2] == ["parametrizations", "original0"]:
lowercase__ : List[str] = key_components[-2] + '''_g'''
elif key_components[-3::2] == ["parametrizations", "original1"]:
lowercase__ : str = key_components[-2] + '''_v'''
if name is not None:
lowercase__ : Optional[int] = key_components[:-3] + [name]
lowercase__ : List[str] = '''.'''.join(__lowerCamelCase )
lowercase__ : List[Any] = key
if flax_key in special_pt_names:
lowercase__ : Any = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
else:
# add weight to pytorch dict
lowercase__ : List[str] = np.asarray(__lowerCamelCase ) if not isinstance(__lowerCamelCase , np.ndarray ) else flax_tensor
lowercase__ : List[str] = torch.from_numpy(__lowerCamelCase )
# remove from missing keys
missing_keys.remove(__lowerCamelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(__lowerCamelCase )
pt_model.load_state_dict(__lowerCamelCase )
# re-transform missing_keys to list
lowercase__ : Optional[Any] = list(__lowerCamelCase )
if len(__lowerCamelCase ) > 0:
logger.warning(
'''Some weights of the Flax model were not used when initializing the PyTorch model'''
f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
''' FlaxBertForSequenceClassification model).''' )
else:
logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n""" )
if len(__lowerCamelCase ) > 0:
logger.warning(
f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
''' use it for predictions and inference.''' )
else:
logger.warning(
f"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
'''If your task is similar to the task the model of the checkpoint was trained on, '''
f"""you can already use {pt_model.__class__.__name__} for predictions without further training.""" )
return pt_model
| 302
| 0
|
"""simple docstring"""
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
lowerCAmelCase_ = 'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'
def __UpperCAmelCase ( ) -> int:
lowercase__ : Optional[Any] = _ask_options(
'''In which compute environment are you running?''' , ['''This machine''', '''AWS (Amazon SageMaker)'''] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
lowercase__ : Any = get_sagemaker_input()
else:
lowercase__ : Union[str, Any] = get_cluster_input()
return config
def __UpperCAmelCase ( __lowerCamelCase=None ) -> Dict:
if subparsers is not None:
lowercase__ : Dict = subparsers.add_parser('''config''' , description=_lowerCAmelCase )
else:
lowercase__ : List[str] = argparse.ArgumentParser('''Accelerate config command''' , description=_lowerCAmelCase )
parser.add_argument(
'''--config_file''' , default=_lowerCAmelCase , help=(
'''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
'''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
'''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
'''with \'huggingface\'.'''
) , )
if subparsers is not None:
parser.set_defaults(func=_lowerCAmelCase )
return parser
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
lowercase__ : int = get_user_input()
if args.config_file is not None:
lowercase__ : List[str] = args.config_file
else:
if not os.path.isdir(_lowerCAmelCase ):
os.makedirs(_lowerCAmelCase )
lowercase__ : Union[str, Any] = default_yaml_config_file
if config_file.endswith('''.json''' ):
config.to_json_file(_lowerCAmelCase )
else:
config.to_yaml_file(_lowerCAmelCase )
print(f"""accelerate configuration saved at {config_file}""" )
def __UpperCAmelCase ( ) -> Tuple:
lowercase__ : int = config_command_parser()
lowercase__ : int = parser.parse_args()
config_command(_lowerCAmelCase )
if __name__ == "__main__":
main()
| 354
|
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Any ,_snake_case : UNetaDModel ,_snake_case : UNetaDModel ,_snake_case : DDPMScheduler ,_snake_case : Any ,) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[int] = value_function
lowercase__ : Optional[int] = unet
lowercase__ : Tuple = scheduler
lowercase__ : Dict = env
lowercase__ : int = env.get_dataset()
lowercase__ : Dict = {}
for key in self.data.keys():
try:
lowercase__ : Optional[Any] = self.data[key].mean()
except: # noqa: E722
pass
lowercase__ : List[Any] = {}
for key in self.data.keys():
try:
lowercase__ : str = self.data[key].std()
except: # noqa: E722
pass
lowercase__ : Tuple = env.observation_space.shape[0]
lowercase__ : Optional[int] = env.action_space.shape[0]
def UpperCAmelCase ( self : str ,_snake_case : Any ,_snake_case : int ) -> Optional[Any]:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def UpperCAmelCase ( self : Dict ,_snake_case : int ,_snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Dict ) -> Optional[int]:
"""simple docstring"""
if type(_snake_case ) is dict:
return {k: self.to_torch(_snake_case ) for k, v in x_in.items()}
elif torch.is_tensor(_snake_case ):
return x_in.to(self.unet.device )
return torch.tensor(_snake_case ,device=self.unet.device )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Any ,_snake_case : int ,_snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
for key, val in cond.items():
lowercase__ : List[Any] = val.clone()
return x_in
def UpperCAmelCase ( self : int ,_snake_case : Optional[int] ,_snake_case : List[Any] ,_snake_case : int ,_snake_case : int ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Any = x.shape[0]
lowercase__ : Dict = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
lowercase__ : Dict = torch.full((batch_size,) ,_snake_case ,device=self.unet.device ,dtype=torch.long )
for _ in range(_snake_case ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
lowercase__ : int = self.value_function(x.permute(0 ,2 ,1 ) ,_snake_case ).sample
lowercase__ : Optional[Any] = torch.autograd.grad([y.sum()] ,[x] )[0]
lowercase__ : List[str] = self.scheduler._get_variance(_snake_case )
lowercase__ : Union[str, Any] = torch.exp(0.5 * posterior_variance )
lowercase__ : Optional[int] = model_std * grad
lowercase__ : Optional[Any] = 0
lowercase__ : str = x.detach()
lowercase__ : Dict = x + scale * grad
lowercase__ : str = self.reset_xa(_snake_case ,_snake_case ,self.action_dim )
lowercase__ : Union[str, Any] = self.unet(x.permute(0 ,2 ,1 ) ,_snake_case ).sample.permute(0 ,2 ,1 )
# TODO: verify deprecation of this kwarg
lowercase__ : Dict = self.scheduler.step(_snake_case ,_snake_case ,_snake_case ,predict_epsilon=_snake_case )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
lowercase__ : Dict = self.reset_xa(_snake_case ,_snake_case ,self.action_dim )
lowercase__ : Union[str, Any] = self.to_torch(_snake_case )
return x, y
def __call__( self : Union[str, Any] ,_snake_case : Any ,_snake_case : Tuple=64 ,_snake_case : Any=32 ,_snake_case : Optional[Any]=2 ,_snake_case : str=0.1 ) -> List[Any]:
"""simple docstring"""
lowercase__ : Any = self.normalize(_snake_case ,'''observations''' )
lowercase__ : Tuple = obs[None].repeat(_snake_case ,axis=0 )
lowercase__ : Dict = {0: self.to_torch(_snake_case )}
lowercase__ : int = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
lowercase__ : Optional[int] = randn_tensor(_snake_case ,device=self.unet.device )
lowercase__ : Tuple = self.reset_xa(_snake_case ,_snake_case ,self.action_dim )
lowercase__ : str = self.to_torch(_snake_case )
# run the diffusion process
lowercase__ , lowercase__ : int = self.run_diffusion(_snake_case ,_snake_case ,_snake_case ,_snake_case )
# sort output trajectories by value
lowercase__ : Optional[Any] = y.argsort(0 ,descending=_snake_case ).squeeze()
lowercase__ : str = x[sorted_idx]
lowercase__ : str = sorted_values[:, :, : self.action_dim]
lowercase__ : Optional[int] = actions.detach().cpu().numpy()
lowercase__ : List[str] = self.de_normalize(_snake_case ,key='''actions''' )
# select the action with the highest value
if y is not None:
lowercase__ : str = 0
else:
# if we didn't run value guiding, select a random action
lowercase__ : str = np.random.randint(0 ,_snake_case )
lowercase__ : int = denorm_actions[selected_index, 0]
return denorm_actions
| 302
| 0
|
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'microsoft/conditional-detr-resnet-50': (
'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
),
}
class __A ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase : List[str] = "conditional_detr"
lowerCAmelCase : Union[str, Any] = ["past_key_values"]
lowerCAmelCase : Any = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self : str ,_snake_case : Dict=True ,_snake_case : Union[str, Any]=None ,_snake_case : Tuple=3 ,_snake_case : str=300 ,_snake_case : Union[str, Any]=6 ,_snake_case : Optional[int]=2_048 ,_snake_case : str=8 ,_snake_case : Optional[int]=6 ,_snake_case : Any=2_048 ,_snake_case : str=8 ,_snake_case : str=0.0 ,_snake_case : List[str]=0.0 ,_snake_case : List[Any]=True ,_snake_case : Tuple="relu" ,_snake_case : Optional[Any]=256 ,_snake_case : Dict=0.1 ,_snake_case : Optional[int]=0.0 ,_snake_case : Dict=0.0 ,_snake_case : int=0.02 ,_snake_case : List[str]=1.0 ,_snake_case : str=False ,_snake_case : Optional[int]="sine" ,_snake_case : Union[str, Any]="resnet50" ,_snake_case : Union[str, Any]=True ,_snake_case : List[Any]=False ,_snake_case : int=2 ,_snake_case : Tuple=5 ,_snake_case : int=2 ,_snake_case : Any=1 ,_snake_case : Optional[Any]=1 ,_snake_case : List[Any]=2 ,_snake_case : List[str]=5 ,_snake_case : Optional[Any]=2 ,_snake_case : Tuple=0.25 ,**_snake_case : Optional[Any] ,) -> Any:
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
lowercase__ : Dict = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
lowercase__ : List[Any] = backbone_config.get('''model_type''' )
lowercase__ : Tuple = CONFIG_MAPPING[backbone_model_type]
lowercase__ : Dict = config_class.from_dict(_SCREAMING_SNAKE_CASE )
lowercase__ : str = use_timm_backbone
lowercase__ : Optional[int] = backbone_config
lowercase__ : Dict = num_channels
lowercase__ : Optional[int] = num_queries
lowercase__ : Dict = d_model
lowercase__ : Optional[int] = encoder_ffn_dim
lowercase__ : Tuple = encoder_layers
lowercase__ : int = encoder_attention_heads
lowercase__ : Any = decoder_ffn_dim
lowercase__ : Optional[int] = decoder_layers
lowercase__ : Any = decoder_attention_heads
lowercase__ : Optional[int] = dropout
lowercase__ : Optional[Any] = attention_dropout
lowercase__ : Union[str, Any] = activation_dropout
lowercase__ : Dict = activation_function
lowercase__ : int = init_std
lowercase__ : Optional[int] = init_xavier_std
lowercase__ : Tuple = encoder_layerdrop
lowercase__ : List[Any] = decoder_layerdrop
lowercase__ : List[str] = encoder_layers
lowercase__ : Optional[Any] = auxiliary_loss
lowercase__ : int = position_embedding_type
lowercase__ : Dict = backbone
lowercase__ : Union[str, Any] = use_pretrained_backbone
lowercase__ : List[Any] = dilation
# Hungarian matcher
lowercase__ : Union[str, Any] = class_cost
lowercase__ : Optional[int] = bbox_cost
lowercase__ : Any = giou_cost
# Loss coefficients
lowercase__ : List[str] = mask_loss_coefficient
lowercase__ : List[Any] = dice_loss_coefficient
lowercase__ : Dict = cls_loss_coefficient
lowercase__ : Any = bbox_loss_coefficient
lowercase__ : Union[str, Any] = giou_loss_coefficient
lowercase__ : List[str] = focal_alpha
super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
@property
def UpperCAmelCase ( self : str ) -> int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def UpperCAmelCase ( self : int ) -> int:
"""simple docstring"""
return self.d_model
def UpperCAmelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : int = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
lowercase__ : Optional[int] = self.backbone_config.to_dict()
lowercase__ : List[Any] = self.__class__.model_type
return output
class __A ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase : Optional[Any] = version.parse("1.11" )
@property
def UpperCAmelCase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def UpperCAmelCase ( self : List[Any] ) -> float:
"""simple docstring"""
return 1e-5
@property
def UpperCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
return 12
| 355
|
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
lowerCAmelCase_ = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n'
lowerCAmelCase_ = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'
lowerCAmelCase_ = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,homepage='''https://github.com/krishnap25/mauve''' ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' ,id='''sequence''' ),
'''references''': datasets.Value('''string''' ,id='''sequence''' ),
} ) ,codebase_urls=['''https://github.com/krishnap25/mauve'''] ,reference_urls=[
'''https://arxiv.org/abs/2102.01454''',
'''https://github.com/krishnap25/mauve''',
] ,)
def UpperCAmelCase ( self : Dict ,_snake_case : Optional[Any] ,_snake_case : Any ,_snake_case : List[str]=None ,_snake_case : Tuple=None ,_snake_case : List[Any]=None ,_snake_case : Any=None ,_snake_case : Optional[int]="auto" ,_snake_case : Optional[int]=-1 ,_snake_case : Optional[int]=0.9 ,_snake_case : Any=5 ,_snake_case : Dict=500 ,_snake_case : Optional[int]="gpt2-large" ,_snake_case : Optional[Any]=-1 ,_snake_case : Tuple=1_024 ,_snake_case : Optional[int]=25 ,_snake_case : Dict=5 ,_snake_case : int=True ,_snake_case : Union[str, Any]=25 ,) -> Any:
"""simple docstring"""
lowercase__ : Any = compute_mauve(
p_text=_snake_case ,q_text=_snake_case ,p_features=_snake_case ,q_features=_snake_case ,p_tokens=_snake_case ,q_tokens=_snake_case ,num_buckets=_snake_case ,pca_max_data=_snake_case ,kmeans_explained_var=_snake_case ,kmeans_num_redo=_snake_case ,kmeans_max_iter=_snake_case ,featurize_model_name=_snake_case ,device_id=_snake_case ,max_text_length=_snake_case ,divergence_curve_discretization_size=_snake_case ,mauve_scaling_factor=_snake_case ,verbose=_snake_case ,seed=_snake_case ,)
return out
| 302
| 0
|
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> Optional[int]:
if isinstance(__lowerCamelCase , __lowerCamelCase ) and isinstance(__lowerCamelCase , __lowerCamelCase ):
lowercase__ : Union[str, Any] = len(set_a.intersection(__lowerCamelCase ) )
if alternative_union:
lowercase__ : str = len(__lowerCamelCase ) + len(__lowerCamelCase )
else:
lowercase__ : str = len(set_a.union(__lowerCamelCase ) )
return intersection / union
if isinstance(__lowerCamelCase , (list, tuple) ) and isinstance(__lowerCamelCase , (list, tuple) ):
lowercase__ : Tuple = [element for element in set_a if element in set_b]
if alternative_union:
lowercase__ : List[Any] = len(__lowerCamelCase ) + len(__lowerCamelCase )
return len(__lowerCamelCase ) / union
else:
lowercase__ : Optional[Any] = set_a + [element for element in set_b if element not in set_a]
return len(__lowerCamelCase ) / len(__lowerCamelCase )
return len(__lowerCamelCase ) / len(__lowerCamelCase )
return None
if __name__ == "__main__":
lowerCAmelCase_ : List[Any] = {'a', 'b', 'c', 'd', 'e'}
lowerCAmelCase_ : Any = {'c', 'd', 'e', 'f', 'h', 'i'}
print(jaccard_similarity(set_a, set_b))
| 356
|
"""simple docstring"""
import math
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
lowercase__ : Tuple = 0
lowercase__ : Tuple = 0
while num > 0:
lowercase__ : int = num % 8
lowercase__ : Tuple = octal + (remainder * math.floor(math.pow(10 , __lowerCamelCase ) ))
counter += 1
lowercase__ : Optional[Any] = math.floor(num / 8 ) # basically /= 8 without remainder if any
# This formatting removes trailing '.0' from `octal`.
return f"""0o{int(__lowerCamelCase )}"""
def __UpperCAmelCase ( ) -> None:
print('''\n2 in octal is:''' )
print(decimal_to_octal(2 ) ) # = 2
print('''\n8 in octal is:''' )
print(decimal_to_octal(8 ) ) # = 10
print('''\n65 in octal is:''' )
print(decimal_to_octal(65 ) ) # = 101
print('''\n216 in octal is:''' )
print(decimal_to_octal(2_16 ) ) # = 330
print('''\n512 in octal is:''' )
print(decimal_to_octal(5_12 ) ) # = 1000
print('''\n''' )
if __name__ == "__main__":
main()
| 302
| 0
|
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def __UpperCAmelCase ( __lowerCamelCase ) -> int:
@wraps(__lowerCamelCase )
def _inner_fn(*__lowerCamelCase , **__lowerCamelCase ):
warnings.warn(
(f"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""") , __lowerCamelCase , )
return fn(*__lowerCamelCase , **__lowerCamelCase )
return _inner_fn
| 357
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
lowerCAmelCase_ = [
'openmmlab/upernet-convnext-tiny',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
lowerCAmelCase_ = 'UperNetConfig'
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : int ,_snake_case : int ,_snake_case : Union[int, Tuple[int, int]] ,_snake_case : Union[int, Tuple[int, int], str] = 0 ,_snake_case : bool = False ,_snake_case : Union[int, Tuple[int, int]] = 1 ,) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[int] = nn.Convad(
in_channels=_snake_case ,out_channels=_snake_case ,kernel_size=_snake_case ,padding=_snake_case ,bias=_snake_case ,dilation=_snake_case ,)
lowercase__ : Tuple = nn.BatchNormad(_snake_case )
lowercase__ : List[str] = nn.ReLU()
def UpperCAmelCase ( self : str ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.conv(_snake_case )
lowercase__ : List[str] = self.batch_norm(_snake_case )
lowercase__ : Tuple = self.activation(_snake_case )
return output
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : int ,_snake_case : int ,_snake_case : int ) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = [
nn.AdaptiveAvgPoolad(_snake_case ),
UperNetConvModule(_snake_case ,_snake_case ,kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(_snake_case ) ,_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : Any = input
for layer in self.layers:
lowercase__ : int = layer(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : Tuple[int, ...] ,_snake_case : int ,_snake_case : int ,_snake_case : bool ) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : int = pool_scales
lowercase__ : Dict = align_corners
lowercase__ : Optional[Any] = in_channels
lowercase__ : Optional[Any] = channels
lowercase__ : int = []
for i, pool_scale in enumerate(_snake_case ):
lowercase__ : Optional[Any] = UperNetPyramidPoolingBlock(pool_scale=_snake_case ,in_channels=_snake_case ,channels=_snake_case )
self.blocks.append(_snake_case )
self.add_module(str(_snake_case ) ,_snake_case )
def UpperCAmelCase ( self : Any ,_snake_case : torch.Tensor ) -> List[torch.Tensor]:
"""simple docstring"""
lowercase__ : int = []
for ppm in self.blocks:
lowercase__ : Any = ppm(_snake_case )
lowercase__ : int = nn.functional.interpolate(
_snake_case ,size=x.size()[2:] ,mode='''bilinear''' ,align_corners=self.align_corners )
ppm_outs.append(_snake_case )
return ppm_outs
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : List[str] ,_snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
super().__init__()
lowercase__ : str = config
lowercase__ : Optional[Any] = config.pool_scales # e.g. (1, 2, 3, 6)
lowercase__ : Optional[Any] = in_channels
lowercase__ : Any = config.hidden_size
lowercase__ : Optional[Any] = False
lowercase__ : Optional[int] = nn.Convad(self.channels ,config.num_labels ,kernel_size=1 )
# PSP Module
lowercase__ : Dict = UperNetPyramidPoolingModule(
self.pool_scales ,self.in_channels[-1] ,self.channels ,align_corners=self.align_corners ,)
lowercase__ : str = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels ,self.channels ,kernel_size=3 ,padding=1 ,)
# FPN Module
lowercase__ : Any = nn.ModuleList()
lowercase__ : Union[str, Any] = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
lowercase__ : List[Any] = UperNetConvModule(_snake_case ,self.channels ,kernel_size=1 )
lowercase__ : Optional[int] = UperNetConvModule(self.channels ,self.channels ,kernel_size=3 ,padding=1 )
self.lateral_convs.append(_snake_case )
self.fpn_convs.append(_snake_case )
lowercase__ : int = UperNetConvModule(
len(self.in_channels ) * self.channels ,self.channels ,kernel_size=3 ,padding=1 ,)
def UpperCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
self.apply(self._init_weights )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[Any] ) -> List[str]:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
module.weight.data.normal_(mean=0.0 ,std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Optional[Any] ) -> str:
"""simple docstring"""
lowercase__ : Dict = inputs[-1]
lowercase__ : Optional[int] = [x]
psp_outs.extend(self.psp_modules(_snake_case ) )
lowercase__ : Optional[Any] = torch.cat(_snake_case ,dim=1 )
lowercase__ : List[str] = self.bottleneck(_snake_case )
return output
def UpperCAmelCase ( self : List[str] ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : Tuple = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(_snake_case ) )
# build top-down path
lowercase__ : List[Any] = len(_snake_case )
for i in range(used_backbone_levels - 1 ,0 ,-1 ):
lowercase__ : Union[str, Any] = laterals[i - 1].shape[2:]
lowercase__ : int = laterals[i - 1] + nn.functional.interpolate(
laterals[i] ,size=_snake_case ,mode='''bilinear''' ,align_corners=self.align_corners )
# build outputs
lowercase__ : List[str] = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 ,0 ,-1 ):
lowercase__ : Any = nn.functional.interpolate(
fpn_outs[i] ,size=fpn_outs[0].shape[2:] ,mode='''bilinear''' ,align_corners=self.align_corners )
lowercase__ : Any = torch.cat(_snake_case ,dim=1 )
lowercase__ : Any = self.fpn_bottleneck(_snake_case )
lowercase__ : str = self.classifier(_snake_case )
return output
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : List[Any] ,_snake_case : int = 2 ,_snake_case : int = 3 ,_snake_case : Union[int, Tuple[int, int]] = 1 ) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : int = config
lowercase__ : Dict = config.auxiliary_in_channels
lowercase__ : Optional[int] = config.auxiliary_channels
lowercase__ : List[Any] = config.auxiliary_num_convs
lowercase__ : List[Any] = config.auxiliary_concat_input
lowercase__ : str = in_index
lowercase__ : Any = (kernel_size // 2) * dilation
lowercase__ : Optional[Any] = []
convs.append(
UperNetConvModule(
self.in_channels ,self.channels ,kernel_size=_snake_case ,padding=_snake_case ,dilation=_snake_case ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels ,self.channels ,kernel_size=_snake_case ,padding=_snake_case ,dilation=_snake_case ) )
if self.num_convs == 0:
lowercase__ : List[str] = nn.Identity()
else:
lowercase__ : Dict = nn.Sequential(*_snake_case )
if self.concat_input:
lowercase__ : int = UperNetConvModule(
self.in_channels + self.channels ,self.channels ,kernel_size=_snake_case ,padding=kernel_size // 2 )
lowercase__ : List[str] = nn.Convad(self.channels ,config.num_labels ,kernel_size=1 )
def UpperCAmelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
self.apply(self._init_weights )
def UpperCAmelCase ( self : List[Any] ,_snake_case : List[Any] ) -> Dict:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
module.weight.data.normal_(mean=0.0 ,std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase ( self : List[str] ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : str = encoder_hidden_states[self.in_index]
lowercase__ : List[str] = self.convs(_snake_case )
if self.concat_input:
lowercase__ : Any = self.conv_cat(torch.cat([hidden_states, output] ,dim=1 ) )
lowercase__ : Dict = self.classifier(_snake_case )
return output
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Any = UperNetConfig
lowerCAmelCase : str = "pixel_values"
lowerCAmelCase : Dict = True
def UpperCAmelCase ( self : int ,_snake_case : str ) -> Optional[int]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def UpperCAmelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def UpperCAmelCase ( self : int ,_snake_case : str ,_snake_case : str=False ) -> List[str]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
lowercase__ : List[Any] = value
lowerCAmelCase_ = R'\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCAmelCase_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes." ,A_ ,)
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Tuple ) -> int:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : int = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
lowercase__ : Any = UperNetHead(_snake_case ,in_channels=self.backbone.channels )
lowercase__ : str = UperNetFCNHead(_snake_case ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length''' ) )
@replace_return_docstrings(output_type=_snake_case ,config_class=_CONFIG_FOR_DOC )
def UpperCAmelCase ( self : Dict ,_snake_case : Optional[torch.Tensor] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[torch.Tensor] = None ,_snake_case : Optional[bool] = None ,) -> Union[tuple, SemanticSegmenterOutput]:
"""simple docstring"""
lowercase__ : int = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : Any = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ : Any = output_attentions if output_attentions is not None else self.config.output_attentions
lowercase__ : Optional[Any] = self.backbone.forward_with_filtered_kwargs(
_snake_case ,output_hidden_states=_snake_case ,output_attentions=_snake_case )
lowercase__ : Optional[int] = outputs.feature_maps
lowercase__ : Tuple = self.decode_head(_snake_case )
lowercase__ : Optional[int] = nn.functional.interpolate(_snake_case ,size=pixel_values.shape[2:] ,mode='''bilinear''' ,align_corners=_snake_case )
lowercase__ : List[str] = None
if self.auxiliary_head is not None:
lowercase__ : str = self.auxiliary_head(_snake_case )
lowercase__ : Dict = nn.functional.interpolate(
_snake_case ,size=pixel_values.shape[2:] ,mode='''bilinear''' ,align_corners=_snake_case )
lowercase__ : Any = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''' )
else:
# compute weighted loss
lowercase__ : Union[str, Any] = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
lowercase__ : List[str] = loss_fct(_snake_case ,_snake_case )
lowercase__ : List[str] = loss_fct(_snake_case ,_snake_case )
lowercase__ : Optional[Any] = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
lowercase__ : Tuple = (logits,) + outputs[1:]
else:
lowercase__ : int = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=_snake_case ,logits=_snake_case ,hidden_states=outputs.hidden_states ,attentions=outputs.attentions ,)
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| 0
|
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
lowerCAmelCase_ = logging.getLogger(__name__)
class __A ( lowerCamelCase__ ):
'''simple docstring'''
def UpperCAmelCase ( self : List[str] ,_snake_case : Tuple ,_snake_case : List[Any] ,_snake_case : Dict=None ,_snake_case : Optional[int]=None ) -> Tuple:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.layer[current_layer](lowercase__ ,lowercase__ ,head_mask[current_layer] )
lowercase__ : List[str] = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top." ,lowerCamelCase__ ,)
class __A ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : Tuple ) -> List[str]:
"""simple docstring"""
super().__init__(lowercase__ )
lowercase__ : str = BertEncoderWithPabee(lowercase__ )
self.init_weights()
lowercase__ : Optional[int] = 0
lowercase__ : int = 0
lowercase__ : Union[str, Any] = 0
lowercase__ : List[str] = 0
def UpperCAmelCase ( self : List[str] ,_snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Optional[int] = threshold
def UpperCAmelCase ( self : Tuple ,_snake_case : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[int] = patience
def UpperCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
lowercase__ : str = 0
lowercase__ : Dict = 0
def UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.inference_layers_num / self.inference_instances_num
lowercase__ : Tuple = (
f"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
f""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
)
print(lowercase__ )
@add_start_docstrings_to_model_forward(lowercase__ )
def UpperCAmelCase ( self : Dict ,_snake_case : int=None ,_snake_case : int=None ,_snake_case : Optional[Any]=None ,_snake_case : Dict=None ,_snake_case : Optional[int]=None ,_snake_case : List[str]=None ,_snake_case : Union[str, Any]=None ,_snake_case : Tuple=None ,_snake_case : List[Any]=None ,_snake_case : Union[str, Any]=None ,_snake_case : Optional[int]=False ,) -> Tuple:
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''' )
elif input_ids is not None:
lowercase__ : str = input_ids.size()
elif inputs_embeds is not None:
lowercase__ : Tuple = inputs_embeds.size()[:-1]
else:
raise ValueError('''You have to specify either input_ids or inputs_embeds''' )
lowercase__ : Optional[Any] = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
lowercase__ : Union[str, Any] = torch.ones(lowercase__ ,device=lowercase__ )
if token_type_ids is None:
lowercase__ : Optional[int] = torch.zeros(lowercase__ ,dtype=torch.long ,device=lowercase__ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
lowercase__ : List[Any] = self.get_extended_attention_mask(lowercase__ ,lowercase__ ,lowercase__ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
lowercase__ , lowercase__ , lowercase__ : Any = encoder_hidden_states.size()
lowercase__ : Optional[Any] = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
lowercase__ : Optional[Any] = torch.ones(lowercase__ ,device=lowercase__ )
lowercase__ : Any = self.invert_attention_mask(lowercase__ )
else:
lowercase__ : Dict = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
lowercase__ : Tuple = self.get_head_mask(lowercase__ ,self.config.num_hidden_layers )
lowercase__ : List[str] = self.embeddings(
input_ids=lowercase__ ,position_ids=lowercase__ ,token_type_ids=lowercase__ ,inputs_embeds=lowercase__ )
lowercase__ : int = embedding_output
if self.training:
lowercase__ : str = []
for i in range(self.config.num_hidden_layers ):
lowercase__ : str = self.encoder.adaptive_forward(
lowercase__ ,current_layer=lowercase__ ,attention_mask=lowercase__ ,head_mask=lowercase__ )
lowercase__ : Tuple = self.pooler(lowercase__ )
lowercase__ : Dict = output_layers[i](output_dropout(lowercase__ ) )
res.append(lowercase__ )
elif self.patience == 0: # Use all layers for inference
lowercase__ : List[str] = self.encoder(
lowercase__ ,attention_mask=lowercase__ ,head_mask=lowercase__ ,encoder_hidden_states=lowercase__ ,encoder_attention_mask=lowercase__ ,)
lowercase__ : List[str] = self.pooler(encoder_outputs[0] )
lowercase__ : Union[str, Any] = [output_layers[self.config.num_hidden_layers - 1](lowercase__ )]
else:
lowercase__ : Optional[Any] = 0
lowercase__ : Tuple = None
lowercase__ : List[Any] = 0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
lowercase__ : List[str] = self.encoder.adaptive_forward(
lowercase__ ,current_layer=lowercase__ ,attention_mask=lowercase__ ,head_mask=lowercase__ )
lowercase__ : List[Any] = self.pooler(lowercase__ )
lowercase__ : Dict = output_layers[i](lowercase__ )
if regression:
lowercase__ : Any = logits.detach()
if patient_result is not None:
lowercase__ : Union[str, Any] = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
lowercase__ : Tuple = 0
else:
lowercase__ : Union[str, Any] = logits.detach().argmax(dim=1 )
if patient_result is not None:
lowercase__ : List[Any] = patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(lowercase__ ) ):
patient_counter += 1
else:
lowercase__ : Union[str, Any] = 0
lowercase__ : Any = logits
if patient_counter == self.patience:
break
lowercase__ : Union[str, Any] = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. " ,lowerCamelCase__ ,)
class __A ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : Optional[int] ) -> int:
"""simple docstring"""
super().__init__(lowercase__ )
lowercase__ : str = config.num_labels
lowercase__ : Optional[Any] = BertModelWithPabee(lowercase__ )
lowercase__ : Tuple = nn.Dropout(config.hidden_dropout_prob )
lowercase__ : Optional[Any] = nn.ModuleList(
[nn.Linear(config.hidden_size ,self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(lowercase__ )
def UpperCAmelCase ( self : Optional[int] ,_snake_case : str=None ,_snake_case : Any=None ,_snake_case : str=None ,_snake_case : Optional[Any]=None ,_snake_case : Optional[Any]=None ,_snake_case : Optional[int]=None ,_snake_case : int=None ,) -> Any:
"""simple docstring"""
lowercase__ : Tuple = self.bert(
input_ids=lowercase__ ,attention_mask=lowercase__ ,token_type_ids=lowercase__ ,position_ids=lowercase__ ,head_mask=lowercase__ ,inputs_embeds=lowercase__ ,output_dropout=self.dropout ,output_layers=self.classifiers ,regression=self.num_labels == 1 ,)
lowercase__ : Union[str, Any] = (logits[-1],)
if labels is not None:
lowercase__ : Union[str, Any] = None
lowercase__ : int = 0
for ix, logits_item in enumerate(lowercase__ ):
if self.num_labels == 1:
# We are doing regression
lowercase__ : int = MSELoss()
lowercase__ : List[str] = loss_fct(logits_item.view(-1 ) ,labels.view(-1 ) )
else:
lowercase__ : int = CrossEntropyLoss()
lowercase__ : Union[str, Any] = loss_fct(logits_item.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
if total_loss is None:
lowercase__ : str = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
lowercase__ : str = (total_loss / total_weights,) + outputs
return outputs
| 358
|
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAmelCase_ = _symbol_database.Default()
lowerCAmelCase_ = _descriptor_pool.Default().AddSerializedFile(
B'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
lowerCAmelCase_ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
lowerCAmelCase_ = None
lowerCAmelCase_ = B'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
lowerCAmelCase_ = 45
lowerCAmelCase_ = 1_581
lowerCAmelCase_ = 1_517
lowerCAmelCase_ = 1_570
lowerCAmelCase_ = 1_584
lowerCAmelCase_ = 1_793
lowerCAmelCase_ = 1_795
lowerCAmelCase_ = 1_916
lowerCAmelCase_ = 1_864
lowerCAmelCase_ = 1_905
lowerCAmelCase_ = 1_919
lowerCAmelCase_ = 2_429
lowerCAmelCase_ = 2_208
lowerCAmelCase_ = 2_418
lowerCAmelCase_ = 2_323
lowerCAmelCase_ = 2_407
# @@protoc_insertion_point(module_scope)
| 302
| 0
|
"""simple docstring"""
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
lowerCAmelCase_ = logging.get_logger(__name__)
@dataclass
class __A :
'''simple docstring'''
def __init__( self : Dict ,_snake_case : Any=False ,_snake_case : Optional[Any]=False ,_snake_case : int=6.0 ,_snake_case : Any=None ,_snake_case : List[Any]=False ,_snake_case : Any=False ,_snake_case : Dict=None ,_snake_case : Dict="fp4" ,_snake_case : Dict=False ,**_snake_case : int ,) -> List[Any]:
"""simple docstring"""
lowercase__ : List[Any] = load_in_abit
lowercase__ : List[str] = load_in_abit
lowercase__ : Optional[Any] = llm_inta_threshold
lowercase__ : int = llm_inta_skip_modules
lowercase__ : List[Any] = llm_inta_enable_fpaa_cpu_offload
lowercase__ : Any = llm_inta_has_fpaa_weight
lowercase__ : Union[str, Any] = bnb_abit_quant_type
lowercase__ : Tuple = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
lowercase__ : Union[str, Any] = torch.floataa
elif isinstance(_snake_case ,_snake_case ):
lowercase__ : Union[str, Any] = getattr(_snake_case ,_snake_case )
elif isinstance(_snake_case ,torch.dtype ):
lowercase__ : Dict = bnb_abit_compute_dtype
else:
raise ValueError('''bnb_4bit_compute_dtype must be a string or a torch.dtype''' )
self.post_init()
def UpperCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
if not isinstance(self.llm_inta_threshold ,_snake_case ):
raise ValueError('''llm_int8_threshold must be a float''' )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules ,_snake_case ):
raise ValueError('''llm_int8_skip_modules must be a list of strings''' )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload ,_snake_case ):
raise ValueError('''llm_int8_enable_fp32_cpu_offload must be a boolean''' )
if not isinstance(self.llm_inta_has_fpaa_weight ,_snake_case ):
raise ValueError('''llm_int8_has_fp16_weight must be a boolean''' )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype ,torch.dtype ):
raise ValueError('''bnb_4bit_compute_dtype must be torch.dtype''' )
if not isinstance(self.bnb_abit_quant_type ,_snake_case ):
raise ValueError('''bnb_4bit_quant_type must be a string''' )
if not isinstance(self.bnb_abit_use_double_quant ,_snake_case ):
raise ValueError('''bnb_4bit_use_double_quant must be a boolean''' )
if self.load_in_abit and not version.parse(importlib.metadata.version('''bitsandbytes''' ) ) >= version.parse(
'''0.39.0''' ):
raise ValueError(
'''4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version''' )
def UpperCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
return self.load_in_abit or self.load_in_abit
def UpperCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def UpperCAmelCase ( cls : str ,_snake_case : int ,_snake_case : Tuple ,**_snake_case : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Any = cls(**_snake_case )
lowercase__ : Any = []
for key, value in kwargs.items():
if hasattr(_snake_case ,_snake_case ):
setattr(_snake_case ,_snake_case ,_snake_case )
to_remove.append(_snake_case )
for key in to_remove:
kwargs.pop(_snake_case ,_snake_case )
if return_unused_kwargs:
return config, kwargs
else:
return config
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Union[str, os.PathLike] ) -> Any:
"""simple docstring"""
with open(_snake_case ,'''w''' ,encoding='''utf-8''' ) as writer:
lowercase__ : str = self.to_dict()
lowercase__ : List[str] = json.dumps(_snake_case ,indent=2 ,sort_keys=_snake_case ) + '\n'
writer.write(_snake_case )
def UpperCAmelCase ( self : Optional[Any] ) -> Dict[str, Any]:
"""simple docstring"""
lowercase__ : int = copy.deepcopy(self.__dict__ )
lowercase__ : Tuple = str(output['''bnb_4bit_compute_dtype'''] ).split('''.''' )[1]
return output
def __repr__( self : Any ) -> Tuple:
"""simple docstring"""
return f"""{self.__class__.__name__} {self.to_json_string()}"""
def UpperCAmelCase ( self : Dict ,_snake_case : bool = True ) -> str:
"""simple docstring"""
if use_diff is True:
lowercase__ : int = self.to_diff_dict()
else:
lowercase__ : List[str] = self.to_dict()
return json.dumps(_snake_case ,indent=2 ,sort_keys=_snake_case ) + "\n"
def UpperCAmelCase ( self : Optional[Any] ) -> Dict[str, Any]:
"""simple docstring"""
lowercase__ : str = self.to_dict()
# get the default config dict
lowercase__ : Tuple = BitsAndBytesConfig().to_dict()
lowercase__ : int = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
lowercase__ : Any = value
return serializable_config_dict
| 359
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Pix2StructConfig',
'Pix2StructTextConfig',
'Pix2StructVisionConfig',
],
'processing_pix2struct': ['Pix2StructProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['Pix2StructImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Pix2StructPreTrainedModel',
'Pix2StructForConditionalGeneration',
'Pix2StructVisionModel',
'Pix2StructTextModel',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 302
| 0
|
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase=None ) -> int:
if attention_mask is None:
lowercase__ : List[str] = tf.cast(tf.math.not_equal(a__ , config.pad_token_id ) , tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class __A :
'''simple docstring'''
lowerCAmelCase : Optional[Any] = OPTConfig
lowerCAmelCase : Tuple = {}
lowerCAmelCase : Any = "gelu"
def __init__( self : int ,_snake_case : Optional[int] ,_snake_case : str=13 ,_snake_case : Optional[Any]=7 ,_snake_case : Union[str, Any]=True ,_snake_case : str=False ,_snake_case : str=99 ,_snake_case : Union[str, Any]=16 ,_snake_case : List[Any]=2 ,_snake_case : Optional[Any]=4 ,_snake_case : Any=4 ,_snake_case : Tuple="gelu" ,_snake_case : Dict=0.1 ,_snake_case : List[Any]=0.1 ,_snake_case : Dict=20 ,_snake_case : Optional[Any]=2 ,_snake_case : Optional[int]=1 ,_snake_case : Dict=0 ,_snake_case : Optional[int]=16 ,_snake_case : List[str]=16 ,) -> str:
"""simple docstring"""
lowercase__ : List[str] = parent
lowercase__ : Union[str, Any] = batch_size
lowercase__ : Any = seq_length
lowercase__ : List[str] = is_training
lowercase__ : Union[str, Any] = use_labels
lowercase__ : Tuple = vocab_size
lowercase__ : Union[str, Any] = hidden_size
lowercase__ : Tuple = num_hidden_layers
lowercase__ : List[Any] = num_attention_heads
lowercase__ : List[Any] = intermediate_size
lowercase__ : Tuple = hidden_act
lowercase__ : Any = hidden_dropout_prob
lowercase__ : Any = attention_probs_dropout_prob
lowercase__ : Dict = max_position_embeddings
lowercase__ : str = eos_token_id
lowercase__ : Optional[int] = pad_token_id
lowercase__ : int = bos_token_id
lowercase__ : Dict = embed_dim
lowercase__ : List[str] = word_embed_proj_dim
lowercase__ : Dict = False
def UpperCAmelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size )
lowercase__ : str = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) ,1 )
lowercase__ : Optional[int] = tf.concat([input_ids, eos_tensor] ,axis=1 )
lowercase__ : str = self.config_cls(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,eos_token_id=self.eos_token_id ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,embed_dim=self.embed_dim ,word_embed_proj_dim=self.word_embed_proj_dim ,is_encoder_decoder=__UpperCamelCase ,**self.config_updates ,)
lowercase__ : Optional[Any] = prepare_opt_inputs_dict(__UpperCamelCase ,__UpperCamelCase )
return config, inputs_dict
def UpperCAmelCase ( self : int ,_snake_case : str ,_snake_case : Tuple ) -> List[str]:
"""simple docstring"""
lowercase__ : Optional[int] = TFOPTModel(config=__UpperCamelCase )
lowercase__ : str = inputs_dict['''input_ids''']
lowercase__ : Any = input_ids[:1, :]
lowercase__ : Dict = inputs_dict['''attention_mask'''][:1, :]
lowercase__ : List[Any] = 1
# first forward pass
lowercase__ : Tuple = model(__UpperCamelCase ,attention_mask=__UpperCamelCase ,use_cache=__UpperCamelCase )
lowercase__ , lowercase__ : List[str] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowercase__ : List[Any] = ids_tensor((self.batch_size, 3) ,config.vocab_size )
lowercase__ : Tuple = tf.cast(ids_tensor((self.batch_size, 3) ,2 ) ,tf.inta )
# append to next input_ids and
lowercase__ : List[str] = tf.concat([input_ids, next_tokens] ,axis=-1 )
lowercase__ : Tuple = tf.concat([attention_mask, next_attn_mask] ,axis=-1 )
lowercase__ : Any = model(__UpperCamelCase ,attention_mask=__UpperCamelCase )[0]
lowercase__ : Union[str, Any] = model(__UpperCamelCase ,attention_mask=__UpperCamelCase ,past_key_values=__UpperCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] ,output_from_past.shape[1] )
# select random slice
lowercase__ : Union[str, Any] = int(ids_tensor((1,) ,output_from_past.shape[-1] ) )
lowercase__ : List[Any] = output_from_no_past[:, -3:, random_slice_idx]
lowercase__ : Optional[int] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__UpperCamelCase ,__UpperCamelCase ,rtol=1e-3 )
@require_tf
class __A ( _lowercase ,_lowercase ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
lowerCAmelCase : int = (TFOPTForCausalLM,) if is_tf_available() else ()
lowerCAmelCase : Optional[int] = (
{"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
)
lowerCAmelCase : int = False
lowerCAmelCase : Dict = False
lowerCAmelCase : Optional[int] = False
lowerCAmelCase : Union[str, Any] = 1_0
def UpperCAmelCase ( self : int ) -> Dict:
"""simple docstring"""
lowercase__ : Any = TFOPTModelTester(self )
lowercase__ : Tuple = ConfigTester(self ,config_class=__UpperCamelCase )
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase )
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
lowercase__ , lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(_snake_case : int ,_snake_case : Any ):
if hasattr(__UpperCamelCase ,'''weight''' ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(__UpperCamelCase ,'''weight''' ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
lowercase__ : Any = model_class(config=__UpperCamelCase )
lowercase__ : List[str] = _get_word_embedding_weight(__UpperCamelCase ,model.get_input_embeddings() )
lowercase__ : List[str] = _get_word_embedding_weight(__UpperCamelCase ,model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(__UpperCamelCase )
lowercase__ : List[str] = _get_word_embedding_weight(__UpperCamelCase ,model.get_input_embeddings() )
lowercase__ : List[Any] = _get_word_embedding_weight(__UpperCamelCase ,model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
lowercase__ : List[Any] = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] ,__UpperCamelCase )
# check that weights remain the same after resizing
lowercase__ : Union[str, Any] = True
for pa, pa in zip(old_input_embeddings.value() ,new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
lowercase__ : Dict = False
self.assertTrue(__UpperCamelCase )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] ,__UpperCamelCase )
lowercase__ : List[Any] = True
for pa, pa in zip(old_output_embeddings.value() ,new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
lowercase__ : Dict = False
self.assertTrue(__UpperCamelCase )
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[Any]:
return tf.constant(a__ , dtype=tf.intaa )
@require_tf
class __A ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : List[str] = 9_9
def UpperCAmelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Tuple = tf.ones((4, 1) ,dtype=tf.intaa ) * 2
lowercase__ : str = tf.concat([ids_tensor((4, 6) ,self.vocab_size - 3 ) + 3, eos_column_vector] ,axis=1 )
lowercase__ : int = input_ids.shape[0]
lowercase__ : Optional[int] = OPTConfig(
vocab_size=self.vocab_size ,hidden_size=24 ,num_hidden_layers=2 ,num_attention_heads=2 ,ffn_dim=32 ,max_position_embeddings=48 ,eos_token_id=2 ,pad_token_id=1 ,bos_token_id=0 ,)
return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCAmelCase ( self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ : List[str] = TFOPTModel.from_pretrained('''facebook/opt-350m''' )
lowercase__ : Union[str, Any] = _long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
lowercase__ : Dict = tf.not_equal(__UpperCamelCase ,model.config.pad_token_id )
with tf.GradientTape():
lowercase__ : str = model(input_ids=__UpperCamelCase ,attention_mask=__UpperCamelCase ).last_hidden_state
lowercase__ : int = (1, 11, 512)
self.assertEqual(output.shape ,__UpperCamelCase )
lowercase__ : int = tf.constant(
[[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]] )
self.assertTrue(np.allclose(output[:, :3, :3] ,__UpperCamelCase ,atol=4e-3 ) )
lowercase__ : str = tf.function(__UpperCamelCase ,jit_compile=__UpperCamelCase )
lowercase__ : Dict = xla_generate(__UpperCamelCase ,__UpperCamelCase )[0]
self.assertTrue(np.allclose(output[:, :3, :3] ,__UpperCamelCase ,atol=4e-2 ) )
@require_tf
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( self : Any ) -> str:
"""simple docstring"""
super().setUp()
lowercase__ : Union[str, Any] = '''facebook/opt-350m'''
def UpperCAmelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ : List[str] = TFOPTForCausalLM.from_pretrained(self.path_model )
lowercase__ : Optional[int] = GPTaTokenizer.from_pretrained(self.path_model )
lowercase__ : List[str] = [
'''Today is a beautiful day and I want to''',
'''In the city of''',
'''Paris is the capital of France and''',
'''Computers and mobile phones have taken''',
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
lowercase__ : str = tokenizer(__UpperCamelCase ,return_tensors='''tf''' ,padding=__UpperCamelCase ,add_special_tokens=__UpperCamelCase )
lowercase__ : List[str] = tf.math.reduce_mean(model(inputs.input_ids ,attention_mask=inputs.attention_mask )[0] ,axis=-1 )
lowercase__ : Optional[Any] = tf.constant(
[
[1.3851, -13.8_923, -10.5_229, -10.7_533, -0.2309, -10.2_384, -0.5365, -9.0947, -5.1670],
[-4.7073, -10.6_276, -3.9415, -21.5_242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
[0.6247, -3.4229, -8.9179, -1.4297, -14.1_650, 1.4146, -9.0218, -0.2703, -0.2703],
[6.4783, -1.9913, -10.7_926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
] )
self.assertTrue(np.allclose(__UpperCamelCase ,__UpperCamelCase ,atol=1e-4 ) )
lowercase__ : int = tf.function(__UpperCamelCase ,jit_compile=__UpperCamelCase )
lowercase__ : Optional[Any] = tf.math.reduce_mean(xla_generate(inputs.input_ids ,attention_mask=inputs.attention_mask )[0] ,axis=-1 )
self.assertTrue(np.allclose(__UpperCamelCase ,__UpperCamelCase ,atol=1e-4 ) )
@require_tf
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
@property
def UpperCAmelCase ( self : int ) -> Tuple:
"""simple docstring"""
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def UpperCAmelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
lowercase__ : List[Any] = '''facebook/opt-125m'''
lowercase__ : Dict = [
'''Today is a beautiful day and I want to''',
'''In the city of New York, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
lowercase__ : str = []
lowercase__ : Optional[int] = GPTaTokenizer.from_pretrained(__UpperCamelCase )
lowercase__ : List[str] = TFOPTForCausalLM.from_pretrained(__UpperCamelCase )
for prompt in self.prompts:
lowercase__ : Optional[int] = tokenizer(__UpperCamelCase ,return_tensors='''tf''' ).input_ids
lowercase__ : Optional[int] = model.generate(__UpperCamelCase ,max_length=10 )
lowercase__ : str = tokenizer.batch_decode(__UpperCamelCase ,skip_special_tokens=__UpperCamelCase )
predicted_outputs += generated_string
self.assertListEqual(__UpperCamelCase ,__UpperCamelCase )
def UpperCAmelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Any = '''facebook/opt-350m'''
lowercase__ : Any = GPTaTokenizer.from_pretrained(__UpperCamelCase )
lowercase__ : List[str] = TFOPTForCausalLM.from_pretrained(__UpperCamelCase )
lowercase__ : Union[str, Any] = '''left'''
# use different length sentences to test batching
lowercase__ : Optional[int] = [
'''Hello, my dog is a little''',
'''Today, I''',
]
lowercase__ : Dict = tokenizer(__UpperCamelCase ,return_tensors='''tf''' ,padding=__UpperCamelCase )
lowercase__ : List[Any] = inputs['''input_ids''']
lowercase__ : List[str] = model.generate(input_ids=__UpperCamelCase ,attention_mask=inputs['''attention_mask'''] )
lowercase__ : Optional[Any] = tokenizer(sentences[0] ,return_tensors='''tf''' ).input_ids
lowercase__ : List[str] = model.generate(input_ids=__UpperCamelCase )
lowercase__ : List[str] = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs['''attention_mask'''][-1] ,tf.intaa ) )
lowercase__ : int = tokenizer(sentences[1] ,return_tensors='''tf''' ).input_ids
lowercase__ : Optional[Any] = model.generate(input_ids=__UpperCamelCase ,max_length=model.config.max_length - num_paddings )
lowercase__ : Tuple = tokenizer.batch_decode(__UpperCamelCase ,skip_special_tokens=__UpperCamelCase )
lowercase__ : Optional[Any] = tokenizer.decode(output_non_padded[0] ,skip_special_tokens=__UpperCamelCase )
lowercase__ : int = tokenizer.decode(output_padded[0] ,skip_special_tokens=__UpperCamelCase )
lowercase__ : Any = [
'''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
'''Today, I was in the middle of a conversation with a friend about the''',
]
self.assertListEqual(__UpperCamelCase ,__UpperCamelCase )
self.assertListEqual(__UpperCamelCase ,[non_padded_sentence, padded_sentence] )
def UpperCAmelCase ( self : List[Any] ) -> str:
"""simple docstring"""
lowercase__ : List[Any] = '''facebook/opt-350m'''
lowercase__ : Optional[Any] = [
'''Today is a beautiful day and I want to''',
'''In the city of San Francisco, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
lowercase__ : Dict = []
lowercase__ : List[str] = GPTaTokenizer.from_pretrained(__UpperCamelCase )
lowercase__ : Any = TFOPTForCausalLM.from_pretrained(__UpperCamelCase )
for prompt in self.prompts:
lowercase__ : Dict = tokenizer(__UpperCamelCase ,return_tensors='''tf''' ).input_ids
lowercase__ : Optional[Any] = model.generate(__UpperCamelCase ,max_length=10 )
lowercase__ : int = tokenizer.batch_decode(__UpperCamelCase ,skip_special_tokens=__UpperCamelCase )
predicted_outputs += generated_string
self.assertListEqual(__UpperCamelCase ,__UpperCamelCase )
| 360
|
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
lowercase__ : List[str] = FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
lowercase__ : List[str] = AutoTokenizer.from_pretrained('''xlm-roberta-base''' )
lowercase__ : List[str] = '''The dog is cute and lives in the garden house'''
lowercase__ : int = jnp.array([tokenizer.encode(_snake_case )] )
lowercase__ : Any = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
lowercase__ : Tuple = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
lowercase__ : Optional[Any] = model(_snake_case )['''last_hidden_state''']
self.assertEqual(output.shape ,_snake_case )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] ,_snake_case ,atol=1e-3 ) )
| 302
| 0
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
set_seed(770)
lowerCAmelCase_ = {
'c_attn': 'att_proj',
'c_proj': 'out_proj',
'c_fc': 'in_proj',
'transformer.': '',
'h.': 'layers.',
'ln_1': 'layernorm_1',
'ln_2': 'layernorm_2',
'ln_f': 'layernorm_final',
'wpe': 'position_embeds_layer',
'wte': 'input_embeds_layer',
}
lowerCAmelCase_ = {
'text_small': {
'repo_id': 'suno/bark',
'file_name': 'text.pt',
},
'coarse_small': {
'repo_id': 'suno/bark',
'file_name': 'coarse.pt',
},
'fine_small': {
'repo_id': 'suno/bark',
'file_name': 'fine.pt',
},
'text': {
'repo_id': 'suno/bark',
'file_name': 'text_2.pt',
},
'coarse': {
'repo_id': 'suno/bark',
'file_name': 'coarse_2.pt',
},
'fine': {
'repo_id': 'suno/bark',
'file_name': 'fine_2.pt',
},
}
lowerCAmelCase_ = os.path.dirname(os.path.abspath(__file__))
lowerCAmelCase_ = os.path.join(os.path.expanduser('~'), '.cache')
lowerCAmelCase_ = os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase=False ) -> Dict:
lowercase__ : Optional[int] = model_type
if use_small:
key += "_small"
return os.path.join(__lowerCamelCase , REMOTE_MODEL_PATHS[key]['''file_name'''] )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
hf_hub_download(repo_id=__lowerCamelCase , filename=__lowerCamelCase , local_dir=__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False , __lowerCamelCase="text" ) -> Any:
if model_type == "text":
lowercase__ : str = BarkSemanticModel
lowercase__ : Optional[Any] = BarkSemanticConfig
lowercase__ : Optional[int] = BarkSemanticGenerationConfig
elif model_type == "coarse":
lowercase__ : Union[str, Any] = BarkCoarseModel
lowercase__ : str = BarkCoarseConfig
lowercase__ : Tuple = BarkCoarseGenerationConfig
elif model_type == "fine":
lowercase__ : str = BarkFineModel
lowercase__ : int = BarkFineConfig
lowercase__ : str = BarkFineGenerationConfig
else:
raise NotImplementedError()
lowercase__ : Any = f"""{model_type}_small""" if use_small else model_type
lowercase__ : Dict = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(__lowerCamelCase ):
logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`.""" )
_download(model_info['''repo_id'''] , model_info['''file_name'''] )
lowercase__ : str = torch.load(__lowerCamelCase , map_location=__lowerCamelCase )
# this is a hack
lowercase__ : Union[str, Any] = checkpoint['''model_args''']
if "input_vocab_size" not in model_args:
lowercase__ : Optional[Any] = model_args['''vocab_size''']
lowercase__ : Tuple = model_args['''vocab_size''']
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
lowercase__ : Optional[int] = model_args.pop('''n_head''' )
lowercase__ : Optional[Any] = model_args.pop('''n_embd''' )
lowercase__ : int = model_args.pop('''n_layer''' )
lowercase__ : Optional[int] = ConfigClass(**checkpoint['''model_args'''] )
lowercase__ : List[Any] = ModelClass(config=__lowerCamelCase )
lowercase__ : List[Any] = GenerationConfigClass()
lowercase__ : List[Any] = model_generation_config
lowercase__ : str = checkpoint['''model''']
# fixup checkpoint
lowercase__ : List[str] = '''_orig_mod.'''
for k, v in list(state_dict.items() ):
if k.startswith(__lowerCamelCase ):
# replace part of the key with corresponding layer name in HF implementation
lowercase__ : int = k[len(__lowerCamelCase ) :]
for old_layer_name in new_layer_name_dict:
lowercase__ : Dict = new_k.replace(__lowerCamelCase , new_layer_name_dict[old_layer_name] )
lowercase__ : str = state_dict.pop(__lowerCamelCase )
lowercase__ : Optional[int] = set(state_dict.keys() ) - set(model.state_dict().keys() )
lowercase__ : Tuple = {k for k in extra_keys if not k.endswith('''.attn.bias''' )}
lowercase__ : Optional[int] = set(model.state_dict().keys() ) - set(state_dict.keys() )
lowercase__ : Dict = {k for k in missing_keys if not k.endswith('''.attn.bias''' )}
if len(__lowerCamelCase ) != 0:
raise ValueError(f"""extra keys found: {extra_keys}""" )
if len(__lowerCamelCase ) != 0:
raise ValueError(f"""missing keys: {missing_keys}""" )
model.load_state_dict(__lowerCamelCase , strict=__lowerCamelCase )
lowercase__ : Optional[Any] = model.num_parameters(exclude_embeddings=__lowerCamelCase )
lowercase__ : int = checkpoint['''best_val_loss'''].item()
logger.info(f"""model loaded: {round(n_params/1E6 , 1 )}M params, {round(__lowerCamelCase , 3 )} loss""" )
model.eval()
model.to(__lowerCamelCase )
del checkpoint, state_dict
return model
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase=False , __lowerCamelCase="text" ) -> Optional[Any]:
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
lowercase__ : List[str] = '''cpu''' # do conversion on cpu
lowercase__ : List[Any] = _get_ckpt_path(__lowerCamelCase , use_small=__lowerCamelCase )
lowercase__ : Optional[int] = _load_model(__lowerCamelCase , __lowerCamelCase , model_type=__lowerCamelCase , use_small=__lowerCamelCase )
# load bark initial model
lowercase__ : int = _bark_load_model(__lowerCamelCase , '''cpu''' , model_type=__lowerCamelCase , use_small=__lowerCamelCase )
if model_type == "text":
lowercase__ : List[str] = bark_model['''model''']
if model.num_parameters(exclude_embeddings=__lowerCamelCase ) != bark_model.get_num_params():
raise ValueError('''initial and new models don\'t have the same number of parameters''' )
# check if same output as the bark model
lowercase__ : int = 5
lowercase__ : Optional[Any] = 10
if model_type in ["text", "coarse"]:
lowercase__ : List[Any] = torch.randint(2_56 , (batch_size, sequence_length) , dtype=torch.int )
lowercase__ : Optional[Any] = bark_model(__lowerCamelCase )[0]
lowercase__ : List[str] = model(__lowerCamelCase )
# take last logits
lowercase__ : Optional[int] = output_new_model_total.logits[:, [-1], :]
else:
lowercase__ : Dict = 3
lowercase__ : List[Any] = 8
lowercase__ : Any = torch.randint(2_56 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
lowercase__ : Dict = model(__lowerCamelCase , __lowerCamelCase )
lowercase__ : Optional[int] = bark_model(__lowerCamelCase , __lowerCamelCase )
lowercase__ : Optional[Any] = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError('''initial and new outputs don\'t have the same shape''' )
if (output_new_model - output_old_model).abs().max().item() > 1E-3:
raise ValueError('''initial and new outputs are not equal''' )
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
model.save_pretrained(__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> int:
lowercase__ : List[Any] = os.path.join(__lowerCamelCase , __lowerCamelCase )
lowercase__ : List[Any] = BarkSemanticConfig.from_pretrained(os.path.join(__lowerCamelCase , '''config.json''' ) )
lowercase__ : Optional[Any] = BarkCoarseConfig.from_pretrained(os.path.join(__lowerCamelCase , '''config.json''' ) )
lowercase__ : Union[str, Any] = BarkFineConfig.from_pretrained(os.path.join(__lowerCamelCase , '''config.json''' ) )
lowercase__ : Union[str, Any] = EncodecConfig.from_pretrained('''facebook/encodec_24khz''' )
lowercase__ : int = BarkSemanticModel.from_pretrained(__lowerCamelCase )
lowercase__ : int = BarkCoarseModel.from_pretrained(__lowerCamelCase )
lowercase__ : List[str] = BarkFineModel.from_pretrained(__lowerCamelCase )
lowercase__ : List[Any] = EncodecModel.from_pretrained('''facebook/encodec_24khz''' )
lowercase__ : Any = BarkConfig.from_sub_model_configs(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
lowercase__ : List[Any] = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
lowercase__ : Optional[int] = BarkModel(__lowerCamelCase )
lowercase__ : Optional[int] = semantic
lowercase__ : Dict = coarseAcoustic
lowercase__ : Union[str, Any] = fineAcoustic
lowercase__ : Any = codec
lowercase__ : str = bark_generation_config
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
bark.save_pretrained(__lowerCamelCase , repo_id=__lowerCamelCase , push_to_hub=__lowerCamelCase )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('model_type', type=str, help='text, coarse or fine.')
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')
lowerCAmelCase_ = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 361
|
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = '#'
class __A :
'''simple docstring'''
def __init__( self : str ) -> None:
"""simple docstring"""
lowercase__ : dict = {}
def UpperCAmelCase ( self : List[str] ,_snake_case : str ) -> None:
"""simple docstring"""
lowercase__ : str = self._trie
for char in text:
if char not in trie:
lowercase__ : Union[str, Any] = {}
lowercase__ : Optional[Any] = trie[char]
lowercase__ : Dict = True
def UpperCAmelCase ( self : Tuple ,_snake_case : str ) -> tuple | list:
"""simple docstring"""
lowercase__ : Optional[Any] = self._trie
for char in prefix:
if char in trie:
lowercase__ : Union[str, Any] = trie[char]
else:
return []
return self._elements(_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : dict ) -> tuple:
"""simple docstring"""
lowercase__ : str = []
for c, v in d.items():
lowercase__ : List[Any] = [''' '''] if c == END else [(c + s) for s in self._elements(_snake_case )]
result.extend(_snake_case )
return tuple(_snake_case )
lowerCAmelCase_ = Trie()
lowerCAmelCase_ = ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
trie.insert_word(word)
def __UpperCAmelCase ( __lowerCamelCase ) -> tuple:
lowercase__ : List[Any] = trie.find_word(__lowerCamelCase )
return tuple(string + word for word in suffixes )
def __UpperCAmelCase ( ) -> None:
print(autocomplete_using_trie('''de''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 302
| 0
|
"""simple docstring"""
class __A :
'''simple docstring'''
def __init__( self : List[Any] ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
lowercase__ : Dict = arr.split(''',''' )
def UpperCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
lowercase__ : Optional[Any] = [int(self.array[0] )] * len(self.array )
lowercase__ : List[str] = [int(self.array[0] )] * len(self.array )
for i in range(1 ,len(self.array ) ):
lowercase__ : Dict = max(
int(self.array[i] ) + sum_value[i - 1] ,int(self.array[i] ) )
lowercase__ : List[Any] = max(sum_value[i] ,rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
lowerCAmelCase_ = input('please input some numbers:')
lowerCAmelCase_ = SubArray(whole_array)
lowerCAmelCase_ = array.solve_sub_array()
print(('the results is:', re))
| 362
|
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
lowerCAmelCase_ = logging.get_logger(__name__)
# General docstring
lowerCAmelCase_ = 'RegNetConfig'
# Base docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = [1, 1_088, 7, 7]
# Image classification docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = 'tabby, tabby cat'
lowerCAmelCase_ = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : int ,_snake_case : int ,_snake_case : int ,_snake_case : int = 3 ,_snake_case : int = 1 ,_snake_case : int = 1 ,_snake_case : Optional[str] = "relu" ,) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = nn.Convad(
_snake_case ,_snake_case ,kernel_size=_snake_case ,stride=_snake_case ,padding=kernel_size // 2 ,groups=_snake_case ,bias=_snake_case ,)
lowercase__ : List[Any] = nn.BatchNormad(_snake_case )
lowercase__ : Optional[int] = ACTaFN[activation] if activation is not None else nn.Identity()
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.convolution(_snake_case )
lowercase__ : Tuple = self.normalization(_snake_case )
lowercase__ : Tuple = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] ,_snake_case : RegNetConfig ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = RegNetConvLayer(
config.num_channels ,config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act )
lowercase__ : str = config.num_channels
def UpperCAmelCase ( self : int ,_snake_case : Dict ) -> str:
"""simple docstring"""
lowercase__ : Union[str, Any] = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
lowercase__ : Optional[int] = self.embedder(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : str ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ) -> Any:
"""simple docstring"""
super().__init__()
lowercase__ : List[str] = nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ,stride=_snake_case ,bias=_snake_case )
lowercase__ : Any = nn.BatchNormad(_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ) -> Tensor:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.convolution(_snake_case )
lowercase__ : Optional[int] = self.normalization(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : int ,_snake_case : int ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Any = nn.AdaptiveAvgPoolad((1, 1) )
lowercase__ : Dict = nn.Sequential(
nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.ReLU() ,nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.Sigmoid() ,)
def UpperCAmelCase ( self : int ,_snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.pooler(_snake_case )
lowercase__ : Union[str, Any] = self.attention(_snake_case )
lowercase__ : List[str] = hidden_state * attention
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> List[str]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = in_channels != out_channels or stride != 1
lowercase__ : Optional[int] = max(1 ,out_channels // config.groups_width )
lowercase__ : str = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : Optional[int] = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : str = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ : Tuple = hidden_state
lowercase__ : Union[str, Any] = self.layer(_snake_case )
lowercase__ : List[Any] = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : Optional[int] = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> Optional[int]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = in_channels != out_channels or stride != 1
lowercase__ : List[str] = max(1 ,out_channels // config.groups_width )
lowercase__ : Tuple = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : str = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetSELayer(_snake_case ,reduced_channels=int(round(in_channels / 4 ) ) ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : Optional[Any] = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ : str = hidden_state
lowercase__ : Optional[Any] = self.layer(_snake_case )
lowercase__ : int = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : str = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ,_snake_case : int = 2 ,) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[Any] = RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer
lowercase__ : Optional[Any] = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
_snake_case ,_snake_case ,_snake_case ,stride=_snake_case ,) ,*[layer(_snake_case ,_snake_case ,_snake_case ) for _ in range(depth - 1 )] ,)
def UpperCAmelCase ( self : Tuple ,_snake_case : int ) -> List[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.layers(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : RegNetConfig ) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : str = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
_snake_case ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,) )
lowercase__ : str = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(_snake_case ,config.depths[1:] ):
self.stages.append(RegNetStage(_snake_case ,_snake_case ,_snake_case ,depth=_snake_case ) )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ,_snake_case : bool = False ,_snake_case : bool = True ) -> BaseModelOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Dict = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase__ : int = hidden_states + (hidden_state,)
lowercase__ : Any = stage_module(_snake_case )
if output_hidden_states:
lowercase__ : Optional[int] = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_snake_case ,hidden_states=_snake_case )
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : int = RegNetConfig
lowerCAmelCase : List[Any] = "regnet"
lowerCAmelCase : Optional[int] = "pixel_values"
lowerCAmelCase : Union[str, Any] = True
def UpperCAmelCase ( self : Any ,_snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
nn.init.kaiming_normal_(module.weight ,mode='''fan_out''' ,nonlinearity='''relu''' )
elif isinstance(_snake_case ,(nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight ,1 )
nn.init.constant_(module.bias ,0 )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Dict ,_snake_case : Any=False ) -> Optional[int]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
lowercase__ : str = value
lowerCAmelCase_ = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCAmelCase_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Any ) -> Tuple:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Any = config
lowercase__ : List[str] = RegNetEmbeddings(_snake_case )
lowercase__ : Any = RegNetEncoder(_snake_case )
lowercase__ : Dict = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,modality='''vision''' ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCAmelCase ( self : Dict ,_snake_case : Tensor ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
"""simple docstring"""
lowercase__ : List[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ : Dict = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : Union[str, Any] = self.embedder(_snake_case )
lowercase__ : List[Any] = self.encoder(
_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : str = encoder_outputs[0]
lowercase__ : Optional[int] = self.pooler(_snake_case )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_snake_case ,pooler_output=_snake_case ,hidden_states=encoder_outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : int ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Optional[Any] = config.num_labels
lowercase__ : int = RegNetModel(_snake_case )
# classification head
lowercase__ : str = nn.Sequential(
nn.Flatten() ,nn.Linear(config.hidden_sizes[-1] ,config.num_labels ) if config.num_labels > 0 else nn.Identity() ,)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCAmelCase ( self : List[Any] ,_snake_case : Optional[torch.FloatTensor] = None ,_snake_case : Optional[torch.LongTensor] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ,) -> ImageClassifierOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Any = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : List[Any] = self.regnet(_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : List[str] = outputs.pooler_output if return_dict else outputs[1]
lowercase__ : Union[str, Any] = self.classifier(_snake_case )
lowercase__ : Optional[int] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase__ : List[Any] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase__ : Dict = '''single_label_classification'''
else:
lowercase__ : Optional[int] = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase__ : Union[str, Any] = MSELoss()
if self.num_labels == 1:
lowercase__ : List[Any] = loss_fct(logits.squeeze() ,labels.squeeze() )
else:
lowercase__ : Tuple = loss_fct(_snake_case ,_snake_case )
elif self.config.problem_type == "single_label_classification":
lowercase__ : Tuple = CrossEntropyLoss()
lowercase__ : str = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase__ : Any = BCEWithLogitsLoss()
lowercase__ : Union[str, Any] = loss_fct(_snake_case ,_snake_case )
if not return_dict:
lowercase__ : Tuple = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_snake_case ,logits=_snake_case ,hidden_states=outputs.hidden_states )
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|
"""simple docstring"""
from math import ceil
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> List[str]:
lowercase__ : Optional[int] = list(range(0 , lowercase__ ) )
lowercase__ : Union[str, Any] = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
lowercase__ : Any = []
for i in device_map_blocks:
if device_map_blocks.count(lowercase__ ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(lowercase__ )
# Missing blocks
lowercase__ : int = [i for i in blocks if i not in device_map_blocks]
lowercase__ : int = [i for i in device_map_blocks if i not in blocks]
if len(lowercase__ ) != 0:
raise ValueError(
'''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
''' These attention blocks were specified more than once: ''' + str(lowercase__ ) )
if len(lowercase__ ) != 0:
raise ValueError(
'''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
'''blocks to a device on the device_map: ''' + str(lowercase__ ) )
if len(lowercase__ ) != 0:
raise ValueError(
'''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
+ str(lowercase__ ) )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Tuple:
lowercase__ : List[str] = list(range(lowercase__ ) )
lowercase__ : str = int(ceil(n_layers / len(lowercase__ ) ) )
lowercase__ : Optional[Any] = [layers[i : i + n_blocks] for i in range(0 , lowercase__ , lowercase__ )]
return dict(zip(lowercase__ , lowercase__ ) )
| 363
|
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = 1.6021E-19 # units = C
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
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|
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase ) -> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __UpperCAmelCase ( __lowerCamelCase ) -> bool:
lowercase__ : Any = 0
lowercase__ : Optional[int] = number
while duplicate > 0:
lowercase__ : Any = divmod(__snake_case , 10 )
fact_sum += factorial(__snake_case )
return fact_sum == number
if __name__ == "__main__":
print('Program to check whether a number is a Krisnamurthy Number or not.')
lowerCAmelCase_ = int(input('Enter number: ').strip())
print(
F'''{number} is {"" if krishnamurthy(number) else "not "}a Krishnamurthy Number.'''
)
| 364
|
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : str = ["pixel_values"]
def __init__( self : Tuple ,_snake_case : bool = True ,_snake_case : Optional[Dict[str, int]] = None ,_snake_case : PILImageResampling = PILImageResampling.BICUBIC ,_snake_case : bool = True ,_snake_case : bool = True ,_snake_case : Union[int, float] = 1 / 255 ,_snake_case : Dict[str, int] = None ,_snake_case : bool = True ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,**_snake_case : Optional[Any] ,) -> None:
"""simple docstring"""
super().__init__(**_snake_case )
lowercase__ : str = size if size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[int] = get_size_dict(_snake_case )
lowercase__ : List[Any] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[int] = get_size_dict(_snake_case ,default_to_square=_snake_case ,param_name='''crop_size''' )
lowercase__ : Tuple = do_resize
lowercase__ : List[Any] = do_rescale
lowercase__ : Any = do_normalize
lowercase__ : List[str] = do_center_crop
lowercase__ : Optional[Any] = crop_size
lowercase__ : Union[str, Any] = size
lowercase__ : Any = resample
lowercase__ : int = rescale_factor
lowercase__ : Tuple = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
lowercase__ : str = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase ( self : str ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : PILImageResampling = PILImageResampling.BILINEAR ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Dict ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : List[str] = get_size_dict(_snake_case )
if "shortest_edge" in size:
lowercase__ : str = get_resize_output_image_size(_snake_case ,size=size['''shortest_edge'''] ,default_to_square=_snake_case )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
lowercase__ : int = (size['''height'''], size['''width'''])
else:
raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}""" )
return resize(_snake_case ,size=_snake_case ,resample=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : List[Any] ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Tuple ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : Optional[Any] = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(_snake_case ,size=(size['''height'''], size['''width''']) ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : np.ndarray ,_snake_case : float ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Optional[int] ) -> np.ndarray:
"""simple docstring"""
return rescale(_snake_case ,scale=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : np.ndarray ,_snake_case : Union[float, List[float]] ,_snake_case : Union[float, List[float]] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Dict ,) -> np.ndarray:
"""simple docstring"""
return normalize(_snake_case ,mean=_snake_case ,std=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : ImageInput ,_snake_case : Optional[bool] = None ,_snake_case : Dict[str, int] = None ,_snake_case : PILImageResampling = None ,_snake_case : bool = None ,_snake_case : int = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[float] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[str, TensorType]] = None ,_snake_case : Union[str, ChannelDimension] = ChannelDimension.FIRST ,**_snake_case : List[str] ,) -> BatchFeature:
"""simple docstring"""
lowercase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
lowercase__ : int = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ : int = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ : Optional[int] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ : Optional[Any] = crop_size if crop_size is not None else self.crop_size
lowercase__ : Tuple = get_size_dict(_snake_case ,param_name='''crop_size''' ,default_to_square=_snake_case )
lowercase__ : Tuple = resample if resample is not None else self.resample
lowercase__ : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ : Union[str, Any] = image_mean if image_mean is not None else self.image_mean
lowercase__ : List[str] = image_std if image_std is not None else self.image_std
lowercase__ : Optional[int] = size if size is not None else self.size
lowercase__ : int = get_size_dict(_snake_case )
if not is_batched(_snake_case ):
lowercase__ : Optional[Any] = [images]
if not valid_images(_snake_case ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
# All transformations expect numpy arrays.
lowercase__ : str = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
lowercase__ : int = [self.resize(image=_snake_case ,size=_snake_case ,resample=_snake_case ) for image in images]
if do_center_crop:
lowercase__ : str = [self.center_crop(image=_snake_case ,size=_snake_case ) for image in images]
if do_rescale:
lowercase__ : Optional[Any] = [self.rescale(image=_snake_case ,scale=_snake_case ) for image in images]
if do_normalize:
lowercase__ : List[str] = [self.normalize(image=_snake_case ,mean=_snake_case ,std=_snake_case ) for image in images]
lowercase__ : Union[str, Any] = [to_channel_dimension_format(_snake_case ,_snake_case ) for image in images]
lowercase__ : Any = {'''pixel_values''': images}
return BatchFeature(data=_snake_case ,tensor_type=_snake_case )
| 302
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|
"""simple docstring"""
import random
from typing import Any
def __UpperCAmelCase ( __lowerCamelCase ) -> Union[str, Any]:
for _ in range(len(_a ) ):
lowercase__ : List[str] = random.randint(0 , len(_a ) - 1 )
lowercase__ : Any = random.randint(0 , len(_a ) - 1 )
lowercase__ : Dict = data[b], data[a]
return data
if __name__ == "__main__":
lowerCAmelCase_ = [0, 1, 2, 3, 4, 5, 6, 7]
lowerCAmelCase_ = ['python', 'says', 'hello', '!']
print('Fisher-Yates Shuffle:')
print('List', integers, strings)
print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 365
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_efficientnet': [
'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EfficientNetConfig',
'EfficientNetOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['EfficientNetImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'EfficientNetForImageClassification',
'EfficientNetModel',
'EfficientNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 302
| 0
|
import os
import string
import sys
lowerCAmelCase_ = 1 << 8
lowerCAmelCase_ = {
"""tab""": ord('\t'),
"""newline""": ord('\r'),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
"""right""": 67 + ARROW_KEY_FLAG,
"""left""": 68 + ARROW_KEY_FLAG,
"""mod_int""": 91,
"""undefined""": sys.maxsize,
"""interrupt""": 3,
"""insert""": 50,
"""delete""": 51,
"""pg_up""": 53,
"""pg_down""": 54,
}
lowerCAmelCase_ = KEYMAP["""up"""]
lowerCAmelCase_ = KEYMAP["""left"""]
if sys.platform == "win32":
lowerCAmelCase_ = []
lowerCAmelCase_ = {
b"""\xe0H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
b"""\x00H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
b"""\xe0P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
b"""\x00P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
b"""\xe0M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
b"""\x00M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
b"""\xe0K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
b"""\x00K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
}
for i in range(10):
lowerCAmelCase_ = ord(str(i))
def __UpperCAmelCase ( ) -> Optional[Any]:
if os.name == "nt":
import msvcrt
lowercase__ : str = '''mbcs'''
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(SCREAMING_SNAKE_CASE_ ) == 0:
# Read the keystroke
lowercase__ : Optional[int] = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
lowercase__ : Tuple = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
lowercase__ : int = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['''mod_int'''] ) )
WIN_CH_BUFFER.append(SCREAMING_SNAKE_CASE_ )
if ord(SCREAMING_SNAKE_CASE_ ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(1_26 ) )
lowercase__ : List[str] = chr(KEYMAP['''esc'''] )
except KeyError:
lowercase__ : List[str] = cha[1]
else:
lowercase__ : int = ch.decode(SCREAMING_SNAKE_CASE_ )
else:
lowercase__ : Union[str, Any] = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
lowercase__ : int = sys.stdin.fileno()
lowercase__ : List[Any] = termios.tcgetattr(SCREAMING_SNAKE_CASE_ )
try:
tty.setraw(SCREAMING_SNAKE_CASE_ )
lowercase__ : Dict = sys.stdin.read(1 )
finally:
termios.tcsetattr(SCREAMING_SNAKE_CASE_ , termios.TCSADRAIN , SCREAMING_SNAKE_CASE_ )
return ch
def __UpperCAmelCase ( ) -> List[Any]:
lowercase__ : List[str] = get_raw_chars()
if ord(SCREAMING_SNAKE_CASE_ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(SCREAMING_SNAKE_CASE_ ) == KEYMAP["esc"]:
lowercase__ : List[Any] = get_raw_chars()
if ord(SCREAMING_SNAKE_CASE_ ) == KEYMAP["mod_int"]:
lowercase__ : str = get_raw_chars()
if ord(SCREAMING_SNAKE_CASE_ ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(SCREAMING_SNAKE_CASE_ ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(SCREAMING_SNAKE_CASE_ ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 366
|
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = "cpu" , __lowerCamelCase = None ) -> None:
lowercase__ : List[str] = torch.load(__lowerCamelCase , map_location=__lowerCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__lowerCamelCase , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
lowercase__ : List[Any] = v.half()
if save_path is None: # overwrite src_path
lowercase__ : Any = src_path
torch.save(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 302
| 0
|
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[Any]:
lowercase__ : Optional[int] = VideoMAEConfig()
set_architecture_configs(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if "finetuned" not in model_name:
lowercase__ : str = False
if "finetuned" in model_name:
lowercase__ : Optional[Any] = '''huggingface/label-files'''
if "kinetics" in model_name:
lowercase__ : Union[str, Any] = 4_00
lowercase__ : List[Any] = '''kinetics400-id2label.json'''
elif "ssv2" in model_name:
lowercase__ : int = 1_74
lowercase__ : Optional[Any] = '''something-something-v2-id2label.json'''
else:
raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.''' )
lowercase__ : Any = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ : int = {int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}
lowercase__ : Dict = idalabel
lowercase__ : Tuple = {v: k for k, v in idalabel.items()}
return config
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Tuple:
if "small" in model_name:
lowercase__ : Optional[int] = 3_84
lowercase__ : Optional[int] = 15_36
lowercase__ : Optional[Any] = 12
lowercase__ : Any = 16
lowercase__ : List[Any] = 12
lowercase__ : Tuple = 3
lowercase__ : List[Any] = 1_92
lowercase__ : List[str] = 7_68
elif "large" in model_name:
lowercase__ : Optional[int] = 10_24
lowercase__ : Dict = 40_96
lowercase__ : Dict = 24
lowercase__ : List[Any] = 16
lowercase__ : Optional[Any] = 12
lowercase__ : List[str] = 8
lowercase__ : Optional[int] = 5_12
lowercase__ : str = 20_48
elif "huge" in model_name:
lowercase__ : Union[str, Any] = 12_80
lowercase__ : int = 51_20
lowercase__ : Tuple = 32
lowercase__ : Union[str, Any] = 16
lowercase__ : Tuple = 12
lowercase__ : Optional[int] = 8
lowercase__ : List[str] = 6_40
lowercase__ : Tuple = 25_60
elif "base" not in model_name:
raise ValueError('''Model name should include either \"small\", \"base\", \"large\", or \"huge\"''' )
def __UpperCAmelCase ( __lowerCamelCase ) -> Dict:
if "encoder." in name:
lowercase__ : str = name.replace('''encoder.''' , '''''' )
if "cls_token" in name:
lowercase__ : List[str] = name.replace('''cls_token''' , '''videomae.embeddings.cls_token''' )
if "decoder_pos_embed" in name:
lowercase__ : List[Any] = name.replace('''decoder_pos_embed''' , '''decoder.decoder_pos_embed''' )
if "pos_embed" in name and "decoder" not in name:
lowercase__ : List[str] = name.replace('''pos_embed''' , '''videomae.embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
lowercase__ : List[str] = name.replace('''patch_embed.proj''' , '''videomae.embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowercase__ : Dict = name.replace('''patch_embed.norm''' , '''videomae.embeddings.norm''' )
if "decoder.blocks" in name:
lowercase__ : List[str] = name.replace('''decoder.blocks''' , '''decoder.decoder_layers''' )
if "blocks" in name:
lowercase__ : Tuple = name.replace('''blocks''' , '''videomae.encoder.layer''' )
if "attn.proj" in name:
lowercase__ : Any = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name and "bias" not in name:
lowercase__ : Tuple = name.replace('''attn''' , '''attention.self''' )
if "attn" in name:
lowercase__ : List[Any] = name.replace('''attn''' , '''attention.attention''' )
if "norm1" in name:
lowercase__ : Union[str, Any] = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
lowercase__ : int = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
lowercase__ : Optional[int] = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
lowercase__ : List[Any] = name.replace('''mlp.fc2''' , '''output.dense''' )
if "decoder_embed" in name:
lowercase__ : Tuple = name.replace('''decoder_embed''' , '''decoder.decoder_embed''' )
if "decoder_norm" in name:
lowercase__ : Union[str, Any] = name.replace('''decoder_norm''' , '''decoder.decoder_norm''' )
if "decoder_pred" in name:
lowercase__ : Tuple = name.replace('''decoder_pred''' , '''decoder.decoder_pred''' )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
lowercase__ : Any = name.replace('''norm.weight''' , '''videomae.layernorm.weight''' )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
lowercase__ : Optional[Any] = name.replace('''norm.bias''' , '''videomae.layernorm.bias''' )
if "head" in name and "decoder" not in name:
lowercase__ : Any = name.replace('''head''' , '''classifier''' )
return name
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> str:
for key in orig_state_dict.copy().keys():
lowercase__ : Any = orig_state_dict.pop(SCREAMING_SNAKE_CASE__ )
if key.startswith('''encoder.''' ):
lowercase__ : Union[str, Any] = key.replace('''encoder.''' , '''''' )
if "qkv" in key:
lowercase__ : str = key.split('''.''' )
if key.startswith('''decoder.blocks''' ):
lowercase__ : Optional[Any] = config.decoder_hidden_size
lowercase__ : List[Any] = int(key_split[2] )
lowercase__ : Any = '''decoder.decoder_layers.'''
if "weight" in key:
lowercase__ : Optional[Any] = val[:dim, :]
lowercase__ : int = val[dim : dim * 2, :]
lowercase__ : Dict = val[-dim:, :]
else:
lowercase__ : Any = config.hidden_size
lowercase__ : Dict = int(key_split[1] )
lowercase__ : int = '''videomae.encoder.layer.'''
if "weight" in key:
lowercase__ : List[Any] = val[:dim, :]
lowercase__ : Any = val[dim : dim * 2, :]
lowercase__ : Any = val[-dim:, :]
else:
lowercase__ : Tuple = val
return orig_state_dict
def __UpperCAmelCase ( ) -> List[Any]:
lowercase__ : List[Any] = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''' )
lowercase__ : List[Any] = np.load(SCREAMING_SNAKE_CASE__ )
return list(SCREAMING_SNAKE_CASE__ )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
lowercase__ : str = get_videomae_config(SCREAMING_SNAKE_CASE__ )
if "finetuned" in model_name:
lowercase__ : Union[str, Any] = VideoMAEForVideoClassification(SCREAMING_SNAKE_CASE__ )
else:
lowercase__ : Dict = VideoMAEForPreTraining(SCREAMING_SNAKE_CASE__ )
# download original checkpoint, hosted on Google Drive
lowercase__ : List[str] = '''pytorch_model.bin'''
gdown.cached_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , quiet=SCREAMING_SNAKE_CASE__ )
lowercase__ : Optional[int] = torch.load(SCREAMING_SNAKE_CASE__ , map_location='''cpu''' )
if "model" in files:
lowercase__ : str = files['''model''']
else:
lowercase__ : List[Any] = files['''module''']
lowercase__ : str = convert_state_dict(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
model.load_state_dict(SCREAMING_SNAKE_CASE__ )
model.eval()
# verify model on basic input
lowercase__ : Optional[Any] = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
lowercase__ : Any = prepare_video()
lowercase__ : int = image_processor(SCREAMING_SNAKE_CASE__ , return_tensors='''pt''' )
if "finetuned" not in model_name:
lowercase__ : Any = hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos''' , filename='''bool_masked_pos.pt''' )
lowercase__ : Union[str, Any] = torch.load(SCREAMING_SNAKE_CASE__ )
lowercase__ : Optional[int] = model(**SCREAMING_SNAKE_CASE__ )
lowercase__ : int = outputs.logits
lowercase__ : int = [
'''videomae-small-finetuned-kinetics''',
'''videomae-small-finetuned-ssv2''',
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
'''videomae-base-short''',
'''videomae-base-short-finetuned-kinetics''',
'''videomae-base''',
'''videomae-base-finetuned-kinetics''',
'''videomae-large''',
'''videomae-large-finetuned-kinetics''',
'''videomae-huge-finetuned-kinetics''',
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
'''videomae-base-short-ssv2''',
'''videomae-base-short-finetuned-ssv2''',
'''videomae-base-ssv2''',
'''videomae-base-finetuned-ssv2''',
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
lowercase__ : List[str] = torch.Size([1, 4_00] )
lowercase__ : Dict = torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7] )
elif model_name == "videomae-small-finetuned-ssv2":
lowercase__ : Optional[Any] = torch.Size([1, 1_74] )
lowercase__ : List[str] = torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5] )
elif model_name == "videomae-base":
lowercase__ : str = torch.Size([1, 14_08, 15_36] )
lowercase__ : Dict = torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]] )
elif model_name == "videomae-base-short":
lowercase__ : Union[str, Any] = torch.Size([1, 14_08, 15_36] )
lowercase__ : Union[str, Any] = torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]] )
# we verified the loss both for normalized and unnormalized targets for this one
lowercase__ : Tuple = torch.tensor([0.5_1_4_2] ) if config.norm_pix_loss else torch.tensor([0.6_4_6_9] )
elif model_name == "videomae-large":
lowercase__ : Optional[Any] = torch.Size([1, 14_08, 15_36] )
lowercase__ : Union[str, Any] = torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]] )
elif model_name == "videomae-large-finetuned-kinetics":
lowercase__ : Dict = torch.Size([1, 4_00] )
lowercase__ : List[str] = torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5] )
elif model_name == "videomae-huge-finetuned-kinetics":
lowercase__ : Any = torch.Size([1, 4_00] )
lowercase__ : str = torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4] )
elif model_name == "videomae-base-short-finetuned-kinetics":
lowercase__ : str = torch.Size([1, 4_00] )
lowercase__ : Optional[Any] = torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3] )
elif model_name == "videomae-base-finetuned-kinetics":
lowercase__ : str = torch.Size([1, 4_00] )
lowercase__ : List[Any] = torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1] )
elif model_name == "videomae-base-short-ssv2":
lowercase__ : str = torch.Size([1, 14_08, 15_36] )
lowercase__ : int = torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
lowercase__ : Optional[int] = torch.Size([1, 1_74] )
lowercase__ : Optional[Any] = torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6] )
elif model_name == "videomae-base-ssv2":
lowercase__ : int = torch.Size([1, 14_08, 15_36] )
lowercase__ : List[Any] = torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]] )
elif model_name == "videomae-base-finetuned-ssv2":
lowercase__ : str = torch.Size([1, 1_74] )
lowercase__ : Any = torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9] )
else:
raise ValueError(f"""Model name not supported. Should be one of {model_names}""" )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE__ , atol=1E-4 )
else:
print('''Logits:''' , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1E-4 )
print('''Logits ok!''' )
# verify loss, if applicable
if model_name == "videomae-base-short":
lowercase__ : Optional[int] = outputs.loss
assert torch.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , atol=1E-4 )
print('''Loss ok!''' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE__ )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
if push_to_hub:
print('''Pushing to the hub...''' )
model.push_to_hub(SCREAMING_SNAKE_CASE__ , organization='''nielsr''' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
type=str,
help=(
'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
' download link.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='/Users/nielsrogge/Documents/VideoMAE/Test',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
lowerCAmelCase_ = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 367
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : UNetaDModel
lowerCAmelCase : ScoreSdeVeScheduler
def __init__( self : Optional[Any] ,_snake_case : UNetaDModel ,_snake_case : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=_snake_case ,scheduler=_snake_case )
@torch.no_grad()
def __call__( self : Any ,_snake_case : int = 1 ,_snake_case : int = 2_000 ,_snake_case : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,_snake_case : Optional[str] = "pil" ,_snake_case : bool = True ,**_snake_case : Any ,) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.unet.config.sample_size
lowercase__ : Dict = (batch_size, 3, img_size, img_size)
lowercase__ : Tuple = self.unet
lowercase__ : Any = randn_tensor(_snake_case ,generator=_snake_case ) * self.scheduler.init_noise_sigma
lowercase__ : Union[str, Any] = sample.to(self.device )
self.scheduler.set_timesteps(_snake_case )
self.scheduler.set_sigmas(_snake_case )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
lowercase__ : Tuple = self.scheduler.sigmas[i] * torch.ones(shape[0] ,device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
lowercase__ : List[str] = self.unet(_snake_case ,_snake_case ).sample
lowercase__ : Optional[Any] = self.scheduler.step_correct(_snake_case ,_snake_case ,generator=_snake_case ).prev_sample
# prediction step
lowercase__ : str = model(_snake_case ,_snake_case ).sample
lowercase__ : List[Any] = self.scheduler.step_pred(_snake_case ,_snake_case ,_snake_case ,generator=_snake_case )
lowercase__ , lowercase__ : Optional[int] = output.prev_sample, output.prev_sample_mean
lowercase__ : Union[str, Any] = sample_mean.clamp(0 ,1 )
lowercase__ : int = sample.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
lowercase__ : Any = self.numpy_to_pil(_snake_case )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=_snake_case )
| 302
| 0
|
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCAmelCase__ ( _a ):
'''simple docstring'''
lowerCAmelCase : List[Any] = """mvp"""
lowerCAmelCase : Any = ["""past_key_values"""]
lowerCAmelCase : List[Any] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : List[str] ,_snake_case : Dict=50_267 ,_snake_case : Union[str, Any]=1_024 ,_snake_case : List[Any]=12 ,_snake_case : Any=4_096 ,_snake_case : List[Any]=16 ,_snake_case : str=12 ,_snake_case : Dict=4_096 ,_snake_case : Union[str, Any]=16 ,_snake_case : Optional[int]=0.0 ,_snake_case : List[Any]=0.0 ,_snake_case : str="gelu" ,_snake_case : Any=1_024 ,_snake_case : Tuple=0.1 ,_snake_case : Any=0.0 ,_snake_case : List[str]=0.0 ,_snake_case : Union[str, Any]=0.02 ,_snake_case : List[str]=0.0 ,_snake_case : str=False ,_snake_case : Optional[int]=True ,_snake_case : str=1 ,_snake_case : Tuple=0 ,_snake_case : str=2 ,_snake_case : List[str]=True ,_snake_case : Union[str, Any]=2 ,_snake_case : str=2 ,_snake_case : Union[str, Any]=False ,_snake_case : Optional[int]=100 ,_snake_case : List[Any]=800 ,**_snake_case : List[str] ,) -> Optional[int]:
"""simple docstring"""
lowercase__ : Union[str, Any] = vocab_size
lowercase__ : Optional[Any] = max_position_embeddings
lowercase__ : Dict = d_model
lowercase__ : List[str] = encoder_ffn_dim
lowercase__ : Optional[int] = encoder_layers
lowercase__ : List[str] = encoder_attention_heads
lowercase__ : Any = decoder_ffn_dim
lowercase__ : str = decoder_layers
lowercase__ : Tuple = decoder_attention_heads
lowercase__ : Tuple = dropout
lowercase__ : Optional[int] = attention_dropout
lowercase__ : List[Any] = activation_dropout
lowercase__ : int = activation_function
lowercase__ : Optional[Any] = init_std
lowercase__ : Union[str, Any] = encoder_layerdrop
lowercase__ : List[str] = decoder_layerdrop
lowercase__ : Any = classifier_dropout
lowercase__ : List[Any] = use_cache
lowercase__ : Optional[int] = encoder_layers
lowercase__ : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
lowercase__ : Dict = use_prompt
lowercase__ : Optional[Any] = prompt_length
lowercase__ : Any = prompt_mid_dim
super().__init__(
pad_token_id=__lowerCamelCase ,bos_token_id=__lowerCamelCase ,eos_token_id=__lowerCamelCase ,is_encoder_decoder=__lowerCamelCase ,decoder_start_token_id=__lowerCamelCase ,forced_eos_token_id=__lowerCamelCase ,**__lowerCamelCase ,)
if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''' ,__lowerCamelCase ):
lowercase__ : Any = self.bos_token_id
warnings.warn(
f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
'''The config can simply be saved and uploaded again to be fixed.''' )
| 368
|
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCAmelCase_ = {
'facebook/maskformer-swin-base-ade': (
'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = "maskformer"
lowerCAmelCase : Any = {"hidden_size": "mask_feature_size"}
lowerCAmelCase : Optional[int] = ["resnet", "swin"]
lowerCAmelCase : str = ["detr"]
def __init__( self : int ,_snake_case : int = 256 ,_snake_case : int = 256 ,_snake_case : float = 0.1 ,_snake_case : bool = False ,_snake_case : Optional[Dict] = None ,_snake_case : Optional[Dict] = None ,_snake_case : float = 0.02 ,_snake_case : float = 1.0 ,_snake_case : float = 1.0 ,_snake_case : float = 1.0 ,_snake_case : float = 20.0 ,_snake_case : Optional[bool] = None ,**_snake_case : Optional[Any] ,) -> Dict:
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowercase__ : Any = SwinConfig(
image_size=384 ,in_channels=3 ,patch_size=4 ,embed_dim=128 ,depths=[2, 2, 18, 2] ,num_heads=[4, 8, 16, 32] ,window_size=12 ,drop_path_rate=0.3 ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] ,)
if isinstance(_snake_case ,_snake_case ):
lowercase__ : List[str] = backbone_config.pop('''model_type''' )
lowercase__ : List[Any] = CONFIG_MAPPING[backbone_model_type]
lowercase__ : str = config_class.from_dict(_snake_case )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
f"""Supported model types: {",".join(self.backbones_supported )}""" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowercase__ : Union[str, Any] = DetrConfig()
else:
# verify that the decoder is supported
lowercase__ : Tuple = (
decoder_config.pop('''model_type''' ) if isinstance(_snake_case ,_snake_case ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f"""Transformer Decoder {decoder_type} not supported, please use one of"""
f""" {",".join(self.decoders_supported )}""" )
if isinstance(_snake_case ,_snake_case ):
lowercase__ : Optional[int] = CONFIG_MAPPING[decoder_type]
lowercase__ : Optional[Any] = config_class.from_dict(_snake_case )
lowercase__ : List[Any] = backbone_config
lowercase__ : List[Any] = decoder_config
# main feature dimension for the model
lowercase__ : List[str] = fpn_feature_size
lowercase__ : int = mask_feature_size
# initializer
lowercase__ : str = init_std
lowercase__ : str = init_xavier_std
# Hungarian matcher && loss
lowercase__ : Optional[int] = cross_entropy_weight
lowercase__ : List[Any] = dice_weight
lowercase__ : List[str] = mask_weight
lowercase__ : str = use_auxiliary_loss
lowercase__ : Optional[int] = no_object_weight
lowercase__ : Optional[Any] = output_auxiliary_logits
lowercase__ : Optional[Any] = self.decoder_config.encoder_attention_heads
lowercase__ : Optional[Any] = self.decoder_config.num_hidden_layers
super().__init__(**_snake_case )
@classmethod
def UpperCAmelCase ( cls : Any ,_snake_case : PretrainedConfig ,_snake_case : PretrainedConfig ,**_snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return cls(
backbone_config=_snake_case ,decoder_config=_snake_case ,**_snake_case ,)
def UpperCAmelCase ( self : str ) -> Dict[str, any]:
"""simple docstring"""
lowercase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase__ : int = self.backbone_config.to_dict()
lowercase__ : List[Any] = self.decoder_config.to_dict()
lowercase__ : List[str] = self.__class__.model_type
return output
| 302
| 0
|
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
lowerCAmelCase_ = 'platform'
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , ) -> List[Any]:
if attention_mask is None:
lowercase__ : Optional[Any] = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
lowercase__ : Tuple = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
lowercase__ : Tuple = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowercase__ : int = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowercase__ : Union[str, Any] = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class __A :
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : Tuple ,_snake_case : List[str]=13 ,_snake_case : int=7 ,_snake_case : Any=True ,_snake_case : Tuple=False ,_snake_case : Tuple=99 ,_snake_case : Optional[int]=16 ,_snake_case : Any=2 ,_snake_case : Optional[Any]=4 ,_snake_case : List[Any]=4 ,_snake_case : Tuple="gelu" ,_snake_case : str=0.1 ,_snake_case : List[str]=0.1 ,_snake_case : List[Any]=32 ,_snake_case : str=2 ,_snake_case : str=1 ,_snake_case : Union[str, Any]=0 ,_snake_case : Tuple=0.02 ,) -> Dict:
"""simple docstring"""
lowercase__ : Optional[int] = parent
lowercase__ : Optional[Any] = batch_size
lowercase__ : Optional[int] = seq_length
lowercase__ : Union[str, Any] = is_training
lowercase__ : Tuple = use_labels
lowercase__ : Optional[Any] = vocab_size
lowercase__ : Optional[Any] = hidden_size
lowercase__ : Optional[int] = num_hidden_layers
lowercase__ : Any = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : Any = hidden_act
lowercase__ : List[str] = hidden_dropout_prob
lowercase__ : Dict = attention_probs_dropout_prob
lowercase__ : int = max_position_embeddings
lowercase__ : str = eos_token_id
lowercase__ : List[Any] = pad_token_id
lowercase__ : List[Any] = bos_token_id
lowercase__ : Union[str, Any] = initializer_range
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : int = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size ) ,3 ,self.vocab_size )
lowercase__ : Optional[Any] = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,dtype=np.intaa )) ,-1 )
lowercase__ : Optional[int] = shift_tokens_right(lowercase_ ,1 ,2 )
lowercase__ : Optional[Any] = BlenderbotConfig(
vocab_size=self.vocab_size ,d_model=self.hidden_size ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,eos_token_id=self.eos_token_id ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,initializer_range=self.initializer_range ,use_cache=lowercase_ ,)
lowercase__ : List[str] = prepare_blenderbot_inputs_dict(lowercase_ ,lowercase_ ,lowercase_ )
return config, inputs_dict
def UpperCAmelCase ( self : int ) -> List[Any]:
"""simple docstring"""
lowercase__ , lowercase__ : int = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCAmelCase ( self : str ,_snake_case : str ,_snake_case : Union[str, Any] ,_snake_case : Optional[int] ) -> Dict:
"""simple docstring"""
lowercase__ : List[Any] = 20
lowercase__ : Dict = model_class_name(lowercase_ )
lowercase__ : Any = model.encode(inputs_dict['''input_ids'''] )
lowercase__ , lowercase__ : Tuple = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
lowercase__ : Tuple = model.init_cache(decoder_input_ids.shape[0] ,lowercase_ ,lowercase_ )
lowercase__ : int = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,dtype='''i4''' )
lowercase__ : str = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
lowercase__ : int = model.decode(
decoder_input_ids[:, :-1] ,lowercase_ ,decoder_attention_mask=lowercase_ ,past_key_values=lowercase_ ,decoder_position_ids=lowercase_ ,)
lowercase__ : str = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype='''i4''' )
lowercase__ : str = model.decode(
decoder_input_ids[:, -1:] ,lowercase_ ,decoder_attention_mask=lowercase_ ,past_key_values=outputs_cache.past_key_values ,decoder_position_ids=lowercase_ ,)
lowercase__ : str = model.decode(lowercase_ ,lowercase_ )
lowercase__ : Optional[int] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 ,msg=f"""Max diff is {diff}""" )
def UpperCAmelCase ( self : Dict ,_snake_case : List[Any] ,_snake_case : int ,_snake_case : Optional[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ : Optional[int] = 20
lowercase__ : List[Any] = model_class_name(lowercase_ )
lowercase__ : List[Any] = model.encode(inputs_dict['''input_ids'''] )
lowercase__ , lowercase__ : int = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
lowercase__ : Dict = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] ,axis=-1 ,)
lowercase__ : int = model.init_cache(decoder_input_ids.shape[0] ,lowercase_ ,lowercase_ )
lowercase__ : Tuple = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
lowercase__ : Optional[Any] = model.decode(
decoder_input_ids[:, :-1] ,lowercase_ ,decoder_attention_mask=lowercase_ ,past_key_values=lowercase_ ,decoder_position_ids=lowercase_ ,)
lowercase__ : List[str] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype='''i4''' )
lowercase__ : List[Any] = model.decode(
decoder_input_ids[:, -1:] ,lowercase_ ,past_key_values=outputs_cache.past_key_values ,decoder_attention_mask=lowercase_ ,decoder_position_ids=lowercase_ ,)
lowercase__ : Tuple = model.decode(lowercase_ ,lowercase_ ,decoder_attention_mask=lowercase_ )
lowercase__ : Optional[int] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 ,msg=f"""Max diff is {diff}""" )
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : List[Any] = 9_9
def UpperCAmelCase ( self : List[str] ) -> str:
"""simple docstring"""
lowercase__ : List[str] = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] ,dtype=np.intaa ,)
lowercase__ : int = input_ids.shape[0]
lowercase__ : int = BlenderbotConfig(
vocab_size=self.vocab_size ,d_model=24 ,encoder_layers=2 ,decoder_layers=2 ,encoder_attention_heads=2 ,decoder_attention_heads=2 ,encoder_ffn_dim=32 ,decoder_ffn_dim=32 ,max_position_embeddings=48 ,eos_token_id=2 ,pad_token_id=1 ,bos_token_id=0 ,)
return config, input_ids, batch_size
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
lowercase__ , lowercase__ , lowercase__ : str = self._get_config_and_data()
lowercase__ : List[Any] = FlaxBlenderbotForConditionalGeneration(lowercase_ )
lowercase__ : List[str] = lm_model(input_ids=lowercase_ )
lowercase__ : Dict = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs['''logits'''].shape ,lowercase_ )
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ : str = BlenderbotConfig(
vocab_size=self.vocab_size ,d_model=14 ,encoder_layers=2 ,decoder_layers=2 ,encoder_attention_heads=2 ,decoder_attention_heads=2 ,encoder_ffn_dim=8 ,decoder_ffn_dim=8 ,max_position_embeddings=48 ,)
lowercase__ : List[str] = FlaxBlenderbotForConditionalGeneration(lowercase_ )
lowercase__ : str = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,dtype=np.intaa )
lowercase__ : List[Any] = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,dtype=np.intaa )
lowercase__ : Tuple = lm_model(input_ids=lowercase_ ,decoder_input_ids=lowercase_ )
lowercase__ : Optional[int] = (*summary.shape, config.vocab_size)
self.assertEqual(outputs['''logits'''].shape ,lowercase_ )
def UpperCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
lowercase__ : Dict = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,dtype=np.intaa )
lowercase__ : Optional[int] = shift_tokens_right(lowercase_ ,1 ,2 )
lowercase__ : int = np.equal(lowercase_ ,1 ).astype(np.floataa ).sum()
lowercase__ : Union[str, Any] = np.equal(lowercase_ ,1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape ,input_ids.shape )
self.assertEqual(lowercase_ ,n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] ,2 ).all() )
@require_flax
class __A ( A_ ,unittest.TestCase ,A_ ):
'''simple docstring'''
lowerCAmelCase : str = True
lowerCAmelCase : Optional[Any] = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowerCAmelCase : str = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def UpperCAmelCase ( self : Any ) -> List[Any]:
"""simple docstring"""
lowercase__ : List[str] = FlaxBlenderbotModelTester(self )
def UpperCAmelCase ( self : int ) -> Tuple:
"""simple docstring"""
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowercase_ ,lowercase_ ,lowercase_ )
def UpperCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
lowercase__ , lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_ ,lowercase_ ,lowercase_ )
def UpperCAmelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowercase__ : Optional[Any] = self._prepare_for_class(lowercase_ ,lowercase_ )
lowercase__ : int = model_class(lowercase_ )
@jax.jit
def encode_jitted(_snake_case : int ,_snake_case : Any=None ,**_snake_case : str ):
return model.encode(input_ids=lowercase_ ,attention_mask=lowercase_ )
with self.subTest('''JIT Enabled''' ):
lowercase__ : List[Any] = encode_jitted(**lowercase_ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
lowercase__ : Union[str, Any] = encode_jitted(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) ,len(lowercase_ ) )
for jitted_output, output in zip(lowercase_ ,lowercase_ ):
self.assertEqual(jitted_output.shape ,output.shape )
def UpperCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowercase__ : Dict = model_class(lowercase_ )
lowercase__ : List[Any] = model.encode(inputs_dict['''input_ids'''] ,inputs_dict['''attention_mask'''] )
lowercase__ : Any = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(_snake_case : int ,_snake_case : Tuple ,_snake_case : Any ):
return model.decode(
decoder_input_ids=lowercase_ ,decoder_attention_mask=lowercase_ ,encoder_outputs=lowercase_ ,)
with self.subTest('''JIT Enabled''' ):
lowercase__ : Optional[int] = decode_jitted(**lowercase_ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
lowercase__ : Optional[int] = decode_jitted(**lowercase_ ).to_tuple()
self.assertEqual(len(lowercase_ ) ,len(lowercase_ ) )
for jitted_output, output in zip(lowercase_ ,lowercase_ ):
self.assertEqual(jitted_output.shape ,output.shape )
@slow
def UpperCAmelCase ( self : Any ) -> Tuple:
"""simple docstring"""
for model_class_name in self.all_model_classes:
lowercase__ : Dict = model_class_name.from_pretrained('''facebook/blenderbot-400M-distill''' )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
lowercase__ : Dict = np.ones((1, 1) ) * model.config.eos_token_id
lowercase__ : Optional[Any] = model(lowercase_ )
self.assertIsNotNone(lowercase_ )
@unittest.skipUnless(jax_device != '''cpu''' ,'''3B test too slow on CPU.''' )
@slow
def UpperCAmelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
lowercase__ : str = {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
lowercase__ : List[Any] = {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}
lowercase__ : str = FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''' ,from_pt=lowercase_ )
lowercase__ : Any = BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''' )
lowercase__ : Any = ['''Sam''']
lowercase__ : Union[str, Any] = tokenizer(lowercase_ ,return_tensors='''jax''' )
lowercase__ : Optional[int] = model.generate(**lowercase_ ,**lowercase_ )
lowercase__ : str = '''Sam is a great name. It means \"sun\" in Gaelic.'''
lowercase__ : Optional[int] = tokenizer.batch_decode(lowercase_ ,**lowercase_ )
assert generated_txt[0].strip() == tgt_text
| 369
|
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]:
lowercase__ : int = [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
lowercase__ : Dict = True if '''large''' in model_name or '''huge''' in model_name else False
lowercase__ : Optional[int] = True if '''large''' in model_name or '''huge''' in model_name else False
lowercase__ : List[Any] = True if '''large''' in model_name or '''huge''' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
lowercase__ : Dict = [3, 3, 3, 3]
lowercase__ : str = [5, 5, 5, 5]
elif "fl4" in model_name:
lowercase__ : List[str] = [4, 4, 4, 4]
lowercase__ : Any = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
lowercase__ : List[str] = [3, 3, 3, 3]
if "lrf" in model_name:
lowercase__ : List[str] = [3, 3, 3, 3]
else:
lowercase__ : Optional[Any] = [2, 2, 2, 2]
if "tiny" in model_name:
lowercase__ : Optional[int] = 96
elif "small" in model_name:
lowercase__ : Union[str, Any] = 96
elif "base" in model_name:
lowercase__ : Tuple = 1_28
elif "large" in model_name:
lowercase__ : Any = 1_92
elif "xlarge" in model_name:
lowercase__ : Any = 2_56
elif "huge" in model_name:
lowercase__ : Union[str, Any] = 3_52
# set label information
lowercase__ : List[Any] = '''huggingface/label-files'''
if "large" in model_name or "huge" in model_name:
lowercase__ : Optional[int] = '''imagenet-22k-id2label.json'''
else:
lowercase__ : Optional[Any] = '''imagenet-1k-id2label.json'''
lowercase__ : Dict = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ : Union[str, Any] = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
lowercase__ : Optional[Any] = {v: k for k, v in idalabel.items()}
lowercase__ : int = FocalNetConfig(
embed_dim=__lowerCamelCase , depths=__lowerCamelCase , focal_levels=__lowerCamelCase , focal_windows=__lowerCamelCase , use_conv_embed=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase , use_post_layernorm=__lowerCamelCase , use_layerscale=__lowerCamelCase , )
return config
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
if "patch_embed.proj" in name:
lowercase__ : Any = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowercase__ : Tuple = name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if "layers" in name:
lowercase__ : Dict = '''encoder.''' + name
if "encoder.layers" in name:
lowercase__ : Tuple = name.replace('''encoder.layers''' , '''encoder.stages''' )
if "downsample.proj" in name:
lowercase__ : Union[str, Any] = name.replace('''downsample.proj''' , '''downsample.projection''' )
if "blocks" in name:
lowercase__ : Optional[Any] = name.replace('''blocks''' , '''layers''' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
lowercase__ : Dict = name.replace('''modulation.f''' , '''modulation.projection_in''' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
lowercase__ : Dict = name.replace('''modulation.h''' , '''modulation.projection_context''' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
lowercase__ : Optional[Any] = name.replace('''modulation.proj''' , '''modulation.projection_out''' )
if name == "norm.weight":
lowercase__ : Dict = '''layernorm.weight'''
if name == "norm.bias":
lowercase__ : Dict = '''layernorm.bias'''
if "head" in name:
lowercase__ : Dict = name.replace('''head''' , '''classifier''' )
else:
lowercase__ : List[Any] = '''focalnet.''' + name
return name
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> List[str]:
# fmt: off
lowercase__ : Any = {
'''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
'''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
'''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
'''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
'''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
'''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
'''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
'''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
'''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
'''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
}
# fmt: on
lowercase__ : Optional[int] = model_name_to_url[model_name]
print('''Checkpoint URL: ''' , __lowerCamelCase )
lowercase__ : str = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location='''cpu''' )['''model''']
# rename keys
for key in state_dict.copy().keys():
lowercase__ : int = state_dict.pop(__lowerCamelCase )
lowercase__ : Any = val
lowercase__ : List[Any] = get_focalnet_config(__lowerCamelCase )
lowercase__ : Optional[int] = FocalNetForImageClassification(__lowerCamelCase )
model.eval()
# load state dict
model.load_state_dict(__lowerCamelCase )
# verify conversion
lowercase__ : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase__ : int = BitImageProcessor(
do_resize=__lowerCamelCase , size={'''shortest_edge''': 2_56} , resample=PILImageResampling.BILINEAR , do_center_crop=__lowerCamelCase , crop_size=2_24 , do_normalize=__lowerCamelCase , image_mean=__lowerCamelCase , image_std=__lowerCamelCase , )
lowercase__ : str = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw )
lowercase__ : List[str] = processor(images=__lowerCamelCase , return_tensors='''pt''' )
lowercase__ : List[str] = transforms.Compose(
[
transforms.Resize(2_56 ),
transforms.CenterCrop(2_24 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
lowercase__ : Optional[Any] = image_transforms(__lowerCamelCase ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , __lowerCamelCase , atol=1E-4 )
lowercase__ : Optional[Any] = model(**__lowerCamelCase )
lowercase__ : Optional[int] = outputs.logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
print('''First values of logits:''' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
lowercase__ : Dict = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] )
elif model_name == "focalnet-tiny-lrf":
lowercase__ : Union[str, Any] = torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5] )
elif model_name == "focalnet-small":
lowercase__ : Optional[int] = torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1] )
elif model_name == "focalnet-small-lrf":
lowercase__ : Dict = torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1] )
elif model_name == "focalnet-base":
lowercase__ : List[str] = torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0] )
elif model_name == "focalnet-base-lrf":
lowercase__ : List[str] = torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8] )
assert torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
print(f"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(f"""{model_name}""" )
processor.push_to_hub(f"""{model_name}""" )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='focalnet-tiny',
type=str,
help='Name of the FocalNet model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub.',
)
lowerCAmelCase_ = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 302
| 0
|
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( __lowerCamelCase ) -> float:
lowercase__ : List[str] = 0.0_0
lowercase__ : Dict = 0
for resistor in resistors:
if resistor <= 0:
lowercase__ : str = f"""Resistor at index {index} has a negative or zero value!"""
raise ValueError(_lowerCamelCase )
first_sum += 1 / float(_lowerCamelCase )
index += 1
return 1 / first_sum
def __UpperCAmelCase ( __lowerCamelCase ) -> float:
lowercase__ : int = 0.0_0
lowercase__ : List[Any] = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowercase__ : Tuple = f"""Resistor at index {index} has a negative value!"""
raise ValueError(_lowerCamelCase )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370
|
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : List[Any] = ["image_processor", "tokenizer"]
lowerCAmelCase : int = "ChineseCLIPImageProcessor"
lowerCAmelCase : str = ("BertTokenizer", "BertTokenizerFast")
def __init__( self : Tuple ,_snake_case : str=None ,_snake_case : Union[str, Any]=None ,**_snake_case : str ) -> Any:
"""simple docstring"""
lowercase__ : Any = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' ,_snake_case ,)
lowercase__ : Tuple = kwargs.pop('''feature_extractor''' )
lowercase__ : Any = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(_snake_case ,_snake_case )
lowercase__ : List[Any] = self.image_processor
def __call__( self : List[Any] ,_snake_case : Optional[int]=None ,_snake_case : Dict=None ,_snake_case : List[Any]=None ,**_snake_case : List[str] ) -> List[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
lowercase__ : str = self.tokenizer(_snake_case ,return_tensors=_snake_case ,**_snake_case )
if images is not None:
lowercase__ : str = self.image_processor(_snake_case ,return_tensors=_snake_case ,**_snake_case )
if text is not None and images is not None:
lowercase__ : Any = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_snake_case ) ,tensor_type=_snake_case )
def UpperCAmelCase ( self : Any ,*_snake_case : List[Any] ,**_snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
return self.tokenizer.batch_decode(*_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Union[str, Any] ,*_snake_case : Tuple ,**_snake_case : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.decode(*_snake_case ,**_snake_case )
@property
def UpperCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.tokenizer.model_input_names
lowercase__ : int = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,_snake_case ,)
return self.image_processor_class
| 302
| 0
|
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class __A :
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : str ,_snake_case : List[str]=13 ,_snake_case : Union[str, Any]=7 ,_snake_case : List[str]=True ,_snake_case : Any=True ,_snake_case : List[str]=True ,_snake_case : Union[str, Any]=True ,_snake_case : List[str]=99 ,_snake_case : Tuple=32 ,_snake_case : List[Any]=2 ,_snake_case : List[str]=4 ,_snake_case : Any=37 ,_snake_case : Dict="gelu" ,_snake_case : str=0.1 ,_snake_case : Optional[Any]=0.1 ,_snake_case : Dict=512 ,_snake_case : List[str]=16 ,_snake_case : Optional[Any]=2 ,_snake_case : int=0.02 ,_snake_case : str=3 ,_snake_case : List[Any]=4 ,_snake_case : Tuple=None ,) -> Optional[int]:
"""simple docstring"""
lowercase__ : List[str] = parent
lowercase__ : int = 13
lowercase__ : Optional[Any] = 7
lowercase__ : int = True
lowercase__ : str = True
lowercase__ : int = True
lowercase__ : Optional[Any] = True
lowercase__ : Optional[Any] = 99
lowercase__ : int = 384
lowercase__ : Union[str, Any] = 2
lowercase__ : Dict = 4
lowercase__ : str = 37
lowercase__ : str = '''gelu'''
lowercase__ : Optional[int] = 0.1
lowercase__ : Union[str, Any] = 0.1
lowercase__ : str = 512
lowercase__ : Tuple = 16
lowercase__ : Any = 2
lowercase__ : Union[str, Any] = 0.02
lowercase__ : Optional[int] = 3
lowercase__ : str = 4
lowercase__ : List[Any] = 128
lowercase__ : Optional[int] = 2
lowercase__ : List[Any] = 9
lowercase__ : str = 1
lowercase__ : List[str] = None
def UpperCAmelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
lowercase__ : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowercase__ : Optional[Any] = None
if self.use_input_mask:
lowercase__ : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : Any = None
if self.use_token_type_ids:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
lowercase__ : Union[str, Any] = None
lowercase__ : Dict = None
lowercase__ : int = None
if self.use_labels:
lowercase__ : Union[str, Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
lowercase__ : int = ids_tensor([self.batch_size] ,self.num_choices )
lowercase__ : List[Any] = ConvBertConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,initializer_range=self.initializer_range ,return_dict=_SCREAMING_SNAKE_CASE ,)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self : Optional[int] ,_snake_case : List[Any] ,_snake_case : Tuple ,_snake_case : Dict ,_snake_case : Optional[int] ,_snake_case : Any ,_snake_case : List[Any] ,_snake_case : Any ) -> Dict:
"""simple docstring"""
lowercase__ : Optional[Any] = TFConvBertModel(config=_SCREAMING_SNAKE_CASE )
lowercase__ : Union[str, Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowercase__ : Optional[Any] = [input_ids, input_mask]
lowercase__ : Tuple = model(_SCREAMING_SNAKE_CASE )
lowercase__ : int = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : List[Any] ,_snake_case : str ,_snake_case : Dict ,_snake_case : List[str] ,_snake_case : List[str] ,_snake_case : Any ,_snake_case : str ,_snake_case : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Optional[int] = TFConvBertForMaskedLM(config=_SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase__ : List[str] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self : str ,_snake_case : Optional[int] ,_snake_case : Optional[Any] ,_snake_case : Union[str, Any] ,_snake_case : Union[str, Any] ,_snake_case : str ,_snake_case : Optional[Any] ,_snake_case : Dict ) -> int:
"""simple docstring"""
lowercase__ : List[str] = self.num_labels
lowercase__ : Dict = TFConvBertForSequenceClassification(config=_SCREAMING_SNAKE_CASE )
lowercase__ : Tuple = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase__ : Optional[int] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[Any] ,_snake_case : Any ,_snake_case : Tuple ,_snake_case : Optional[int] ,_snake_case : List[str] ,_snake_case : List[str] ,_snake_case : Optional[int] ) -> int:
"""simple docstring"""
lowercase__ : List[Any] = self.num_choices
lowercase__ : Union[str, Any] = TFConvBertForMultipleChoice(config=_SCREAMING_SNAKE_CASE )
lowercase__ : Union[str, Any] = tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE ,1 ) ,(1, self.num_choices, 1) )
lowercase__ : Optional[int] = tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE ,1 ) ,(1, self.num_choices, 1) )
lowercase__ : int = tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE ,1 ) ,(1, self.num_choices, 1) )
lowercase__ : Optional[Any] = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
lowercase__ : str = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def UpperCAmelCase ( self : Dict ,_snake_case : Tuple ,_snake_case : Union[str, Any] ,_snake_case : Any ,_snake_case : Union[str, Any] ,_snake_case : Union[str, Any] ,_snake_case : Optional[int] ,_snake_case : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Optional[int] = self.num_labels
lowercase__ : Optional[int] = TFConvBertForTokenClassification(config=_SCREAMING_SNAKE_CASE )
lowercase__ : int = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase__ : str = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase ( self : Tuple ,_snake_case : Union[str, Any] ,_snake_case : int ,_snake_case : Tuple ,_snake_case : int ,_snake_case : Union[str, Any] ,_snake_case : Optional[Any] ,_snake_case : Dict ) -> str:
"""simple docstring"""
lowercase__ : int = TFConvBertForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
lowercase__ : List[Any] = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
lowercase__ : int = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Dict = self.prepare_config_and_inputs()
(
lowercase__
) : Dict = config_and_inputs
lowercase__ : Optional[Any] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __A ( A_ ,A_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : List[str] = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
lowerCAmelCase : Union[str, Any] = (
{
"feature-extraction": TFConvBertModel,
"fill-mask": TFConvBertForMaskedLM,
"question-answering": TFConvBertForQuestionAnswering,
"text-classification": TFConvBertForSequenceClassification,
"token-classification": TFConvBertForTokenClassification,
"zero-shot": TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCAmelCase : List[Any] = False
lowerCAmelCase : Union[str, Any] = False
lowerCAmelCase : Optional[Any] = False
def UpperCAmelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
lowercase__ : int = TFConvBertModelTester(self )
lowercase__ : Tuple = ConfigTester(self ,config_class=_SCREAMING_SNAKE_CASE ,hidden_size=37 )
def UpperCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self : int ) -> Any:
"""simple docstring"""
lowercase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE )
@slow
def UpperCAmelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Any = True
lowercase__ : Optional[int] = True
if hasattr(_SCREAMING_SNAKE_CASE ,'''use_cache''' ):
lowercase__ : List[str] = True
lowercase__ : Optional[int] = getattr(self.model_tester ,'''encoder_seq_length''' ,self.model_tester.seq_length )
lowercase__ : Any = getattr(self.model_tester ,'''key_length''' ,_SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
lowercase__ : List[Any] = self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
lowercase__ : Tuple = model_class(_SCREAMING_SNAKE_CASE )
lowercase__ : str = len(model(_SCREAMING_SNAKE_CASE ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE ,saved_model=_SCREAMING_SNAKE_CASE )
lowercase__ : str = os.path.join(_SCREAMING_SNAKE_CASE ,'''saved_model''' ,'''1''' )
lowercase__ : Union[str, Any] = tf.keras.models.load_model(_SCREAMING_SNAKE_CASE )
lowercase__ : Dict = model(_SCREAMING_SNAKE_CASE )
if self.is_encoder_decoder:
lowercase__ : int = outputs['''encoder_hidden_states''']
lowercase__ : List[str] = outputs['''encoder_attentions''']
else:
lowercase__ : Optional[int] = outputs['''hidden_states''']
lowercase__ : int = outputs['''attentions''']
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) ,_SCREAMING_SNAKE_CASE )
lowercase__ : int = getattr(
self.model_tester ,'''expected_num_hidden_layers''' ,self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) ,_SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) ,[self.model_tester.seq_length, self.model_tester.hidden_size] ,)
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] ,)
@slow
def UpperCAmelCase ( self : Any ) -> str:
"""simple docstring"""
lowercase__ : Optional[int] = TFConvBertModel.from_pretrained('''YituTech/conv-bert-base''' )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[str] = True
lowercase__ : Optional[Any] = getattr(self.model_tester ,'''decoder_seq_length''' ,self.model_tester.seq_length )
lowercase__ : str = getattr(self.model_tester ,'''encoder_seq_length''' ,self.model_tester.seq_length )
lowercase__ : List[str] = getattr(self.model_tester ,'''key_length''' ,_SCREAMING_SNAKE_CASE )
lowercase__ : Union[str, Any] = getattr(self.model_tester ,'''key_length''' ,_SCREAMING_SNAKE_CASE )
def check_decoder_attentions_output(_snake_case : str ):
lowercase__ : List[str] = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(out_len % 2 ,0 )
lowercase__ : int = outputs.decoder_attentions
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] ,)
def check_encoder_attentions_output(_snake_case : List[Any] ):
lowercase__ : List[str] = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) ,self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] ,)
for model_class in self.all_model_classes:
lowercase__ : Optional[Any] = True
lowercase__ : List[Any] = False
lowercase__ : Dict = model_class(_SCREAMING_SNAKE_CASE )
lowercase__ : Any = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) )
lowercase__ : int = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(config.output_hidden_states ,_SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
if self.is_encoder_decoder:
lowercase__ : Optional[Any] = model_class(_SCREAMING_SNAKE_CASE )
lowercase__ : List[Any] = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) )
self.assertEqual(config.output_hidden_states ,_SCREAMING_SNAKE_CASE )
check_decoder_attentions_output(_SCREAMING_SNAKE_CASE )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
lowercase__ : Optional[int] = True
lowercase__ : Tuple = model_class(_SCREAMING_SNAKE_CASE )
lowercase__ : Union[str, Any] = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) )
self.assertEqual(config.output_hidden_states ,_SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
# Check attention is always last and order is fine
lowercase__ : Optional[int] = True
lowercase__ : Any = True
lowercase__ : Optional[Any] = model_class(_SCREAMING_SNAKE_CASE )
lowercase__ : List[Any] = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) ,len(_SCREAMING_SNAKE_CASE ) )
self.assertEqual(model.config.output_hidden_states ,_SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
@require_tf
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCAmelCase ( self : List[str] ) -> str:
"""simple docstring"""
lowercase__ : int = TFConvBertModel.from_pretrained('''YituTech/conv-bert-base''' )
lowercase__ : Any = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowercase__ : str = model(_SCREAMING_SNAKE_CASE )[0]
lowercase__ : Tuple = [1, 6, 768]
self.assertEqual(output.shape ,_SCREAMING_SNAKE_CASE )
lowercase__ : Any = tf.constant(
[
[
[-0.0347_5493, -0.468_6034, -0.3063_8832],
[0.2263_7248, -0.2698_8646, -0.742_3424],
[0.1032_4868, -0.4501_3508, -0.5828_0784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] ,_SCREAMING_SNAKE_CASE ,atol=1e-4 )
| 371
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
'tokenization_roberta': ['RobertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['RobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'RobertaForCausalLM',
'RobertaForMaskedLM',
'RobertaForMultipleChoice',
'RobertaForQuestionAnswering',
'RobertaForSequenceClassification',
'RobertaForTokenClassification',
'RobertaModel',
'RobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRobertaForCausalLM',
'TFRobertaForMaskedLM',
'TFRobertaForMultipleChoice',
'TFRobertaForQuestionAnswering',
'TFRobertaForSequenceClassification',
'TFRobertaForTokenClassification',
'TFRobertaMainLayer',
'TFRobertaModel',
'TFRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'FlaxRobertaForCausalLM',
'FlaxRobertaForMaskedLM',
'FlaxRobertaForMultipleChoice',
'FlaxRobertaForQuestionAnswering',
'FlaxRobertaForSequenceClassification',
'FlaxRobertaForTokenClassification',
'FlaxRobertaModel',
'FlaxRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 302
| 0
|
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 350
|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowerCAmelCase_ = logging.getLogger(__name__)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowerCAmelCase : Optional[str] = field(
default="NER" ,metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
lowerCAmelCase : bool = field(default=A_ ,metadata={"help": "Set this flag to use fast tokenization."} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : str = field(
metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} ,)
lowerCAmelCase : int = field(
default=1_2_8 ,metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} ,)
lowerCAmelCase : bool = field(
default=A_ ,metadata={"help": "Overwrite the cached training and evaluation sets"} )
def __UpperCAmelCase ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase__ : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase__ , lowercase__ , lowercase__ : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase__ , lowercase__ , lowercase__ : List[str] = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
lowercase__ : str = import_module('''tasks''' )
try:
lowercase__ : List[str] = getattr(__lowerCamelCase , model_args.task_type )
lowercase__ : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __lowerCamelCase )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
lowercase__ : Union[str, Any] = token_classification_task.get_labels(data_args.labels )
lowercase__ : Dict[int, str] = dict(enumerate(__lowerCamelCase ) )
lowercase__ : Optional[int] = len(__lowerCamelCase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase__ : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid={label: i for i, label in enumerate(__lowerCamelCase )} , cache_dir=model_args.cache_dir , )
lowercase__ : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
lowercase__ : str = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__lowerCamelCase , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase__ : str = (
TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowercase__ : str = (
TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(__lowerCamelCase , __lowerCamelCase ) -> Tuple[List[int], List[int]]:
lowercase__ : Tuple = np.argmax(__lowerCamelCase , axis=2 )
lowercase__ , lowercase__ : Tuple = preds.shape
lowercase__ : List[str] = [[] for _ in range(__lowerCamelCase )]
lowercase__ : Tuple = [[] for _ in range(__lowerCamelCase )]
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(__lowerCamelCase ) -> Dict:
lowercase__ , lowercase__ : List[Any] = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(__lowerCamelCase , __lowerCamelCase ),
"precision": precision_score(__lowerCamelCase , __lowerCamelCase ),
"recall": recall_score(__lowerCamelCase , __lowerCamelCase ),
"f1": fa_score(__lowerCamelCase , __lowerCamelCase ),
}
# Data collator
lowercase__ : Tuple = DataCollatorWithPadding(__lowerCamelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase__ : str = Trainer(
model=__lowerCamelCase , args=__lowerCamelCase , train_dataset=__lowerCamelCase , eval_dataset=__lowerCamelCase , compute_metrics=__lowerCamelCase , data_collator=__lowerCamelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase__ : int = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase__ : Optional[int] = trainer.evaluate()
lowercase__ : Union[str, Any] = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __lowerCamelCase , __lowerCamelCase )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__lowerCamelCase )
# Predict
if training_args.do_predict:
lowercase__ : Optional[int] = TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = trainer.predict(__lowerCamelCase )
lowercase__ , lowercase__ : Tuple = align_predictions(__lowerCamelCase , __lowerCamelCase )
lowercase__ : Dict = os.path.join(training_args.output_dir , '''test_results.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
for key, value in metrics.items():
logger.info(''' %s = %s''' , __lowerCamelCase , __lowerCamelCase )
writer.write('''%s = %s\n''' % (key, value) )
# Save predictions
lowercase__ : Dict = os.path.join(training_args.output_dir , '''test_predictions.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
with open(os.path.join(data_args.data_dir , '''test.txt''' ) , '''r''' ) as f:
token_classification_task.write_predictions_to_file(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return results
def __UpperCAmelCase ( __lowerCamelCase ) -> List[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 302
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|
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]:
lowercase__ : Tuple = [2, 2, 6, 2] if "tiny" in model_name else [2, 2, 18, 2]
lowercase__ : List[str] = True if "large" in model_name or "huge" in model_name else False
lowercase__ : List[Any] = True if "large" in model_name or "huge" in model_name else False
lowercase__ : Union[str, Any] = True if "large" in model_name or "huge" in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
lowercase__ : Optional[int] = [3, 3, 3, 3]
lowercase__ : Dict = [5, 5, 5, 5]
elif "fl4" in model_name:
lowercase__ : Tuple = [4, 4, 4, 4]
lowercase__ : Optional[int] = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
lowercase__ : int = [3, 3, 3, 3]
if "lrf" in model_name:
lowercase__ : Tuple = [3, 3, 3, 3]
else:
lowercase__ : Optional[Any] = [2, 2, 2, 2]
if "tiny" in model_name:
lowercase__ : Any = 96
elif "small" in model_name:
lowercase__ : Union[str, Any] = 96
elif "base" in model_name:
lowercase__ : Optional[Any] = 1_28
elif "large" in model_name:
lowercase__ : List[str] = 1_92
elif "xlarge" in model_name:
lowercase__ : Any = 2_56
elif "huge" in model_name:
lowercase__ : Dict = 3_52
# set label information
lowercase__ : List[Any] = "huggingface/label-files"
if "large" in model_name or "huge" in model_name:
lowercase__ : Dict = "imagenet-22k-id2label.json"
else:
lowercase__ : str = "imagenet-1k-id2label.json"
lowercase__ : str = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ : Optional[Any] = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowercase__ : Tuple = {v: k for k, v in idalabel.items()}
lowercase__ : str = FocalNetConfig(
embed_dim=_SCREAMING_SNAKE_CASE , depths=_SCREAMING_SNAKE_CASE , focal_levels=_SCREAMING_SNAKE_CASE , focal_windows=_SCREAMING_SNAKE_CASE , use_conv_embed=_SCREAMING_SNAKE_CASE , idalabel=_SCREAMING_SNAKE_CASE , labelaid=_SCREAMING_SNAKE_CASE , use_post_layernorm=_SCREAMING_SNAKE_CASE , use_layerscale=_SCREAMING_SNAKE_CASE , )
return config
def __UpperCAmelCase ( __lowerCamelCase ) -> List[str]:
if "patch_embed.proj" in name:
lowercase__ : List[Any] = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowercase__ : Union[str, Any] = name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if "layers" in name:
lowercase__ : List[Any] = "encoder." + name
if "encoder.layers" in name:
lowercase__ : Any = name.replace('''encoder.layers''' , '''encoder.stages''' )
if "downsample.proj" in name:
lowercase__ : List[str] = name.replace('''downsample.proj''' , '''downsample.projection''' )
if "blocks" in name:
lowercase__ : str = name.replace('''blocks''' , '''layers''' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
lowercase__ : int = name.replace('''modulation.f''' , '''modulation.projection_in''' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
lowercase__ : Union[str, Any] = name.replace('''modulation.h''' , '''modulation.projection_context''' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
lowercase__ : Dict = name.replace('''modulation.proj''' , '''modulation.projection_out''' )
if name == "norm.weight":
lowercase__ : Tuple = "layernorm.weight"
if name == "norm.bias":
lowercase__ : str = "layernorm.bias"
if "head" in name:
lowercase__ : List[Any] = name.replace('''head''' , '''classifier''' )
else:
lowercase__ : Tuple = "focalnet." + name
return name
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> Any:
lowercase__ : str = {
"focalnet-tiny": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth",
"focalnet-tiny-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth",
"focalnet-small": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth",
"focalnet-small-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth",
"focalnet-base": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth",
"focalnet-base-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth",
"focalnet-large-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth",
"focalnet-large-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth",
"focalnet-xlarge-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth",
"focalnet-xlarge-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth",
}
# fmt: on
lowercase__ : List[Any] = model_name_to_url[model_name]
print('''Checkpoint URL: ''' , _SCREAMING_SNAKE_CASE )
lowercase__ : str = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location='''cpu''' )["model"]
# rename keys
for key in state_dict.copy().keys():
lowercase__ : Union[str, Any] = state_dict.pop(_SCREAMING_SNAKE_CASE )
lowercase__ : List[str] = val
lowercase__ : Any = get_focalnet_config(_SCREAMING_SNAKE_CASE )
lowercase__ : Tuple = FocalNetForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
# load state dict
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# verify conversion
lowercase__ : str = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowercase__ : int = BitImageProcessor(
do_resize=_SCREAMING_SNAKE_CASE , size={'''shortest_edge''': 2_56} , resample=PILImageResampling.BILINEAR , do_center_crop=_SCREAMING_SNAKE_CASE , crop_size=2_24 , do_normalize=_SCREAMING_SNAKE_CASE , image_mean=_SCREAMING_SNAKE_CASE , image_std=_SCREAMING_SNAKE_CASE , )
lowercase__ : Any = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
lowercase__ : List[str] = processor(images=_SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
lowercase__ : Any = transforms.Compose(
[
transforms.Resize(2_56 ),
transforms.CenterCrop(2_24 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
lowercase__ : List[Any] = image_transforms(_SCREAMING_SNAKE_CASE ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , _SCREAMING_SNAKE_CASE , atol=1E-4 )
lowercase__ : int = model(**_SCREAMING_SNAKE_CASE )
lowercase__ : Dict = outputs.logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
print('''First values of logits:''' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
lowercase__ : Dict = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] )
elif model_name == "focalnet-tiny-lrf":
lowercase__ : Tuple = torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5] )
elif model_name == "focalnet-small":
lowercase__ : int = torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1] )
elif model_name == "focalnet-small-lrf":
lowercase__ : Dict = torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1] )
elif model_name == "focalnet-base":
lowercase__ : Any = torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0] )
elif model_name == "focalnet-base-lrf":
lowercase__ : str = torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8] )
assert torch.allclose(outputs.logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
print(f"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(f"""{model_name}""" )
processor.push_to_hub(f"""{model_name}""" )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='focalnet-tiny',
type=str,
help='Name of the FocalNet model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub.',
)
lowerCAmelCase_ = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 351
|
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase_ = 16
lowerCAmelCase_ = 32
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = 16 ) -> Optional[int]:
lowercase__ : Optional[int] = AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowercase__ : List[str] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : List[str] = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=__lowerCamelCase , max_length=__lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase__ : Dict = datasets.map(
__lowerCamelCase , batched=__lowerCamelCase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : int = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ : List[str] = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase__ : List[str] = 16
elif accelerator.mixed_precision != "no":
lowercase__ : List[Any] = 8
else:
lowercase__ : Optional[int] = None
return tokenizer.pad(
__lowerCamelCase , padding='''longest''' , max_length=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_tensors='''pt''' , )
# Instantiate dataloaders.
lowercase__ : Dict = DataLoader(
tokenized_datasets['''train'''] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
lowercase__ : Union[str, Any] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowerCAmelCase_ = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Tuple:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , __lowerCamelCase ) == "1":
lowercase__ : Any = 2
# Initialize accelerator
lowercase__ : str = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : List[Any] = config['''lr''']
lowercase__ : Union[str, Any] = int(config['''num_epochs'''] )
lowercase__ : List[str] = int(config['''seed'''] )
lowercase__ : Any = int(config['''batch_size'''] )
lowercase__ : int = evaluate.load('''glue''' , '''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__lowerCamelCase )
def inner_training_loop(__lowerCamelCase ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__lowerCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : Optional[Any] = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=__lowerCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase__ : str = model.to(accelerator.device )
# Instantiate optimizer
lowercase__ : Optional[int] = AdamW(params=model.parameters() , lr=__lowerCamelCase )
lowercase__ , lowercase__ : List[str] = get_dataloaders(__lowerCamelCase , __lowerCamelCase )
# Instantiate scheduler
lowercase__ : Optional[Any] = get_linear_schedule_with_warmup(
optimizer=__lowerCamelCase , num_warmup_steps=1_00 , num_training_steps=(len(__lowerCamelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = accelerator.prepare(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# Now we train the model
for epoch in range(__lowerCamelCase ):
model.train()
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase__ : int = model(**__lowerCamelCase )
lowercase__ : Optional[int] = outputs.loss
accelerator.backward(__lowerCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ : Tuple = model(**__lowerCamelCase )
lowercase__ : Dict = outputs.logits.argmax(dim=-1 )
lowercase__ , lowercase__ : Any = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=__lowerCamelCase , references=__lowerCamelCase , )
lowercase__ : Optional[int] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , __lowerCamelCase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( ) -> Tuple:
lowercase__ : List[str] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=__lowerCamelCase , default=__lowerCamelCase , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
lowercase__ : Union[str, Any] = parser.parse_args()
lowercase__ : Union[str, Any] = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
main()
| 302
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|
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
lowerCAmelCase_ = 'pt'
elif is_tf_available():
lowerCAmelCase_ = 'tf'
else:
lowerCAmelCase_ = 'jax'
class __A ( A_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Tuple = PerceiverTokenizer
lowerCAmelCase : Any = False
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
super().setUp()
lowercase__ : Optional[Any] = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def UpperCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
def UpperCAmelCase ( self : Dict ,**_snake_case : Any ) -> PerceiverTokenizer:
"""simple docstring"""
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : Tuple ,_snake_case : int=False ,_snake_case : Optional[int]=20 ,_snake_case : Optional[int]=5 ) -> Tuple[str, list]:
"""simple docstring"""
lowercase__ : List[str] = []
for i in range(len(_snake_case ) ):
try:
lowercase__ : Union[str, Any] = tokenizer.decode([i] ,clean_up_tokenization_spaces=_snake_case )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowercase__ : Optional[int] = list(filter(lambda _snake_case : re.match(r'''^[ a-zA-Z]+$''' ,t[1] ) ,_snake_case ) )
lowercase__ : Union[str, Any] = list(filter(lambda _snake_case : [t[0]] == tokenizer.encode(t[1] ,add_special_tokens=_snake_case ) ,_snake_case ) )
if max_length is not None and len(_snake_case ) > max_length:
lowercase__ : Dict = toks[:max_length]
if min_length is not None and len(_snake_case ) < min_length and len(_snake_case ) > 0:
while len(_snake_case ) < min_length:
lowercase__ : str = toks + toks
# toks_str = [t[1] for t in toks]
lowercase__ : Optional[Any] = [t[0] for t in toks]
# Ensure consistency
lowercase__ : Any = tokenizer.decode(_snake_case ,clean_up_tokenization_spaces=_snake_case )
if " " not in output_txt and len(_snake_case ) > 1:
lowercase__ : Any = (
tokenizer.decode([toks_ids[0]] ,clean_up_tokenization_spaces=_snake_case )
+ ''' '''
+ tokenizer.decode(toks_ids[1:] ,clean_up_tokenization_spaces=_snake_case )
)
if with_prefix_space:
lowercase__ : Dict = ''' ''' + output_txt
lowercase__ : str = tokenizer.encode(_snake_case ,add_special_tokens=_snake_case )
return output_txt, output_ids
def UpperCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Any = self.perceiver_tokenizer
lowercase__ : int = '''Unicode €.'''
lowercase__ : List[Any] = tokenizer(_snake_case )
lowercase__ : Any = [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
self.assertEqual(encoded['''input_ids'''] ,_snake_case )
# decoding
lowercase__ : Optional[Any] = tokenizer.decode(_snake_case )
self.assertEqual(_snake_case ,'''[CLS]Unicode €.[SEP]''' )
lowercase__ : Union[str, Any] = tokenizer('''e è é ê ë''' )
lowercase__ : Dict = [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
self.assertEqual(encoded['''input_ids'''] ,_snake_case )
# decoding
lowercase__ : int = tokenizer.decode(_snake_case )
self.assertEqual(_snake_case ,'''[CLS]e è é ê ë[SEP]''' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) ) ,'''[CLS]e è é ê ë[SEP]''' )
def UpperCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.perceiver_tokenizer
lowercase__ : List[Any] = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
# fmt: off
lowercase__ : int = [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
# fmt: on
lowercase__ : List[str] = tokenizer(_snake_case ,padding=_snake_case ,return_tensors=_snake_case )
self.assertIsInstance(_snake_case ,_snake_case )
if FRAMEWORK != "jax":
lowercase__ : Any = list(batch.input_ids.numpy()[0] )
else:
lowercase__ : Tuple = list(batch.input_ids.tolist()[0] )
self.assertListEqual(_snake_case ,_snake_case )
self.assertEqual((2, 38) ,batch.input_ids.shape )
self.assertEqual((2, 38) ,batch.attention_mask.shape )
def UpperCAmelCase ( self : int ) -> Tuple:
"""simple docstring"""
lowercase__ : Optional[Any] = self.perceiver_tokenizer
lowercase__ : int = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
lowercase__ : int = tokenizer(_snake_case ,padding=_snake_case ,return_tensors=_snake_case )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('''input_ids''' ,_snake_case )
self.assertIn('''attention_mask''' ,_snake_case )
self.assertNotIn('''decoder_input_ids''' ,_snake_case )
self.assertNotIn('''decoder_attention_mask''' ,_snake_case )
def UpperCAmelCase ( self : List[Any] ) -> int:
"""simple docstring"""
lowercase__ : Optional[int] = self.perceiver_tokenizer
lowercase__ : List[Any] = [
'''Summary of the text.''',
'''Another summary.''',
]
lowercase__ : Any = tokenizer(
text_target=_snake_case ,max_length=32 ,padding='''max_length''' ,truncation=_snake_case ,return_tensors=_snake_case )
self.assertEqual(32 ,targets['''input_ids'''].shape[1] )
def UpperCAmelCase ( self : Any ) -> List[Any]:
"""simple docstring"""
lowercase__ : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length ,42 )
# Now let's start the test
lowercase__ : Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
lowercase__ : Any = tempfile.mkdtemp()
lowercase__ : Optional[int] = ''' He is very happy, UNwant\u00E9d,running'''
lowercase__ : Dict = tokenizer.encode(_snake_case ,add_special_tokens=_snake_case )
tokenizer.save_pretrained(_snake_case )
lowercase__ : Optional[Any] = tokenizer.__class__.from_pretrained(_snake_case )
lowercase__ : Optional[Any] = after_tokenizer.encode(_snake_case ,add_special_tokens=_snake_case )
self.assertListEqual(_snake_case ,_snake_case )
shutil.rmtree(_snake_case )
lowercase__ : List[Any] = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
lowercase__ : Union[str, Any] = tempfile.mkdtemp()
lowercase__ : List[str] = ''' He is very happy, UNwant\u00E9d,running'''
tokenizer.add_tokens(['''bim''', '''bambam'''] )
lowercase__ : str = tokenizer.additional_special_tokens
additional_special_tokens.append('''new_additional_special_token''' )
tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
lowercase__ : Optional[Any] = tokenizer.encode(_snake_case ,add_special_tokens=_snake_case )
tokenizer.save_pretrained(_snake_case )
lowercase__ : int = tokenizer.__class__.from_pretrained(_snake_case )
lowercase__ : List[Any] = after_tokenizer.encode(_snake_case ,add_special_tokens=_snake_case )
self.assertListEqual(_snake_case ,_snake_case )
self.assertIn('''new_additional_special_token''' ,after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length ,42 )
lowercase__ : Optional[Any] = tokenizer.__class__.from_pretrained(_snake_case ,model_max_length=43 )
self.assertEqual(tokenizer.model_max_length ,43 )
shutil.rmtree(_snake_case )
def UpperCAmelCase ( self : List[str] ) -> str:
"""simple docstring"""
lowercase__ : Tuple = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(_snake_case )
with open(os.path.join(_snake_case ,'''special_tokens_map.json''' ) ,encoding='''utf-8''' ) as json_file:
lowercase__ : int = json.load(_snake_case )
with open(os.path.join(_snake_case ,'''tokenizer_config.json''' ) ,encoding='''utf-8''' ) as json_file:
lowercase__ : List[str] = json.load(_snake_case )
lowercase__ : Any = [f"""<extra_id_{i}>""" for i in range(125 )]
lowercase__ : List[Any] = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
lowercase__ : Union[str, Any] = added_tokens_extra_ids + [
'''an_additional_special_token'''
]
with open(os.path.join(_snake_case ,'''special_tokens_map.json''' ) ,'''w''' ,encoding='''utf-8''' ) as outfile:
json.dump(_snake_case ,_snake_case )
with open(os.path.join(_snake_case ,'''tokenizer_config.json''' ) ,'''w''' ,encoding='''utf-8''' ) as outfile:
json.dump(_snake_case ,_snake_case )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
lowercase__ : Dict = tokenizer_class.from_pretrained(
_snake_case ,)
self.assertIn(
'''an_additional_special_token''' ,tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
['''an_additional_special_token'''] ,tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token'''] ) ) ,)
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
lowercase__ : Dict = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' ,lstrip=_snake_case )]
lowercase__ : Optional[int] = tokenizer_class.from_pretrained(
_snake_case ,additional_special_tokens=_snake_case ,)
self.assertIn('''a_new_additional_special_token''' ,tokenizer.additional_special_tokens )
self.assertEqual(
['''a_new_additional_special_token'''] ,tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token'''] ) ) ,)
def UpperCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
lowercase__ : str = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([178] ) ,'''�''' )
def UpperCAmelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
def UpperCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
pass
def UpperCAmelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
pass
def UpperCAmelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
pass
def UpperCAmelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ : int = self.get_tokenizers(fast=_snake_case ,do_lower_case=_snake_case )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
lowercase__ : str = ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
lowercase__ : Union[str, Any] = tokenizer.convert_tokens_to_string(_snake_case )
self.assertIsInstance(_snake_case ,_snake_case )
| 352
|
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( self : int ) -> str:
"""simple docstring"""
lowercase__ : List[Any] = '''hf-internal-testing/tiny-random-t5'''
lowercase__ : List[Any] = AutoTokenizer.from_pretrained(_snake_case )
lowercase__ : int = AutoModelForSeqaSeqLM.from_pretrained(_snake_case )
lowercase__ : str = tokenizer('''This is me''' ,return_tensors='''pt''' )
lowercase__ : Tuple = model.to_bettertransformer()
self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
lowercase__ : Optional[int] = model.generate(**_snake_case )
lowercase__ : List[Any] = model.reverse_bettertransformer()
self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_snake_case )
lowercase__ : Tuple = AutoModelForSeqaSeqLM.from_pretrained(_snake_case )
self.assertFalse(
any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
lowercase__ : int = model_reloaded.generate(**_snake_case )
self.assertTrue(torch.allclose(_snake_case ,_snake_case ) )
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : List[str] = '''hf-internal-testing/tiny-random-t5'''
lowercase__ : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_snake_case )
lowercase__ : Union[str, Any] = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(_snake_case ):
model.save_pretrained(_snake_case )
lowercase__ : int = model.reverse_bettertransformer()
model.save_pretrained(_snake_case )
| 302
| 0
|
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ):
while a != 0:
lowercase__ , lowercase__ : Union[str, Any] = b % a, a
return b
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ):
if gcd(UpperCAmelCase_ , UpperCAmelCase_ ) != 1:
lowercase__ : Tuple = f"""mod inverse of {a!r} and {m!r} does not exist"""
raise ValueError(UpperCAmelCase_ )
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = 1, 0, a
lowercase__ , lowercase__ , lowercase__ : str = 0, 1, m
while va != 0:
lowercase__ : Tuple = ua // va
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : List[str] = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 353
|
"""simple docstring"""
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> Any:
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
if not is_sharded:
lowercase__ : List[str] = os.path.abspath(__lowerCamelCase )
logger.info(f"""Loading PyTorch weights from {pt_path}""" )
lowercase__ : List[Any] = torch.load(__lowerCamelCase , map_location='''cpu''' )
logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.""" )
lowercase__ : int = convert_pytorch_state_dict_to_flax(__lowerCamelCase , __lowerCamelCase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
lowercase__ : Dict = convert_pytorch_sharded_state_dict_to_flax(__lowerCamelCase , __lowerCamelCase )
return flax_state_dict
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> (Tuple[str], np.ndarray):
def is_key_or_prefix_key_in_dict(__lowerCamelCase ) -> bool:
return len(set(__lowerCamelCase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
lowercase__ : int = pt_tuple_key[:-1] + ('''scale''',)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
lowercase__ : Union[str, Any] = pt_tuple_key[:-1] + ('''mean''',)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
lowercase__ : Any = pt_tuple_key[:-1] + ('''var''',)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
lowercase__ : Tuple = pt_tuple_key[:-1] + ('''embedding''',)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowercase__ : str = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
lowercase__ : str = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowercase__ : Union[str, Any] = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
lowercase__ : Optional[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowercase__ : Optional[int] = pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowercase__ : List[Any] = pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
lowercase__ : List[str] = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
lowercase__ : List[str] = pt_tuple_key[-2] + '''_g'''
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
lowercase__ : List[str] = pt_tuple_key[-2] + '''_v'''
if name is not None:
lowercase__ : Optional[Any] = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
# convert pytorch tensor to numpy
lowercase__ : Optional[Any] = {k: v.numpy() for k, v in pt_state_dict.items()}
lowercase__ : List[Any] = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
lowercase__ : str = flax_model.params['''params''']
else:
lowercase__ : Optional[int] = flax_model.params
lowercase__ : Optional[Any] = flatten_dict(__lowerCamelCase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowercase__ : Tuple = flatten_dict(flax_model.params['''batch_stats'''] )
random_flax_state_dict.update(__lowerCamelCase )
lowercase__ : int = {}
lowercase__ : List[str] = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
lowercase__ : Union[str, Any] = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase__ : Optional[Any] = tuple(pt_key.split('''.''' ) )
# remove base model prefix if necessary
lowercase__ : Union[str, Any] = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase__ : Union[str, Any] = pt_tuple_key[1:]
# Correctly rename weight parameters
lowercase__ , lowercase__ : List[str] = rename_key_and_reshape_tensor(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# add model prefix if necessary
lowercase__ : Union[str, Any] = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowercase__ : Dict = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
lowercase__ : int = jnp.asarray(__lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__lowerCamelCase , __lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
lowercase__ : Tuple = jnp.asarray(__lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
lowercase__ : Any = jnp.asarray(__lowerCamelCase )
return unflatten_dict(__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Dict:
import torch
# Load the index
lowercase__ : Dict = {}
for shard_file in shard_filenames:
# load using msgpack utils
lowercase__ : Optional[int] = torch.load(__lowerCamelCase )
lowercase__ : str = {k: v.numpy() for k, v in pt_state_dict.items()}
lowercase__ : Dict = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowercase__ : Optional[Any] = flax_model.params['''params''']
lowercase__ : List[Any] = flatten_dict(__lowerCamelCase )
random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats'''] ) )
else:
lowercase__ : Union[str, Any] = flax_model.params
lowercase__ : Tuple = flatten_dict(__lowerCamelCase )
lowercase__ : Tuple = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
lowercase__ : int = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase__ : List[str] = tuple(pt_key.split('''.''' ) )
# remove base model prefix if necessary
lowercase__ : Tuple = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase__ : List[str] = pt_tuple_key[1:]
# Correctly rename weight parameters
lowercase__ , lowercase__ : str = rename_key_and_reshape_tensor(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# add model prefix if necessary
lowercase__ : Union[str, Any] = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowercase__ : Dict = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
lowercase__ : Union[str, Any] = jnp.asarray(__lowerCamelCase )
continue
if "var" in flax_key[-1]:
lowercase__ : str = jnp.asarray(__lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__lowerCamelCase , __lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
lowercase__ : List[str] = jnp.asarray(__lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
lowercase__ : Union[str, Any] = jnp.asarray(__lowerCamelCase )
return unflatten_dict(__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
lowercase__ : List[str] = os.path.abspath(__lowerCamelCase )
logger.info(f"""Loading Flax weights from {flax_checkpoint_path}""" )
# import correct flax class
lowercase__ : Optional[int] = getattr(__lowerCamelCase , '''Flax''' + model.__class__.__name__ )
# load flax weight dict
with open(__lowerCamelCase , '''rb''' ) as state_f:
try:
lowercase__ : str = from_bytes(__lowerCamelCase , state_f.read() )
except UnpicklingError:
raise EnvironmentError(f"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """ )
return load_flax_weights_in_pytorch_model(__lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> List[str]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
# check if we have bf16 weights
lowercase__ : Any = flatten_dict(jax.tree_util.tree_map(lambda __lowerCamelCase : x.dtype == jnp.bfloataa , __lowerCamelCase ) ).values()
if any(__lowerCamelCase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
'''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
'''before loading those in PyTorch model.''' )
lowercase__ : Union[str, Any] = jax.tree_util.tree_map(
lambda __lowerCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , __lowerCamelCase )
lowercase__ : Tuple = flatten_dict(__lowerCamelCase )
lowercase__ : List[str] = pt_model.state_dict()
lowercase__ : int = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split('''.''' )[0] for k in pt_model_dict.keys()}
)
lowercase__ : int = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split('''.''' )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
lowercase__ : List[str] = []
lowercase__ : Tuple = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
lowercase__ : List[Any] = flax_key_tuple[0] == pt_model.base_model_prefix
lowercase__ : Optional[int] = '''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase__ : Tuple = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
lowercase__ : Optional[Any] = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowerCamelCase ) not in pt_model_dict:
# conv layer
lowercase__ : Dict = flax_key_tuple[:-1] + ('''weight''',)
lowercase__ : List[str] = jnp.transpose(__lowerCamelCase , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCamelCase ) not in pt_model_dict:
# linear layer
lowercase__ : Optional[int] = flax_key_tuple[:-1] + ('''weight''',)
lowercase__ : str = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
lowercase__ : Dict = flax_key_tuple[:-1] + ('''weight''',)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
lowercase__ : Any = flax_key_tuple[:-1] + ('''running_mean''',)
elif "var" in flax_key_tuple[-1]:
lowercase__ : Dict = flax_key_tuple[:-1] + ('''running_var''',)
if "batch_stats" in flax_state:
lowercase__ : Union[str, Any] = '''.'''.join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
lowercase__ : Dict = '''.'''.join(__lowerCamelCase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
lowercase__ : Optional[int] = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
lowercase__ : str = key.split('''.''' )
lowercase__ : Optional[Any] = None
if key_components[-3::2] == ["parametrizations", "original0"]:
lowercase__ : List[str] = key_components[-2] + '''_g'''
elif key_components[-3::2] == ["parametrizations", "original1"]:
lowercase__ : str = key_components[-2] + '''_v'''
if name is not None:
lowercase__ : Optional[int] = key_components[:-3] + [name]
lowercase__ : List[str] = '''.'''.join(__lowerCamelCase )
lowercase__ : List[Any] = key
if flax_key in special_pt_names:
lowercase__ : Any = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
else:
# add weight to pytorch dict
lowercase__ : List[str] = np.asarray(__lowerCamelCase ) if not isinstance(__lowerCamelCase , np.ndarray ) else flax_tensor
lowercase__ : List[str] = torch.from_numpy(__lowerCamelCase )
# remove from missing keys
missing_keys.remove(__lowerCamelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(__lowerCamelCase )
pt_model.load_state_dict(__lowerCamelCase )
# re-transform missing_keys to list
lowercase__ : Optional[Any] = list(__lowerCamelCase )
if len(__lowerCamelCase ) > 0:
logger.warning(
'''Some weights of the Flax model were not used when initializing the PyTorch model'''
f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
''' FlaxBertForSequenceClassification model).''' )
else:
logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n""" )
if len(__lowerCamelCase ) > 0:
logger.warning(
f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
''' use it for predictions and inference.''' )
else:
logger.warning(
f"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
'''If your task is similar to the task the model of the checkpoint was trained on, '''
f"""you can already use {pt_model.__class__.__name__} for predictions without further training.""" )
return pt_model
| 302
| 0
|
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
lowerCAmelCase_ = False, False, False
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = None
lowerCAmelCase : Union[str, Any] = True
lowerCAmelCase : Union[str, Any] = True
lowerCAmelCase : Tuple = None
# Automatically constructed
lowerCAmelCase : Union[str, Any] = "dict"
lowerCAmelCase : Optional[int] = pa.struct({"bytes": pa.binary(), "path": pa.string()} )
lowerCAmelCase : Optional[Any] = field(default="Audio" ,init=lowerCamelCase__ ,repr=lowerCamelCase__ )
def __call__( self : str ) -> Optional[Any]:
"""simple docstring"""
return self.pa_type
def UpperCAmelCase ( self : Any ,_snake_case : Union[str, bytes, dict] ) -> List[str]:
"""simple docstring"""
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError('''To support encoding audio data, please install \'soundfile\'.''' ) from err
if isinstance(_snake_case ,_snake_case ):
return {"bytes": None, "path": value}
elif isinstance(_snake_case ,_snake_case ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
lowercase__ : List[Any] = BytesIO()
sf.write(_snake_case ,value['''array'''] ,value['''sampling_rate'''] ,format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith('''pcm''' ):
# "PCM" only has raw audio bytes
if value.get('''sampling_rate''' ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object''' )
if value.get('''bytes''' ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
lowercase__ : int = np.frombuffer(value['''bytes'''] ,dtype=np.intaa ).astype(np.floataa ) / 32_767
else:
lowercase__ : Tuple = np.memmap(value['''path'''] ,dtype='''h''' ,mode='''r''' ).astype(np.floataa ) / 32_767
lowercase__ : Dict = BytesIO(bytes() )
sf.write(_snake_case ,_snake_case ,value['''sampling_rate'''] ,format='''wav''' )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get('''path''' )}
elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
else:
raise ValueError(
f"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : dict ,_snake_case : Optional[Dict[str, Union[str, bool, None]]] = None ) -> Union[str, Any]:
"""simple docstring"""
if not self.decode:
raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )
lowercase__ : Any = (value["path"], BytesIO(value['''bytes'''] )) if value["bytes"] is not None else (value["path"], None)
if path is None and file is None:
raise ValueError(f"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}.""" )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.''' ) from err
lowercase__ : Dict = xsplitext(_snake_case )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
'''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
'''You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. ''' )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
'''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
'''You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. ''' )
if file is None:
lowercase__ : Optional[int] = token_per_repo_id or {}
lowercase__ : Optional[Any] = path.split('''::''' )[-1]
try:
lowercase__ : int = string_to_dict(_snake_case ,config.HUB_DATASETS_URL )["repo_id"]
lowercase__ : List[Any] = token_per_repo_id[repo_id]
except (ValueError, KeyError):
lowercase__ : Dict = None
with xopen(_snake_case ,'''rb''' ,use_auth_token=_snake_case ) as f:
lowercase__ : Optional[int] = sf.read(_snake_case )
else:
lowercase__ : Union[str, Any] = sf.read(_snake_case )
lowercase__ : Any = array.T
if self.mono:
lowercase__ : Optional[int] = librosa.to_mono(_snake_case )
if self.sampling_rate and self.sampling_rate != sampling_rate:
lowercase__ : Union[str, Any] = librosa.resample(_snake_case ,orig_sr=_snake_case ,target_sr=self.sampling_rate )
lowercase__ : Union[str, Any] = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def UpperCAmelCase ( self : str ) -> List[str]:
"""simple docstring"""
from .features import Value
if self.decode:
raise ValueError('''Cannot flatten a decoded Audio feature.''' )
return {
"bytes": Value('''binary''' ),
"path": Value('''string''' ),
}
def UpperCAmelCase ( self : List[Any] ,_snake_case : Union[pa.StringArray, pa.StructArray] ) -> Tuple:
"""simple docstring"""
if pa.types.is_string(storage.type ):
lowercase__ : Union[str, Any] = pa.array([None] * len(_snake_case ) ,type=pa.binary() )
lowercase__ : Optional[int] = pa.StructArray.from_arrays([bytes_array, storage] ,['''bytes''', '''path'''] ,mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase__ : Any = pa.array([None] * len(_snake_case ) ,type=pa.string() )
lowercase__ : Dict = pa.StructArray.from_arrays([storage, path_array] ,['''bytes''', '''path'''] ,mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('''array''' ):
lowercase__ : Union[str, Any] = pa.array([Audio().encode_example(_snake_case ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('''bytes''' ) >= 0:
lowercase__ : Optional[Any] = storage.field('''bytes''' )
else:
lowercase__ : Any = pa.array([None] * len(_snake_case ) ,type=pa.binary() )
if storage.type.get_field_index('''path''' ) >= 0:
lowercase__ : Dict = storage.field('''path''' )
else:
lowercase__ : Any = pa.array([None] * len(_snake_case ) ,type=pa.string() )
lowercase__ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] ,['''bytes''', '''path'''] ,mask=storage.is_null() )
return array_cast(_snake_case ,self.pa_type )
def UpperCAmelCase ( self : str ,_snake_case : pa.StructArray ) -> Dict:
"""simple docstring"""
@no_op_if_value_is_null
def path_to_bytes(_snake_case : List[Any] ):
with xopen(_snake_case ,'''rb''' ) as f:
lowercase__ : str = f.read()
return bytes_
lowercase__ : str = pa.array(
[
(path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
for x in storage.to_pylist()
] ,type=pa.binary() ,)
lowercase__ : List[str] = pa.array(
[os.path.basename(_snake_case ) if path is not None else None for path in storage.field('''path''' ).to_pylist()] ,type=pa.string() ,)
lowercase__ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] ,['''bytes''', '''path'''] ,mask=bytes_array.is_null() )
return array_cast(_snake_case ,self.pa_type )
| 354
|
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Any ,_snake_case : UNetaDModel ,_snake_case : UNetaDModel ,_snake_case : DDPMScheduler ,_snake_case : Any ,) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[int] = value_function
lowercase__ : Optional[int] = unet
lowercase__ : Tuple = scheduler
lowercase__ : Dict = env
lowercase__ : int = env.get_dataset()
lowercase__ : Dict = {}
for key in self.data.keys():
try:
lowercase__ : Optional[Any] = self.data[key].mean()
except: # noqa: E722
pass
lowercase__ : List[Any] = {}
for key in self.data.keys():
try:
lowercase__ : str = self.data[key].std()
except: # noqa: E722
pass
lowercase__ : Tuple = env.observation_space.shape[0]
lowercase__ : Optional[int] = env.action_space.shape[0]
def UpperCAmelCase ( self : str ,_snake_case : Any ,_snake_case : int ) -> Optional[Any]:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def UpperCAmelCase ( self : Dict ,_snake_case : int ,_snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Dict ) -> Optional[int]:
"""simple docstring"""
if type(_snake_case ) is dict:
return {k: self.to_torch(_snake_case ) for k, v in x_in.items()}
elif torch.is_tensor(_snake_case ):
return x_in.to(self.unet.device )
return torch.tensor(_snake_case ,device=self.unet.device )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Any ,_snake_case : int ,_snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
for key, val in cond.items():
lowercase__ : List[Any] = val.clone()
return x_in
def UpperCAmelCase ( self : int ,_snake_case : Optional[int] ,_snake_case : List[Any] ,_snake_case : int ,_snake_case : int ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Any = x.shape[0]
lowercase__ : Dict = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
lowercase__ : Dict = torch.full((batch_size,) ,_snake_case ,device=self.unet.device ,dtype=torch.long )
for _ in range(_snake_case ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
lowercase__ : int = self.value_function(x.permute(0 ,2 ,1 ) ,_snake_case ).sample
lowercase__ : Optional[Any] = torch.autograd.grad([y.sum()] ,[x] )[0]
lowercase__ : List[str] = self.scheduler._get_variance(_snake_case )
lowercase__ : Union[str, Any] = torch.exp(0.5 * posterior_variance )
lowercase__ : Optional[int] = model_std * grad
lowercase__ : Optional[Any] = 0
lowercase__ : str = x.detach()
lowercase__ : Dict = x + scale * grad
lowercase__ : str = self.reset_xa(_snake_case ,_snake_case ,self.action_dim )
lowercase__ : Union[str, Any] = self.unet(x.permute(0 ,2 ,1 ) ,_snake_case ).sample.permute(0 ,2 ,1 )
# TODO: verify deprecation of this kwarg
lowercase__ : Dict = self.scheduler.step(_snake_case ,_snake_case ,_snake_case ,predict_epsilon=_snake_case )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
lowercase__ : Dict = self.reset_xa(_snake_case ,_snake_case ,self.action_dim )
lowercase__ : Union[str, Any] = self.to_torch(_snake_case )
return x, y
def __call__( self : Union[str, Any] ,_snake_case : Any ,_snake_case : Tuple=64 ,_snake_case : Any=32 ,_snake_case : Optional[Any]=2 ,_snake_case : str=0.1 ) -> List[Any]:
"""simple docstring"""
lowercase__ : Any = self.normalize(_snake_case ,'''observations''' )
lowercase__ : Tuple = obs[None].repeat(_snake_case ,axis=0 )
lowercase__ : Dict = {0: self.to_torch(_snake_case )}
lowercase__ : int = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
lowercase__ : Optional[int] = randn_tensor(_snake_case ,device=self.unet.device )
lowercase__ : Tuple = self.reset_xa(_snake_case ,_snake_case ,self.action_dim )
lowercase__ : str = self.to_torch(_snake_case )
# run the diffusion process
lowercase__ , lowercase__ : int = self.run_diffusion(_snake_case ,_snake_case ,_snake_case ,_snake_case )
# sort output trajectories by value
lowercase__ : Optional[Any] = y.argsort(0 ,descending=_snake_case ).squeeze()
lowercase__ : str = x[sorted_idx]
lowercase__ : str = sorted_values[:, :, : self.action_dim]
lowercase__ : Optional[int] = actions.detach().cpu().numpy()
lowercase__ : List[str] = self.de_normalize(_snake_case ,key='''actions''' )
# select the action with the highest value
if y is not None:
lowercase__ : str = 0
else:
# if we didn't run value guiding, select a random action
lowercase__ : str = np.random.randint(0 ,_snake_case )
lowercase__ : int = denorm_actions[selected_index, 0]
return denorm_actions
| 302
| 0
|
def __UpperCAmelCase ( __lowerCamelCase ) -> Dict:
lowercase__ : Any = len(__lowerCamelCase )
lowercase__ : Tuple = sum(__lowerCamelCase )
lowercase__ : Any = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
lowercase__ : Optional[int] = True
for i in range(1 , s + 1 ):
lowercase__ : List[str] = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
lowercase__ : Any = dp[i][j - 1]
if arr[i - 1] <= j:
lowercase__ : Optional[Any] = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
lowercase__ : int = s - 2 * j
break
return diff
| 355
|
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
lowerCAmelCase_ = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n'
lowerCAmelCase_ = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'
lowerCAmelCase_ = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,homepage='''https://github.com/krishnap25/mauve''' ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' ,id='''sequence''' ),
'''references''': datasets.Value('''string''' ,id='''sequence''' ),
} ) ,codebase_urls=['''https://github.com/krishnap25/mauve'''] ,reference_urls=[
'''https://arxiv.org/abs/2102.01454''',
'''https://github.com/krishnap25/mauve''',
] ,)
def UpperCAmelCase ( self : Dict ,_snake_case : Optional[Any] ,_snake_case : Any ,_snake_case : List[str]=None ,_snake_case : Tuple=None ,_snake_case : List[Any]=None ,_snake_case : Any=None ,_snake_case : Optional[int]="auto" ,_snake_case : Optional[int]=-1 ,_snake_case : Optional[int]=0.9 ,_snake_case : Any=5 ,_snake_case : Dict=500 ,_snake_case : Optional[int]="gpt2-large" ,_snake_case : Optional[Any]=-1 ,_snake_case : Tuple=1_024 ,_snake_case : Optional[int]=25 ,_snake_case : Dict=5 ,_snake_case : int=True ,_snake_case : Union[str, Any]=25 ,) -> Any:
"""simple docstring"""
lowercase__ : Any = compute_mauve(
p_text=_snake_case ,q_text=_snake_case ,p_features=_snake_case ,q_features=_snake_case ,p_tokens=_snake_case ,q_tokens=_snake_case ,num_buckets=_snake_case ,pca_max_data=_snake_case ,kmeans_explained_var=_snake_case ,kmeans_num_redo=_snake_case ,kmeans_max_iter=_snake_case ,featurize_model_name=_snake_case ,device_id=_snake_case ,max_text_length=_snake_case ,divergence_curve_discretization_size=_snake_case ,mauve_scaling_factor=_snake_case ,verbose=_snake_case ,seed=_snake_case ,)
return out
| 302
| 0
|
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase_ : Optional[Any] = 'docs/source/en/_toctree.yml'
def __UpperCAmelCase ( __lowerCamelCase ) -> Dict:
lowercase__ : Tuple = defaultdict(__UpperCAmelCase )
lowercase__ : Dict = []
lowercase__ : Tuple = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({'''local''': doc['''local'''], '''title''': doc['''title''']} )
else:
new_doc_list.append(__UpperCAmelCase )
lowercase__ : Any = new_doc_list
lowercase__ : List[Any] = [key for key, value in counts.items() if value > 1]
lowercase__ : List[Any] = []
for duplicate_key in duplicates:
lowercase__ : Optional[int] = list({doc['''title'''] for doc in doc_list if doc['''local'''] == duplicate_key} )
if len(__UpperCAmelCase ) > 1:
raise ValueError(
f"""{duplicate_key} is present several times in the documentation table of content at """
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if '''local''' not in counts or counts[doc['''local''']] == 1] )
lowercase__ : str = sorted(__UpperCAmelCase , key=lambda __lowerCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__UpperCAmelCase ) > 1:
raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.''' )
overview_doc.extend(__UpperCAmelCase )
# Sort
return overview_doc
def __UpperCAmelCase ( __lowerCamelCase=False ) -> str:
with open(__UpperCAmelCase , encoding='''utf-8''' ) as f:
lowercase__ : Union[str, Any] = yaml.safe_load(f.read() )
# Get to the API doc
lowercase__ : List[str] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase__ : Any = content[api_idx]['''sections''']
# Then to the model doc
lowercase__ : Union[str, Any] = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
lowercase__ : str = api_doc[scheduler_idx]['''sections''']
lowercase__ : List[str] = clean_doc_toc(__UpperCAmelCase )
lowercase__ : Optional[Any] = False
if new_scheduler_doc != scheduler_doc:
lowercase__ : Any = True
if overwrite:
lowercase__ : Tuple = new_scheduler_doc
if diff:
if overwrite:
lowercase__ : List[str] = api_doc
with open(__UpperCAmelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(__UpperCAmelCase , allow_unicode=__UpperCAmelCase ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
def __UpperCAmelCase ( __lowerCamelCase=False ) -> Optional[Any]:
with open(__UpperCAmelCase , encoding='''utf-8''' ) as f:
lowercase__ : Optional[Any] = yaml.safe_load(f.read() )
# Get to the API doc
lowercase__ : List[Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase__ : List[Any] = content[api_idx]['''sections''']
# Then to the model doc
lowercase__ : Tuple = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
lowercase__ : Dict = False
lowercase__ : str = api_doc[pipeline_idx]['''sections''']
lowercase__ : int = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
lowercase__ : str = pipeline_doc['''section''']
lowercase__ : Optional[Any] = clean_doc_toc(__UpperCAmelCase )
if overwrite:
lowercase__ : Union[str, Any] = new_sub_pipeline_doc
new_pipeline_docs.append(__UpperCAmelCase )
# sort overall pipeline doc
lowercase__ : Union[str, Any] = clean_doc_toc(__UpperCAmelCase )
if new_pipeline_docs != pipeline_docs:
lowercase__ : Optional[int] = True
if overwrite:
lowercase__ : int = new_pipeline_docs
if diff:
if overwrite:
lowercase__ : Optional[int] = api_doc
with open(__UpperCAmelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(__UpperCAmelCase , allow_unicode=__UpperCAmelCase ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
lowerCAmelCase_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
lowerCAmelCase_ : Dict = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 356
|
"""simple docstring"""
import math
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
lowercase__ : Tuple = 0
lowercase__ : Tuple = 0
while num > 0:
lowercase__ : int = num % 8
lowercase__ : Tuple = octal + (remainder * math.floor(math.pow(10 , __lowerCamelCase ) ))
counter += 1
lowercase__ : Optional[Any] = math.floor(num / 8 ) # basically /= 8 without remainder if any
# This formatting removes trailing '.0' from `octal`.
return f"""0o{int(__lowerCamelCase )}"""
def __UpperCAmelCase ( ) -> None:
print('''\n2 in octal is:''' )
print(decimal_to_octal(2 ) ) # = 2
print('''\n8 in octal is:''' )
print(decimal_to_octal(8 ) ) # = 10
print('''\n65 in octal is:''' )
print(decimal_to_octal(65 ) ) # = 101
print('''\n216 in octal is:''' )
print(decimal_to_octal(2_16 ) ) # = 330
print('''\n512 in octal is:''' )
print(decimal_to_octal(5_12 ) ) # = 1000
print('''\n''' )
if __name__ == "__main__":
main()
| 302
| 0
|
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCAmelCase_ = logging.getLogger()
@unittest.skip("Temporarily disable the doc tests." )
@require_torch
@require_tf
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( self : Dict ,_snake_case : Optional[int] ,_snake_case : int = None ,_snake_case : Optional[Any] = None ,_snake_case : Optional[Any] = None ,_snake_case : str = True ,) -> Dict:
"""simple docstring"""
lowercase__ : List[Any] = [file for file in os.listdir(lowerCamelCase__ ) if os.path.isfile(os.path.join(lowerCamelCase__ ,lowerCamelCase__ ) )]
if identifier is not None:
lowercase__ : str = [file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
for n_ in n_identifier:
lowercase__ : List[str] = [file for file in files if n_ not in file]
else:
lowercase__ : Optional[int] = [file for file in files if n_identifier not in file]
lowercase__ : List[str] = ignore_files or []
ignore_files.append('''__init__.py''' )
lowercase__ : Optional[Any] = [file for file in files if file not in ignore_files]
for file in files:
# Open all files
print('''Testing''' ,lowerCamelCase__ )
if only_modules:
lowercase__ : int = file.split('''.''' )[0]
try:
lowercase__ : Union[str, Any] = getattr(lowerCamelCase__ ,lowerCamelCase__ )
lowercase__ : List[Any] = doctest.DocTestSuite(lowerCamelCase__ )
lowercase__ : Tuple = unittest.TextTestRunner().run(lowerCamelCase__ )
self.assertIs(len(result.failures ) ,0 )
except AttributeError:
logger.info(f"""{module_identifier} is not a module.""" )
else:
lowercase__ : Optional[int] = doctest.testfile(str('''..''' / directory / file ) ,optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed ,0 )
def UpperCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
lowercase__ : int = Path('''src/transformers''' )
lowercase__ : List[str] = '''modeling'''
lowercase__ : Any = [
'''modeling_ctrl.py''',
'''modeling_tf_ctrl.py''',
]
self.analyze_directory(lowerCamelCase__ ,identifier=lowerCamelCase__ ,ignore_files=lowerCamelCase__ )
def UpperCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
lowercase__ : List[Any] = Path('''src/transformers''' )
lowercase__ : List[Any] = '''tokenization'''
self.analyze_directory(lowerCamelCase__ ,identifier=lowerCamelCase__ )
def UpperCAmelCase ( self : str ) -> List[str]:
"""simple docstring"""
lowercase__ : Tuple = Path('''src/transformers''' )
lowercase__ : List[str] = '''configuration'''
self.analyze_directory(lowerCamelCase__ ,identifier=lowerCamelCase__ )
def UpperCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
lowercase__ : Any = Path('''src/transformers''' )
lowercase__ : Optional[Any] = ['''configuration''', '''modeling''', '''tokenization''']
self.analyze_directory(lowerCamelCase__ ,n_identifier=lowerCamelCase__ )
def UpperCAmelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = Path('''docs/source''' )
lowercase__ : str = ['''favicon.ico''']
self.analyze_directory(lowerCamelCase__ ,ignore_files=lowerCamelCase__ ,only_modules=lowerCamelCase__ )
| 357
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
lowerCAmelCase_ = [
'openmmlab/upernet-convnext-tiny',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
lowerCAmelCase_ = 'UperNetConfig'
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : int ,_snake_case : int ,_snake_case : Union[int, Tuple[int, int]] ,_snake_case : Union[int, Tuple[int, int], str] = 0 ,_snake_case : bool = False ,_snake_case : Union[int, Tuple[int, int]] = 1 ,) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[int] = nn.Convad(
in_channels=_snake_case ,out_channels=_snake_case ,kernel_size=_snake_case ,padding=_snake_case ,bias=_snake_case ,dilation=_snake_case ,)
lowercase__ : Tuple = nn.BatchNormad(_snake_case )
lowercase__ : List[str] = nn.ReLU()
def UpperCAmelCase ( self : str ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.conv(_snake_case )
lowercase__ : List[str] = self.batch_norm(_snake_case )
lowercase__ : Tuple = self.activation(_snake_case )
return output
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : int ,_snake_case : int ,_snake_case : int ) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = [
nn.AdaptiveAvgPoolad(_snake_case ),
UperNetConvModule(_snake_case ,_snake_case ,kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(_snake_case ) ,_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : Any = input
for layer in self.layers:
lowercase__ : int = layer(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : Tuple[int, ...] ,_snake_case : int ,_snake_case : int ,_snake_case : bool ) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : int = pool_scales
lowercase__ : Dict = align_corners
lowercase__ : Optional[Any] = in_channels
lowercase__ : Optional[Any] = channels
lowercase__ : int = []
for i, pool_scale in enumerate(_snake_case ):
lowercase__ : Optional[Any] = UperNetPyramidPoolingBlock(pool_scale=_snake_case ,in_channels=_snake_case ,channels=_snake_case )
self.blocks.append(_snake_case )
self.add_module(str(_snake_case ) ,_snake_case )
def UpperCAmelCase ( self : Any ,_snake_case : torch.Tensor ) -> List[torch.Tensor]:
"""simple docstring"""
lowercase__ : int = []
for ppm in self.blocks:
lowercase__ : Any = ppm(_snake_case )
lowercase__ : int = nn.functional.interpolate(
_snake_case ,size=x.size()[2:] ,mode='''bilinear''' ,align_corners=self.align_corners )
ppm_outs.append(_snake_case )
return ppm_outs
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : List[str] ,_snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
super().__init__()
lowercase__ : str = config
lowercase__ : Optional[Any] = config.pool_scales # e.g. (1, 2, 3, 6)
lowercase__ : Optional[Any] = in_channels
lowercase__ : Any = config.hidden_size
lowercase__ : Optional[Any] = False
lowercase__ : Optional[int] = nn.Convad(self.channels ,config.num_labels ,kernel_size=1 )
# PSP Module
lowercase__ : Dict = UperNetPyramidPoolingModule(
self.pool_scales ,self.in_channels[-1] ,self.channels ,align_corners=self.align_corners ,)
lowercase__ : str = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels ,self.channels ,kernel_size=3 ,padding=1 ,)
# FPN Module
lowercase__ : Any = nn.ModuleList()
lowercase__ : Union[str, Any] = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
lowercase__ : List[Any] = UperNetConvModule(_snake_case ,self.channels ,kernel_size=1 )
lowercase__ : Optional[int] = UperNetConvModule(self.channels ,self.channels ,kernel_size=3 ,padding=1 )
self.lateral_convs.append(_snake_case )
self.fpn_convs.append(_snake_case )
lowercase__ : int = UperNetConvModule(
len(self.in_channels ) * self.channels ,self.channels ,kernel_size=3 ,padding=1 ,)
def UpperCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
self.apply(self._init_weights )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[Any] ) -> List[str]:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
module.weight.data.normal_(mean=0.0 ,std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Optional[Any] ) -> str:
"""simple docstring"""
lowercase__ : Dict = inputs[-1]
lowercase__ : Optional[int] = [x]
psp_outs.extend(self.psp_modules(_snake_case ) )
lowercase__ : Optional[Any] = torch.cat(_snake_case ,dim=1 )
lowercase__ : List[str] = self.bottleneck(_snake_case )
return output
def UpperCAmelCase ( self : List[str] ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : Tuple = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(_snake_case ) )
# build top-down path
lowercase__ : List[Any] = len(_snake_case )
for i in range(used_backbone_levels - 1 ,0 ,-1 ):
lowercase__ : Union[str, Any] = laterals[i - 1].shape[2:]
lowercase__ : int = laterals[i - 1] + nn.functional.interpolate(
laterals[i] ,size=_snake_case ,mode='''bilinear''' ,align_corners=self.align_corners )
# build outputs
lowercase__ : List[str] = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 ,0 ,-1 ):
lowercase__ : Any = nn.functional.interpolate(
fpn_outs[i] ,size=fpn_outs[0].shape[2:] ,mode='''bilinear''' ,align_corners=self.align_corners )
lowercase__ : Any = torch.cat(_snake_case ,dim=1 )
lowercase__ : Any = self.fpn_bottleneck(_snake_case )
lowercase__ : str = self.classifier(_snake_case )
return output
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : List[Any] ,_snake_case : int = 2 ,_snake_case : int = 3 ,_snake_case : Union[int, Tuple[int, int]] = 1 ) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : int = config
lowercase__ : Dict = config.auxiliary_in_channels
lowercase__ : Optional[int] = config.auxiliary_channels
lowercase__ : List[Any] = config.auxiliary_num_convs
lowercase__ : List[Any] = config.auxiliary_concat_input
lowercase__ : str = in_index
lowercase__ : Any = (kernel_size // 2) * dilation
lowercase__ : Optional[Any] = []
convs.append(
UperNetConvModule(
self.in_channels ,self.channels ,kernel_size=_snake_case ,padding=_snake_case ,dilation=_snake_case ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels ,self.channels ,kernel_size=_snake_case ,padding=_snake_case ,dilation=_snake_case ) )
if self.num_convs == 0:
lowercase__ : List[str] = nn.Identity()
else:
lowercase__ : Dict = nn.Sequential(*_snake_case )
if self.concat_input:
lowercase__ : int = UperNetConvModule(
self.in_channels + self.channels ,self.channels ,kernel_size=_snake_case ,padding=kernel_size // 2 )
lowercase__ : List[str] = nn.Convad(self.channels ,config.num_labels ,kernel_size=1 )
def UpperCAmelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
self.apply(self._init_weights )
def UpperCAmelCase ( self : List[Any] ,_snake_case : List[Any] ) -> Dict:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
module.weight.data.normal_(mean=0.0 ,std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase ( self : List[str] ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : str = encoder_hidden_states[self.in_index]
lowercase__ : List[str] = self.convs(_snake_case )
if self.concat_input:
lowercase__ : Any = self.conv_cat(torch.cat([hidden_states, output] ,dim=1 ) )
lowercase__ : Dict = self.classifier(_snake_case )
return output
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Any = UperNetConfig
lowerCAmelCase : str = "pixel_values"
lowerCAmelCase : Dict = True
def UpperCAmelCase ( self : int ,_snake_case : str ) -> Optional[int]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def UpperCAmelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def UpperCAmelCase ( self : int ,_snake_case : str ,_snake_case : str=False ) -> List[str]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
lowercase__ : List[Any] = value
lowerCAmelCase_ = R'\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCAmelCase_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes." ,A_ ,)
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Tuple ) -> int:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : int = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
lowercase__ : Any = UperNetHead(_snake_case ,in_channels=self.backbone.channels )
lowercase__ : str = UperNetFCNHead(_snake_case ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length''' ) )
@replace_return_docstrings(output_type=_snake_case ,config_class=_CONFIG_FOR_DOC )
def UpperCAmelCase ( self : Dict ,_snake_case : Optional[torch.Tensor] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[torch.Tensor] = None ,_snake_case : Optional[bool] = None ,) -> Union[tuple, SemanticSegmenterOutput]:
"""simple docstring"""
lowercase__ : int = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : Any = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ : Any = output_attentions if output_attentions is not None else self.config.output_attentions
lowercase__ : Optional[Any] = self.backbone.forward_with_filtered_kwargs(
_snake_case ,output_hidden_states=_snake_case ,output_attentions=_snake_case )
lowercase__ : Optional[int] = outputs.feature_maps
lowercase__ : Tuple = self.decode_head(_snake_case )
lowercase__ : Optional[int] = nn.functional.interpolate(_snake_case ,size=pixel_values.shape[2:] ,mode='''bilinear''' ,align_corners=_snake_case )
lowercase__ : List[str] = None
if self.auxiliary_head is not None:
lowercase__ : str = self.auxiliary_head(_snake_case )
lowercase__ : Dict = nn.functional.interpolate(
_snake_case ,size=pixel_values.shape[2:] ,mode='''bilinear''' ,align_corners=_snake_case )
lowercase__ : Any = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''' )
else:
# compute weighted loss
lowercase__ : Union[str, Any] = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
lowercase__ : List[str] = loss_fct(_snake_case ,_snake_case )
lowercase__ : List[str] = loss_fct(_snake_case ,_snake_case )
lowercase__ : Optional[Any] = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
lowercase__ : Tuple = (logits,) + outputs[1:]
else:
lowercase__ : int = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=_snake_case ,logits=_snake_case ,hidden_states=outputs.hidden_states ,attentions=outputs.attentions ,)
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|
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase=0.9_9_9 , __lowerCamelCase="cosine" , ) -> int:
if alpha_transform_type == "cosine":
def alpha_bar_fn(__lowerCamelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__lowerCamelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
lowercase__ : Optional[Any] = []
for i in range(A_ ):
lowercase__ : str = i / num_diffusion_timesteps
lowercase__ : Union[str, Any] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(A_ ) / alpha_bar_fn(A_ ) , A_ ) )
return torch.tensor(A_ , dtype=torch.floataa )
class __A ( a_ ,a_ ):
'''simple docstring'''
lowerCAmelCase : Dict = [e.name for e in KarrasDiffusionSchedulers]
lowerCAmelCase : Optional[Any] = 2
@register_to_config
def __init__( self : Optional[int] ,_snake_case : int = 1_000 ,_snake_case : float = 0.0_0085 ,_snake_case : float = 0.012 ,_snake_case : str = "linear" ,_snake_case : Optional[Union[np.ndarray, List[float]]] = None ,_snake_case : str = "epsilon" ,_snake_case : Optional[bool] = False ,_snake_case : Optional[bool] = False ,_snake_case : float = 1.0 ,_snake_case : str = "linspace" ,_snake_case : int = 0 ,) -> Dict:
"""simple docstring"""
if trained_betas is not None:
lowercase__ : Any = torch.tensor(lowercase_ ,dtype=torch.floataa )
elif beta_schedule == "linear":
lowercase__ : Any = torch.linspace(lowercase_ ,lowercase_ ,lowercase_ ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowercase__ : Optional[Any] = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,lowercase_ ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowercase__ : List[str] = betas_for_alpha_bar(lowercase_ ,alpha_transform_type='''cosine''' )
elif beta_schedule == "exp":
lowercase__ : Optional[int] = betas_for_alpha_bar(lowercase_ ,alpha_transform_type='''exp''' )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
lowercase__ : List[str] = 1.0 - self.betas
lowercase__ : Any = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(lowercase_ ,lowercase_ ,lowercase_ )
lowercase__ : Optional[Any] = use_karras_sigmas
def UpperCAmelCase ( self : Optional[int] ,_snake_case : Union[str, Any] ,_snake_case : Dict=None ) -> Tuple:
"""simple docstring"""
if schedule_timesteps is None:
lowercase__ : Union[str, Any] = self.timesteps
lowercase__ : Union[str, Any] = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
lowercase__ : Union[str, Any] = 1 if len(lowercase_ ) > 1 else 0
else:
lowercase__ : Dict = timestep.cpu().item() if torch.is_tensor(lowercase_ ) else timestep
lowercase__ : Optional[int] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def UpperCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def UpperCAmelCase ( self : str ,_snake_case : torch.FloatTensor ,_snake_case : Union[float, torch.FloatTensor] ,) -> Any:
"""simple docstring"""
lowercase__ : Optional[int] = self.index_for_timestep(lowercase_ )
lowercase__ : Dict = self.sigmas[step_index]
lowercase__ : int = sample / ((sigma**2 + 1) ** 0.5)
return sample
def UpperCAmelCase ( self : int ,_snake_case : int ,_snake_case : Union[str, torch.device] = None ,_snake_case : Optional[int] = None ,) -> Optional[int]:
"""simple docstring"""
lowercase__ : List[Any] = num_inference_steps
lowercase__ : int = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
lowercase__ : Any = np.linspace(0 ,num_train_timesteps - 1 ,lowercase_ ,dtype=lowercase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
lowercase__ : Dict = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowercase__ : Tuple = (np.arange(0 ,lowercase_ ) * step_ratio).round()[::-1].copy().astype(lowercase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
lowercase__ : str = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowercase__ : str = (np.arange(lowercase_ ,0 ,-step_ratio )).round().copy().astype(lowercase_ )
timesteps -= 1
else:
raise ValueError(
f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.""" )
lowercase__ : List[Any] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
lowercase__ : str = np.log(lowercase_ )
lowercase__ : int = np.interp(lowercase_ ,np.arange(0 ,len(lowercase_ ) ) ,lowercase_ )
if self.config.use_karras_sigmas:
lowercase__ : Any = self._convert_to_karras(in_sigmas=lowercase_ ,num_inference_steps=self.num_inference_steps )
lowercase__ : Optional[int] = np.array([self._sigma_to_t(lowercase_ ,lowercase_ ) for sigma in sigmas] )
lowercase__ : Dict = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
lowercase__ : Union[str, Any] = torch.from_numpy(lowercase_ ).to(device=lowercase_ )
lowercase__ : Union[str, Any] = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )
lowercase__ : List[str] = torch.from_numpy(lowercase_ )
lowercase__ : Tuple = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )
if str(lowercase_ ).startswith('''mps''' ):
# mps does not support float64
lowercase__ : List[str] = timesteps.to(lowercase_ ,dtype=torch.floataa )
else:
lowercase__ : Any = timesteps.to(device=lowercase_ )
# empty dt and derivative
lowercase__ : Dict = None
lowercase__ : Optional[int] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
lowercase__ : Dict = defaultdict(lowercase_ )
def UpperCAmelCase ( self : Any ,_snake_case : Dict ,_snake_case : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ : List[Any] = np.log(lowercase_ )
# get distribution
lowercase__ : List[Any] = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
lowercase__ : str = np.cumsum((dists >= 0) ,axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
lowercase__ : Any = low_idx + 1
lowercase__ : Tuple = log_sigmas[low_idx]
lowercase__ : Dict = log_sigmas[high_idx]
# interpolate sigmas
lowercase__ : List[Any] = (low - log_sigma) / (low - high)
lowercase__ : int = np.clip(lowercase_ ,0 ,1 )
# transform interpolation to time range
lowercase__ : Any = (1 - w) * low_idx + w * high_idx
lowercase__ : List[str] = t.reshape(sigma.shape )
return t
def UpperCAmelCase ( self : Optional[int] ,_snake_case : torch.FloatTensor ,_snake_case : Union[str, Any] ) -> Tuple:
"""simple docstring"""
lowercase__ : float = in_sigmas[-1].item()
lowercase__ : float = in_sigmas[0].item()
lowercase__ : str = 7.0 # 7.0 is the value used in the paper
lowercase__ : Optional[int] = np.linspace(0 ,1 ,lowercase_ )
lowercase__ : List[Any] = sigma_min ** (1 / rho)
lowercase__ : List[Any] = sigma_max ** (1 / rho)
lowercase__ : List[str] = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
@property
def UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return self.dt is None
def UpperCAmelCase ( self : str ,_snake_case : Union[torch.FloatTensor, np.ndarray] ,_snake_case : Union[float, torch.FloatTensor] ,_snake_case : Union[torch.FloatTensor, np.ndarray] ,_snake_case : bool = True ,) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : List[Any] = self.index_for_timestep(lowercase_ )
# advance index counter by 1
lowercase__ : Dict = timestep.cpu().item() if torch.is_tensor(lowercase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
lowercase__ : int = self.sigmas[step_index]
lowercase__ : Dict = self.sigmas[step_index + 1]
else:
# 2nd order / Heun's method
lowercase__ : Union[str, Any] = self.sigmas[step_index - 1]
lowercase__ : List[Any] = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
lowercase__ : Union[str, Any] = 0
lowercase__ : List[Any] = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
lowercase__ : List[Any] = sigma_hat if self.state_in_first_order else sigma_next
lowercase__ : Any = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
lowercase__ : int = sigma_hat if self.state_in_first_order else sigma_next
lowercase__ : Optional[int] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
lowercase__ : int = model_output
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.config.clip_sample:
lowercase__ : Union[str, Any] = pred_original_sample.clamp(
-self.config.clip_sample_range ,self.config.clip_sample_range )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
lowercase__ : List[Any] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
lowercase__ : str = sigma_next - sigma_hat
# store for 2nd order step
lowercase__ : Optional[int] = derivative
lowercase__ : Dict = dt
lowercase__ : Optional[Any] = sample
else:
# 2. 2nd order / Heun's method
lowercase__ : List[str] = (sample - pred_original_sample) / sigma_next
lowercase__ : int = (self.prev_derivative + derivative) / 2
# 3. take prev timestep & sample
lowercase__ : str = self.dt
lowercase__ : List[Any] = self.sample
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
lowercase__ : List[Any] = None
lowercase__ : str = None
lowercase__ : Optional[Any] = None
lowercase__ : Union[str, Any] = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=lowercase_ )
def UpperCAmelCase ( self : List[Any] ,_snake_case : torch.FloatTensor ,_snake_case : torch.FloatTensor ,_snake_case : torch.FloatTensor ,) -> List[str]:
"""simple docstring"""
lowercase__ : Tuple = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_ ):
# mps does not support float64
lowercase__ : Dict = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
lowercase__ : str = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
lowercase__ : int = self.timesteps.to(original_samples.device )
lowercase__ : Union[str, Any] = timesteps.to(original_samples.device )
lowercase__ : int = [self.index_for_timestep(lowercase_ ,lowercase_ ) for t in timesteps]
lowercase__ : Dict = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
lowercase__ : Optional[Any] = sigma.unsqueeze(-1 )
lowercase__ : List[Any] = original_samples + noise * sigma
return noisy_samples
def __len__( self : Dict ) -> List[str]:
"""simple docstring"""
return self.config.num_train_timesteps
| 358
|
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAmelCase_ = _symbol_database.Default()
lowerCAmelCase_ = _descriptor_pool.Default().AddSerializedFile(
B'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
lowerCAmelCase_ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
lowerCAmelCase_ = None
lowerCAmelCase_ = B'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
lowerCAmelCase_ = 45
lowerCAmelCase_ = 1_581
lowerCAmelCase_ = 1_517
lowerCAmelCase_ = 1_570
lowerCAmelCase_ = 1_584
lowerCAmelCase_ = 1_793
lowerCAmelCase_ = 1_795
lowerCAmelCase_ = 1_916
lowerCAmelCase_ = 1_864
lowerCAmelCase_ = 1_905
lowerCAmelCase_ = 1_919
lowerCAmelCase_ = 2_429
lowerCAmelCase_ = 2_208
lowerCAmelCase_ = 2_418
lowerCAmelCase_ = 2_323
lowerCAmelCase_ = 2_407
# @@protoc_insertion_point(module_scope)
| 302
| 0
|
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
lowercase__ : List[Any] = sorted(zip(_lowercase , _lowercase ) , key=lambda __lowerCamelCase : x[0] / x[1] , reverse=_lowercase )
lowercase__ , lowercase__ : List[Any] = [i[0] for i in r], [i[1] for i in r]
lowercase__ : List[str] = list(accumulate(_lowercase ) )
lowercase__ : List[str] = bisect(_lowercase , _lowercase )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Pix2StructConfig',
'Pix2StructTextConfig',
'Pix2StructVisionConfig',
],
'processing_pix2struct': ['Pix2StructProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['Pix2StructImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Pix2StructPreTrainedModel',
'Pix2StructForConditionalGeneration',
'Pix2StructVisionModel',
'Pix2StructTextModel',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 302
| 0
|
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __UpperCAmelCase ( __lowerCamelCase ) -> Dict:
lowercase__ : int = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(__lowerCamelCase ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
lowercase__ : Union[str, Any] = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError('''This matrix has no inverse.''' )
# Creates a copy of the matrix with swapped positions of the elements
lowercase__ : Dict = [[0.0, 0.0], [0.0, 0.0]]
lowercase__ : Dict = matrix[1][1], matrix[0][0]
lowercase__ : Optional[Any] = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(__lowerCamelCase ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(__lowerCamelCase ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
lowercase__ : Optional[int] = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError('''This matrix has no inverse.''' )
# Creating cofactor matrix
lowercase__ : List[Any] = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
lowercase__ : Dict = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
lowercase__ : List[Any] = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
lowercase__ : int = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
lowercase__ : Dict = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
lowercase__ : Optional[int] = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
lowercase__ : Optional[Any] = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
lowercase__ : Optional[Any] = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
lowercase__ : str = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
lowercase__ : Optional[Any] = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
lowercase__ : Any = array(__lowerCamelCase )
for i in range(3 ):
for j in range(3 ):
lowercase__ : Optional[int] = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
lowercase__ : int = array(__lowerCamelCase )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(__lowerCamelCase )
# Calculate the inverse of the matrix
return [[float(d(__lowerCamelCase ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError('''Please provide a matrix of size 2x2 or 3x3.''' )
| 360
|
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
lowercase__ : List[str] = FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
lowercase__ : List[str] = AutoTokenizer.from_pretrained('''xlm-roberta-base''' )
lowercase__ : List[str] = '''The dog is cute and lives in the garden house'''
lowercase__ : int = jnp.array([tokenizer.encode(_snake_case )] )
lowercase__ : Any = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
lowercase__ : Tuple = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
lowercase__ : Optional[Any] = model(_snake_case )['''last_hidden_state''']
self.assertEqual(output.shape ,_snake_case )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] ,_snake_case ,atol=1e-3 ) )
| 302
| 0
|
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
lowerCAmelCase_ = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
for attribute in key.split('''.''' ):
lowercase__ : List[str] = getattr(_a , _a )
if weight_type is not None:
lowercase__ : Union[str, Any] = getattr(_a , _a ).shape
else:
lowercase__ : List[str] = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
lowercase__ : List[Any] = value
elif weight_type == "weight_g":
lowercase__ : Optional[int] = value
elif weight_type == "weight_v":
lowercase__ : int = value
elif weight_type == "bias":
lowercase__ : Optional[int] = value
else:
lowercase__ : Optional[Any] = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Any:
lowercase__ : Optional[Any] = []
lowercase__ : List[Any] = fairseq_model.state_dict()
lowercase__ : Optional[Any] = hf_model.feature_extractor
lowercase__ : str = hf_model.adapter
for name, value in fairseq_dict.items():
lowercase__ : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
_a , _a , _a , _a , hf_model.config.feat_extract_norm == '''group''' , )
lowercase__ : Dict = True
elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ):
load_adapter(_a , _a , _a , _a )
lowercase__ : List[Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
lowercase__ : Optional[Any] = True
if "*" in mapped_key:
lowercase__ : Any = name.split(_a )[0].split('''.''' )[-2]
lowercase__ : List[str] = mapped_key.replace('''*''' , _a )
if "weight_g" in name:
lowercase__ : Any = '''weight_g'''
elif "weight_v" in name:
lowercase__ : Dict = '''weight_v'''
elif "bias" in name:
lowercase__ : Optional[int] = '''bias'''
elif "weight" in name:
lowercase__ : str = '''weight'''
else:
lowercase__ : int = None
set_recursively(_a , _a , _a , _a , _a )
continue
if not is_used:
unused_weights.append(_a )
logger.warning(f"""Unused weights: {unused_weights}""" )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
lowercase__ : Union[str, Any] = full_name.split('''conv_layers.''' )[-1]
lowercase__ : Optional[Any] = name.split('''.''' )
lowercase__ : Any = int(items[0] )
lowercase__ : List[Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
lowercase__ : Optional[int] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
lowercase__ : Optional[int] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
lowercase__ : List[str] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
lowercase__ : str = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_a )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
lowercase__ : int = full_name.split('''adaptor.''' )[-1]
lowercase__ : List[Any] = name.split('''.''' )
if items[1].isdigit():
lowercase__ : List[str] = int(items[1] )
else:
lowercase__ : Optional[Any] = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
lowercase__ : Any = value
logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}.""" )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
lowercase__ : Union[str, Any] = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
lowercase__ : Tuple = value
logger.info(f"""Adapter proj layer bias was initialized from {full_name}.""" )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
lowercase__ : Any = value
logger.info(f"""Adapter proj layer weight was initialized from {full_name}.""" )
elif isinstance(_a , _a ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
lowercase__ : Union[str, Any] = value
logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}.""" )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
lowercase__ : Tuple = value
logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}.""" )
else:
unused_weights.append(_a )
def __UpperCAmelCase ( __lowerCamelCase ) -> Union[str, Any]:
lowercase__ , lowercase__ : Union[str, Any] = emb.weight.shape
lowercase__ : List[str] = nn.Linear(_a , _a , bias=_a )
lowercase__ : Tuple = emb.weight.data
return lin_layer
@torch.no_grad()
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> Optional[Any]:
lowercase__ : int = WavaVecaConfig.from_pretrained(
_a , add_adapter=_a , adapter_stride=_a , adapter_kernel_size=_a , use_auth_token=_a , output_hidden_size=_a , )
lowercase__ : str = MBartConfig.from_pretrained(_a )
# load model
lowercase__ , lowercase__ , lowercase__ : Optional[int] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={
'''config_yaml''': config_yaml_path,
'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ),
'''w2v_path''': checkpoint_path,
'''load_pretrained_decoder_from''': None,
} , )
lowercase__ : str = model[0].eval()
# load feature extractor
lowercase__ : Optional[Any] = WavaVecaFeatureExtractor.from_pretrained(_a , use_auth_token=_a )
# set weights for wav2vec2 encoder
lowercase__ : str = WavaVecaModel(_a )
recursively_load_weights_wavaveca(model.encoder , _a )
# load decoder weights
lowercase__ : Any = MBartForCausalLM(_a )
lowercase__ , lowercase__ : Dict = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=_a )
logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )
lowercase__ : str = SpeechEncoderDecoderModel(encoder=_a , decoder=_a )
lowercase__ : Tuple = False
lowercase__ : Dict = MBartaaTokenizer(_a )
tokenizer.save_pretrained(_a )
lowercase__ : str = hf_wavavec.config.to_dict()
lowercase__ : str = tokenizer.pad_token_id
lowercase__ : Optional[Any] = tokenizer.bos_token_id
lowercase__ : Dict = tokenizer.eos_token_id
lowercase__ : str = '''mbart50'''
lowercase__ : List[str] = '''wav2vec2'''
lowercase__ : Tuple = tokenizer.eos_token_id
lowercase__ : Optional[Any] = 25_00_04
lowercase__ : Any = tokenizer.eos_token_id
lowercase__ : Optional[Any] = SpeechEncoderDecoderConfig.from_dict(_a )
hf_wavavec.save_pretrained(_a )
feature_extractor.save_pretrained(_a )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
parser.add_argument(
'--encoder_config_path',
default='facebook/wav2vec2-xls-r-1b',
type=str,
help='Path to hf encoder wav2vec2 checkpoint config',
)
parser.add_argument(
'--decoder_config_path',
default='facebook/mbart-large-50-one-to-many-mmt',
type=str,
help='Path to hf decoder checkpoint config',
)
parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
parser.add_argument('--encoder_output_dim', default=1_024, type=int, help='encoder output dim')
parser.add_argument('--start_token_id', default=250_004, type=int, help='`decoder_start_token_id` of model config')
lowerCAmelCase_ = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 361
|
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = '#'
class __A :
'''simple docstring'''
def __init__( self : str ) -> None:
"""simple docstring"""
lowercase__ : dict = {}
def UpperCAmelCase ( self : List[str] ,_snake_case : str ) -> None:
"""simple docstring"""
lowercase__ : str = self._trie
for char in text:
if char not in trie:
lowercase__ : Union[str, Any] = {}
lowercase__ : Optional[Any] = trie[char]
lowercase__ : Dict = True
def UpperCAmelCase ( self : Tuple ,_snake_case : str ) -> tuple | list:
"""simple docstring"""
lowercase__ : Optional[Any] = self._trie
for char in prefix:
if char in trie:
lowercase__ : Union[str, Any] = trie[char]
else:
return []
return self._elements(_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : dict ) -> tuple:
"""simple docstring"""
lowercase__ : str = []
for c, v in d.items():
lowercase__ : List[Any] = [''' '''] if c == END else [(c + s) for s in self._elements(_snake_case )]
result.extend(_snake_case )
return tuple(_snake_case )
lowerCAmelCase_ = Trie()
lowerCAmelCase_ = ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
trie.insert_word(word)
def __UpperCAmelCase ( __lowerCamelCase ) -> tuple:
lowercase__ : List[Any] = trie.find_word(__lowerCamelCase )
return tuple(string + word for word in suffixes )
def __UpperCAmelCase ( ) -> None:
print(autocomplete_using_trie('''de''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 302
| 0
|
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> int:
return int((input_a, input_a).count(0 ) != 0 )
def __UpperCAmelCase ( ) -> None:
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 362
|
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
lowerCAmelCase_ = logging.get_logger(__name__)
# General docstring
lowerCAmelCase_ = 'RegNetConfig'
# Base docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = [1, 1_088, 7, 7]
# Image classification docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = 'tabby, tabby cat'
lowerCAmelCase_ = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : int ,_snake_case : int ,_snake_case : int ,_snake_case : int = 3 ,_snake_case : int = 1 ,_snake_case : int = 1 ,_snake_case : Optional[str] = "relu" ,) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = nn.Convad(
_snake_case ,_snake_case ,kernel_size=_snake_case ,stride=_snake_case ,padding=kernel_size // 2 ,groups=_snake_case ,bias=_snake_case ,)
lowercase__ : List[Any] = nn.BatchNormad(_snake_case )
lowercase__ : Optional[int] = ACTaFN[activation] if activation is not None else nn.Identity()
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.convolution(_snake_case )
lowercase__ : Tuple = self.normalization(_snake_case )
lowercase__ : Tuple = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] ,_snake_case : RegNetConfig ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = RegNetConvLayer(
config.num_channels ,config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act )
lowercase__ : str = config.num_channels
def UpperCAmelCase ( self : int ,_snake_case : Dict ) -> str:
"""simple docstring"""
lowercase__ : Union[str, Any] = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
lowercase__ : Optional[int] = self.embedder(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : str ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ) -> Any:
"""simple docstring"""
super().__init__()
lowercase__ : List[str] = nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ,stride=_snake_case ,bias=_snake_case )
lowercase__ : Any = nn.BatchNormad(_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ) -> Tensor:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.convolution(_snake_case )
lowercase__ : Optional[int] = self.normalization(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : int ,_snake_case : int ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Any = nn.AdaptiveAvgPoolad((1, 1) )
lowercase__ : Dict = nn.Sequential(
nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.ReLU() ,nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.Sigmoid() ,)
def UpperCAmelCase ( self : int ,_snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.pooler(_snake_case )
lowercase__ : Union[str, Any] = self.attention(_snake_case )
lowercase__ : List[str] = hidden_state * attention
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> List[str]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = in_channels != out_channels or stride != 1
lowercase__ : Optional[int] = max(1 ,out_channels // config.groups_width )
lowercase__ : str = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : Optional[int] = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : str = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ : Tuple = hidden_state
lowercase__ : Union[str, Any] = self.layer(_snake_case )
lowercase__ : List[Any] = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : Optional[int] = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> Optional[int]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = in_channels != out_channels or stride != 1
lowercase__ : List[str] = max(1 ,out_channels // config.groups_width )
lowercase__ : Tuple = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : str = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetSELayer(_snake_case ,reduced_channels=int(round(in_channels / 4 ) ) ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : Optional[Any] = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ : str = hidden_state
lowercase__ : Optional[Any] = self.layer(_snake_case )
lowercase__ : int = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : str = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ,_snake_case : int = 2 ,) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[Any] = RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer
lowercase__ : Optional[Any] = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
_snake_case ,_snake_case ,_snake_case ,stride=_snake_case ,) ,*[layer(_snake_case ,_snake_case ,_snake_case ) for _ in range(depth - 1 )] ,)
def UpperCAmelCase ( self : Tuple ,_snake_case : int ) -> List[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.layers(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : RegNetConfig ) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : str = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
_snake_case ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,) )
lowercase__ : str = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(_snake_case ,config.depths[1:] ):
self.stages.append(RegNetStage(_snake_case ,_snake_case ,_snake_case ,depth=_snake_case ) )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ,_snake_case : bool = False ,_snake_case : bool = True ) -> BaseModelOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Dict = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase__ : int = hidden_states + (hidden_state,)
lowercase__ : Any = stage_module(_snake_case )
if output_hidden_states:
lowercase__ : Optional[int] = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_snake_case ,hidden_states=_snake_case )
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : int = RegNetConfig
lowerCAmelCase : List[Any] = "regnet"
lowerCAmelCase : Optional[int] = "pixel_values"
lowerCAmelCase : Union[str, Any] = True
def UpperCAmelCase ( self : Any ,_snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
nn.init.kaiming_normal_(module.weight ,mode='''fan_out''' ,nonlinearity='''relu''' )
elif isinstance(_snake_case ,(nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight ,1 )
nn.init.constant_(module.bias ,0 )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Dict ,_snake_case : Any=False ) -> Optional[int]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
lowercase__ : str = value
lowerCAmelCase_ = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCAmelCase_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Any ) -> Tuple:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Any = config
lowercase__ : List[str] = RegNetEmbeddings(_snake_case )
lowercase__ : Any = RegNetEncoder(_snake_case )
lowercase__ : Dict = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,modality='''vision''' ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCAmelCase ( self : Dict ,_snake_case : Tensor ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
"""simple docstring"""
lowercase__ : List[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ : Dict = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : Union[str, Any] = self.embedder(_snake_case )
lowercase__ : List[Any] = self.encoder(
_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : str = encoder_outputs[0]
lowercase__ : Optional[int] = self.pooler(_snake_case )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_snake_case ,pooler_output=_snake_case ,hidden_states=encoder_outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : int ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Optional[Any] = config.num_labels
lowercase__ : int = RegNetModel(_snake_case )
# classification head
lowercase__ : str = nn.Sequential(
nn.Flatten() ,nn.Linear(config.hidden_sizes[-1] ,config.num_labels ) if config.num_labels > 0 else nn.Identity() ,)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCAmelCase ( self : List[Any] ,_snake_case : Optional[torch.FloatTensor] = None ,_snake_case : Optional[torch.LongTensor] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ,) -> ImageClassifierOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Any = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : List[Any] = self.regnet(_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : List[str] = outputs.pooler_output if return_dict else outputs[1]
lowercase__ : Union[str, Any] = self.classifier(_snake_case )
lowercase__ : Optional[int] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase__ : List[Any] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase__ : Dict = '''single_label_classification'''
else:
lowercase__ : Optional[int] = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase__ : Union[str, Any] = MSELoss()
if self.num_labels == 1:
lowercase__ : List[Any] = loss_fct(logits.squeeze() ,labels.squeeze() )
else:
lowercase__ : Tuple = loss_fct(_snake_case ,_snake_case )
elif self.config.problem_type == "single_label_classification":
lowercase__ : Tuple = CrossEntropyLoss()
lowercase__ : str = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase__ : Any = BCEWithLogitsLoss()
lowercase__ : Union[str, Any] = loss_fct(_snake_case ,_snake_case )
if not return_dict:
lowercase__ : Tuple = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_snake_case ,logits=_snake_case ,hidden_states=outputs.hidden_states )
| 302
| 0
|
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( a__ ):
'''simple docstring'''
lowerCAmelCase : int = ["""image_processor""", """tokenizer"""]
lowerCAmelCase : List[Any] = """CLIPImageProcessor"""
lowerCAmelCase : Any = ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")
def __init__( self : Dict ,_snake_case : Optional[Any]=None ,_snake_case : Dict=None ,**_snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Any = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' ,lowerCAmelCase__ ,)
lowercase__ : Tuple = kwargs.pop('''feature_extractor''' )
lowercase__ : int = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(lowerCAmelCase__ ,lowerCAmelCase__ )
def __call__( self : Optional[Any] ,_snake_case : Optional[int]=None ,_snake_case : Dict=None ,_snake_case : Optional[int]=None ,**_snake_case : List[str] ) -> Union[str, Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
lowercase__ : int = self.tokenizer(lowerCAmelCase__ ,return_tensors=lowerCAmelCase__ ,**lowerCAmelCase__ )
if images is not None:
lowercase__ : List[str] = self.image_processor(lowerCAmelCase__ ,return_tensors=lowerCAmelCase__ ,**lowerCAmelCase__ )
if text is not None and images is not None:
lowercase__ : Optional[Any] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowerCAmelCase__ ) ,tensor_type=lowerCAmelCase__ )
def UpperCAmelCase ( self : Optional[Any] ,*_snake_case : Tuple ,**_snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase__ ,**lowerCAmelCase__ )
def UpperCAmelCase ( self : int ,*_snake_case : Optional[int] ,**_snake_case : List[Any] ) -> List[str]:
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase__ ,**lowerCAmelCase__ )
@property
def UpperCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
lowercase__ : List[Any] = self.tokenizer.model_input_names
lowercase__ : Optional[int] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 363
|
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = 1.6021E-19 # units = C
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 302
| 0
|
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowerCAmelCase_ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|'),
datarow=DataRow('', '|', '|'),
padding=1,
with_header_hide=None,
)
lowerCAmelCase_ = []
lowerCAmelCase_ = []
lowerCAmelCase_ = {"""type""": """section""", """text""": {"""type""": """plain_text""", """text""": """No failed tests! 🤗""", """emoji""": True}}
lowerCAmelCase_ = [
{
"""type""": """header""",
"""text""": {
"""type""": """plain_text""",
"""text""": F'''🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results''',
"""emoji""": True,
},
}
]
lowerCAmelCase_ = 0
for log in Path().glob('*.log'):
lowerCAmelCase_ = 0
with open(log, 'r') as f:
for line in f:
lowerCAmelCase_ = json.loads(line)
if line.get('nodeid', '') != "":
lowerCAmelCase_ = line["""nodeid"""]
if line.get('duration', None) is not None:
lowerCAmelCase_ = F'''{line["duration"]:.4f}'''
if line.get('outcome', '') == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split('_')[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
lowerCAmelCase_ = []
log.unlink()
lowerCAmelCase_ = """"""
lowerCAmelCase_ = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += F"*{name[1:]}: {num_failed} failed test*\n"
else:
message += F"*{name[1:]}: {num_failed} failed tests*\n"
lowerCAmelCase_ = []
lowerCAmelCase_ = {}
for test in failed_tests:
lowerCAmelCase_ = test[0].split('::')
lowerCAmelCase_ = data[0].split('/')[-1]
if data[0] not in filesafailed:
lowerCAmelCase_ = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
lowerCAmelCase_ = [test[0] for test in failed_table]
lowerCAmelCase_ = list(set(files))
# Count number of instances in failed_tests
lowerCAmelCase_ = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
lowerCAmelCase_ = tabulate(
table,
headers=['Test Location', 'Num Failed'],
tablefmt=hf_table_format,
stralign='right',
)
message += F"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 3_000:
lowerCAmelCase_ = """Too many failed tests, please see the full report in the Action results."""
lowerCAmelCase_ = len(err) + 10
lowerCAmelCase_ = message[: 3_000 - offset] + F'''\n...\n```\n{err}'''
print(F'''### {message}''')
else:
lowerCAmelCase_ = """No failed tests! 🤗"""
print(F'''## {message}''')
payload.append(no_error_payload)
if os.environ.get('TEST_TYPE', '') != "":
from slack_sdk import WebClient
lowerCAmelCase_ = WebClient(token=os.environ['SLACK_API_TOKEN'])
if message != "No failed tests! 🤗":
lowerCAmelCase_ = {
"""type""": """section""",
"""text""": {
"""type""": """mrkdwn""",
"""text""": message,
},
}
payload.append(md_report)
lowerCAmelCase_ = {
"""type""": """section""",
"""text""": {
"""type""": """mrkdwn""",
"""text""": """*For more details:*""",
},
"""accessory""": {
"""type""": """button""",
"""text""": {
"""type""": """plain_text""",
"""text""": """Check Action results""",
"""emoji""": True,
},
"""url""": F'''https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}''',
},
}
payload.append(action_button)
lowerCAmelCase_ = {
"""type""": """context""",
"""elements""": [
{
"""type""": """plain_text""",
"""text""": F'''Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}''',
}
],
}
payload.append(date_report)
lowerCAmelCase_ = client.chat_postMessage(channel='#accelerate-ci-daily', text=message, blocks=payload)
lowerCAmelCase_ = response.data["""ts"""]
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
lowerCAmelCase_ = """"""
for i, row in enumerate(test_failures):
if row[0] != test_class:
lowerCAmelCase_ = row[0]
else:
lowerCAmelCase_ = """"""
lowerCAmelCase_ = {
"""type""": """section""",
"""text""": {
"""type""": """mrkdwn""",
"""text""": F'''Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```''',
},
}
client.chat_postMessage(
channel='#accelerate-ci-daily',
thread_ts=ts,
blocks=[payload],
)
| 364
|
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : str = ["pixel_values"]
def __init__( self : Tuple ,_snake_case : bool = True ,_snake_case : Optional[Dict[str, int]] = None ,_snake_case : PILImageResampling = PILImageResampling.BICUBIC ,_snake_case : bool = True ,_snake_case : bool = True ,_snake_case : Union[int, float] = 1 / 255 ,_snake_case : Dict[str, int] = None ,_snake_case : bool = True ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,**_snake_case : Optional[Any] ,) -> None:
"""simple docstring"""
super().__init__(**_snake_case )
lowercase__ : str = size if size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[int] = get_size_dict(_snake_case )
lowercase__ : List[Any] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[int] = get_size_dict(_snake_case ,default_to_square=_snake_case ,param_name='''crop_size''' )
lowercase__ : Tuple = do_resize
lowercase__ : List[Any] = do_rescale
lowercase__ : Any = do_normalize
lowercase__ : List[str] = do_center_crop
lowercase__ : Optional[Any] = crop_size
lowercase__ : Union[str, Any] = size
lowercase__ : Any = resample
lowercase__ : int = rescale_factor
lowercase__ : Tuple = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
lowercase__ : str = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase ( self : str ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : PILImageResampling = PILImageResampling.BILINEAR ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Dict ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : List[str] = get_size_dict(_snake_case )
if "shortest_edge" in size:
lowercase__ : str = get_resize_output_image_size(_snake_case ,size=size['''shortest_edge'''] ,default_to_square=_snake_case )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
lowercase__ : int = (size['''height'''], size['''width'''])
else:
raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}""" )
return resize(_snake_case ,size=_snake_case ,resample=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : List[Any] ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Tuple ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : Optional[Any] = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(_snake_case ,size=(size['''height'''], size['''width''']) ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : np.ndarray ,_snake_case : float ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Optional[int] ) -> np.ndarray:
"""simple docstring"""
return rescale(_snake_case ,scale=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : np.ndarray ,_snake_case : Union[float, List[float]] ,_snake_case : Union[float, List[float]] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Dict ,) -> np.ndarray:
"""simple docstring"""
return normalize(_snake_case ,mean=_snake_case ,std=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : ImageInput ,_snake_case : Optional[bool] = None ,_snake_case : Dict[str, int] = None ,_snake_case : PILImageResampling = None ,_snake_case : bool = None ,_snake_case : int = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[float] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[str, TensorType]] = None ,_snake_case : Union[str, ChannelDimension] = ChannelDimension.FIRST ,**_snake_case : List[str] ,) -> BatchFeature:
"""simple docstring"""
lowercase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
lowercase__ : int = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ : int = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ : Optional[int] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ : Optional[Any] = crop_size if crop_size is not None else self.crop_size
lowercase__ : Tuple = get_size_dict(_snake_case ,param_name='''crop_size''' ,default_to_square=_snake_case )
lowercase__ : Tuple = resample if resample is not None else self.resample
lowercase__ : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ : Union[str, Any] = image_mean if image_mean is not None else self.image_mean
lowercase__ : List[str] = image_std if image_std is not None else self.image_std
lowercase__ : Optional[int] = size if size is not None else self.size
lowercase__ : int = get_size_dict(_snake_case )
if not is_batched(_snake_case ):
lowercase__ : Optional[Any] = [images]
if not valid_images(_snake_case ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
# All transformations expect numpy arrays.
lowercase__ : str = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
lowercase__ : int = [self.resize(image=_snake_case ,size=_snake_case ,resample=_snake_case ) for image in images]
if do_center_crop:
lowercase__ : str = [self.center_crop(image=_snake_case ,size=_snake_case ) for image in images]
if do_rescale:
lowercase__ : Optional[Any] = [self.rescale(image=_snake_case ,scale=_snake_case ) for image in images]
if do_normalize:
lowercase__ : List[str] = [self.normalize(image=_snake_case ,mean=_snake_case ,std=_snake_case ) for image in images]
lowercase__ : Union[str, Any] = [to_channel_dimension_format(_snake_case ,_snake_case ) for image in images]
lowercase__ : Any = {'''pixel_values''': images}
return BatchFeature(data=_snake_case ,tensor_type=_snake_case )
| 302
| 0
|
"""simple docstring"""
import random
class __A :
'''simple docstring'''
@staticmethod
def UpperCAmelCase ( _snake_case : int ) -> tuple[list[int], list[int]]:
"""simple docstring"""
lowercase__ : Dict = [ord(lowerCAmelCase_ ) for i in text]
lowercase__ : Dict = []
lowercase__ : Optional[int] = []
for i in plain:
lowercase__ : str = random.randint(1 ,300 )
lowercase__ : Dict = (i + k) * k
cipher.append(lowerCAmelCase_ )
key.append(lowerCAmelCase_ )
return cipher, key
@staticmethod
def UpperCAmelCase ( _snake_case : Dict ,_snake_case : Any ) -> str:
"""simple docstring"""
lowercase__ : Optional[int] = []
for i in range(len(lowerCAmelCase_ ) ):
lowercase__ : Optional[int] = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(lowerCAmelCase_ ) )
return "".join(lowerCAmelCase_ )
if __name__ == "__main__":
lowerCAmelCase_ ,lowerCAmelCase_ = Onepad().encrypt('Hello')
print(c, k)
print(Onepad().decrypt(c, k))
| 365
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_efficientnet': [
'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EfficientNetConfig',
'EfficientNetOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['EfficientNetImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'EfficientNetForImageClassification',
'EfficientNetModel',
'EfficientNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 302
| 0
|
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __A ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def UpperCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
return datasets.DatasetInfo(
features=datasets.Features({'''content''': datasets.Value('''string''' )} ) ,supervised_keys=SCREAMING_SNAKE_CASE_ ,)
def UpperCAmelCase ( self : Tuple ,_snake_case : Any ,_snake_case : List[Any] ) -> Any:
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={'''examples''': get_test_dummy_examples()} )]
def UpperCAmelCase ( self : Any ,_snake_case : int ,_snake_case : Any ) -> Optional[int]:
"""simple docstring"""
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(SCREAMING_SNAKE_CASE_ )
class __A ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def UpperCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
return datasets.DatasetInfo(
features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) ,supervised_keys=SCREAMING_SNAKE_CASE_ ,)
def UpperCAmelCase ( self : Dict ,_snake_case : List[str] ,_snake_case : List[str] ) -> Optional[int]:
"""simple docstring"""
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={'''examples''': get_test_nested_examples()} )
]
def UpperCAmelCase ( self : Optional[int] ,_snake_case : Dict ,_snake_case : List[str] ) -> Dict:
"""simple docstring"""
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(SCREAMING_SNAKE_CASE_ )
def __UpperCAmelCase ( ) -> Optional[int]:
return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''] )]
def __UpperCAmelCase ( ) -> Union[str, Any]:
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''] )]
class __A ( _lowerCAmelCase ):
'''simple docstring'''
@require_beam
def UpperCAmelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ : Any = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
lowercase__ : Optional[int] = DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE_ ,beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(SCREAMING_SNAKE_CASE_ ,builder.name ,'''default''' ,'''0.0.0''' ,f"""{builder.name}-train.arrow""" ) ) )
self.assertDictEqual(builder.info.features ,datasets.Features({'''content''': datasets.Value('''string''' )} ) )
lowercase__ : Any = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples ,SCREAMING_SNAKE_CASE_ )
self.assertDictEqual(dset['''train'''][0] ,get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] ,get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_ ,builder.name ,'''default''' ,'''0.0.0''' ,'''dataset_info.json''' ) ) )
del dset
@require_beam
def UpperCAmelCase ( self : str ) -> List[str]:
"""simple docstring"""
import apache_beam as beam
lowercase__ : List[str] = beam.io.parquetio.WriteToParquet
lowercase__ : Any = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
lowercase__ : Dict = DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE_ ,beam_runner='''DirectRunner''' )
with patch('''apache_beam.io.parquetio.WriteToParquet''' ) as write_parquet_mock:
lowercase__ : List[Any] = partial(SCREAMING_SNAKE_CASE_ ,num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
SCREAMING_SNAKE_CASE_ ,builder.name ,'''default''' ,'''0.0.0''' ,f"""{builder.name}-train-00000-of-00002.arrow""" ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
SCREAMING_SNAKE_CASE_ ,builder.name ,'''default''' ,'''0.0.0''' ,f"""{builder.name}-train-00000-of-00002.arrow""" ) ) )
self.assertDictEqual(builder.info.features ,datasets.Features({'''content''': datasets.Value('''string''' )} ) )
lowercase__ : Any = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples ,SCREAMING_SNAKE_CASE_ )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset['''train''']['''content'''] ) ,sorted(['''foo''', '''bar''', '''foobar'''] ) )
self.assertTrue(
os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_ ,builder.name ,'''default''' ,'''0.0.0''' ,'''dataset_info.json''' ) ) )
del dset
@require_beam
def UpperCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_cache_dir:
lowercase__ : int = DummyBeamDataset(cache_dir=SCREAMING_SNAKE_CASE_ )
self.assertRaises(datasets.builder.MissingBeamOptions ,builder.download_and_prepare )
@require_beam
def UpperCAmelCase ( self : Tuple ) -> Any:
"""simple docstring"""
lowercase__ : Optional[Any] = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
lowercase__ : int = NestedBeamDataset(cache_dir=SCREAMING_SNAKE_CASE_ ,beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(SCREAMING_SNAKE_CASE_ ,builder.name ,'''default''' ,'''0.0.0''' ,f"""{builder.name}-train.arrow""" ) ) )
self.assertDictEqual(
builder.info.features ,datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) )
lowercase__ : Any = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows ,SCREAMING_SNAKE_CASE_ )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples ,SCREAMING_SNAKE_CASE_ )
self.assertDictEqual(dset['''train'''][0] ,get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] ,get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_ ,builder.name ,'''default''' ,'''0.0.0''' ,'''dataset_info.json''' ) ) )
del dset
| 366
|
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = "cpu" , __lowerCamelCase = None ) -> None:
lowercase__ : List[str] = torch.load(__lowerCamelCase , map_location=__lowerCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__lowerCamelCase , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
lowercase__ : List[Any] = v.half()
if save_path is None: # overwrite src_path
lowercase__ : Any = src_path
torch.save(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 302
| 0
|
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class __A :
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : Optional[int] ,_snake_case : int=13 ,_snake_case : Optional[int]=7 ,_snake_case : Tuple=True ,_snake_case : Any=True ,_snake_case : List[str]=True ,_snake_case : Tuple=True ,_snake_case : str=99 ,_snake_case : str=16 ,_snake_case : Dict=36 ,_snake_case : List[str]=6 ,_snake_case : Any=6 ,_snake_case : Any=6 ,_snake_case : Union[str, Any]=37 ,_snake_case : str="gelu" ,_snake_case : List[Any]=0.1 ,_snake_case : Optional[int]=0.1 ,_snake_case : Optional[int]=512 ,_snake_case : List[str]=16 ,_snake_case : Tuple=2 ,_snake_case : Dict=0.02 ,_snake_case : Tuple=3 ,_snake_case : Union[str, Any]=4 ,_snake_case : Optional[int]=None ,) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = parent
lowercase__ : str = batch_size
lowercase__ : Optional[Any] = seq_length
lowercase__ : Optional[int] = is_training
lowercase__ : Optional[int] = use_input_mask
lowercase__ : List[Any] = use_token_type_ids
lowercase__ : Tuple = use_labels
lowercase__ : Optional[Any] = vocab_size
lowercase__ : List[Any] = embedding_size
lowercase__ : Any = hidden_size
lowercase__ : Any = num_hidden_layers
lowercase__ : Union[str, Any] = num_hidden_groups
lowercase__ : List[str] = num_attention_heads
lowercase__ : Any = intermediate_size
lowercase__ : List[Any] = hidden_act
lowercase__ : List[str] = hidden_dropout_prob
lowercase__ : Union[str, Any] = attention_probs_dropout_prob
lowercase__ : Dict = max_position_embeddings
lowercase__ : Union[str, Any] = type_vocab_size
lowercase__ : List[Any] = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : Union[str, Any] = num_labels
lowercase__ : Optional[Any] = num_choices
lowercase__ : Optional[int] = scope
def UpperCAmelCase ( self : Any ) -> int:
"""simple docstring"""
lowercase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowercase__ : List[str] = None
if self.use_input_mask:
lowercase__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : Optional[int] = None
if self.use_token_type_ids:
lowercase__ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
lowercase__ : Union[str, Any] = None
lowercase__ : List[Any] = None
lowercase__ : int = None
if self.use_labels:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowercase__ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
lowercase__ : Tuple = ids_tensor([self.batch_size] ,self.num_choices )
lowercase__ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self : int ) -> Tuple:
"""simple docstring"""
return AlbertConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,initializer_range=self.initializer_range ,num_hidden_groups=self.num_hidden_groups ,)
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : List[str] ,_snake_case : str ,_snake_case : Optional[int] ,_snake_case : Tuple ,_snake_case : int ,_snake_case : str ,_snake_case : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[int] = AlbertModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowercase__ : List[str] = model(UpperCamelCase__ ,attention_mask=UpperCamelCase__ ,token_type_ids=UpperCamelCase__ )
lowercase__ : str = model(UpperCamelCase__ ,token_type_ids=UpperCamelCase__ )
lowercase__ : int = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape ,(self.batch_size, self.hidden_size) )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : str ,_snake_case : Union[str, Any] ,_snake_case : Optional[Any] ,_snake_case : str ,_snake_case : Optional[int] ,_snake_case : Dict ,_snake_case : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ : Optional[Any] = AlbertForPreTraining(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowercase__ : List[Any] = model(
UpperCamelCase__ ,attention_mask=UpperCamelCase__ ,token_type_ids=UpperCamelCase__ ,labels=UpperCamelCase__ ,sentence_order_label=UpperCamelCase__ ,)
self.parent.assertEqual(result.prediction_logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape ,(self.batch_size, config.num_labels) )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[int] ,_snake_case : Union[str, Any] ,_snake_case : Optional[int] ,_snake_case : int ,_snake_case : Optional[int] ,_snake_case : Optional[int] ,_snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : str = AlbertForMaskedLM(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowercase__ : Dict = model(UpperCamelCase__ ,attention_mask=UpperCamelCase__ ,token_type_ids=UpperCamelCase__ ,labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self : Dict ,_snake_case : int ,_snake_case : Optional[int] ,_snake_case : Any ,_snake_case : Optional[Any] ,_snake_case : Union[str, Any] ,_snake_case : int ,_snake_case : Dict ) -> Any:
"""simple docstring"""
lowercase__ : int = AlbertForQuestionAnswering(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowercase__ : str = model(
UpperCamelCase__ ,attention_mask=UpperCamelCase__ ,token_type_ids=UpperCamelCase__ ,start_positions=UpperCamelCase__ ,end_positions=UpperCamelCase__ ,)
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : Tuple ,_snake_case : List[str] ,_snake_case : List[str] ,_snake_case : int ,_snake_case : Optional[Any] ,_snake_case : Tuple ,_snake_case : Union[str, Any] ,_snake_case : str ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Optional[int] = self.num_labels
lowercase__ : List[str] = AlbertForSequenceClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowercase__ : Dict = model(UpperCamelCase__ ,attention_mask=UpperCamelCase__ ,token_type_ids=UpperCamelCase__ ,labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[int] ,_snake_case : Union[str, Any] ,_snake_case : List[str] ,_snake_case : int ,_snake_case : Optional[Any] ,_snake_case : List[str] ,_snake_case : str ) -> Tuple:
"""simple docstring"""
lowercase__ : Tuple = self.num_labels
lowercase__ : int = AlbertForTokenClassification(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowercase__ : str = model(UpperCamelCase__ ,attention_mask=UpperCamelCase__ ,token_type_ids=UpperCamelCase__ ,labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase ( self : Any ,_snake_case : List[str] ,_snake_case : Optional[Any] ,_snake_case : int ,_snake_case : Any ,_snake_case : Dict ,_snake_case : int ,_snake_case : str ) -> Tuple:
"""simple docstring"""
lowercase__ : int = self.num_choices
lowercase__ : List[Any] = AlbertForMultipleChoice(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
lowercase__ : Tuple = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
lowercase__ : Union[str, Any] = token_type_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
lowercase__ : Tuple = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
lowercase__ : Union[str, Any] = model(
UpperCamelCase__ ,attention_mask=UpperCamelCase__ ,token_type_ids=UpperCamelCase__ ,labels=UpperCamelCase__ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def UpperCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
lowercase__ : Optional[int] = self.prepare_config_and_inputs()
(
lowercase__
) : List[str] = config_and_inputs
lowercase__ : Dict = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Any = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCAmelCase : List[Any] = (
{
"feature-extraction": AlbertModel,
"fill-mask": AlbertForMaskedLM,
"question-answering": AlbertForQuestionAnswering,
"text-classification": AlbertForSequenceClassification,
"token-classification": AlbertForTokenClassification,
"zero-shot": AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase : Dict = True
def UpperCAmelCase ( self : Dict ,_snake_case : Dict ,_snake_case : Dict ,_snake_case : int=False ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Any = super()._prepare_for_class(UpperCamelCase__ ,UpperCamelCase__ ,return_labels=UpperCamelCase__ )
if return_labels:
if model_class in get_values(UpperCamelCase__ ):
lowercase__ : Optional[int] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) ,dtype=torch.long ,device=UpperCamelCase__ )
lowercase__ : List[Any] = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=UpperCamelCase__ )
return inputs_dict
def UpperCAmelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Any = AlbertModelTester(self )
lowercase__ : Any = ConfigTester(self ,config_class=UpperCamelCase__ ,hidden_size=37 )
def UpperCAmelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def UpperCAmelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__ )
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )
def UpperCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__ )
def UpperCAmelCase ( self : Any ) -> Tuple:
"""simple docstring"""
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__ )
def UpperCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__ )
def UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase__ : int = type
self.model_tester.create_and_check_model(*UpperCamelCase__ )
@slow
def UpperCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : List[Any] = AlbertModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Tuple = AlbertModel.from_pretrained('''albert-base-v2''' )
lowercase__ : Dict = torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
lowercase__ : Union[str, Any] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowercase__ : Any = model(UpperCamelCase__ ,attention_mask=UpperCamelCase__ )[0]
lowercase__ : int = torch.Size((1, 11, 768) )
self.assertEqual(output.shape ,UpperCamelCase__ )
lowercase__ : Any = torch.tensor(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,UpperCamelCase__ ,atol=1e-4 ) )
| 367
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : UNetaDModel
lowerCAmelCase : ScoreSdeVeScheduler
def __init__( self : Optional[Any] ,_snake_case : UNetaDModel ,_snake_case : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=_snake_case ,scheduler=_snake_case )
@torch.no_grad()
def __call__( self : Any ,_snake_case : int = 1 ,_snake_case : int = 2_000 ,_snake_case : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,_snake_case : Optional[str] = "pil" ,_snake_case : bool = True ,**_snake_case : Any ,) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.unet.config.sample_size
lowercase__ : Dict = (batch_size, 3, img_size, img_size)
lowercase__ : Tuple = self.unet
lowercase__ : Any = randn_tensor(_snake_case ,generator=_snake_case ) * self.scheduler.init_noise_sigma
lowercase__ : Union[str, Any] = sample.to(self.device )
self.scheduler.set_timesteps(_snake_case )
self.scheduler.set_sigmas(_snake_case )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
lowercase__ : Tuple = self.scheduler.sigmas[i] * torch.ones(shape[0] ,device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
lowercase__ : List[str] = self.unet(_snake_case ,_snake_case ).sample
lowercase__ : Optional[Any] = self.scheduler.step_correct(_snake_case ,_snake_case ,generator=_snake_case ).prev_sample
# prediction step
lowercase__ : str = model(_snake_case ,_snake_case ).sample
lowercase__ : List[Any] = self.scheduler.step_pred(_snake_case ,_snake_case ,_snake_case ,generator=_snake_case )
lowercase__ , lowercase__ : Optional[int] = output.prev_sample, output.prev_sample_mean
lowercase__ : Union[str, Any] = sample_mean.clamp(0 ,1 )
lowercase__ : int = sample.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
lowercase__ : Any = self.numpy_to_pil(_snake_case )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=_snake_case )
| 302
| 0
|
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase ) -> int:
assert column_title.isupper()
lowercase__ : Tuple = 0
lowercase__ : int = len(lowerCamelCase_ ) - 1
lowercase__ : Any = 0
while index >= 0:
lowercase__ : Dict = (ord(column_title[index] ) - 64) * pow(26 , lowerCamelCase_ )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 368
|
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCAmelCase_ = {
'facebook/maskformer-swin-base-ade': (
'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = "maskformer"
lowerCAmelCase : Any = {"hidden_size": "mask_feature_size"}
lowerCAmelCase : Optional[int] = ["resnet", "swin"]
lowerCAmelCase : str = ["detr"]
def __init__( self : int ,_snake_case : int = 256 ,_snake_case : int = 256 ,_snake_case : float = 0.1 ,_snake_case : bool = False ,_snake_case : Optional[Dict] = None ,_snake_case : Optional[Dict] = None ,_snake_case : float = 0.02 ,_snake_case : float = 1.0 ,_snake_case : float = 1.0 ,_snake_case : float = 1.0 ,_snake_case : float = 20.0 ,_snake_case : Optional[bool] = None ,**_snake_case : Optional[Any] ,) -> Dict:
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowercase__ : Any = SwinConfig(
image_size=384 ,in_channels=3 ,patch_size=4 ,embed_dim=128 ,depths=[2, 2, 18, 2] ,num_heads=[4, 8, 16, 32] ,window_size=12 ,drop_path_rate=0.3 ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] ,)
if isinstance(_snake_case ,_snake_case ):
lowercase__ : List[str] = backbone_config.pop('''model_type''' )
lowercase__ : List[Any] = CONFIG_MAPPING[backbone_model_type]
lowercase__ : str = config_class.from_dict(_snake_case )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
f"""Supported model types: {",".join(self.backbones_supported )}""" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowercase__ : Union[str, Any] = DetrConfig()
else:
# verify that the decoder is supported
lowercase__ : Tuple = (
decoder_config.pop('''model_type''' ) if isinstance(_snake_case ,_snake_case ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f"""Transformer Decoder {decoder_type} not supported, please use one of"""
f""" {",".join(self.decoders_supported )}""" )
if isinstance(_snake_case ,_snake_case ):
lowercase__ : Optional[int] = CONFIG_MAPPING[decoder_type]
lowercase__ : Optional[Any] = config_class.from_dict(_snake_case )
lowercase__ : List[Any] = backbone_config
lowercase__ : List[Any] = decoder_config
# main feature dimension for the model
lowercase__ : List[str] = fpn_feature_size
lowercase__ : int = mask_feature_size
# initializer
lowercase__ : str = init_std
lowercase__ : str = init_xavier_std
# Hungarian matcher && loss
lowercase__ : Optional[int] = cross_entropy_weight
lowercase__ : List[Any] = dice_weight
lowercase__ : List[str] = mask_weight
lowercase__ : str = use_auxiliary_loss
lowercase__ : Optional[int] = no_object_weight
lowercase__ : Optional[Any] = output_auxiliary_logits
lowercase__ : Optional[Any] = self.decoder_config.encoder_attention_heads
lowercase__ : Optional[Any] = self.decoder_config.num_hidden_layers
super().__init__(**_snake_case )
@classmethod
def UpperCAmelCase ( cls : Any ,_snake_case : PretrainedConfig ,_snake_case : PretrainedConfig ,**_snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return cls(
backbone_config=_snake_case ,decoder_config=_snake_case ,**_snake_case ,)
def UpperCAmelCase ( self : str ) -> Dict[str, any]:
"""simple docstring"""
lowercase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase__ : int = self.backbone_config.to_dict()
lowercase__ : List[Any] = self.decoder_config.to_dict()
lowercase__ : List[str] = self.__class__.model_type
return output
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|
"""simple docstring"""
import string
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
lowercase__ : Any = ''''''
for i in sequence:
lowercase__ : List[Any] = ord(__UpperCamelCase )
if 65 <= extract <= 90:
output += chr(1_55 - extract )
elif 97 <= extract <= 1_22:
output += chr(2_19 - extract )
else:
output += i
return output
def __UpperCAmelCase ( __lowerCamelCase ) -> List[Any]:
lowercase__ : List[str] = string.ascii_letters
lowercase__ : Union[str, Any] = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(__UpperCamelCase )] if c in letters else c for c in sequence )
def __UpperCAmelCase ( ) -> Optional[Any]:
from timeit import timeit
print('''Running performance benchmarks...''' )
lowercase__ : Union[str, Any] = '''from string import printable ; from __main__ import atbash, atbash_slow'''
print(f"""> atbash_slow(): {timeit("atbash_slow(printable)" , setup=__UpperCamelCase )} seconds""" )
print(f"""> atbash(): {timeit("atbash(printable)" , setup=__UpperCamelCase )} seconds""" )
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(F'''{example} encrypted in atbash: {atbash(example)}''')
benchmark()
| 369
|
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]:
lowercase__ : int = [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
lowercase__ : Dict = True if '''large''' in model_name or '''huge''' in model_name else False
lowercase__ : Optional[int] = True if '''large''' in model_name or '''huge''' in model_name else False
lowercase__ : List[Any] = True if '''large''' in model_name or '''huge''' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
lowercase__ : Dict = [3, 3, 3, 3]
lowercase__ : str = [5, 5, 5, 5]
elif "fl4" in model_name:
lowercase__ : List[str] = [4, 4, 4, 4]
lowercase__ : Any = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
lowercase__ : List[str] = [3, 3, 3, 3]
if "lrf" in model_name:
lowercase__ : List[str] = [3, 3, 3, 3]
else:
lowercase__ : Optional[Any] = [2, 2, 2, 2]
if "tiny" in model_name:
lowercase__ : Optional[int] = 96
elif "small" in model_name:
lowercase__ : Union[str, Any] = 96
elif "base" in model_name:
lowercase__ : Tuple = 1_28
elif "large" in model_name:
lowercase__ : Any = 1_92
elif "xlarge" in model_name:
lowercase__ : Any = 2_56
elif "huge" in model_name:
lowercase__ : Union[str, Any] = 3_52
# set label information
lowercase__ : List[Any] = '''huggingface/label-files'''
if "large" in model_name or "huge" in model_name:
lowercase__ : Optional[int] = '''imagenet-22k-id2label.json'''
else:
lowercase__ : Optional[Any] = '''imagenet-1k-id2label.json'''
lowercase__ : Dict = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ : Union[str, Any] = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
lowercase__ : Optional[Any] = {v: k for k, v in idalabel.items()}
lowercase__ : int = FocalNetConfig(
embed_dim=__lowerCamelCase , depths=__lowerCamelCase , focal_levels=__lowerCamelCase , focal_windows=__lowerCamelCase , use_conv_embed=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase , use_post_layernorm=__lowerCamelCase , use_layerscale=__lowerCamelCase , )
return config
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
if "patch_embed.proj" in name:
lowercase__ : Any = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowercase__ : Tuple = name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if "layers" in name:
lowercase__ : Dict = '''encoder.''' + name
if "encoder.layers" in name:
lowercase__ : Tuple = name.replace('''encoder.layers''' , '''encoder.stages''' )
if "downsample.proj" in name:
lowercase__ : Union[str, Any] = name.replace('''downsample.proj''' , '''downsample.projection''' )
if "blocks" in name:
lowercase__ : Optional[Any] = name.replace('''blocks''' , '''layers''' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
lowercase__ : Dict = name.replace('''modulation.f''' , '''modulation.projection_in''' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
lowercase__ : Dict = name.replace('''modulation.h''' , '''modulation.projection_context''' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
lowercase__ : Optional[Any] = name.replace('''modulation.proj''' , '''modulation.projection_out''' )
if name == "norm.weight":
lowercase__ : Dict = '''layernorm.weight'''
if name == "norm.bias":
lowercase__ : Dict = '''layernorm.bias'''
if "head" in name:
lowercase__ : Dict = name.replace('''head''' , '''classifier''' )
else:
lowercase__ : List[Any] = '''focalnet.''' + name
return name
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> List[str]:
# fmt: off
lowercase__ : Any = {
'''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
'''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
'''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
'''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
'''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
'''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
'''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
'''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
'''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
'''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
}
# fmt: on
lowercase__ : Optional[int] = model_name_to_url[model_name]
print('''Checkpoint URL: ''' , __lowerCamelCase )
lowercase__ : str = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location='''cpu''' )['''model''']
# rename keys
for key in state_dict.copy().keys():
lowercase__ : int = state_dict.pop(__lowerCamelCase )
lowercase__ : Any = val
lowercase__ : List[Any] = get_focalnet_config(__lowerCamelCase )
lowercase__ : Optional[int] = FocalNetForImageClassification(__lowerCamelCase )
model.eval()
# load state dict
model.load_state_dict(__lowerCamelCase )
# verify conversion
lowercase__ : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase__ : int = BitImageProcessor(
do_resize=__lowerCamelCase , size={'''shortest_edge''': 2_56} , resample=PILImageResampling.BILINEAR , do_center_crop=__lowerCamelCase , crop_size=2_24 , do_normalize=__lowerCamelCase , image_mean=__lowerCamelCase , image_std=__lowerCamelCase , )
lowercase__ : str = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw )
lowercase__ : List[str] = processor(images=__lowerCamelCase , return_tensors='''pt''' )
lowercase__ : List[str] = transforms.Compose(
[
transforms.Resize(2_56 ),
transforms.CenterCrop(2_24 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
lowercase__ : Optional[Any] = image_transforms(__lowerCamelCase ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , __lowerCamelCase , atol=1E-4 )
lowercase__ : Optional[Any] = model(**__lowerCamelCase )
lowercase__ : Optional[int] = outputs.logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
print('''First values of logits:''' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
lowercase__ : Dict = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] )
elif model_name == "focalnet-tiny-lrf":
lowercase__ : Union[str, Any] = torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5] )
elif model_name == "focalnet-small":
lowercase__ : Optional[int] = torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1] )
elif model_name == "focalnet-small-lrf":
lowercase__ : Dict = torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1] )
elif model_name == "focalnet-base":
lowercase__ : List[str] = torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0] )
elif model_name == "focalnet-base-lrf":
lowercase__ : List[str] = torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8] )
assert torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
print(f"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(f"""{model_name}""" )
processor.push_to_hub(f"""{model_name}""" )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='focalnet-tiny',
type=str,
help='Name of the FocalNet model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub.',
)
lowerCAmelCase_ = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 302
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|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCAmelCase_ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
lowerCAmelCase_ = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCAmelCase_ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : Optional[str] = field(
default="cifar10" ,metadata={"help": "Name of a dataset from the datasets package"} )
lowerCAmelCase : Optional[str] = field(
default=_lowerCAmelCase ,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
lowerCAmelCase : Optional[str] = field(
default=_lowerCAmelCase ,metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."} ,)
lowerCAmelCase : Optional[str] = field(default=_lowerCAmelCase ,metadata={"help": "A folder containing the training data."} )
lowerCAmelCase : Optional[str] = field(default=_lowerCAmelCase ,metadata={"help": "A folder containing the validation data."} )
lowerCAmelCase : Optional[float] = field(
default=0.15 ,metadata={"help": "Percent to split off of train for validation."} )
lowerCAmelCase : int = field(default=3_2 ,metadata={"help": "The size of the square patches to use for masking."} )
lowerCAmelCase : float = field(
default=0.6 ,metadata={"help": "Percentage of patches to mask."} ,)
lowerCAmelCase : Optional[int] = field(
default=_lowerCAmelCase ,metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} ,)
lowerCAmelCase : Optional[int] = field(
default=_lowerCAmelCase ,metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} ,)
def UpperCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
lowercase__ : Optional[Any] = {}
if self.train_dir is not None:
lowercase__ : Optional[int] = self.train_dir
if self.validation_dir is not None:
lowercase__ : int = self.validation_dir
lowercase__ : str = data_files if data_files else None
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : str = field(
default=_lowerCAmelCase ,metadata={
"help": (
"The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
"checkpoint identifier on the hub. "
"Don't set if you want to train a model from scratch."
)
} ,)
lowerCAmelCase : Optional[str] = field(
default=_lowerCAmelCase ,metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_lowerCAmelCase )} ,)
lowerCAmelCase : Optional[str] = field(
default=_lowerCAmelCase ,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowerCAmelCase : Optional[str] = field(
default=_lowerCAmelCase ,metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
} ,)
lowerCAmelCase : Optional[str] = field(
default=_lowerCAmelCase ,metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"} ,)
lowerCAmelCase : str = field(
default="main" ,metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} ,)
lowerCAmelCase : str = field(default=_lowerCAmelCase ,metadata={"help": "Name or path of preprocessor config."} )
lowerCAmelCase : bool = field(
default=_lowerCAmelCase ,metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} ,)
lowerCAmelCase : Optional[int] = field(
default=_lowerCAmelCase ,metadata={
"help": (
"The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
)
} ,)
lowerCAmelCase : Optional[int] = field(
default=_lowerCAmelCase ,metadata={
"help": (
"The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
)
} ,)
lowerCAmelCase : Optional[int] = field(
default=_lowerCAmelCase ,metadata={"help": "Stride to use for the encoder."} ,)
class __A :
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Union[str, Any]=192 ,_snake_case : Optional[int]=32 ,_snake_case : List[Any]=4 ,_snake_case : str=0.6 ) -> str:
"""simple docstring"""
lowercase__ : List[str] = input_size
lowercase__ : Optional[int] = mask_patch_size
lowercase__ : str = model_patch_size
lowercase__ : List[Any] = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError('''Input size must be divisible by mask patch size''' )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError('''Mask patch size must be divisible by model patch size''' )
lowercase__ : Dict = self.input_size // self.mask_patch_size
lowercase__ : Tuple = self.mask_patch_size // self.model_patch_size
lowercase__ : List[Any] = self.rand_size**2
lowercase__ : Optional[Any] = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__( self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ : List[Any] = np.random.permutation(self.token_count )[: self.mask_count]
lowercase__ : List[str] = np.zeros(self.token_count ,dtype=_lowercase )
lowercase__ : Dict = 1
lowercase__ : Dict = mask.reshape((self.rand_size, self.rand_size) )
lowercase__ : Any = mask.repeat(self.scale ,axis=0 ).repeat(self.scale ,axis=1 )
return torch.tensor(mask.flatten() )
def __UpperCAmelCase ( __lowerCamelCase ) -> Dict:
lowercase__ : Optional[int] = torch.stack([example['''pixel_values'''] for example in examples] )
lowercase__ : Tuple = torch.stack([example['''mask'''] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def __UpperCAmelCase ( ) -> List[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase__ : Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase__ , lowercase__ , lowercase__ : Tuple = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase__ , lowercase__ , lowercase__ : Tuple = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_mim''' , snake_case_ , snake_case_ )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowercase__ : str = training_args.get_process_log_level()
logger.setLevel(snake_case_ )
transformers.utils.logging.set_verbosity(snake_case_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
lowercase__ : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase__ : List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset.
lowercase__ : Any = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
lowercase__ : Tuple = None if '''validation''' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , snake_case_ ) and data_args.train_val_split > 0.0:
lowercase__ : List[str] = ds['''train'''].train_test_split(data_args.train_val_split )
lowercase__ : Any = split['''train''']
lowercase__ : Dict = split['''test''']
# Create config
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase__ : List[Any] = {
'''cache_dir''': model_args.cache_dir,
'''revision''': model_args.model_revision,
'''use_auth_token''': True if model_args.use_auth_token else None,
}
if model_args.config_name_or_path:
lowercase__ : int = AutoConfig.from_pretrained(model_args.config_name_or_path , **snake_case_ )
elif model_args.model_name_or_path:
lowercase__ : List[str] = AutoConfig.from_pretrained(model_args.model_name_or_path , **snake_case_ )
else:
lowercase__ : Union[str, Any] = CONFIG_MAPPING[model_args.model_type]()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.config_overrides is not None:
logger.info(f"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(f"""New config: {config}""" )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(snake_case_ , '''decoder_type''' ):
lowercase__ : Tuple = '''simmim'''
# adapt config
lowercase__ : List[Any] = model_args.image_size if model_args.image_size is not None else config.image_size
lowercase__ : Optional[Any] = model_args.patch_size if model_args.patch_size is not None else config.patch_size
lowercase__ : Dict = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
'''image_size''': model_args.image_size,
'''patch_size''': model_args.patch_size,
'''encoder_stride''': model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
lowercase__ : List[str] = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **snake_case_ )
elif model_args.model_name_or_path:
lowercase__ : Dict = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **snake_case_ )
else:
lowercase__ : Any = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
lowercase__ : int = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
lowercase__ : Optional[int] = AutoModelForMaskedImageModeling.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=snake_case_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('''Training new model from scratch''' )
lowercase__ : Any = AutoModelForMaskedImageModeling.from_config(snake_case_ )
if training_args.do_train:
lowercase__ : Any = ds['''train'''].column_names
else:
lowercase__ : Tuple = ds['''validation'''].column_names
if data_args.image_column_name is not None:
lowercase__ : List[Any] = data_args.image_column_name
elif "image" in column_names:
lowercase__ : Optional[int] = '''image'''
elif "img" in column_names:
lowercase__ : Any = '''img'''
else:
lowercase__ : str = column_names[0]
# transformations as done in original SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
lowercase__ : int = Compose(
[
Lambda(lambda __lowerCamelCase : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.6_7, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
lowercase__ : Optional[int] = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(__lowerCamelCase ):
lowercase__ : int = [transforms(snake_case_ ) for image in examples[image_column_name]]
lowercase__ : Optional[int] = [mask_generator() for i in range(len(examples[image_column_name] ) )]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('''--do_train requires a train dataset''' )
if data_args.max_train_samples is not None:
lowercase__ : Dict = ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(snake_case_ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('''--do_eval requires a validation dataset''' )
if data_args.max_eval_samples is not None:
lowercase__ : Optional[Any] = (
ds['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(snake_case_ )
# Initialize our trainer
lowercase__ : int = Trainer(
model=snake_case_ , args=snake_case_ , train_dataset=ds['''train'''] if training_args.do_train else None , eval_dataset=ds['''validation'''] if training_args.do_eval else None , tokenizer=snake_case_ , data_collator=snake_case_ , )
# Training
if training_args.do_train:
lowercase__ : Union[str, Any] = None
if training_args.resume_from_checkpoint is not None:
lowercase__ : Dict = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase__ : Optional[Any] = last_checkpoint
lowercase__ : Tuple = trainer.train(resume_from_checkpoint=snake_case_ )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowercase__ : Optional[int] = trainer.evaluate()
trainer.log_metrics('''eval''' , snake_case_ )
trainer.save_metrics('''eval''' , snake_case_ )
# Write model card and (optionally) push to hub
lowercase__ : Any = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''masked-image-modeling''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''masked-image-modeling'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**snake_case_ )
else:
trainer.create_model_card(**snake_case_ )
if __name__ == "__main__":
main()
| 370
|
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : List[Any] = ["image_processor", "tokenizer"]
lowerCAmelCase : int = "ChineseCLIPImageProcessor"
lowerCAmelCase : str = ("BertTokenizer", "BertTokenizerFast")
def __init__( self : Tuple ,_snake_case : str=None ,_snake_case : Union[str, Any]=None ,**_snake_case : str ) -> Any:
"""simple docstring"""
lowercase__ : Any = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' ,_snake_case ,)
lowercase__ : Tuple = kwargs.pop('''feature_extractor''' )
lowercase__ : Any = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(_snake_case ,_snake_case )
lowercase__ : List[Any] = self.image_processor
def __call__( self : List[Any] ,_snake_case : Optional[int]=None ,_snake_case : Dict=None ,_snake_case : List[Any]=None ,**_snake_case : List[str] ) -> List[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
lowercase__ : str = self.tokenizer(_snake_case ,return_tensors=_snake_case ,**_snake_case )
if images is not None:
lowercase__ : str = self.image_processor(_snake_case ,return_tensors=_snake_case ,**_snake_case )
if text is not None and images is not None:
lowercase__ : Any = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_snake_case ) ,tensor_type=_snake_case )
def UpperCAmelCase ( self : Any ,*_snake_case : List[Any] ,**_snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
return self.tokenizer.batch_decode(*_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Union[str, Any] ,*_snake_case : Tuple ,**_snake_case : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.decode(*_snake_case ,**_snake_case )
@property
def UpperCAmelCase ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.tokenizer.model_input_names
lowercase__ : int = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,_snake_case ,)
return self.image_processor_class
| 302
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase_ = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['SpeechEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['FlaxSpeechEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 371
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
'tokenization_roberta': ['RobertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['RobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'RobertaForCausalLM',
'RobertaForMaskedLM',
'RobertaForMultipleChoice',
'RobertaForQuestionAnswering',
'RobertaForSequenceClassification',
'RobertaForTokenClassification',
'RobertaModel',
'RobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRobertaForCausalLM',
'TFRobertaForMaskedLM',
'TFRobertaForMultipleChoice',
'TFRobertaForQuestionAnswering',
'TFRobertaForSequenceClassification',
'TFRobertaForTokenClassification',
'TFRobertaMainLayer',
'TFRobertaModel',
'TFRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'FlaxRobertaForCausalLM',
'FlaxRobertaForMaskedLM',
'FlaxRobertaForMultipleChoice',
'FlaxRobertaForQuestionAnswering',
'FlaxRobertaForSequenceClassification',
'FlaxRobertaForTokenClassification',
'FlaxRobertaModel',
'FlaxRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 302
| 0
|
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
lowerCAmelCase_ = logging.get_logger(__name__)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : str = field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys() )} )
lowerCAmelCase : str = field(
metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."} )
lowerCAmelCase : int = field(
default=1_2_8 ,metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} ,)
lowerCAmelCase : bool = field(
default=snake_case_ ,metadata={"help": "Overwrite the cached training and evaluation sets"} )
def UpperCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
lowercase__ : str = self.task_name.lower()
class __A ( snake_case_ ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = "train"
lowerCAmelCase : Optional[int] = "dev"
lowerCAmelCase : int = "test"
class __A ( snake_case_ ):
'''simple docstring'''
lowerCAmelCase : GlueDataTrainingArguments
lowerCAmelCase : str
lowerCAmelCase : List[InputFeatures]
def __init__( self : Tuple ,_snake_case : GlueDataTrainingArguments ,_snake_case : PreTrainedTokenizerBase ,_snake_case : Optional[int] = None ,_snake_case : Union[str, Split] = Split.train ,_snake_case : Optional[str] = None ,) -> Any:
"""simple docstring"""
warnings.warn(
'''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
'''library. You can have a look at this example script for pointers: '''
'''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py''' ,_A ,)
lowercase__ : Optional[Any] = args
lowercase__ : int = glue_processors[args.task_name]()
lowercase__ : Dict = glue_output_modes[args.task_name]
if isinstance(_A ,_A ):
try:
lowercase__ : int = Split[mode]
except KeyError:
raise KeyError('''mode is not a valid split name''' )
# Load data features from cache or dataset file
lowercase__ : Dict = os.path.join(
cache_dir if cache_dir is not None else args.data_dir ,f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}""" ,)
lowercase__ : Optional[Any] = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowercase__ : Union[str, Any] = label_list[2], label_list[1]
lowercase__ : Optional[Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowercase__ : List[Any] = cached_features_file + '.lock'
with FileLock(_A ):
if os.path.exists(_A ) and not args.overwrite_cache:
lowercase__ : int = time.time()
lowercase__ : Tuple = torch.load(_A )
logger.info(
f"""Loading features from cached file {cached_features_file} [took %.3f s]""" ,time.time() - start )
else:
logger.info(f"""Creating features from dataset file at {args.data_dir}""" )
if mode == Split.dev:
lowercase__ : Union[str, Any] = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
lowercase__ : Any = self.processor.get_test_examples(args.data_dir )
else:
lowercase__ : Optional[Any] = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
lowercase__ : List[Any] = examples[:limit_length]
lowercase__ : List[Any] = glue_convert_examples_to_features(
_A ,_A ,max_length=args.max_seq_length ,label_list=_A ,output_mode=self.output_mode ,)
lowercase__ : int = time.time()
torch.save(self.features ,_A )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )
def __len__( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
return len(self.features )
def __getitem__( self : Optional[Any] ,_snake_case : int ) -> InputFeatures:
"""simple docstring"""
return self.features[i]
def UpperCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
return self.label_list
| 350
|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowerCAmelCase_ = logging.getLogger(__name__)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowerCAmelCase : Optional[str] = field(
default="NER" ,metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
lowerCAmelCase : bool = field(default=A_ ,metadata={"help": "Set this flag to use fast tokenization."} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : str = field(
metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} ,)
lowerCAmelCase : int = field(
default=1_2_8 ,metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} ,)
lowerCAmelCase : bool = field(
default=A_ ,metadata={"help": "Overwrite the cached training and evaluation sets"} )
def __UpperCAmelCase ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase__ : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase__ , lowercase__ , lowercase__ : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase__ , lowercase__ , lowercase__ : List[str] = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
lowercase__ : str = import_module('''tasks''' )
try:
lowercase__ : List[str] = getattr(__lowerCamelCase , model_args.task_type )
lowercase__ : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __lowerCamelCase )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
lowercase__ : Union[str, Any] = token_classification_task.get_labels(data_args.labels )
lowercase__ : Dict[int, str] = dict(enumerate(__lowerCamelCase ) )
lowercase__ : Optional[int] = len(__lowerCamelCase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase__ : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid={label: i for i, label in enumerate(__lowerCamelCase )} , cache_dir=model_args.cache_dir , )
lowercase__ : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
lowercase__ : str = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__lowerCamelCase , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase__ : str = (
TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowercase__ : str = (
TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(__lowerCamelCase , __lowerCamelCase ) -> Tuple[List[int], List[int]]:
lowercase__ : Tuple = np.argmax(__lowerCamelCase , axis=2 )
lowercase__ , lowercase__ : Tuple = preds.shape
lowercase__ : List[str] = [[] for _ in range(__lowerCamelCase )]
lowercase__ : Tuple = [[] for _ in range(__lowerCamelCase )]
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(__lowerCamelCase ) -> Dict:
lowercase__ , lowercase__ : List[Any] = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(__lowerCamelCase , __lowerCamelCase ),
"precision": precision_score(__lowerCamelCase , __lowerCamelCase ),
"recall": recall_score(__lowerCamelCase , __lowerCamelCase ),
"f1": fa_score(__lowerCamelCase , __lowerCamelCase ),
}
# Data collator
lowercase__ : Tuple = DataCollatorWithPadding(__lowerCamelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase__ : str = Trainer(
model=__lowerCamelCase , args=__lowerCamelCase , train_dataset=__lowerCamelCase , eval_dataset=__lowerCamelCase , compute_metrics=__lowerCamelCase , data_collator=__lowerCamelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase__ : int = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase__ : Optional[int] = trainer.evaluate()
lowercase__ : Union[str, Any] = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __lowerCamelCase , __lowerCamelCase )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__lowerCamelCase )
# Predict
if training_args.do_predict:
lowercase__ : Optional[int] = TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = trainer.predict(__lowerCamelCase )
lowercase__ , lowercase__ : Tuple = align_predictions(__lowerCamelCase , __lowerCamelCase )
lowercase__ : Dict = os.path.join(training_args.output_dir , '''test_results.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
for key, value in metrics.items():
logger.info(''' %s = %s''' , __lowerCamelCase , __lowerCamelCase )
writer.write('''%s = %s\n''' % (key, value) )
# Save predictions
lowercase__ : Dict = os.path.join(training_args.output_dir , '''test_predictions.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
with open(os.path.join(data_args.data_dir , '''test.txt''' ) , '''r''' ) as f:
token_classification_task.write_predictions_to_file(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return results
def __UpperCAmelCase ( __lowerCamelCase ) -> List[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 302
| 0
|
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase = "The quick brown fox jumps over the lazy dog" , ) -> Any:
lowercase__ : List[Any] = set()
# Replace all the whitespace in our sentence
lowercase__ : int = input_str.replace(''' ''' , '''''' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(A__ ) == 26
def __UpperCAmelCase ( __lowerCamelCase = "The quick brown fox jumps over the lazy dog" , ) -> Tuple:
lowercase__ : Dict = [False] * 26
for char in input_str:
if char.islower():
lowercase__ : Optional[int] = True
elif char.isupper():
lowercase__ : Optional[Any] = True
return all(A__ )
def __UpperCAmelCase ( __lowerCamelCase = "The quick brown fox jumps over the lazy dog" , ) -> List[Any]:
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def __UpperCAmelCase ( ) -> List[Any]:
from timeit import timeit
lowercase__ : Tuple = """from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
print(timeit('''is_pangram()''' , setup=A__ ) )
print(timeit('''is_pangram_faster()''' , setup=A__ ) )
print(timeit('''is_pangram_fastest()''' , setup=A__ ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 351
|
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase_ = 16
lowerCAmelCase_ = 32
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = 16 ) -> Optional[int]:
lowercase__ : Optional[int] = AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowercase__ : List[str] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : List[str] = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=__lowerCamelCase , max_length=__lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase__ : Dict = datasets.map(
__lowerCamelCase , batched=__lowerCamelCase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : int = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ : List[str] = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase__ : List[str] = 16
elif accelerator.mixed_precision != "no":
lowercase__ : List[Any] = 8
else:
lowercase__ : Optional[int] = None
return tokenizer.pad(
__lowerCamelCase , padding='''longest''' , max_length=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_tensors='''pt''' , )
# Instantiate dataloaders.
lowercase__ : Dict = DataLoader(
tokenized_datasets['''train'''] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
lowercase__ : Union[str, Any] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowerCAmelCase_ = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Tuple:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , __lowerCamelCase ) == "1":
lowercase__ : Any = 2
# Initialize accelerator
lowercase__ : str = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : List[Any] = config['''lr''']
lowercase__ : Union[str, Any] = int(config['''num_epochs'''] )
lowercase__ : List[str] = int(config['''seed'''] )
lowercase__ : Any = int(config['''batch_size'''] )
lowercase__ : int = evaluate.load('''glue''' , '''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__lowerCamelCase )
def inner_training_loop(__lowerCamelCase ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__lowerCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : Optional[Any] = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=__lowerCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase__ : str = model.to(accelerator.device )
# Instantiate optimizer
lowercase__ : Optional[int] = AdamW(params=model.parameters() , lr=__lowerCamelCase )
lowercase__ , lowercase__ : List[str] = get_dataloaders(__lowerCamelCase , __lowerCamelCase )
# Instantiate scheduler
lowercase__ : Optional[Any] = get_linear_schedule_with_warmup(
optimizer=__lowerCamelCase , num_warmup_steps=1_00 , num_training_steps=(len(__lowerCamelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = accelerator.prepare(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# Now we train the model
for epoch in range(__lowerCamelCase ):
model.train()
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase__ : int = model(**__lowerCamelCase )
lowercase__ : Optional[int] = outputs.loss
accelerator.backward(__lowerCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ : Tuple = model(**__lowerCamelCase )
lowercase__ : Dict = outputs.logits.argmax(dim=-1 )
lowercase__ , lowercase__ : Any = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=__lowerCamelCase , references=__lowerCamelCase , )
lowercase__ : Optional[int] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , __lowerCamelCase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( ) -> Tuple:
lowercase__ : List[str] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=__lowerCamelCase , default=__lowerCamelCase , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
lowercase__ : Union[str, Any] = parser.parse_args()
lowercase__ : Union[str, Any] = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
main()
| 302
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|
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __A :
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : List[Any] ,_snake_case : Union[str, Any]=sys.maxsize ) -> Optional[int]:
"""simple docstring"""
lowercase__ : List[str] = '''bilinear'''
lowercase__ : List[str] = max_size
lowercase__ : List[Any] = short_edge_length
def __call__( self : List[str] ,_snake_case : List[str] ) -> List[str]:
"""simple docstring"""
lowercase__ : int = []
for img in imgs:
lowercase__ : Dict = img.shape[:2]
# later: provide list and randomly choose index for resize
lowercase__ : str = np.random.randint(self.short_edge_length[0] ,self.short_edge_length[1] + 1 )
if size == 0:
return img
lowercase__ : List[Any] = size * 1.0 / min(snake_case_ ,snake_case_ )
if h < w:
lowercase__ : Dict = size, scale * w
else:
lowercase__ : Optional[Any] = scale * h, size
if max(snake_case_ ,snake_case_ ) > self.max_size:
lowercase__ : Tuple = self.max_size * 1.0 / max(snake_case_ ,snake_case_ )
lowercase__ : List[Any] = newh * scale
lowercase__ : int = neww * scale
lowercase__ : Dict = int(neww + 0.5 )
lowercase__ : Dict = int(newh + 0.5 )
if img.dtype == np.uinta:
lowercase__ : Optional[Any] = Image.fromarray(snake_case_ )
lowercase__ : str = pil_image.resize((neww, newh) ,PILImageResampling.BILINEAR )
lowercase__ : Optional[Any] = np.asarray(snake_case_ )
else:
lowercase__ : Tuple = img.permute(2 ,0 ,1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
lowercase__ : Any = nn.functional.interpolate(
snake_case_ ,(newh, neww) ,mode=self.interp_method ,align_corners=snake_case_ ).squeeze(0 )
img_augs.append(snake_case_ )
return img_augs
class __A :
'''simple docstring'''
def __init__( self : int ,_snake_case : List[Any] ) -> int:
"""simple docstring"""
lowercase__ : Dict = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,cfg.INPUT.MAX_SIZE_TEST )
lowercase__ : Union[str, Any] = cfg.INPUT.FORMAT
lowercase__ : Dict = cfg.SIZE_DIVISIBILITY
lowercase__ : List[Any] = cfg.PAD_VALUE
lowercase__ : Dict = cfg.INPUT.MAX_SIZE_TEST
lowercase__ : List[Any] = cfg.MODEL.DEVICE
lowercase__ : Tuple = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) ,1 ,1 )
lowercase__ : int = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) ,1 ,1 )
lowercase__ : Union[str, Any] = lambda _snake_case : (x - self.pixel_mean) / self.pixel_std
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Dict ) -> Any:
"""simple docstring"""
lowercase__ : List[str] = tuple(max(snake_case_ ) for s in zip(*[img.shape for img in images] ) )
lowercase__ : List[Any] = [im.shape[-2:] for im in images]
lowercase__ : Optional[int] = [
nn.functional.pad(
snake_case_ ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,value=self.pad_value ,)
for size, im in zip(snake_case_ ,snake_case_ )
]
return torch.stack(snake_case_ ), torch.tensor(snake_case_ )
def __call__( self : List[str] ,_snake_case : int ,_snake_case : Dict=False ) -> List[str]:
"""simple docstring"""
with torch.no_grad():
if not isinstance(snake_case_ ,snake_case_ ):
lowercase__ : str = [images]
if single_image:
assert len(snake_case_ ) == 1
for i in range(len(snake_case_ ) ):
if isinstance(images[i] ,torch.Tensor ):
images.insert(snake_case_ ,images.pop(snake_case_ ).to(self.device ).float() )
elif not isinstance(images[i] ,torch.Tensor ):
images.insert(
snake_case_ ,torch.as_tensor(img_tensorize(images.pop(snake_case_ ) ,input_format=self.input_format ) )
.to(self.device )
.float() ,)
# resize smallest edge
lowercase__ : Optional[Any] = torch.tensor([im.shape[:2] for im in images] )
lowercase__ : int = self.aug(snake_case_ )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
lowercase__ : str = [self.normalizer(snake_case_ ) for x in images]
# now pad them to do the following operations
lowercase__ : List[Any] = self.pad(snake_case_ )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
lowercase__ : str = torch.true_divide(snake_case_ ,snake_case_ )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> str:
assert torch.isfinite(lowerCAmelCase__ ).all(), "Box tensor contains infinite or NaN!"
lowercase__ : Optional[Any] = box_size
tensor[:, 0].clamp_(min=0 , max=lowerCAmelCase__ )
tensor[:, 1].clamp_(min=0 , max=lowerCAmelCase__ )
tensor[:, 2].clamp_(min=0 , max=lowerCAmelCase__ )
tensor[:, 3].clamp_(min=0 , max=lowerCAmelCase__ )
| 352
|
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( self : int ) -> str:
"""simple docstring"""
lowercase__ : List[Any] = '''hf-internal-testing/tiny-random-t5'''
lowercase__ : List[Any] = AutoTokenizer.from_pretrained(_snake_case )
lowercase__ : int = AutoModelForSeqaSeqLM.from_pretrained(_snake_case )
lowercase__ : str = tokenizer('''This is me''' ,return_tensors='''pt''' )
lowercase__ : Tuple = model.to_bettertransformer()
self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
lowercase__ : Optional[int] = model.generate(**_snake_case )
lowercase__ : List[Any] = model.reverse_bettertransformer()
self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_snake_case )
lowercase__ : Tuple = AutoModelForSeqaSeqLM.from_pretrained(_snake_case )
self.assertFalse(
any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
lowercase__ : int = model_reloaded.generate(**_snake_case )
self.assertTrue(torch.allclose(_snake_case ,_snake_case ) )
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : List[str] = '''hf-internal-testing/tiny-random-t5'''
lowercase__ : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_snake_case )
lowercase__ : Union[str, Any] = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(_snake_case ):
model.save_pretrained(_snake_case )
lowercase__ : int = model.reverse_bettertransformer()
model.save_pretrained(_snake_case )
| 302
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|
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {"""vocab_file""": """spiece.model"""}
lowerCAmelCase_ = {
"""vocab_file""": {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
}
}
# TODO(PVP) - this should be removed in Transformers v5
lowerCAmelCase_ = {
"""t5-small""": 512,
"""t5-base""": 512,
"""t5-large""": 512,
"""t5-3b""": 512,
"""t5-11b""": 512,
}
lowerCAmelCase_ = """▁"""
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = VOCAB_FILES_NAMES
lowerCAmelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase : int = ["input_ids", "attention_mask"]
def __init__( self : Tuple ,_snake_case : Any ,_snake_case : Optional[int]="</s>" ,_snake_case : Tuple="<unk>" ,_snake_case : Optional[int]="<pad>" ,_snake_case : Dict=100 ,_snake_case : Dict=None ,_snake_case : Optional[Dict[str, Any]] = None ,_snake_case : List[Any]=True ,**_snake_case : int ,) -> None:
"""simple docstring"""
if extra_ids > 0 and additional_special_tokens is None:
lowercase__ : Any = [f"""<extra_id_{i}>""" for i in range(_snake_case )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
lowercase__ : Any = len(set(filter(lambda _snake_case : bool('''extra_id''' in str(_snake_case ) ) ,_snake_case ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
lowercase__ : Optional[int] = legacy
lowercase__ : int = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_snake_case ,unk_token=_snake_case ,pad_token=_snake_case ,extra_ids=_snake_case ,additional_special_tokens=_snake_case ,sp_model_kwargs=self.sp_model_kwargs ,legacy=_snake_case ,**_snake_case ,)
lowercase__ : Any = vocab_file
lowercase__ : Optional[int] = extra_ids
lowercase__ : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_snake_case )
@staticmethod
def UpperCAmelCase ( _snake_case : int ,_snake_case : Optional[Any] ,_snake_case : Any ) -> Union[str, Any]:
"""simple docstring"""
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
lowercase__ : List[str] = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f""" {pretrained_model_name_or_path} automatically truncating your input to"""
f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' ,_snake_case ,)
return max_model_length
@property
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
return self.sp_model.get_piece_size() + self._extra_ids
def UpperCAmelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Dict = {self.convert_ids_to_tokens(_snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCAmelCase ( self : int ,_snake_case : List[int] ,_snake_case : Optional[List[int]] = None ,_snake_case : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_snake_case ,token_ids_a=_snake_case ,already_has_special_tokens=_snake_case )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_snake_case )) + [1]
return ([0] * len(_snake_case )) + [1] + ([0] * len(_snake_case )) + [1]
def UpperCAmelCase ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
return list(
set(filter(lambda _snake_case : bool(re.search(r'''<extra_id_\d+>''' ,_snake_case ) ) is not None ,self.additional_special_tokens ) ) )
def UpperCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
return [self._convert_token_to_id(_snake_case ) for token in self.get_sentinel_tokens()]
def UpperCAmelCase ( self : Optional[int] ,_snake_case : List[int] ) -> List[int]:
"""simple docstring"""
if len(_snake_case ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[int] ,_snake_case : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
lowercase__ : Tuple = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def UpperCAmelCase ( self : Dict ,_snake_case : List[int] ,_snake_case : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
lowercase__ : int = self._add_eos_if_not_present(_snake_case )
if token_ids_a is None:
return token_ids_a
else:
lowercase__ : int = self._add_eos_if_not_present(_snake_case )
return token_ids_a + token_ids_a
def __getstate__( self : str ) -> Tuple:
"""simple docstring"""
lowercase__ : Optional[Any] = self.__dict__.copy()
lowercase__ : List[str] = None
return state
def __setstate__( self : Any ,_snake_case : Dict ) -> Any:
"""simple docstring"""
lowercase__ : List[str] = d
# for backward compatibility
if not hasattr(self ,'''sp_model_kwargs''' ):
lowercase__ : Optional[Any] = {}
lowercase__ : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCAmelCase ( self : Tuple ,_snake_case : "TextInput" ,**_snake_case : Optional[int] ) -> List[str]:
"""simple docstring"""
if not self.legacy:
lowercase__ : Union[str, Any] = SPIECE_UNDERLINE + text.replace(_snake_case ,''' ''' )
return super().tokenize(_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[Any] ,**_snake_case : Tuple ) -> Tuple:
"""simple docstring"""
if not self.legacy:
lowercase__ : Union[str, Any] = text.startswith(_snake_case )
if is_first:
lowercase__ : List[str] = text[1:]
lowercase__ : int = self.sp_model.encode(_snake_case ,out_type=_snake_case )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_snake_case ):
lowercase__ : List[Any] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def UpperCAmelCase ( self : str ,_snake_case : Optional[int] ) -> Dict:
"""simple docstring"""
if token.startswith('''<extra_id_''' ):
lowercase__ : Optional[Any] = re.match(r'''<extra_id_(\d+)>''' ,_snake_case )
lowercase__ : Optional[int] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : Tuple ) -> Optional[int]:
"""simple docstring"""
if index < self.sp_model.get_piece_size():
lowercase__ : Any = self.sp_model.IdToPiece(_snake_case )
else:
lowercase__ : Dict = f"""<extra_id_{self.vocab_size - 1 - index}>"""
return token
def UpperCAmelCase ( self : Optional[int] ,_snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ : Optional[int] = []
lowercase__ : Dict = ''''''
lowercase__ : Optional[int] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_snake_case ) + token
lowercase__ : Any = True
lowercase__ : List[Any] = []
else:
current_sub_tokens.append(_snake_case )
lowercase__ : Optional[int] = False
out_string += self.sp_model.decode(_snake_case )
return out_string.strip()
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : str ,_snake_case : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_snake_case ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase__ : Any = os.path.join(
_snake_case ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_snake_case ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,_snake_case )
elif not os.path.isfile(self.vocab_file ):
with open(_snake_case ,'''wb''' ) as fi:
lowercase__ : Any = self.sp_model.serialized_model_proto()
fi.write(_snake_case )
return (out_vocab_file,)
| 353
|
"""simple docstring"""
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> Any:
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
if not is_sharded:
lowercase__ : List[str] = os.path.abspath(__lowerCamelCase )
logger.info(f"""Loading PyTorch weights from {pt_path}""" )
lowercase__ : List[Any] = torch.load(__lowerCamelCase , map_location='''cpu''' )
logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.""" )
lowercase__ : int = convert_pytorch_state_dict_to_flax(__lowerCamelCase , __lowerCamelCase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
lowercase__ : Dict = convert_pytorch_sharded_state_dict_to_flax(__lowerCamelCase , __lowerCamelCase )
return flax_state_dict
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> (Tuple[str], np.ndarray):
def is_key_or_prefix_key_in_dict(__lowerCamelCase ) -> bool:
return len(set(__lowerCamelCase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
lowercase__ : int = pt_tuple_key[:-1] + ('''scale''',)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
lowercase__ : Union[str, Any] = pt_tuple_key[:-1] + ('''mean''',)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
lowercase__ : Any = pt_tuple_key[:-1] + ('''var''',)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
lowercase__ : Tuple = pt_tuple_key[:-1] + ('''embedding''',)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowercase__ : str = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
lowercase__ : str = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowercase__ : Union[str, Any] = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
lowercase__ : Optional[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowercase__ : Optional[int] = pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowercase__ : List[Any] = pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
lowercase__ : List[str] = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
lowercase__ : List[str] = pt_tuple_key[-2] + '''_g'''
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
lowercase__ : List[str] = pt_tuple_key[-2] + '''_v'''
if name is not None:
lowercase__ : Optional[Any] = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
# convert pytorch tensor to numpy
lowercase__ : Optional[Any] = {k: v.numpy() for k, v in pt_state_dict.items()}
lowercase__ : List[Any] = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
lowercase__ : str = flax_model.params['''params''']
else:
lowercase__ : Optional[int] = flax_model.params
lowercase__ : Optional[Any] = flatten_dict(__lowerCamelCase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowercase__ : Tuple = flatten_dict(flax_model.params['''batch_stats'''] )
random_flax_state_dict.update(__lowerCamelCase )
lowercase__ : int = {}
lowercase__ : List[str] = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
lowercase__ : Union[str, Any] = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase__ : Optional[Any] = tuple(pt_key.split('''.''' ) )
# remove base model prefix if necessary
lowercase__ : Union[str, Any] = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase__ : Union[str, Any] = pt_tuple_key[1:]
# Correctly rename weight parameters
lowercase__ , lowercase__ : List[str] = rename_key_and_reshape_tensor(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# add model prefix if necessary
lowercase__ : Union[str, Any] = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowercase__ : Dict = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
lowercase__ : int = jnp.asarray(__lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__lowerCamelCase , __lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
lowercase__ : Tuple = jnp.asarray(__lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
lowercase__ : Any = jnp.asarray(__lowerCamelCase )
return unflatten_dict(__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Dict:
import torch
# Load the index
lowercase__ : Dict = {}
for shard_file in shard_filenames:
# load using msgpack utils
lowercase__ : Optional[int] = torch.load(__lowerCamelCase )
lowercase__ : str = {k: v.numpy() for k, v in pt_state_dict.items()}
lowercase__ : Dict = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
lowercase__ : Optional[Any] = flax_model.params['''params''']
lowercase__ : List[Any] = flatten_dict(__lowerCamelCase )
random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats'''] ) )
else:
lowercase__ : Union[str, Any] = flax_model.params
lowercase__ : Tuple = flatten_dict(__lowerCamelCase )
lowercase__ : Tuple = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
lowercase__ : int = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase__ : List[str] = tuple(pt_key.split('''.''' ) )
# remove base model prefix if necessary
lowercase__ : Tuple = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase__ : List[str] = pt_tuple_key[1:]
# Correctly rename weight parameters
lowercase__ , lowercase__ : str = rename_key_and_reshape_tensor(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# add model prefix if necessary
lowercase__ : Union[str, Any] = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
lowercase__ : Dict = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
lowercase__ : Union[str, Any] = jnp.asarray(__lowerCamelCase )
continue
if "var" in flax_key[-1]:
lowercase__ : str = jnp.asarray(__lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__lowerCamelCase , __lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
lowercase__ : List[str] = jnp.asarray(__lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
lowercase__ : Union[str, Any] = jnp.asarray(__lowerCamelCase )
return unflatten_dict(__lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
lowercase__ : List[str] = os.path.abspath(__lowerCamelCase )
logger.info(f"""Loading Flax weights from {flax_checkpoint_path}""" )
# import correct flax class
lowercase__ : Optional[int] = getattr(__lowerCamelCase , '''Flax''' + model.__class__.__name__ )
# load flax weight dict
with open(__lowerCamelCase , '''rb''' ) as state_f:
try:
lowercase__ : str = from_bytes(__lowerCamelCase , state_f.read() )
except UnpicklingError:
raise EnvironmentError(f"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """ )
return load_flax_weights_in_pytorch_model(__lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> List[str]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
# check if we have bf16 weights
lowercase__ : Any = flatten_dict(jax.tree_util.tree_map(lambda __lowerCamelCase : x.dtype == jnp.bfloataa , __lowerCamelCase ) ).values()
if any(__lowerCamelCase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
'''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
'''before loading those in PyTorch model.''' )
lowercase__ : Union[str, Any] = jax.tree_util.tree_map(
lambda __lowerCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , __lowerCamelCase )
lowercase__ : Tuple = flatten_dict(__lowerCamelCase )
lowercase__ : List[str] = pt_model.state_dict()
lowercase__ : int = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split('''.''' )[0] for k in pt_model_dict.keys()}
)
lowercase__ : int = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split('''.''' )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
lowercase__ : List[str] = []
lowercase__ : Tuple = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
lowercase__ : List[Any] = flax_key_tuple[0] == pt_model.base_model_prefix
lowercase__ : Optional[int] = '''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
lowercase__ : Tuple = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
lowercase__ : Optional[Any] = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowerCamelCase ) not in pt_model_dict:
# conv layer
lowercase__ : Dict = flax_key_tuple[:-1] + ('''weight''',)
lowercase__ : List[str] = jnp.transpose(__lowerCamelCase , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCamelCase ) not in pt_model_dict:
# linear layer
lowercase__ : Optional[int] = flax_key_tuple[:-1] + ('''weight''',)
lowercase__ : str = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
lowercase__ : Dict = flax_key_tuple[:-1] + ('''weight''',)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
lowercase__ : Any = flax_key_tuple[:-1] + ('''running_mean''',)
elif "var" in flax_key_tuple[-1]:
lowercase__ : Dict = flax_key_tuple[:-1] + ('''running_var''',)
if "batch_stats" in flax_state:
lowercase__ : Union[str, Any] = '''.'''.join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
lowercase__ : Dict = '''.'''.join(__lowerCamelCase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
lowercase__ : Optional[int] = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
lowercase__ : str = key.split('''.''' )
lowercase__ : Optional[Any] = None
if key_components[-3::2] == ["parametrizations", "original0"]:
lowercase__ : List[str] = key_components[-2] + '''_g'''
elif key_components[-3::2] == ["parametrizations", "original1"]:
lowercase__ : str = key_components[-2] + '''_v'''
if name is not None:
lowercase__ : Optional[int] = key_components[:-3] + [name]
lowercase__ : List[str] = '''.'''.join(__lowerCamelCase )
lowercase__ : List[Any] = key
if flax_key in special_pt_names:
lowercase__ : Any = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
else:
# add weight to pytorch dict
lowercase__ : List[str] = np.asarray(__lowerCamelCase ) if not isinstance(__lowerCamelCase , np.ndarray ) else flax_tensor
lowercase__ : List[str] = torch.from_numpy(__lowerCamelCase )
# remove from missing keys
missing_keys.remove(__lowerCamelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(__lowerCamelCase )
pt_model.load_state_dict(__lowerCamelCase )
# re-transform missing_keys to list
lowercase__ : Optional[Any] = list(__lowerCamelCase )
if len(__lowerCamelCase ) > 0:
logger.warning(
'''Some weights of the Flax model were not used when initializing the PyTorch model'''
f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
''' FlaxBertForSequenceClassification model).''' )
else:
logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n""" )
if len(__lowerCamelCase ) > 0:
logger.warning(
f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
''' use it for predictions and inference.''' )
else:
logger.warning(
f"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
'''If your task is similar to the task the model of the checkpoint was trained on, '''
f"""you can already use {pt_model.__class__.__name__} for predictions without further training.""" )
return pt_model
| 302
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|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase_ = 16
lowerCAmelCase_ = 32
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = 16 ) -> Union[str, Any]:
lowercase__ : Dict = AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowercase__ : List[Any] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : str = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=__lowerCamelCase , max_length=__lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase__ : Optional[int] = datasets.map(
__lowerCamelCase , batched=__lowerCamelCase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : Dict = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ : Any = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase__ : Any = 16
elif accelerator.mixed_precision != "no":
lowercase__ : List[Any] = 8
else:
lowercase__ : Optional[int] = None
return tokenizer.pad(
__lowerCamelCase , padding='''longest''' , max_length=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_tensors='''pt''' , )
# Instantiate dataloaders.
lowercase__ : str = DataLoader(
tokenized_datasets['''train'''] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
lowercase__ : List[str] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowerCAmelCase_ = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , __lowerCamelCase ) == "1":
lowercase__ : Union[str, Any] = 2
# New Code #
lowercase__ : Dict = int(args.gradient_accumulation_steps )
# Initialize accelerator
lowercase__ : str = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=__lowerCamelCase )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
'''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`''' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : List[str] = config['''lr''']
lowercase__ : int = int(config['''num_epochs'''] )
lowercase__ : Dict = int(config['''seed'''] )
lowercase__ : Any = int(config['''batch_size'''] )
lowercase__ : Union[str, Any] = evaluate.load('''glue''' , '''mrpc''' )
set_seed(__lowerCamelCase )
lowercase__ , lowercase__ : str = get_dataloaders(__lowerCamelCase , __lowerCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : Tuple = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=__lowerCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase__ : Dict = model.to(accelerator.device )
# Instantiate optimizer
lowercase__ : List[Any] = AdamW(params=model.parameters() , lr=__lowerCamelCase )
# Instantiate scheduler
lowercase__ : List[Any] = get_linear_schedule_with_warmup(
optimizer=__lowerCamelCase , num_warmup_steps=1_00 , num_training_steps=(len(__lowerCamelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : Any = accelerator.prepare(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# Now we train the model
for epoch in range(__lowerCamelCase ):
model.train()
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(__lowerCamelCase ):
lowercase__ : int = model(**__lowerCamelCase )
lowercase__ : Optional[Any] = output.loss
accelerator.backward(__lowerCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ : Optional[int] = model(**__lowerCamelCase )
lowercase__ : List[str] = outputs.logits.argmax(dim=-1 )
lowercase__ , lowercase__ : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=__lowerCamelCase , references=__lowerCamelCase , )
lowercase__ : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , __lowerCamelCase )
def __UpperCAmelCase ( ) -> Union[str, Any]:
lowercase__ : Tuple = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=__lowerCamelCase , default=__lowerCamelCase , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
# New Code #
parser.add_argument(
'''--gradient_accumulation_steps''' , type=__lowerCamelCase , default=1 , help='''The number of minibatches to be ran before gradients are accumulated.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
lowercase__ : Optional[Any] = parser.parse_args()
lowercase__ : Union[str, Any] = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
main()
| 354
|
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Any ,_snake_case : UNetaDModel ,_snake_case : UNetaDModel ,_snake_case : DDPMScheduler ,_snake_case : Any ,) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[int] = value_function
lowercase__ : Optional[int] = unet
lowercase__ : Tuple = scheduler
lowercase__ : Dict = env
lowercase__ : int = env.get_dataset()
lowercase__ : Dict = {}
for key in self.data.keys():
try:
lowercase__ : Optional[Any] = self.data[key].mean()
except: # noqa: E722
pass
lowercase__ : List[Any] = {}
for key in self.data.keys():
try:
lowercase__ : str = self.data[key].std()
except: # noqa: E722
pass
lowercase__ : Tuple = env.observation_space.shape[0]
lowercase__ : Optional[int] = env.action_space.shape[0]
def UpperCAmelCase ( self : str ,_snake_case : Any ,_snake_case : int ) -> Optional[Any]:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def UpperCAmelCase ( self : Dict ,_snake_case : int ,_snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Dict ) -> Optional[int]:
"""simple docstring"""
if type(_snake_case ) is dict:
return {k: self.to_torch(_snake_case ) for k, v in x_in.items()}
elif torch.is_tensor(_snake_case ):
return x_in.to(self.unet.device )
return torch.tensor(_snake_case ,device=self.unet.device )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Any ,_snake_case : int ,_snake_case : List[Any] ) -> Tuple:
"""simple docstring"""
for key, val in cond.items():
lowercase__ : List[Any] = val.clone()
return x_in
def UpperCAmelCase ( self : int ,_snake_case : Optional[int] ,_snake_case : List[Any] ,_snake_case : int ,_snake_case : int ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Any = x.shape[0]
lowercase__ : Dict = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
lowercase__ : Dict = torch.full((batch_size,) ,_snake_case ,device=self.unet.device ,dtype=torch.long )
for _ in range(_snake_case ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
lowercase__ : int = self.value_function(x.permute(0 ,2 ,1 ) ,_snake_case ).sample
lowercase__ : Optional[Any] = torch.autograd.grad([y.sum()] ,[x] )[0]
lowercase__ : List[str] = self.scheduler._get_variance(_snake_case )
lowercase__ : Union[str, Any] = torch.exp(0.5 * posterior_variance )
lowercase__ : Optional[int] = model_std * grad
lowercase__ : Optional[Any] = 0
lowercase__ : str = x.detach()
lowercase__ : Dict = x + scale * grad
lowercase__ : str = self.reset_xa(_snake_case ,_snake_case ,self.action_dim )
lowercase__ : Union[str, Any] = self.unet(x.permute(0 ,2 ,1 ) ,_snake_case ).sample.permute(0 ,2 ,1 )
# TODO: verify deprecation of this kwarg
lowercase__ : Dict = self.scheduler.step(_snake_case ,_snake_case ,_snake_case ,predict_epsilon=_snake_case )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
lowercase__ : Dict = self.reset_xa(_snake_case ,_snake_case ,self.action_dim )
lowercase__ : Union[str, Any] = self.to_torch(_snake_case )
return x, y
def __call__( self : Union[str, Any] ,_snake_case : Any ,_snake_case : Tuple=64 ,_snake_case : Any=32 ,_snake_case : Optional[Any]=2 ,_snake_case : str=0.1 ) -> List[Any]:
"""simple docstring"""
lowercase__ : Any = self.normalize(_snake_case ,'''observations''' )
lowercase__ : Tuple = obs[None].repeat(_snake_case ,axis=0 )
lowercase__ : Dict = {0: self.to_torch(_snake_case )}
lowercase__ : int = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
lowercase__ : Optional[int] = randn_tensor(_snake_case ,device=self.unet.device )
lowercase__ : Tuple = self.reset_xa(_snake_case ,_snake_case ,self.action_dim )
lowercase__ : str = self.to_torch(_snake_case )
# run the diffusion process
lowercase__ , lowercase__ : int = self.run_diffusion(_snake_case ,_snake_case ,_snake_case ,_snake_case )
# sort output trajectories by value
lowercase__ : Optional[Any] = y.argsort(0 ,descending=_snake_case ).squeeze()
lowercase__ : str = x[sorted_idx]
lowercase__ : str = sorted_values[:, :, : self.action_dim]
lowercase__ : Optional[int] = actions.detach().cpu().numpy()
lowercase__ : List[str] = self.de_normalize(_snake_case ,key='''actions''' )
# select the action with the highest value
if y is not None:
lowercase__ : str = 0
else:
# if we didn't run value guiding, select a random action
lowercase__ : str = np.random.randint(0 ,_snake_case )
lowercase__ : int = denorm_actions[selected_index, 0]
return denorm_actions
| 302
| 0
|
from math import ceil
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
lowercase__ : Union[str, Any] = list(range(0 , __lowerCamelCase ) )
lowercase__ : Dict = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
lowercase__ : Union[str, Any] = []
for i in device_map_blocks:
if device_map_blocks.count(__lowerCamelCase ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(__lowerCamelCase )
# Missing blocks
lowercase__ : Union[str, Any] = [i for i in blocks if i not in device_map_blocks]
lowercase__ : Optional[Any] = [i for i in device_map_blocks if i not in blocks]
if len(__lowerCamelCase ) != 0:
raise ValueError(
'''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
''' These attention blocks were specified more than once: ''' + str(__lowerCamelCase ) )
if len(__lowerCamelCase ) != 0:
raise ValueError(
'''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
'''blocks to a device on the device_map: ''' + str(__lowerCamelCase ) )
if len(__lowerCamelCase ) != 0:
raise ValueError(
'''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
+ str(__lowerCamelCase ) )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
lowercase__ : List[str] = list(range(__lowerCamelCase ) )
lowercase__ : Dict = int(ceil(n_layers / len(__lowerCamelCase ) ) )
lowercase__ : Dict = [layers[i : i + n_blocks] for i in range(0 , __lowerCamelCase , __lowerCamelCase )]
return dict(zip(__lowerCamelCase , __lowerCamelCase ) )
| 355
|
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
lowerCAmelCase_ = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n'
lowerCAmelCase_ = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'
lowerCAmelCase_ = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,homepage='''https://github.com/krishnap25/mauve''' ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' ,id='''sequence''' ),
'''references''': datasets.Value('''string''' ,id='''sequence''' ),
} ) ,codebase_urls=['''https://github.com/krishnap25/mauve'''] ,reference_urls=[
'''https://arxiv.org/abs/2102.01454''',
'''https://github.com/krishnap25/mauve''',
] ,)
def UpperCAmelCase ( self : Dict ,_snake_case : Optional[Any] ,_snake_case : Any ,_snake_case : List[str]=None ,_snake_case : Tuple=None ,_snake_case : List[Any]=None ,_snake_case : Any=None ,_snake_case : Optional[int]="auto" ,_snake_case : Optional[int]=-1 ,_snake_case : Optional[int]=0.9 ,_snake_case : Any=5 ,_snake_case : Dict=500 ,_snake_case : Optional[int]="gpt2-large" ,_snake_case : Optional[Any]=-1 ,_snake_case : Tuple=1_024 ,_snake_case : Optional[int]=25 ,_snake_case : Dict=5 ,_snake_case : int=True ,_snake_case : Union[str, Any]=25 ,) -> Any:
"""simple docstring"""
lowercase__ : Any = compute_mauve(
p_text=_snake_case ,q_text=_snake_case ,p_features=_snake_case ,q_features=_snake_case ,p_tokens=_snake_case ,q_tokens=_snake_case ,num_buckets=_snake_case ,pca_max_data=_snake_case ,kmeans_explained_var=_snake_case ,kmeans_num_redo=_snake_case ,kmeans_max_iter=_snake_case ,featurize_model_name=_snake_case ,device_id=_snake_case ,max_text_length=_snake_case ,divergence_curve_discretization_size=_snake_case ,mauve_scaling_factor=_snake_case ,verbose=_snake_case ,seed=_snake_case ,)
return out
| 302
| 0
|
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase ) -> List[str]:
if not numbers:
return 0
if not isinstance(lowerCamelCase_ , (list, tuple) ) or not all(
isinstance(lowerCamelCase_ , lowerCamelCase_ ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
lowercase__ : Tuple = numbers[0]
for i in range(1 , len(lowerCamelCase_ ) ):
# update the maximum and minimum subarray products
lowercase__ : Any = numbers[i]
if number < 0:
lowercase__ : str = min_till_now, max_till_now
lowercase__ : Any = max(lowerCamelCase_ , max_till_now * number )
lowercase__ : Any = min(lowerCamelCase_ , min_till_now * number )
# update the maximum product found till now
lowercase__ : Optional[int] = max(lowerCamelCase_ , lowerCamelCase_ )
return max_prod
| 356
|
"""simple docstring"""
import math
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
lowercase__ : Tuple = 0
lowercase__ : Tuple = 0
while num > 0:
lowercase__ : int = num % 8
lowercase__ : Tuple = octal + (remainder * math.floor(math.pow(10 , __lowerCamelCase ) ))
counter += 1
lowercase__ : Optional[Any] = math.floor(num / 8 ) # basically /= 8 without remainder if any
# This formatting removes trailing '.0' from `octal`.
return f"""0o{int(__lowerCamelCase )}"""
def __UpperCAmelCase ( ) -> None:
print('''\n2 in octal is:''' )
print(decimal_to_octal(2 ) ) # = 2
print('''\n8 in octal is:''' )
print(decimal_to_octal(8 ) ) # = 10
print('''\n65 in octal is:''' )
print(decimal_to_octal(65 ) ) # = 101
print('''\n216 in octal is:''' )
print(decimal_to_octal(2_16 ) ) # = 330
print('''\n512 in octal is:''' )
print(decimal_to_octal(5_12 ) ) # = 1000
print('''\n''' )
if __name__ == "__main__":
main()
| 302
| 0
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
def __init__( self : int ,*_snake_case : str ,**_snake_case : List[str] ) -> None:
"""simple docstring"""
warnings.warn(
'''The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use VideoMAEImageProcessor instead.''' ,_A ,)
super().__init__(*_A ,**_A )
| 357
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
lowerCAmelCase_ = [
'openmmlab/upernet-convnext-tiny',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
lowerCAmelCase_ = 'UperNetConfig'
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : int ,_snake_case : int ,_snake_case : Union[int, Tuple[int, int]] ,_snake_case : Union[int, Tuple[int, int], str] = 0 ,_snake_case : bool = False ,_snake_case : Union[int, Tuple[int, int]] = 1 ,) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[int] = nn.Convad(
in_channels=_snake_case ,out_channels=_snake_case ,kernel_size=_snake_case ,padding=_snake_case ,bias=_snake_case ,dilation=_snake_case ,)
lowercase__ : Tuple = nn.BatchNormad(_snake_case )
lowercase__ : List[str] = nn.ReLU()
def UpperCAmelCase ( self : str ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.conv(_snake_case )
lowercase__ : List[str] = self.batch_norm(_snake_case )
lowercase__ : Tuple = self.activation(_snake_case )
return output
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : int ,_snake_case : int ,_snake_case : int ) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = [
nn.AdaptiveAvgPoolad(_snake_case ),
UperNetConvModule(_snake_case ,_snake_case ,kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(_snake_case ) ,_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : Any = input
for layer in self.layers:
lowercase__ : int = layer(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : Tuple[int, ...] ,_snake_case : int ,_snake_case : int ,_snake_case : bool ) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : int = pool_scales
lowercase__ : Dict = align_corners
lowercase__ : Optional[Any] = in_channels
lowercase__ : Optional[Any] = channels
lowercase__ : int = []
for i, pool_scale in enumerate(_snake_case ):
lowercase__ : Optional[Any] = UperNetPyramidPoolingBlock(pool_scale=_snake_case ,in_channels=_snake_case ,channels=_snake_case )
self.blocks.append(_snake_case )
self.add_module(str(_snake_case ) ,_snake_case )
def UpperCAmelCase ( self : Any ,_snake_case : torch.Tensor ) -> List[torch.Tensor]:
"""simple docstring"""
lowercase__ : int = []
for ppm in self.blocks:
lowercase__ : Any = ppm(_snake_case )
lowercase__ : int = nn.functional.interpolate(
_snake_case ,size=x.size()[2:] ,mode='''bilinear''' ,align_corners=self.align_corners )
ppm_outs.append(_snake_case )
return ppm_outs
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : List[str] ,_snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
super().__init__()
lowercase__ : str = config
lowercase__ : Optional[Any] = config.pool_scales # e.g. (1, 2, 3, 6)
lowercase__ : Optional[Any] = in_channels
lowercase__ : Any = config.hidden_size
lowercase__ : Optional[Any] = False
lowercase__ : Optional[int] = nn.Convad(self.channels ,config.num_labels ,kernel_size=1 )
# PSP Module
lowercase__ : Dict = UperNetPyramidPoolingModule(
self.pool_scales ,self.in_channels[-1] ,self.channels ,align_corners=self.align_corners ,)
lowercase__ : str = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels ,self.channels ,kernel_size=3 ,padding=1 ,)
# FPN Module
lowercase__ : Any = nn.ModuleList()
lowercase__ : Union[str, Any] = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
lowercase__ : List[Any] = UperNetConvModule(_snake_case ,self.channels ,kernel_size=1 )
lowercase__ : Optional[int] = UperNetConvModule(self.channels ,self.channels ,kernel_size=3 ,padding=1 )
self.lateral_convs.append(_snake_case )
self.fpn_convs.append(_snake_case )
lowercase__ : int = UperNetConvModule(
len(self.in_channels ) * self.channels ,self.channels ,kernel_size=3 ,padding=1 ,)
def UpperCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
self.apply(self._init_weights )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[Any] ) -> List[str]:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
module.weight.data.normal_(mean=0.0 ,std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Optional[Any] ) -> str:
"""simple docstring"""
lowercase__ : Dict = inputs[-1]
lowercase__ : Optional[int] = [x]
psp_outs.extend(self.psp_modules(_snake_case ) )
lowercase__ : Optional[Any] = torch.cat(_snake_case ,dim=1 )
lowercase__ : List[str] = self.bottleneck(_snake_case )
return output
def UpperCAmelCase ( self : List[str] ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : Tuple = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(_snake_case ) )
# build top-down path
lowercase__ : List[Any] = len(_snake_case )
for i in range(used_backbone_levels - 1 ,0 ,-1 ):
lowercase__ : Union[str, Any] = laterals[i - 1].shape[2:]
lowercase__ : int = laterals[i - 1] + nn.functional.interpolate(
laterals[i] ,size=_snake_case ,mode='''bilinear''' ,align_corners=self.align_corners )
# build outputs
lowercase__ : List[str] = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 ,0 ,-1 ):
lowercase__ : Any = nn.functional.interpolate(
fpn_outs[i] ,size=fpn_outs[0].shape[2:] ,mode='''bilinear''' ,align_corners=self.align_corners )
lowercase__ : Any = torch.cat(_snake_case ,dim=1 )
lowercase__ : Any = self.fpn_bottleneck(_snake_case )
lowercase__ : str = self.classifier(_snake_case )
return output
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : List[Any] ,_snake_case : int = 2 ,_snake_case : int = 3 ,_snake_case : Union[int, Tuple[int, int]] = 1 ) -> None:
"""simple docstring"""
super().__init__()
lowercase__ : int = config
lowercase__ : Dict = config.auxiliary_in_channels
lowercase__ : Optional[int] = config.auxiliary_channels
lowercase__ : List[Any] = config.auxiliary_num_convs
lowercase__ : List[Any] = config.auxiliary_concat_input
lowercase__ : str = in_index
lowercase__ : Any = (kernel_size // 2) * dilation
lowercase__ : Optional[Any] = []
convs.append(
UperNetConvModule(
self.in_channels ,self.channels ,kernel_size=_snake_case ,padding=_snake_case ,dilation=_snake_case ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels ,self.channels ,kernel_size=_snake_case ,padding=_snake_case ,dilation=_snake_case ) )
if self.num_convs == 0:
lowercase__ : List[str] = nn.Identity()
else:
lowercase__ : Dict = nn.Sequential(*_snake_case )
if self.concat_input:
lowercase__ : int = UperNetConvModule(
self.in_channels + self.channels ,self.channels ,kernel_size=_snake_case ,padding=kernel_size // 2 )
lowercase__ : List[str] = nn.Convad(self.channels ,config.num_labels ,kernel_size=1 )
def UpperCAmelCase ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
self.apply(self._init_weights )
def UpperCAmelCase ( self : List[Any] ,_snake_case : List[Any] ) -> Dict:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
module.weight.data.normal_(mean=0.0 ,std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase ( self : List[str] ,_snake_case : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
lowercase__ : str = encoder_hidden_states[self.in_index]
lowercase__ : List[str] = self.convs(_snake_case )
if self.concat_input:
lowercase__ : Any = self.conv_cat(torch.cat([hidden_states, output] ,dim=1 ) )
lowercase__ : Dict = self.classifier(_snake_case )
return output
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Any = UperNetConfig
lowerCAmelCase : str = "pixel_values"
lowerCAmelCase : Dict = True
def UpperCAmelCase ( self : int ,_snake_case : str ) -> Optional[int]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def UpperCAmelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def UpperCAmelCase ( self : int ,_snake_case : str ,_snake_case : str=False ) -> List[str]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
lowercase__ : List[Any] = value
lowerCAmelCase_ = R'\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCAmelCase_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes." ,A_ ,)
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Tuple ) -> int:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : int = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
lowercase__ : Any = UperNetHead(_snake_case ,in_channels=self.backbone.channels )
lowercase__ : str = UperNetFCNHead(_snake_case ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length''' ) )
@replace_return_docstrings(output_type=_snake_case ,config_class=_CONFIG_FOR_DOC )
def UpperCAmelCase ( self : Dict ,_snake_case : Optional[torch.Tensor] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[torch.Tensor] = None ,_snake_case : Optional[bool] = None ,) -> Union[tuple, SemanticSegmenterOutput]:
"""simple docstring"""
lowercase__ : int = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : Any = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ : Any = output_attentions if output_attentions is not None else self.config.output_attentions
lowercase__ : Optional[Any] = self.backbone.forward_with_filtered_kwargs(
_snake_case ,output_hidden_states=_snake_case ,output_attentions=_snake_case )
lowercase__ : Optional[int] = outputs.feature_maps
lowercase__ : Tuple = self.decode_head(_snake_case )
lowercase__ : Optional[int] = nn.functional.interpolate(_snake_case ,size=pixel_values.shape[2:] ,mode='''bilinear''' ,align_corners=_snake_case )
lowercase__ : List[str] = None
if self.auxiliary_head is not None:
lowercase__ : str = self.auxiliary_head(_snake_case )
lowercase__ : Dict = nn.functional.interpolate(
_snake_case ,size=pixel_values.shape[2:] ,mode='''bilinear''' ,align_corners=_snake_case )
lowercase__ : Any = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''' )
else:
# compute weighted loss
lowercase__ : Union[str, Any] = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
lowercase__ : List[str] = loss_fct(_snake_case ,_snake_case )
lowercase__ : List[str] = loss_fct(_snake_case ,_snake_case )
lowercase__ : Optional[Any] = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
lowercase__ : Tuple = (logits,) + outputs[1:]
else:
lowercase__ : int = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=_snake_case ,logits=_snake_case ,hidden_states=outputs.hidden_states ,attentions=outputs.attentions ,)
| 302
| 0
|
lowerCAmelCase_ = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
lowerCAmelCase_ = {
'm': 0,
'km': 3,
'Mm': 6,
'Gm': 9,
'Tm': 12,
'Pm': 15,
'Em': 18,
'Zm': 21,
'Ym': 24,
}
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Tuple:
lowercase__ : Optional[int] = from_type.lower().strip('''s''' )
lowercase__ : Optional[Any] = to_type.lower().strip('''s''' )
lowercase__ : Dict = UNIT_SYMBOL.get(_A , _A )
lowercase__ : Optional[Any] = UNIT_SYMBOL.get(_A , _A )
if from_sanitized not in METRIC_CONVERSION:
lowercase__ : Optional[Any] = (
f"""Invalid 'from_type' value: {from_type!r}.\n"""
f"""Conversion abbreviations are: {", ".join(_A )}"""
)
raise ValueError(_A )
if to_sanitized not in METRIC_CONVERSION:
lowercase__ : Union[str, Any] = (
f"""Invalid 'to_type' value: {to_type!r}.\n"""
f"""Conversion abbreviations are: {", ".join(_A )}"""
)
raise ValueError(_A )
lowercase__ : List[Any] = METRIC_CONVERSION[from_sanitized]
lowercase__ : int = METRIC_CONVERSION[to_sanitized]
lowercase__ : Tuple = 1
if from_exponent > to_exponent:
lowercase__ : Optional[int] = from_exponent - to_exponent
else:
lowercase__ : Optional[Any] = -(to_exponent - from_exponent)
return value * pow(10 , _A )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 358
|
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAmelCase_ = _symbol_database.Default()
lowerCAmelCase_ = _descriptor_pool.Default().AddSerializedFile(
B'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
lowerCAmelCase_ = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
lowerCAmelCase_ = None
lowerCAmelCase_ = B'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
lowerCAmelCase_ = 45
lowerCAmelCase_ = 1_581
lowerCAmelCase_ = 1_517
lowerCAmelCase_ = 1_570
lowerCAmelCase_ = 1_584
lowerCAmelCase_ = 1_793
lowerCAmelCase_ = 1_795
lowerCAmelCase_ = 1_916
lowerCAmelCase_ = 1_864
lowerCAmelCase_ = 1_905
lowerCAmelCase_ = 1_919
lowerCAmelCase_ = 2_429
lowerCAmelCase_ = 2_208
lowerCAmelCase_ = 2_418
lowerCAmelCase_ = 2_323
lowerCAmelCase_ = 2_407
# @@protoc_insertion_point(module_scope)
| 302
| 0
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase_ = 16
lowerCAmelCase_ = 32
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = 16 ) -> Optional[Any]:
lowercase__ : Optional[int] = AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowercase__ : int = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : str = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase__ : Optional[Any] = datasets.map(
lowerCAmelCase__ , batched=lowerCAmelCase__ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ : Tuple = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ : List[str] = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase__ : Dict = 16
elif accelerator.mixed_precision != "no":
lowercase__ : List[str] = 8
else:
lowercase__ : Optional[int] = None
return tokenizer.pad(
lowerCAmelCase__ , padding='''longest''' , max_length=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_tensors='''pt''' , )
# Instantiate dataloaders.
lowercase__ : List[Any] = DataLoader(
tokenized_datasets['''train'''] , shuffle=lowerCAmelCase__ , collate_fn=lowerCAmelCase__ , batch_size=lowerCAmelCase__ )
lowercase__ : str = DataLoader(
tokenized_datasets['''validation'''] , shuffle=lowerCAmelCase__ , collate_fn=lowerCAmelCase__ , batch_size=lowerCAmelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowerCAmelCase_ = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , lowerCAmelCase__ ) == "1":
lowercase__ : Optional[int] = 2
# New Code #
lowercase__ : List[str] = int(args.gradient_accumulation_steps )
lowercase__ : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
lowercase__ : Any = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=lowerCAmelCase__ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : Union[str, Any] = config['''lr''']
lowercase__ : Optional[int] = int(config['''num_epochs'''] )
lowercase__ : Any = int(config['''seed'''] )
lowercase__ : List[str] = int(config['''batch_size'''] )
lowercase__ : List[Any] = evaluate.load('''glue''' , '''mrpc''' )
set_seed(lowerCAmelCase__ )
lowercase__ , lowercase__ : str = get_dataloaders(lowerCAmelCase__ , lowerCAmelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : Tuple = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=lowerCAmelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase__ : Dict = model.to(accelerator.device )
# Instantiate optimizer
lowercase__ : Optional[Any] = AdamW(params=model.parameters() , lr=lowerCAmelCase__ )
# Instantiate scheduler
lowercase__ : List[Any] = get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase__ , num_warmup_steps=1_00 , num_training_steps=(len(lowerCAmelCase__ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : List[str] = accelerator.prepare(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# Now we train the model
for epoch in range(lowerCAmelCase__ ):
model.train()
with LocalSGD(
accelerator=lowerCAmelCase__ , model=lowerCAmelCase__ , local_sgd_steps=lowerCAmelCase__ , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(lowerCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(lowerCAmelCase__ ):
lowercase__ : str = model(**lowerCAmelCase__ )
lowercase__ : int = output.loss
accelerator.backward(lowerCAmelCase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(lowerCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ : Optional[Any] = model(**lowerCAmelCase__ )
lowercase__ : Optional[int] = outputs.logits.argmax(dim=-1 )
lowercase__ , lowercase__ : int = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=lowerCAmelCase__ , references=lowerCAmelCase__ , )
lowercase__ : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , lowerCAmelCase__ )
def __UpperCAmelCase ( ) -> str:
lowercase__ : List[str] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=lowerCAmelCase__ , default=lowerCAmelCase__ , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
# New Code #
parser.add_argument(
'''--gradient_accumulation_steps''' , type=lowerCAmelCase__ , default=1 , help='''The number of minibatches to be ran before gradients are accumulated.''' , )
parser.add_argument(
'''--local_sgd_steps''' , type=lowerCAmelCase__ , default=8 , help='''Number of local SGD steps or None to disable local SGD''' )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
lowercase__ : Union[str, Any] = parser.parse_args()
lowercase__ : Union[str, Any] = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
main()
| 359
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Pix2StructConfig',
'Pix2StructTextConfig',
'Pix2StructVisionConfig',
],
'processing_pix2struct': ['Pix2StructProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['Pix2StructImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Pix2StructPreTrainedModel',
'Pix2StructForConditionalGeneration',
'Pix2StructVisionModel',
'Pix2StructTextModel',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 302
| 0
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase=False , __lowerCamelCase=False , __lowerCamelCase=False ) -> Union[str, Any]:
lowercase__ : Union[str, Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""transformer.blocks.{i}.norm1.weight""", f"""vilt.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""transformer.blocks.{i}.norm1.bias""", f"""vilt.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(f"""transformer.blocks.{i}.attn.proj.weight""", f"""vilt.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append(
(f"""transformer.blocks.{i}.attn.proj.bias""", f"""vilt.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""transformer.blocks.{i}.norm2.weight""", f"""vilt.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""transformer.blocks.{i}.norm2.bias""", f"""vilt.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append(
(f"""transformer.blocks.{i}.mlp.fc1.weight""", f"""vilt.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""transformer.blocks.{i}.mlp.fc1.bias""", f"""vilt.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""transformer.blocks.{i}.mlp.fc2.weight""", f"""vilt.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""transformer.blocks.{i}.mlp.fc2.bias""", f"""vilt.encoder.layer.{i}.output.dense.bias""") )
# embeddings
rename_keys.extend(
[
# text embeddings
('''text_embeddings.word_embeddings.weight''', '''vilt.embeddings.text_embeddings.word_embeddings.weight'''),
(
'''text_embeddings.position_embeddings.weight''',
'''vilt.embeddings.text_embeddings.position_embeddings.weight''',
),
('''text_embeddings.position_ids''', '''vilt.embeddings.text_embeddings.position_ids'''),
(
'''text_embeddings.token_type_embeddings.weight''',
'''vilt.embeddings.text_embeddings.token_type_embeddings.weight''',
),
('''text_embeddings.LayerNorm.weight''', '''vilt.embeddings.text_embeddings.LayerNorm.weight'''),
('''text_embeddings.LayerNorm.bias''', '''vilt.embeddings.text_embeddings.LayerNorm.bias'''),
# patch embeddings
('''transformer.cls_token''', '''vilt.embeddings.cls_token'''),
('''transformer.patch_embed.proj.weight''', '''vilt.embeddings.patch_embeddings.projection.weight'''),
('''transformer.patch_embed.proj.bias''', '''vilt.embeddings.patch_embeddings.projection.bias'''),
('''transformer.pos_embed''', '''vilt.embeddings.position_embeddings'''),
# token type embeddings
('''token_type_embeddings.weight''', '''vilt.embeddings.token_type_embeddings.weight'''),
] )
# final layernorm + pooler
rename_keys.extend(
[
('''transformer.norm.weight''', '''vilt.layernorm.weight'''),
('''transformer.norm.bias''', '''vilt.layernorm.bias'''),
('''pooler.dense.weight''', '''vilt.pooler.dense.weight'''),
('''pooler.dense.bias''', '''vilt.pooler.dense.bias'''),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('''vqa_classifier.0.weight''', '''classifier.0.weight'''),
('''vqa_classifier.0.bias''', '''classifier.0.bias'''),
('''vqa_classifier.1.weight''', '''classifier.1.weight'''),
('''vqa_classifier.1.bias''', '''classifier.1.bias'''),
('''vqa_classifier.3.weight''', '''classifier.3.weight'''),
('''vqa_classifier.3.bias''', '''classifier.3.bias'''),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('''nlvr2_classifier.0.weight''', '''classifier.0.weight'''),
('''nlvr2_classifier.0.bias''', '''classifier.0.bias'''),
('''nlvr2_classifier.1.weight''', '''classifier.1.weight'''),
('''nlvr2_classifier.1.bias''', '''classifier.1.bias'''),
('''nlvr2_classifier.3.weight''', '''classifier.3.weight'''),
('''nlvr2_classifier.3.bias''', '''classifier.3.bias'''),
] )
else:
pass
return rename_keys
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> List[str]:
for i in range(config.num_hidden_layers ):
lowercase__ : Tuple = '''vilt.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase__ : Union[str, Any] = state_dict.pop(f"""transformer.blocks.{i}.attn.qkv.weight""" )
lowercase__ : Optional[Any] = state_dict.pop(f"""transformer.blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
lowercase__ : str = in_proj_weight[
: config.hidden_size, :
]
lowercase__ : Optional[Any] = in_proj_bias[: config.hidden_size]
lowercase__ : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase__ : Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase__ : Any = in_proj_weight[
-config.hidden_size :, :
]
lowercase__ : Optional[Any] = in_proj_bias[-config.hidden_size :]
def __UpperCAmelCase ( __lowerCamelCase ) -> Tuple:
lowercase__ : str = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(__lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
lowercase__ : int = dct.pop(__lowerCamelCase )
lowercase__ : Optional[int] = val
@torch.no_grad()
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> Union[str, Any]:
lowercase__ : Tuple = ViltConfig(image_size=3_84 , patch_size=32 , tie_word_embeddings=__lowerCamelCase )
lowercase__ : Optional[int] = False
lowercase__ : List[Any] = False
lowercase__ : Optional[int] = False
lowercase__ : Union[str, Any] = False
if "vqa" in checkpoint_url:
lowercase__ : int = True
lowercase__ : Optional[Any] = 31_29
lowercase__ : Optional[Any] = '''huggingface/label-files'''
lowercase__ : List[Any] = '''vqa2-id2label.json'''
lowercase__ : Dict = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ : Union[str, Any] = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
lowercase__ : Optional[Any] = idalabel
lowercase__ : List[str] = {v: k for k, v in idalabel.items()}
lowercase__ : int = ViltForQuestionAnswering(__lowerCamelCase )
elif "nlvr" in checkpoint_url:
lowercase__ : int = True
lowercase__ : Optional[Any] = 2
lowercase__ : str = {0: '''False''', 1: '''True'''}
lowercase__ : Union[str, Any] = {v: k for k, v in config.idalabel.items()}
lowercase__ : Optional[Any] = 3
lowercase__ : List[Any] = ViltForImagesAndTextClassification(__lowerCamelCase )
elif "irtr" in checkpoint_url:
lowercase__ : int = True
lowercase__ : Any = ViltForImageAndTextRetrieval(__lowerCamelCase )
elif "mlm_itm" in checkpoint_url:
lowercase__ : Optional[Any] = True
lowercase__ : str = ViltForMaskedLM(__lowerCamelCase )
else:
raise ValueError('''Unknown model type''' )
# load state_dict of original model, remove and rename some keys
lowercase__ : int = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location='''cpu''' )['''state_dict''']
lowercase__ : Union[str, Any] = create_rename_keys(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for src, dest in rename_keys:
rename_key(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
read_in_q_k_v(__lowerCamelCase , __lowerCamelCase )
if mlm_model or irtr_model:
lowercase__ : Tuple = ['''itm_score.fc.weight''', '''itm_score.fc.bias''']
for k in ignore_keys:
state_dict.pop(__lowerCamelCase , __lowerCamelCase )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
lowercase__ , lowercase__ : List[str] = model.load_state_dict(__lowerCamelCase , strict=__lowerCamelCase )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(__lowerCamelCase )
# Define processor
lowercase__ : List[Any] = ViltImageProcessor(size=3_84 )
lowercase__ : int = BertTokenizer.from_pretrained('''bert-base-uncased''' )
lowercase__ : str = ViltProcessor(__lowerCamelCase , __lowerCamelCase )
# Forward pass on example inputs (image + text)
if nlvr_model:
lowercase__ : List[Any] = Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg''' , stream=__lowerCamelCase ).raw )
lowercase__ : Any = Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg''' , stream=__lowerCamelCase ).raw )
lowercase__ : Tuple = (
'''The left image contains twice the number of dogs as the right image, and at least two dogs in total are'''
''' standing.'''
)
lowercase__ : List[Any] = processor(__lowerCamelCase , __lowerCamelCase , return_tensors='''pt''' )
lowercase__ : str = processor(__lowerCamelCase , __lowerCamelCase , return_tensors='''pt''' )
lowercase__ : Optional[int] = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
lowercase__ : Union[str, Any] = Image.open(requests.get('''http://images.cocodataset.org/val2017/000000039769.jpg''' , stream=__lowerCamelCase ).raw )
if mlm_model:
lowercase__ : int = '''a bunch of [MASK] laying on a [MASK].'''
else:
lowercase__ : Optional[int] = '''How many cats are there?'''
lowercase__ : str = processor(__lowerCamelCase , __lowerCamelCase , return_tensors='''pt''' )
lowercase__ : Optional[Any] = model(**__lowerCamelCase )
# Verify outputs
if mlm_model:
lowercase__ : List[Any] = torch.Size([1, 11, 3_05_22] )
lowercase__ : Any = torch.tensor([-1_2.5_0_6_1, -1_2.5_1_2_3, -1_2.5_1_7_4] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , __lowerCamelCase , atol=1E-4 )
# verify masked token prediction equals "cats"
lowercase__ : Any = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
lowercase__ : Any = torch.Size([1, 31_29] )
lowercase__ : Tuple = torch.tensor([-1_5.9_4_9_5, -1_8.1_4_7_2, -1_0.3_0_4_1] )
assert torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , __lowerCamelCase , atol=1E-4 )
# verify vqa prediction equals "2"
lowercase__ : List[Any] = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
lowercase__ : Optional[Any] = torch.Size([1, 2] )
lowercase__ : Dict = torch.tensor([-2.8_7_2_1, 2.1_2_9_1] )
assert torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
print(f"""Saving model and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt',
type=str,
help='URL of the checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
lowerCAmelCase_ = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 360
|
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
lowercase__ : List[str] = FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base''' )
lowercase__ : List[str] = AutoTokenizer.from_pretrained('''xlm-roberta-base''' )
lowercase__ : List[str] = '''The dog is cute and lives in the garden house'''
lowercase__ : int = jnp.array([tokenizer.encode(_snake_case )] )
lowercase__ : Any = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
lowercase__ : Tuple = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
lowercase__ : Optional[Any] = model(_snake_case )['''last_hidden_state''']
self.assertEqual(output.shape ,_snake_case )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] ,_snake_case ,atol=1e-3 ) )
| 302
| 0
|
"""simple docstring"""
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __A :
'''simple docstring'''
def __init__( self : str ,_snake_case : List[str] ,_snake_case : int=13 ,_snake_case : Optional[int]=30 ,_snake_case : Any=2 ,_snake_case : Any=3 ,_snake_case : str=True ,_snake_case : int=True ,_snake_case : Optional[Any]=32 ,_snake_case : str=5 ,_snake_case : Union[str, Any]=4 ,_snake_case : Optional[int]=37 ,_snake_case : Dict="gelu" ,_snake_case : Tuple=0.1 ,_snake_case : List[str]=0.1 ,_snake_case : Union[str, Any]=10 ,_snake_case : Optional[Any]=0.02 ,_snake_case : Any=None ,) -> Tuple:
"""simple docstring"""
lowercase__ : Optional[Any] = parent
lowercase__ : Dict = batch_size
lowercase__ : Union[str, Any] = image_size
lowercase__ : List[str] = patch_size
lowercase__ : Any = num_channels
lowercase__ : Tuple = is_training
lowercase__ : Dict = use_labels
lowercase__ : Union[str, Any] = hidden_size
lowercase__ : Union[str, Any] = num_hidden_layers
lowercase__ : Any = num_attention_heads
lowercase__ : Optional[Any] = intermediate_size
lowercase__ : Optional[int] = hidden_act
lowercase__ : str = hidden_dropout_prob
lowercase__ : List[Any] = attention_probs_dropout_prob
lowercase__ : Dict = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : int = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase__ : Dict = (image_size // patch_size) ** 2
lowercase__ : Tuple = num_patches + 1
def UpperCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
lowercase__ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Any = None
if self.use_labels:
lowercase__ : Union[str, Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowercase__ : List[Any] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
return ViTMSNConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,initializer_range=self.initializer_range ,)
def UpperCAmelCase ( self : List[str] ,_snake_case : List[str] ,_snake_case : Union[str, Any] ,_snake_case : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ : List[Any] = ViTMSNModel(config=__A )
model.to(__A )
model.eval()
lowercase__ : Any = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : List[str] ,_snake_case : Any ,_snake_case : Any ,_snake_case : List[str] ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.type_sequence_label_size
lowercase__ : List[str] = ViTMSNForImageClassification(__A )
model.to(__A )
model.eval()
lowercase__ : Optional[Any] = model(__A ,labels=__A )
print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}''' )
print('''Labels: {labels}''' )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowercase__ : Union[str, Any] = 1
lowercase__ : List[str] = ViTMSNForImageClassification(__A )
model.to(__A )
model.eval()
lowercase__ : Any = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ : Tuple = model(__A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase ( self : str ) -> Any:
"""simple docstring"""
lowercase__ : Dict = self.prepare_config_and_inputs()
lowercase__ : List[str] = config_and_inputs
lowercase__ : Any = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __A ( lowerCamelCase__ ,lowerCamelCase__ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : str = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
lowerCAmelCase : Optional[int] = (
{"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase : Dict = False
lowerCAmelCase : int = False
lowerCAmelCase : Any = False
lowerCAmelCase : str = False
def UpperCAmelCase ( self : str ) -> Tuple:
"""simple docstring"""
lowercase__ : Optional[int] = ViTMSNModelTester(self )
lowercase__ : Union[str, Any] = ConfigTester(self ,config_class=__A ,has_text_modality=__A ,hidden_size=37 )
def UpperCAmelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
def UpperCAmelCase ( self : int ) -> str:
"""simple docstring"""
pass
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Tuple = model_class(__A )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
lowercase__ : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A ,nn.Linear ) )
def UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : str = model_class(__A )
lowercase__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Dict = [*signature.parameters.keys()]
lowercase__ : List[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,__A )
def UpperCAmelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def UpperCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__A )
@slow
def UpperCAmelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : List[str] = ViTMSNModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def __UpperCAmelCase ( ) -> Any:
lowercase__ : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None
@slow
def UpperCAmelCase ( self : List[str] ) -> str:
"""simple docstring"""
torch.manual_seed(2 )
lowercase__ : Optional[int] = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(__A )
lowercase__ : List[Any] = self.default_image_processor
lowercase__ : List[str] = prepare_img()
lowercase__ : Dict = image_processor(images=__A ,return_tensors='''pt''' ).to(__A )
# forward pass
with torch.no_grad():
lowercase__ : Any = model(**__A )
# verify the logits
lowercase__ : Union[str, Any] = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape ,__A )
lowercase__ : int = torch.tensor([-0.0803, -0.4454, -0.2375] ).to(__A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,__A ,atol=1e-4 ) )
| 361
|
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = '#'
class __A :
'''simple docstring'''
def __init__( self : str ) -> None:
"""simple docstring"""
lowercase__ : dict = {}
def UpperCAmelCase ( self : List[str] ,_snake_case : str ) -> None:
"""simple docstring"""
lowercase__ : str = self._trie
for char in text:
if char not in trie:
lowercase__ : Union[str, Any] = {}
lowercase__ : Optional[Any] = trie[char]
lowercase__ : Dict = True
def UpperCAmelCase ( self : Tuple ,_snake_case : str ) -> tuple | list:
"""simple docstring"""
lowercase__ : Optional[Any] = self._trie
for char in prefix:
if char in trie:
lowercase__ : Union[str, Any] = trie[char]
else:
return []
return self._elements(_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : dict ) -> tuple:
"""simple docstring"""
lowercase__ : str = []
for c, v in d.items():
lowercase__ : List[Any] = [''' '''] if c == END else [(c + s) for s in self._elements(_snake_case )]
result.extend(_snake_case )
return tuple(_snake_case )
lowerCAmelCase_ = Trie()
lowerCAmelCase_ = ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
trie.insert_word(word)
def __UpperCAmelCase ( __lowerCamelCase ) -> tuple:
lowercase__ : List[Any] = trie.find_word(__lowerCamelCase )
return tuple(string + word for word in suffixes )
def __UpperCAmelCase ( ) -> None:
print(autocomplete_using_trie('''de''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 302
| 0
|
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_available():
import torch
lowerCAmelCase_ = logging.get_logger(__name__)
@add_end_docstrings(
A_ ,r"\n top_k (`int`, defaults to 5):\n The number of predictions to return.\n targets (`str` or `List[str]`, *optional*):\n When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n token will be used (with a warning, and that might be slower).\n\n " ,)
class __A ( A_ ):
'''simple docstring'''
def UpperCAmelCase ( self : Any ,_snake_case : Union[str, Any] ) -> np.ndarray:
"""simple docstring"""
if self.framework == "tf":
lowercase__ : Dict = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
elif self.framework == "pt":
lowercase__ : List[Any] = torch.nonzero(input_ids == self.tokenizer.mask_token_id ,as_tuple=__a )
else:
raise ValueError('''Unsupported framework''' )
return masked_index
def UpperCAmelCase ( self : str ,_snake_case : str ) -> np.ndarray:
"""simple docstring"""
lowercase__ : List[Any] = self.get_masked_index(__a )
lowercase__ : Dict = np.prod(masked_index.shape )
if numel < 1:
raise PipelineException(
'''fill-mask''' ,self.model.base_model_prefix ,f"""No mask_token ({self.tokenizer.mask_token}) found on the input""" ,)
def UpperCAmelCase ( self : Tuple ,_snake_case : Union[str, Any] ) -> int:
"""simple docstring"""
if isinstance(__a ,__a ):
for model_input in model_inputs:
self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0] )
else:
for input_ids in model_inputs["input_ids"]:
self._ensure_exactly_one_mask_token(__a )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[int] ,_snake_case : Dict=None ,**_snake_case : Optional[int] ) -> Dict[str, GenericTensor]:
"""simple docstring"""
if return_tensors is None:
lowercase__ : Dict = self.framework
lowercase__ : List[Any] = self.tokenizer(__a ,return_tensors=__a )
self.ensure_exactly_one_mask_token(__a )
return model_inputs
def UpperCAmelCase ( self : List[Any] ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
lowercase__ : int = self.model(**__a )
lowercase__ : Any = model_inputs['''input_ids''']
return model_outputs
def UpperCAmelCase ( self : Optional[int] ,_snake_case : Dict ,_snake_case : str=5 ,_snake_case : int=None ) -> Optional[int]:
"""simple docstring"""
if target_ids is not None and target_ids.shape[0] < top_k:
lowercase__ : int = target_ids.shape[0]
lowercase__ : Tuple = model_outputs['''input_ids'''][0]
lowercase__ : str = model_outputs['''logits''']
if self.framework == "tf":
lowercase__ : Optional[int] = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]
lowercase__ : List[Any] = outputs.numpy()
lowercase__ : Optional[int] = outputs[0, masked_index, :]
lowercase__ : str = stable_softmax(__a ,axis=-1 )
if target_ids is not None:
lowercase__ : int = tf.gather_nd(tf.squeeze(__a ,0 ) ,target_ids.reshape(-1 ,1 ) )
lowercase__ : Dict = tf.expand_dims(__a ,0 )
lowercase__ : Dict = tf.math.top_k(__a ,k=__a )
lowercase__ , lowercase__ : str = topk.values.numpy(), topk.indices.numpy()
else:
lowercase__ : int = torch.nonzero(input_ids == self.tokenizer.mask_token_id ,as_tuple=__a ).squeeze(-1 )
# Fill mask pipeline supports only one ${mask_token} per sample
lowercase__ : Dict = outputs[0, masked_index, :]
lowercase__ : Tuple = logits.softmax(dim=-1 )
if target_ids is not None:
lowercase__ : Dict = probs[..., target_ids]
lowercase__ , lowercase__ : str = probs.topk(__a )
lowercase__ : int = []
lowercase__ : Union[str, Any] = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist() ,predictions.tolist() ) ):
lowercase__ : List[Any] = []
for v, p in zip(_values ,_predictions ):
# Copy is important since we're going to modify this array in place
lowercase__ : Dict = input_ids.numpy().copy()
if target_ids is not None:
lowercase__ : Optional[Any] = target_ids[p].tolist()
lowercase__ : str = p
# Filter padding out:
lowercase__ : Union[str, Any] = tokens[np.where(tokens != self.tokenizer.pad_token_id )]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
lowercase__ : Any = self.tokenizer.decode(__a ,skip_special_tokens=__a )
lowercase__ : Tuple = {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p] ), '''sequence''': sequence}
row.append(__a )
result.append(__a )
if single_mask:
return result[0]
return result
def UpperCAmelCase ( self : Dict ,_snake_case : Any ,_snake_case : Dict=None ) -> Any:
"""simple docstring"""
if isinstance(__a ,__a ):
lowercase__ : Optional[Any] = [targets]
try:
lowercase__ : Optional[int] = self.tokenizer.get_vocab()
except Exception:
lowercase__ : Tuple = {}
lowercase__ : Dict = []
for target in targets:
lowercase__ : int = vocab.get(__a ,__a )
if id_ is None:
lowercase__ : Union[str, Any] = self.tokenizer(
__a ,add_special_tokens=__a ,return_attention_mask=__a ,return_token_type_ids=__a ,max_length=1 ,truncation=__a ,)['''input_ids''']
if len(__a ) == 0:
logger.warning(
f"""The specified target token `{target}` does not exist in the model vocabulary. """
'''We cannot replace it with anything meaningful, ignoring it''' )
continue
lowercase__ : Dict = input_ids[0]
# XXX: If users encounter this pass
# it becomes pretty slow, so let's make sure
# The warning enables them to fix the input to
# get faster performance.
logger.warning(
f"""The specified target token `{target}` does not exist in the model vocabulary. """
f"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`.""" )
target_ids.append(id_ )
lowercase__ : Dict = list(set(__a ) )
if len(__a ) == 0:
raise ValueError('''At least one target must be provided when passed.''' )
lowercase__ : List[str] = np.array(__a )
return target_ids
def UpperCAmelCase ( self : str ,_snake_case : Union[str, Any]=None ,_snake_case : Dict=None ) -> Optional[int]:
"""simple docstring"""
lowercase__ : List[str] = {}
if targets is not None:
lowercase__ : Tuple = self.get_target_ids(__a ,__a )
lowercase__ : Optional[int] = target_ids
if top_k is not None:
lowercase__ : Union[str, Any] = top_k
if self.tokenizer.mask_token_id is None:
raise PipelineException(
'''fill-mask''' ,self.model.base_model_prefix ,'''The tokenizer does not define a `mask_token`.''' )
return {}, {}, postprocess_params
def __call__( self : Optional[Any] ,_snake_case : str ,*_snake_case : Optional[Any] ,**_snake_case : List[str] ) -> Any:
"""simple docstring"""
lowercase__ : str = super().__call__(__a ,**__a )
if isinstance(__a ,__a ) and len(__a ) == 1:
return outputs[0]
return outputs
| 362
|
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
lowerCAmelCase_ = logging.get_logger(__name__)
# General docstring
lowerCAmelCase_ = 'RegNetConfig'
# Base docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = [1, 1_088, 7, 7]
# Image classification docstring
lowerCAmelCase_ = 'facebook/regnet-y-040'
lowerCAmelCase_ = 'tabby, tabby cat'
lowerCAmelCase_ = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : int ,_snake_case : int ,_snake_case : int ,_snake_case : int = 3 ,_snake_case : int = 1 ,_snake_case : int = 1 ,_snake_case : Optional[str] = "relu" ,) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = nn.Convad(
_snake_case ,_snake_case ,kernel_size=_snake_case ,stride=_snake_case ,padding=kernel_size // 2 ,groups=_snake_case ,bias=_snake_case ,)
lowercase__ : List[Any] = nn.BatchNormad(_snake_case )
lowercase__ : Optional[int] = ACTaFN[activation] if activation is not None else nn.Identity()
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.convolution(_snake_case )
lowercase__ : Tuple = self.normalization(_snake_case )
lowercase__ : Tuple = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] ,_snake_case : RegNetConfig ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = RegNetConvLayer(
config.num_channels ,config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act )
lowercase__ : str = config.num_channels
def UpperCAmelCase ( self : int ,_snake_case : Dict ) -> str:
"""simple docstring"""
lowercase__ : Union[str, Any] = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
lowercase__ : Optional[int] = self.embedder(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : str ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ) -> Any:
"""simple docstring"""
super().__init__()
lowercase__ : List[str] = nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ,stride=_snake_case ,bias=_snake_case )
lowercase__ : Any = nn.BatchNormad(_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ) -> Tensor:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.convolution(_snake_case )
lowercase__ : Optional[int] = self.normalization(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : int ,_snake_case : int ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Any = nn.AdaptiveAvgPoolad((1, 1) )
lowercase__ : Dict = nn.Sequential(
nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.ReLU() ,nn.Convad(_snake_case ,_snake_case ,kernel_size=1 ) ,nn.Sigmoid() ,)
def UpperCAmelCase ( self : int ,_snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.pooler(_snake_case )
lowercase__ : Union[str, Any] = self.attention(_snake_case )
lowercase__ : List[str] = hidden_state * attention
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> List[str]:
"""simple docstring"""
super().__init__()
lowercase__ : Tuple = in_channels != out_channels or stride != 1
lowercase__ : Optional[int] = max(1 ,out_channels // config.groups_width )
lowercase__ : str = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : Optional[int] = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : str = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ : Tuple = hidden_state
lowercase__ : Union[str, Any] = self.layer(_snake_case )
lowercase__ : List[Any] = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : Optional[int] = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 1 ) -> Optional[int]:
"""simple docstring"""
super().__init__()
lowercase__ : List[Any] = in_channels != out_channels or stride != 1
lowercase__ : List[str] = max(1 ,out_channels // config.groups_width )
lowercase__ : Tuple = (
RegNetShortCut(_snake_case ,_snake_case ,stride=_snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase__ : str = nn.Sequential(
RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=config.hidden_act ) ,RegNetConvLayer(_snake_case ,_snake_case ,stride=_snake_case ,groups=_snake_case ,activation=config.hidden_act ) ,RegNetSELayer(_snake_case ,reduced_channels=int(round(in_channels / 4 ) ) ) ,RegNetConvLayer(_snake_case ,_snake_case ,kernel_size=1 ,activation=_snake_case ) ,)
lowercase__ : Optional[Any] = ACTaFN[config.hidden_act]
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ : str = hidden_state
lowercase__ : Optional[Any] = self.layer(_snake_case )
lowercase__ : int = self.shortcut(_snake_case )
hidden_state += residual
lowercase__ : str = self.activation(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,_snake_case : RegNetConfig ,_snake_case : int ,_snake_case : int ,_snake_case : int = 2 ,_snake_case : int = 2 ,) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ : Optional[Any] = RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer
lowercase__ : Optional[Any] = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
_snake_case ,_snake_case ,_snake_case ,stride=_snake_case ,) ,*[layer(_snake_case ,_snake_case ,_snake_case ) for _ in range(depth - 1 )] ,)
def UpperCAmelCase ( self : Tuple ,_snake_case : int ) -> List[Any]:
"""simple docstring"""
lowercase__ : List[str] = self.layers(_snake_case )
return hidden_state
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict ,_snake_case : RegNetConfig ) -> List[Any]:
"""simple docstring"""
super().__init__()
lowercase__ : str = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
_snake_case ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,) )
lowercase__ : str = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(_snake_case ,config.depths[1:] ):
self.stages.append(RegNetStage(_snake_case ,_snake_case ,_snake_case ,depth=_snake_case ) )
def UpperCAmelCase ( self : List[str] ,_snake_case : Tensor ,_snake_case : bool = False ,_snake_case : bool = True ) -> BaseModelOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Dict = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase__ : int = hidden_states + (hidden_state,)
lowercase__ : Any = stage_module(_snake_case )
if output_hidden_states:
lowercase__ : Optional[int] = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_snake_case ,hidden_states=_snake_case )
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : int = RegNetConfig
lowerCAmelCase : List[Any] = "regnet"
lowerCAmelCase : Optional[int] = "pixel_values"
lowerCAmelCase : Union[str, Any] = True
def UpperCAmelCase ( self : Any ,_snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
if isinstance(_snake_case ,nn.Convad ):
nn.init.kaiming_normal_(module.weight ,mode='''fan_out''' ,nonlinearity='''relu''' )
elif isinstance(_snake_case ,(nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight ,1 )
nn.init.constant_(module.bias ,0 )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Dict ,_snake_case : Any=False ) -> Optional[int]:
"""simple docstring"""
if isinstance(_snake_case ,_snake_case ):
lowercase__ : str = value
lowerCAmelCase_ = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowerCAmelCase_ = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_snake_case : Any ) -> Tuple:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Any = config
lowercase__ : List[str] = RegNetEmbeddings(_snake_case )
lowercase__ : Any = RegNetEncoder(_snake_case )
lowercase__ : Dict = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,modality='''vision''' ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCAmelCase ( self : Dict ,_snake_case : Tensor ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
"""simple docstring"""
lowercase__ : List[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase__ : Dict = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : Union[str, Any] = self.embedder(_snake_case )
lowercase__ : List[Any] = self.encoder(
_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : str = encoder_outputs[0]
lowercase__ : Optional[int] = self.pooler(_snake_case )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_snake_case ,pooler_output=_snake_case ,hidden_states=encoder_outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " ,A_ ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class __A ( A_ ):
'''simple docstring'''
def __init__( self : int ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
super().__init__(_snake_case )
lowercase__ : Optional[Any] = config.num_labels
lowercase__ : int = RegNetModel(_snake_case )
# classification head
lowercase__ : str = nn.Sequential(
nn.Flatten() ,nn.Linear(config.hidden_sizes[-1] ,config.num_labels ) if config.num_labels > 0 else nn.Identity() ,)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_snake_case )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=_snake_case ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCAmelCase ( self : List[Any] ,_snake_case : Optional[torch.FloatTensor] = None ,_snake_case : Optional[torch.LongTensor] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[bool] = None ,) -> ImageClassifierOutputWithNoAttention:
"""simple docstring"""
lowercase__ : Any = return_dict if return_dict is not None else self.config.use_return_dict
lowercase__ : List[Any] = self.regnet(_snake_case ,output_hidden_states=_snake_case ,return_dict=_snake_case )
lowercase__ : List[str] = outputs.pooler_output if return_dict else outputs[1]
lowercase__ : Union[str, Any] = self.classifier(_snake_case )
lowercase__ : Optional[int] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase__ : List[Any] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase__ : Dict = '''single_label_classification'''
else:
lowercase__ : Optional[int] = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowercase__ : Union[str, Any] = MSELoss()
if self.num_labels == 1:
lowercase__ : List[Any] = loss_fct(logits.squeeze() ,labels.squeeze() )
else:
lowercase__ : Tuple = loss_fct(_snake_case ,_snake_case )
elif self.config.problem_type == "single_label_classification":
lowercase__ : Tuple = CrossEntropyLoss()
lowercase__ : str = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase__ : Any = BCEWithLogitsLoss()
lowercase__ : Union[str, Any] = loss_fct(_snake_case ,_snake_case )
if not return_dict:
lowercase__ : Tuple = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_snake_case ,logits=_snake_case ,hidden_states=outputs.hidden_states )
| 302
| 0
|
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
lowerCAmelCase_ = datasets.utils.logging.get_logger(__name__)
lowerCAmelCase_ = ['names', 'prefix']
lowerCAmelCase_ = ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
lowerCAmelCase_ = ['encoding_errors', 'on_bad_lines']
lowerCAmelCase_ = ['date_format']
@dataclass
class __A ( datasets.BuilderConfig ):
'''simple docstring'''
lowerCAmelCase : Tuple = ","
lowerCAmelCase : List[str] = None
lowerCAmelCase : List[str] = "infer"
lowerCAmelCase : str = None
lowerCAmelCase : int = None
lowerCAmelCase : Tuple = None
lowerCAmelCase : Any = None
lowerCAmelCase : Dict = None
lowerCAmelCase : Optional[int] = True
lowerCAmelCase : Any = None
lowerCAmelCase : List[Any] = None
lowerCAmelCase : Dict = None
lowerCAmelCase : Dict = None
lowerCAmelCase : List[Any] = False
lowerCAmelCase : Union[str, Any] = None
lowerCAmelCase : List[str] = None
lowerCAmelCase : int = None
lowerCAmelCase : List[Any] = True
lowerCAmelCase : Any = True
lowerCAmelCase : str = False
lowerCAmelCase : Union[str, Any] = True
lowerCAmelCase : Union[str, Any] = None
lowerCAmelCase : Any = "."
lowerCAmelCase : List[Any] = None
lowerCAmelCase : Optional[Any] = "\""
lowerCAmelCase : Dict = 0
lowerCAmelCase : Tuple = None
lowerCAmelCase : Any = None
lowerCAmelCase : List[Any] = None
lowerCAmelCase : Union[str, Any] = None
lowerCAmelCase : Tuple = True
lowerCAmelCase : List[str] = True
lowerCAmelCase : int = 0
lowerCAmelCase : Optional[Any] = True
lowerCAmelCase : Dict = False
lowerCAmelCase : str = None
lowerCAmelCase : str = 1_0_0_0_0
lowerCAmelCase : str = None
lowerCAmelCase : List[str] = "strict"
lowerCAmelCase : str = "error"
lowerCAmelCase : Union[str, Any] = None
def UpperCAmelCase ( self : str ) -> List[str]:
"""simple docstring"""
if self.delimiter is not None:
lowercase__ : Dict = self.delimiter
if self.column_names is not None:
lowercase__ : int = self.column_names
@property
def UpperCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ : List[Any] = {
'''sep''': self.sep,
'''header''': self.header,
'''names''': self.names,
'''index_col''': self.index_col,
'''usecols''': self.usecols,
'''prefix''': self.prefix,
'''mangle_dupe_cols''': self.mangle_dupe_cols,
'''engine''': self.engine,
'''converters''': self.converters,
'''true_values''': self.true_values,
'''false_values''': self.false_values,
'''skipinitialspace''': self.skipinitialspace,
'''skiprows''': self.skiprows,
'''nrows''': self.nrows,
'''na_values''': self.na_values,
'''keep_default_na''': self.keep_default_na,
'''na_filter''': self.na_filter,
'''verbose''': self.verbose,
'''skip_blank_lines''': self.skip_blank_lines,
'''thousands''': self.thousands,
'''decimal''': self.decimal,
'''lineterminator''': self.lineterminator,
'''quotechar''': self.quotechar,
'''quoting''': self.quoting,
'''escapechar''': self.escapechar,
'''comment''': self.comment,
'''encoding''': self.encoding,
'''dialect''': self.dialect,
'''error_bad_lines''': self.error_bad_lines,
'''warn_bad_lines''': self.warn_bad_lines,
'''skipfooter''': self.skipfooter,
'''doublequote''': self.doublequote,
'''memory_map''': self.memory_map,
'''float_precision''': self.float_precision,
'''chunksize''': self.chunksize,
'''encoding_errors''': self.encoding_errors,
'''on_bad_lines''': self.on_bad_lines,
'''date_format''': self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() ,UpperCamelCase__ ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class __A ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
lowerCAmelCase : Tuple = CsvConfig
def UpperCAmelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[Any] ) -> List[str]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
lowercase__ : Union[str, Any] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(UpperCamelCase__ ,(str, list, tuple) ):
lowercase__ : Any = data_files
if isinstance(UpperCamelCase__ ,UpperCamelCase__ ):
lowercase__ : List[str] = [files]
lowercase__ : Tuple = [dl_manager.iter_files(UpperCamelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={'''files''': files} )]
lowercase__ : Dict = []
for split_name, files in data_files.items():
if isinstance(UpperCamelCase__ ,UpperCamelCase__ ):
lowercase__ : Dict = [files]
lowercase__ : List[Any] = [dl_manager.iter_files(UpperCamelCase__ ) for file in files]
splits.append(datasets.SplitGenerator(name=UpperCamelCase__ ,gen_kwargs={'''files''': files} ) )
return splits
def UpperCAmelCase ( self : str ,_snake_case : Any ) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
lowercase__ : Any = self.config.features.arrow_schema
if all(not require_storage_cast(UpperCamelCase__ ) for feature in self.config.features.values() ):
# cheaper cast
lowercase__ : Tuple = pa.Table.from_arrays([pa_table[field.name] for field in schema] ,schema=UpperCamelCase__ )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
lowercase__ : Any = table_cast(UpperCamelCase__ ,UpperCamelCase__ )
return pa_table
def UpperCAmelCase ( self : Dict ,_snake_case : str ) -> Tuple:
"""simple docstring"""
lowercase__ : List[Any] = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
lowercase__ : Union[str, Any] = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(UpperCamelCase__ ) else object
for name, dtype, feature in zip(schema.names ,schema.types ,self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__ ) ):
lowercase__ : Optional[int] = pd.read_csv(UpperCamelCase__ ,iterator=UpperCamelCase__ ,dtype=UpperCamelCase__ ,**self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(UpperCamelCase__ ):
lowercase__ : int = pa.Table.from_pandas(UpperCamelCase__ )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(UpperCamelCase__ )
except ValueError as e:
logger.error(f"""Failed to read file \'{file}\' with error {type(UpperCamelCase__ )}: {e}""" )
raise
| 363
|
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = 1.6021E-19 # units = C
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 302
| 0
|
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __A ( snake_case__ ,snake_case__ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : List[str] = IFInpaintingPipeline
lowerCAmelCase : Tuple = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
lowerCAmelCase : int = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowerCAmelCase : List[Any] = PipelineTesterMixin.required_optional_params - {"""latents"""}
def UpperCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
return self._get_dummy_components()
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : str ,_snake_case : Union[str, Any]=0 ) -> Optional[Any]:
"""simple docstring"""
if str(_A ).startswith('''mps''' ):
lowercase__ : str = torch.manual_seed(_A )
else:
lowercase__ : Union[str, Any] = torch.Generator(device=_A ).manual_seed(_A )
lowercase__ : Optional[int] = floats_tensor((1, 3, 32, 32) ,rng=random.Random(_A ) ).to(_A )
lowercase__ : str = floats_tensor((1, 3, 32, 32) ,rng=random.Random(_A ) ).to(_A )
lowercase__ : Optional[Any] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() ,reason='''XFormers attention is only available with CUDA and `xformers` installed''' ,)
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def UpperCAmelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' ,reason='''float16 requires CUDA''' )
def UpperCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1e-1 )
def UpperCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def UpperCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
self._test_save_load_local()
def UpperCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 ,)
| 364
|
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : str = ["pixel_values"]
def __init__( self : Tuple ,_snake_case : bool = True ,_snake_case : Optional[Dict[str, int]] = None ,_snake_case : PILImageResampling = PILImageResampling.BICUBIC ,_snake_case : bool = True ,_snake_case : bool = True ,_snake_case : Union[int, float] = 1 / 255 ,_snake_case : Dict[str, int] = None ,_snake_case : bool = True ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,**_snake_case : Optional[Any] ,) -> None:
"""simple docstring"""
super().__init__(**_snake_case )
lowercase__ : str = size if size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[int] = get_size_dict(_snake_case )
lowercase__ : List[Any] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[int] = get_size_dict(_snake_case ,default_to_square=_snake_case ,param_name='''crop_size''' )
lowercase__ : Tuple = do_resize
lowercase__ : List[Any] = do_rescale
lowercase__ : Any = do_normalize
lowercase__ : List[str] = do_center_crop
lowercase__ : Optional[Any] = crop_size
lowercase__ : Union[str, Any] = size
lowercase__ : Any = resample
lowercase__ : int = rescale_factor
lowercase__ : Tuple = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
lowercase__ : str = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase ( self : str ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : PILImageResampling = PILImageResampling.BILINEAR ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Dict ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : List[str] = get_size_dict(_snake_case )
if "shortest_edge" in size:
lowercase__ : str = get_resize_output_image_size(_snake_case ,size=size['''shortest_edge'''] ,default_to_square=_snake_case )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
lowercase__ : int = (size['''height'''], size['''width'''])
else:
raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}""" )
return resize(_snake_case ,size=_snake_case ,resample=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : List[Any] ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Tuple ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : Optional[Any] = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(_snake_case ,size=(size['''height'''], size['''width''']) ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : np.ndarray ,_snake_case : float ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Optional[int] ) -> np.ndarray:
"""simple docstring"""
return rescale(_snake_case ,scale=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : np.ndarray ,_snake_case : Union[float, List[float]] ,_snake_case : Union[float, List[float]] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Dict ,) -> np.ndarray:
"""simple docstring"""
return normalize(_snake_case ,mean=_snake_case ,std=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : ImageInput ,_snake_case : Optional[bool] = None ,_snake_case : Dict[str, int] = None ,_snake_case : PILImageResampling = None ,_snake_case : bool = None ,_snake_case : int = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[float] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[str, TensorType]] = None ,_snake_case : Union[str, ChannelDimension] = ChannelDimension.FIRST ,**_snake_case : List[str] ,) -> BatchFeature:
"""simple docstring"""
lowercase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
lowercase__ : int = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ : int = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ : Optional[int] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ : Optional[Any] = crop_size if crop_size is not None else self.crop_size
lowercase__ : Tuple = get_size_dict(_snake_case ,param_name='''crop_size''' ,default_to_square=_snake_case )
lowercase__ : Tuple = resample if resample is not None else self.resample
lowercase__ : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ : Union[str, Any] = image_mean if image_mean is not None else self.image_mean
lowercase__ : List[str] = image_std if image_std is not None else self.image_std
lowercase__ : Optional[int] = size if size is not None else self.size
lowercase__ : int = get_size_dict(_snake_case )
if not is_batched(_snake_case ):
lowercase__ : Optional[Any] = [images]
if not valid_images(_snake_case ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
# All transformations expect numpy arrays.
lowercase__ : str = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
lowercase__ : int = [self.resize(image=_snake_case ,size=_snake_case ,resample=_snake_case ) for image in images]
if do_center_crop:
lowercase__ : str = [self.center_crop(image=_snake_case ,size=_snake_case ) for image in images]
if do_rescale:
lowercase__ : Optional[Any] = [self.rescale(image=_snake_case ,scale=_snake_case ) for image in images]
if do_normalize:
lowercase__ : List[str] = [self.normalize(image=_snake_case ,mean=_snake_case ,std=_snake_case ) for image in images]
lowercase__ : Union[str, Any] = [to_channel_dimension_format(_snake_case ,_snake_case ) for image in images]
lowercase__ : Any = {'''pixel_values''': images}
return BatchFeature(data=_snake_case ,tensor_type=_snake_case )
| 302
| 0
|
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __UpperCAmelCase ( ) -> Dict:
lowercase__ : int = 9, 14 # noqa: F841
lowercase__ : Tuple = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
lowercase__ : int = defaultdict(snake_case__ )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
lowercase__ : int = mst(snake_case__ )
lowercase__ : Dict = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
lowercase__ : Optional[Any] = tuple(answer[:2] )
lowercase__ : Any = tuple(edge[::-1] )
assert edge in result or reverse in result
| 365
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_efficientnet': [
'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EfficientNetConfig',
'EfficientNetOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['EfficientNetImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'EfficientNetForImageClassification',
'EfficientNetModel',
'EfficientNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 302
| 0
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[Any]:
lowercase__ : Any = '''huggingface/label-files'''
lowercase__ : Any = '''imagenet-1k-id2label.json'''
lowercase__ : Optional[Any] = json.load(open(hf_hub_download(lowerCAmelCase__ , lowerCAmelCase__ , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ : Optional[Any] = {int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
lowercase__ : List[str] = {v: k for k, v in idalabel.items()}
lowercase__ : Union[str, Any] = '''std_conv''' if '''bit''' in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
lowercase__ : Any = BitConfig(
conv_layer=lowerCAmelCase__ , num_labels=10_00 , idalabel=lowerCAmelCase__ , labelaid=lowerCAmelCase__ , )
return config
def __UpperCAmelCase ( __lowerCamelCase ) -> Tuple:
if "stem.conv" in name:
lowercase__ : List[str] = name.replace('''stem.conv''' , '''bit.embedder.convolution''' )
if "blocks" in name:
lowercase__ : Tuple = name.replace('''blocks''' , '''layers''' )
if "head.fc" in name:
lowercase__ : Union[str, Any] = name.replace('''head.fc''' , '''classifier.1''' )
if name.startswith('''norm''' ):
lowercase__ : Tuple = '''bit.''' + name
if "bit" not in name and "classifier" not in name:
lowercase__ : int = '''bit.encoder.''' + name
return name
def __UpperCAmelCase ( ) -> str:
lowercase__ : Union[str, Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase__ : Optional[Any] = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw )
return im
@torch.no_grad()
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> Tuple:
lowercase__ : Any = get_config(lowerCAmelCase__ )
# load original model from timm
lowercase__ : str = create_model(lowerCAmelCase__ , pretrained=lowerCAmelCase__ )
timm_model.eval()
# load state_dict of original model
lowercase__ : List[Any] = timm_model.state_dict()
for key in state_dict.copy().keys():
lowercase__ : Optional[int] = state_dict.pop(lowerCAmelCase__ )
lowercase__ : int = val.squeeze() if '''head''' in key else val
# load HuggingFace model
lowercase__ : List[str] = BitForImageClassification(lowerCAmelCase__ )
model.eval()
model.load_state_dict(lowerCAmelCase__ )
# create image processor
lowercase__ : List[str] = create_transform(**resolve_data_config({} , model=lowerCAmelCase__ ) )
lowercase__ : Optional[int] = transform.transforms
lowercase__ : int = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
lowercase__ : Union[str, Any] = BitImageProcessor(
do_resize=lowerCAmelCase__ , size={'''shortest_edge''': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=lowerCAmelCase__ , crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} , do_normalize=lowerCAmelCase__ , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
lowercase__ : Optional[int] = prepare_img()
lowercase__ : List[Any] = transform(lowerCAmelCase__ ).unsqueeze(0 )
lowercase__ : Optional[int] = processor(lowerCAmelCase__ , return_tensors='''pt''' ).pixel_values
# verify pixel values
assert torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ )
# verify logits
with torch.no_grad():
lowercase__ : Union[str, Any] = model(lowerCAmelCase__ )
lowercase__ : Union[str, Any] = outputs.logits
print('''Logits:''' , logits[0, :3] )
print('''Predicted class:''' , model.config.idalabel[logits.argmax(-1 ).item()] )
lowercase__ : Dict = timm_model(lowerCAmelCase__ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(lowerCAmelCase__ , outputs.logits , atol=1E-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
print(f"""Saving model {model_name} and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowerCAmelCase__ )
processor.save_pretrained(lowerCAmelCase__ )
if push_to_hub:
print(f"""Pushing model {model_name} and processor to the hub""" )
model.push_to_hub(f"""ybelkada/{model_name}""" )
processor.push_to_hub(f"""ybelkada/{model_name}""" )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='resnetv2_50x1_bitm',
type=str,
help='Name of the BiT timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model to the hub.',
)
lowerCAmelCase_ = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 366
|
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = "cpu" , __lowerCamelCase = None ) -> None:
lowercase__ : List[str] = torch.load(__lowerCamelCase , map_location=__lowerCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__lowerCamelCase , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
lowercase__ : List[Any] = v.half()
if save_path is None: # overwrite src_path
lowercase__ : Any = src_path
torch.save(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
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import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase=0.9_9_9 , __lowerCamelCase="cosine" , ) -> List[str]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(__lowerCamelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__lowerCamelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
lowercase__ : Optional[Any] = []
for i in range(__lowerCamelCase ):
lowercase__ : Any = i / num_diffusion_timesteps
lowercase__ : Any = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__lowerCamelCase ) / alpha_bar_fn(__lowerCamelCase ) , __lowerCamelCase ) )
return torch.tensor(__lowerCamelCase , dtype=torch.floataa )
class __A ( UpperCAmelCase_ ,UpperCAmelCase_ ):
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = [e.name for e in KarrasDiffusionSchedulers]
lowerCAmelCase : Dict = 2
@register_to_config
def __init__( self : int ,_snake_case : Tuple = 1_000 ,_snake_case : Union[str, Any] = 0.0_0085 ,_snake_case : Optional[int] = 0.012 ,_snake_case : Union[str, Any] = "linear" ,_snake_case : Union[str, Any] = None ,_snake_case : int = "epsilon" ,_snake_case : Any = "linspace" ,_snake_case : List[str] = 0 ,) -> Optional[int]:
"""simple docstring"""
if trained_betas is not None:
lowercase__ : Any = torch.tensor(__lowercase ,dtype=torch.floataa )
elif beta_schedule == "linear":
lowercase__ : str = torch.linspace(__lowercase ,__lowercase ,__lowercase ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowercase__ : List[str] = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,__lowercase ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowercase__ : Optional[int] = betas_for_alpha_bar(__lowercase )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
lowercase__ : Any = 1.0 - self.betas
lowercase__ : Optional[int] = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(__lowercase ,__lowercase ,__lowercase )
def UpperCAmelCase ( self : int ,_snake_case : Union[str, Any] ,_snake_case : List[Any]=None ) -> Any:
"""simple docstring"""
if schedule_timesteps is None:
lowercase__ : Dict = self.timesteps
lowercase__ : List[str] = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
lowercase__ : int = 1 if len(__lowercase ) > 1 else 0
else:
lowercase__ : str = timestep.cpu().item() if torch.is_tensor(__lowercase ) else timestep
lowercase__ : Union[str, Any] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def UpperCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def UpperCAmelCase ( self : int ,_snake_case : Dict ,_snake_case : int ,) -> torch.FloatTensor:
"""simple docstring"""
lowercase__ : Dict = self.index_for_timestep(__lowercase )
if self.state_in_first_order:
lowercase__ : Dict = self.sigmas[step_index]
else:
lowercase__ : int = self.sigmas_interpol[step_index]
lowercase__ : Tuple = sample / ((sigma**2 + 1) ** 0.5)
return sample
def UpperCAmelCase ( self : Dict ,_snake_case : Union[str, Any] ,_snake_case : List[Any] = None ,_snake_case : Tuple = None ,) -> Tuple:
"""simple docstring"""
lowercase__ : Optional[Any] = num_inference_steps
lowercase__ : Any = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
lowercase__ : List[Any] = np.linspace(0 ,num_train_timesteps - 1 ,__lowercase ,dtype=__lowercase )[::-1].copy()
elif self.config.timestep_spacing == "leading":
lowercase__ : Dict = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowercase__ : List[str] = (np.arange(0 ,__lowercase ) * step_ratio).round()[::-1].copy().astype(__lowercase )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
lowercase__ : Optional[Any] = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowercase__ : Dict = (np.arange(__lowercase ,0 ,-step_ratio )).round().copy().astype(__lowercase )
timesteps -= 1
else:
raise ValueError(
f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
lowercase__ : List[str] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
lowercase__ : Tuple = torch.from_numpy(np.log(__lowercase ) ).to(__lowercase )
lowercase__ : Union[str, Any] = np.interp(__lowercase ,np.arange(0 ,len(__lowercase ) ) ,__lowercase )
lowercase__ : Any = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
lowercase__ : Optional[Any] = torch.from_numpy(__lowercase ).to(device=__lowercase )
# interpolate sigmas
lowercase__ : Optional[int] = sigmas.log().lerp(sigmas.roll(1 ).log() ,0.5 ).exp()
lowercase__ : Tuple = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
lowercase__ : str = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(__lowercase ).startswith('''mps''' ):
# mps does not support float64
lowercase__ : Any = torch.from_numpy(__lowercase ).to(__lowercase ,dtype=torch.floataa )
else:
lowercase__ : List[str] = torch.from_numpy(__lowercase ).to(__lowercase )
# interpolate timesteps
lowercase__ : int = self.sigma_to_t(__lowercase ).to(__lowercase ,dtype=timesteps.dtype )
lowercase__ : Union[str, Any] = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,dim=-1 ).flatten()
lowercase__ : Optional[Any] = torch.cat([timesteps[:1], interleaved_timesteps] )
lowercase__ : Union[str, Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
lowercase__ : str = defaultdict(__lowercase )
def UpperCAmelCase ( self : str ,_snake_case : Tuple ) -> List[str]:
"""simple docstring"""
lowercase__ : Optional[Any] = sigma.log()
# get distribution
lowercase__ : Optional[Any] = log_sigma - self.log_sigmas[:, None]
# get sigmas range
lowercase__ : Tuple = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
lowercase__ : List[str] = low_idx + 1
lowercase__ : str = self.log_sigmas[low_idx]
lowercase__ : List[str] = self.log_sigmas[high_idx]
# interpolate sigmas
lowercase__ : Tuple = (low - log_sigma) / (low - high)
lowercase__ : List[str] = w.clamp(0 ,1 )
# transform interpolation to time range
lowercase__ : Tuple = (1 - w) * low_idx + w * high_idx
lowercase__ : Optional[int] = t.view(sigma.shape )
return t
@property
def UpperCAmelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
return self.sample is None
def UpperCAmelCase ( self : int ,_snake_case : int ,_snake_case : Union[str, Any] ,_snake_case : List[str] ,_snake_case : str = True ,) -> Union[SchedulerOutput, Tuple]:
"""simple docstring"""
lowercase__ : Union[str, Any] = self.index_for_timestep(__lowercase )
# advance index counter by 1
lowercase__ : int = timestep.cpu().item() if torch.is_tensor(__lowercase ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
lowercase__ : List[Any] = self.sigmas[step_index]
lowercase__ : List[str] = self.sigmas_interpol[step_index + 1]
lowercase__ : Optional[Any] = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
lowercase__ : List[Any] = self.sigmas[step_index - 1]
lowercase__ : List[Any] = self.sigmas_interpol[step_index]
lowercase__ : str = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
lowercase__ : str = 0
lowercase__ : Optional[int] = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
lowercase__ : Optional[Any] = sigma_hat if self.state_in_first_order else sigma_interpol
lowercase__ : List[Any] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
lowercase__ : Dict = sigma_hat if self.state_in_first_order else sigma_interpol
lowercase__ : List[Any] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError('''prediction_type not implemented yet: sample''' )
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
lowercase__ : List[Any] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
lowercase__ : Union[str, Any] = sigma_interpol - sigma_hat
# store for 2nd order step
lowercase__ : Any = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
lowercase__ : Union[str, Any] = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
lowercase__ : Tuple = sigma_next - sigma_hat
lowercase__ : Tuple = self.sample
lowercase__ : int = None
lowercase__ : Optional[Any] = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__lowercase )
def UpperCAmelCase ( self : int ,_snake_case : int ,_snake_case : List[str] ,_snake_case : List[str] ,) -> torch.FloatTensor:
"""simple docstring"""
lowercase__ : Any = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(__lowercase ):
# mps does not support float64
lowercase__ : Dict = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
lowercase__ : Optional[Any] = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
lowercase__ : Any = self.timesteps.to(original_samples.device )
lowercase__ : List[str] = timesteps.to(original_samples.device )
lowercase__ : List[Any] = [self.index_for_timestep(__lowercase ,__lowercase ) for t in timesteps]
lowercase__ : Any = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
lowercase__ : Any = sigma.unsqueeze(-1 )
lowercase__ : Optional[int] = original_samples + noise * sigma
return noisy_samples
def __len__( self : int ) -> List[Any]:
"""simple docstring"""
return self.config.num_train_timesteps
| 367
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : UNetaDModel
lowerCAmelCase : ScoreSdeVeScheduler
def __init__( self : Optional[Any] ,_snake_case : UNetaDModel ,_snake_case : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=_snake_case ,scheduler=_snake_case )
@torch.no_grad()
def __call__( self : Any ,_snake_case : int = 1 ,_snake_case : int = 2_000 ,_snake_case : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,_snake_case : Optional[str] = "pil" ,_snake_case : bool = True ,**_snake_case : Any ,) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
lowercase__ : Optional[Any] = self.unet.config.sample_size
lowercase__ : Dict = (batch_size, 3, img_size, img_size)
lowercase__ : Tuple = self.unet
lowercase__ : Any = randn_tensor(_snake_case ,generator=_snake_case ) * self.scheduler.init_noise_sigma
lowercase__ : Union[str, Any] = sample.to(self.device )
self.scheduler.set_timesteps(_snake_case )
self.scheduler.set_sigmas(_snake_case )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
lowercase__ : Tuple = self.scheduler.sigmas[i] * torch.ones(shape[0] ,device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
lowercase__ : List[str] = self.unet(_snake_case ,_snake_case ).sample
lowercase__ : Optional[Any] = self.scheduler.step_correct(_snake_case ,_snake_case ,generator=_snake_case ).prev_sample
# prediction step
lowercase__ : str = model(_snake_case ,_snake_case ).sample
lowercase__ : List[Any] = self.scheduler.step_pred(_snake_case ,_snake_case ,_snake_case ,generator=_snake_case )
lowercase__ , lowercase__ : Optional[int] = output.prev_sample, output.prev_sample_mean
lowercase__ : Union[str, Any] = sample_mean.clamp(0 ,1 )
lowercase__ : int = sample.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
lowercase__ : Any = self.numpy_to_pil(_snake_case )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=_snake_case )
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|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = torch.device('cpu')
def __UpperCAmelCase ( ) -> Any:
lowercase__ : List[str] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase__ : Dict = Image.open(requests.get(snake_case_ , stream=snake_case_ ).raw )
return im
def __UpperCAmelCase ( __lowerCamelCase ) -> List[Any]:
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02] )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
lowercase__ : Optional[Any] = dct.pop(snake_case_ )
lowercase__ : Tuple = val
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]:
lowercase__ : Optional[Any] = []
for k in state_dict.keys():
lowercase__ : Dict = k
if ".pwconv" in k:
lowercase__ : int = k_new.replace('''.pwconv''' , '''.point_wise_conv''' )
if ".dwconv" in k:
lowercase__ : List[Any] = k_new.replace('''.dwconv''' , '''.depth_wise_conv''' )
if ".Proj." in k:
lowercase__ : List[str] = k_new.replace('''.Proj.''' , '''.proj.''' )
if "patch_embed" in k_new:
lowercase__ : int = k_new.replace('''patch_embed''' , '''swiftformer.patch_embed.patch_embedding''' )
if "network" in k_new:
lowercase__ : Any = k_new.split('''.''' )
if ls[2].isdigit():
lowercase__ : Dict = '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:] )
else:
lowercase__ : Optional[int] = k_new.replace('''network''' , '''swiftformer.encoder.network''' )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
lowercase__ : Dict = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
lowercase__ : List[str] = 10_00
lowercase__ : Tuple = '''huggingface/label-files'''
lowercase__ : Dict = '''imagenet-1k-id2label.json'''
lowercase__ : Dict = json.load(open(hf_hub_download(snake_case_ , snake_case_ , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ : Optional[Any] = {int(snake_case_ ): v for k, v in idalabel.items()}
lowercase__ : Dict = idalabel
lowercase__ : Optional[Any] = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
lowercase__ : int = [3, 3, 6, 4]
lowercase__ : List[str] = [48, 56, 1_12, 2_20]
elif swiftformer_name == "swiftformer_s":
lowercase__ : List[str] = [3, 3, 9, 6]
lowercase__ : Dict = [48, 64, 1_68, 2_24]
elif swiftformer_name == "swiftformer_l1":
lowercase__ : Union[str, Any] = [4, 3, 10, 5]
lowercase__ : str = [48, 96, 1_92, 3_84]
elif swiftformer_name == "swiftformer_l3":
lowercase__ : Optional[int] = [4, 4, 12, 6]
lowercase__ : Any = [64, 1_28, 3_20, 5_12]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith('''https''' ):
lowercase__ : List[Any] = torch.hub.load_state_dict_from_url(snake_case_ , map_location='''cpu''' , check_hash=snake_case_ )
else:
lowercase__ : Optional[Any] = torch.load(snake_case_ , map_location='''cpu''' )
lowercase__ : int = checkpoint
lowercase__ : List[str] = create_rename_keys(snake_case_ )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(snake_case_ , snake_case_ , snake_case_ )
# load HuggingFace model
lowercase__ : Any = SwiftFormerForImageClassification(snake_case_ ).eval()
hf_model.load_state_dict(snake_case_ )
# prepare test inputs
lowercase__ : Union[str, Any] = prepare_img()
lowercase__ : List[Any] = ViTImageProcessor.from_pretrained('''preprocessor_config''' )
lowercase__ : str = processor(images=snake_case_ , return_tensors='''pt''' )
# compare outputs from both models
lowercase__ : Optional[int] = get_expected_output(snake_case_ )
lowercase__ : Dict = hf_model(inputs['''pixel_values'''] ).logits
assert hf_logits.shape == torch.Size([1, 10_00] )
assert torch.allclose(hf_logits[0, 0:5] , snake_case_ , atol=1E-3 )
Path(snake_case_ ).mkdir(exist_ok=snake_case_ )
print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}""" )
hf_model.save_pretrained(snake_case_ )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--swiftformer_name',
default='swiftformer_xs',
choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
type=str,
help='Name of the SwiftFormer model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='./converted_outputs/',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')
lowerCAmelCase_ = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 368
|
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCAmelCase_ = {
'facebook/maskformer-swin-base-ade': (
'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = "maskformer"
lowerCAmelCase : Any = {"hidden_size": "mask_feature_size"}
lowerCAmelCase : Optional[int] = ["resnet", "swin"]
lowerCAmelCase : str = ["detr"]
def __init__( self : int ,_snake_case : int = 256 ,_snake_case : int = 256 ,_snake_case : float = 0.1 ,_snake_case : bool = False ,_snake_case : Optional[Dict] = None ,_snake_case : Optional[Dict] = None ,_snake_case : float = 0.02 ,_snake_case : float = 1.0 ,_snake_case : float = 1.0 ,_snake_case : float = 1.0 ,_snake_case : float = 20.0 ,_snake_case : Optional[bool] = None ,**_snake_case : Optional[Any] ,) -> Dict:
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowercase__ : Any = SwinConfig(
image_size=384 ,in_channels=3 ,patch_size=4 ,embed_dim=128 ,depths=[2, 2, 18, 2] ,num_heads=[4, 8, 16, 32] ,window_size=12 ,drop_path_rate=0.3 ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] ,)
if isinstance(_snake_case ,_snake_case ):
lowercase__ : List[str] = backbone_config.pop('''model_type''' )
lowercase__ : List[Any] = CONFIG_MAPPING[backbone_model_type]
lowercase__ : str = config_class.from_dict(_snake_case )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
f"""Supported model types: {",".join(self.backbones_supported )}""" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowercase__ : Union[str, Any] = DetrConfig()
else:
# verify that the decoder is supported
lowercase__ : Tuple = (
decoder_config.pop('''model_type''' ) if isinstance(_snake_case ,_snake_case ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f"""Transformer Decoder {decoder_type} not supported, please use one of"""
f""" {",".join(self.decoders_supported )}""" )
if isinstance(_snake_case ,_snake_case ):
lowercase__ : Optional[int] = CONFIG_MAPPING[decoder_type]
lowercase__ : Optional[Any] = config_class.from_dict(_snake_case )
lowercase__ : List[Any] = backbone_config
lowercase__ : List[Any] = decoder_config
# main feature dimension for the model
lowercase__ : List[str] = fpn_feature_size
lowercase__ : int = mask_feature_size
# initializer
lowercase__ : str = init_std
lowercase__ : str = init_xavier_std
# Hungarian matcher && loss
lowercase__ : Optional[int] = cross_entropy_weight
lowercase__ : List[Any] = dice_weight
lowercase__ : List[str] = mask_weight
lowercase__ : str = use_auxiliary_loss
lowercase__ : Optional[int] = no_object_weight
lowercase__ : Optional[Any] = output_auxiliary_logits
lowercase__ : Optional[Any] = self.decoder_config.encoder_attention_heads
lowercase__ : Optional[Any] = self.decoder_config.num_hidden_layers
super().__init__(**_snake_case )
@classmethod
def UpperCAmelCase ( cls : Any ,_snake_case : PretrainedConfig ,_snake_case : PretrainedConfig ,**_snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return cls(
backbone_config=_snake_case ,decoder_config=_snake_case ,**_snake_case ,)
def UpperCAmelCase ( self : str ) -> Dict[str, any]:
"""simple docstring"""
lowercase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase__ : int = self.backbone_config.to_dict()
lowercase__ : List[Any] = self.decoder_config.to_dict()
lowercase__ : List[str] = self.__class__.model_type
return output
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"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class __A ( _UpperCamelCase ):
'''simple docstring'''
lowerCAmelCase : List[Any] = 'roberta'
def __init__( self : Optional[Any] ,_snake_case : Tuple=50_265 ,_snake_case : int=768 ,_snake_case : Optional[Any]=12 ,_snake_case : List[Any]=12 ,_snake_case : Tuple=3_072 ,_snake_case : List[Any]="gelu" ,_snake_case : Optional[int]=0.1 ,_snake_case : str=0.1 ,_snake_case : Dict=512 ,_snake_case : str=2 ,_snake_case : Dict=0.02 ,_snake_case : List[str]=1e-12 ,_snake_case : Any=1 ,_snake_case : Optional[int]=0 ,_snake_case : Tuple=2 ,_snake_case : Optional[int]="absolute" ,_snake_case : Tuple=True ,_snake_case : str=None ,**_snake_case : Optional[Any] ,) -> Dict:
"""simple docstring"""
super().__init__(pad_token_id=_UpperCAmelCase ,bos_token_id=_UpperCAmelCase ,eos_token_id=_UpperCAmelCase ,**_UpperCAmelCase )
lowercase__ : Optional[int] = vocab_size
lowercase__ : Tuple = hidden_size
lowercase__ : int = num_hidden_layers
lowercase__ : str = num_attention_heads
lowercase__ : Dict = hidden_act
lowercase__ : Any = intermediate_size
lowercase__ : Tuple = hidden_dropout_prob
lowercase__ : Any = attention_probs_dropout_prob
lowercase__ : Optional[int] = max_position_embeddings
lowercase__ : List[str] = type_vocab_size
lowercase__ : Union[str, Any] = initializer_range
lowercase__ : Any = layer_norm_eps
lowercase__ : Optional[Any] = position_embedding_type
lowercase__ : List[str] = use_cache
lowercase__ : str = classifier_dropout
class __A ( _UpperCamelCase ):
'''simple docstring'''
@property
def UpperCAmelCase ( self : List[str] ) -> str:
"""simple docstring"""
if self.task == "multiple-choice":
lowercase__ : Optional[Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowercase__ : Dict = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
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|
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]:
lowercase__ : int = [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
lowercase__ : Dict = True if '''large''' in model_name or '''huge''' in model_name else False
lowercase__ : Optional[int] = True if '''large''' in model_name or '''huge''' in model_name else False
lowercase__ : List[Any] = True if '''large''' in model_name or '''huge''' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
lowercase__ : Dict = [3, 3, 3, 3]
lowercase__ : str = [5, 5, 5, 5]
elif "fl4" in model_name:
lowercase__ : List[str] = [4, 4, 4, 4]
lowercase__ : Any = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
lowercase__ : List[str] = [3, 3, 3, 3]
if "lrf" in model_name:
lowercase__ : List[str] = [3, 3, 3, 3]
else:
lowercase__ : Optional[Any] = [2, 2, 2, 2]
if "tiny" in model_name:
lowercase__ : Optional[int] = 96
elif "small" in model_name:
lowercase__ : Union[str, Any] = 96
elif "base" in model_name:
lowercase__ : Tuple = 1_28
elif "large" in model_name:
lowercase__ : Any = 1_92
elif "xlarge" in model_name:
lowercase__ : Any = 2_56
elif "huge" in model_name:
lowercase__ : Union[str, Any] = 3_52
# set label information
lowercase__ : List[Any] = '''huggingface/label-files'''
if "large" in model_name or "huge" in model_name:
lowercase__ : Optional[int] = '''imagenet-22k-id2label.json'''
else:
lowercase__ : Optional[Any] = '''imagenet-1k-id2label.json'''
lowercase__ : Dict = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ : Union[str, Any] = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
lowercase__ : Optional[Any] = {v: k for k, v in idalabel.items()}
lowercase__ : int = FocalNetConfig(
embed_dim=__lowerCamelCase , depths=__lowerCamelCase , focal_levels=__lowerCamelCase , focal_windows=__lowerCamelCase , use_conv_embed=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase , use_post_layernorm=__lowerCamelCase , use_layerscale=__lowerCamelCase , )
return config
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
if "patch_embed.proj" in name:
lowercase__ : Any = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowercase__ : Tuple = name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if "layers" in name:
lowercase__ : Dict = '''encoder.''' + name
if "encoder.layers" in name:
lowercase__ : Tuple = name.replace('''encoder.layers''' , '''encoder.stages''' )
if "downsample.proj" in name:
lowercase__ : Union[str, Any] = name.replace('''downsample.proj''' , '''downsample.projection''' )
if "blocks" in name:
lowercase__ : Optional[Any] = name.replace('''blocks''' , '''layers''' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
lowercase__ : Dict = name.replace('''modulation.f''' , '''modulation.projection_in''' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
lowercase__ : Dict = name.replace('''modulation.h''' , '''modulation.projection_context''' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
lowercase__ : Optional[Any] = name.replace('''modulation.proj''' , '''modulation.projection_out''' )
if name == "norm.weight":
lowercase__ : Dict = '''layernorm.weight'''
if name == "norm.bias":
lowercase__ : Dict = '''layernorm.bias'''
if "head" in name:
lowercase__ : Dict = name.replace('''head''' , '''classifier''' )
else:
lowercase__ : List[Any] = '''focalnet.''' + name
return name
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> List[str]:
# fmt: off
lowercase__ : Any = {
'''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
'''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
'''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
'''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
'''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
'''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
'''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
'''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
'''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
'''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
}
# fmt: on
lowercase__ : Optional[int] = model_name_to_url[model_name]
print('''Checkpoint URL: ''' , __lowerCamelCase )
lowercase__ : str = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location='''cpu''' )['''model''']
# rename keys
for key in state_dict.copy().keys():
lowercase__ : int = state_dict.pop(__lowerCamelCase )
lowercase__ : Any = val
lowercase__ : List[Any] = get_focalnet_config(__lowerCamelCase )
lowercase__ : Optional[int] = FocalNetForImageClassification(__lowerCamelCase )
model.eval()
# load state dict
model.load_state_dict(__lowerCamelCase )
# verify conversion
lowercase__ : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase__ : int = BitImageProcessor(
do_resize=__lowerCamelCase , size={'''shortest_edge''': 2_56} , resample=PILImageResampling.BILINEAR , do_center_crop=__lowerCamelCase , crop_size=2_24 , do_normalize=__lowerCamelCase , image_mean=__lowerCamelCase , image_std=__lowerCamelCase , )
lowercase__ : str = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw )
lowercase__ : List[str] = processor(images=__lowerCamelCase , return_tensors='''pt''' )
lowercase__ : List[str] = transforms.Compose(
[
transforms.Resize(2_56 ),
transforms.CenterCrop(2_24 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
lowercase__ : Optional[Any] = image_transforms(__lowerCamelCase ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , __lowerCamelCase , atol=1E-4 )
lowercase__ : Optional[Any] = model(**__lowerCamelCase )
lowercase__ : Optional[int] = outputs.logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
print('''First values of logits:''' , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
lowercase__ : Dict = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] )
elif model_name == "focalnet-tiny-lrf":
lowercase__ : Union[str, Any] = torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5] )
elif model_name == "focalnet-small":
lowercase__ : Optional[int] = torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1] )
elif model_name == "focalnet-small-lrf":
lowercase__ : Dict = torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1] )
elif model_name == "focalnet-base":
lowercase__ : List[str] = torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0] )
elif model_name == "focalnet-base-lrf":
lowercase__ : List[str] = torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8] )
assert torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
print(f"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(f"""{model_name}""" )
processor.push_to_hub(f"""{model_name}""" )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='focalnet-tiny',
type=str,
help='Name of the FocalNet model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub.',
)
lowerCAmelCase_ = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
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