code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = FileLock(str(tmpdir / 'foo.lock' ) )
SCREAMING_SNAKE_CASE_ : List[str] = FileLock(str(tmpdir / 'foo.lock' ) )
SCREAMING_SNAKE_CASE_ : Tuple = 0.01
with locka.acquire():
with pytest.raises(A__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = time.time()
locka.acquire(A__ )
assert time.time() - _start > timeout
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : List[Any] = 'a' * 1_0_0_0 + '.lock'
SCREAMING_SNAKE_CASE_ : int = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith('.lock' )
assert not locka._lock_file.endswith(A__ )
assert len(os.path.basename(locka._lock_file ) ) <= 2_5_5
SCREAMING_SNAKE_CASE_ : Optional[int] = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(A__ ):
locka.acquire(0 )
| 101 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["""MBartTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["""MBartTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MBartForCausalLM""",
"""MBartForConditionalGeneration""",
"""MBartForQuestionAnswering""",
"""MBartForSequenceClassification""",
"""MBartModel""",
"""MBartPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""TFMBartForConditionalGeneration""",
"""TFMBartModel""",
"""TFMBartPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""FlaxMBartForConditionalGeneration""",
"""FlaxMBartForQuestionAnswering""",
"""FlaxMBartForSequenceClassification""",
"""FlaxMBartModel""",
"""FlaxMBartPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
"""simple docstring"""
__magic_name__ : str = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": """--""", """N""": """-.""",
"""O""": """---""", """P""": """.--.""", """Q""": """--.-""", """R""": """.-.""", """S""": """...""", """T""": """-""", """U""": """..-""",
"""V""": """...-""", """W""": """.--""", """X""": """-..-""", """Y""": """-.--""", """Z""": """--..""", """1""": """.----""",
"""2""": """..---""", """3""": """...--""", """4""": """....-""", """5""": """.....""", """6""": """-....""", """7""": """--...""",
"""8""": """---..""", """9""": """----.""", """0""": """-----""", """&""": """.-...""", """@""": """.--.-.""",
""":""": """---...""", """,""": """--..--""", """.""": """.-.-.-""", """'""": """.----.""", """\"""": """.-..-.""",
"""?""": """..--..""", """/""": """-..-.""", """=""": """-...-""", """+""": """.-.-.""", """-""": """-....-""",
"""(""": """-.--.""", """)""": """-.--.-""", """!""": """-.-.--""", """ """: """/"""
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
__magic_name__ : int = {value: key for key, value in MORSE_CODE_DICT.items()}
def UpperCamelCase (SCREAMING_SNAKE_CASE ):
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def UpperCamelCase (SCREAMING_SNAKE_CASE ):
return "".join(REVERSE_DICT[char] for char in message.split() )
def UpperCamelCase ():
UpperCamelCase : Any = """Morse code here!"""
print(SCREAMING_SNAKE_CASE )
UpperCamelCase : Optional[int] = encrypt(SCREAMING_SNAKE_CASE )
print(SCREAMING_SNAKE_CASE )
UpperCamelCase : Optional[Any] = decrypt(SCREAMING_SNAKE_CASE )
print(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 102 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase_ = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
from typing import Any
import requests
snake_case = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
snake_case = BASE_URL + '''/user'''
# https://github.com/settings/tokens
snake_case = os.environ.get('''USER_TOKEN''', '''''')
def snake_case ( lowerCAmelCase_ ) -> dict[Any, Any]:
_snake_case = {
'''Authorization''': f"""token {auth_token}""",
'''Accept''': '''application/vnd.github.v3+json''',
}
return requests.get(lowerCAmelCase_ , headers=lowerCAmelCase_ ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(F"{key}: {value}")
else:
raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')
| 103 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
UpperCamelCase_ = logging.getLogger(__name__)
def _lowerCAmelCase ( __magic_name__ : List[Any] , __magic_name__ : str ) -> Union[str, Any]:
return (preds == labels).mean()
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys() )} )
lowerCamelCase_ = field(metadata={'help': 'Should contain the data files for the task.'} )
lowerCamelCase_ = field(
default=1_28 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _lowerCAmelCase ( ) -> 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 : Dict =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase , lowercase , lowercase : List[Any] =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.''' )
# 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''' , __magic_name__ )
# Set seed
set_seed(training_args.seed )
try:
lowercase : Any =processors[data_args.task_name]()
lowercase : Optional[int] =processor.get_labels()
lowercase : str =len(__magic_name__ )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase : List[str] =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__magic_name__ , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
lowercase : int =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 , )
lowercase : Any =AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__magic_name__ , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase : int =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__magic_name__ , task=data_args.task_name , 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 : Union[str, Any] =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__magic_name__ , task=data_args.task_name , 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 compute_metrics(__magic_name__ : EvalPrediction ) -> Dict:
lowercase : Dict =np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__magic_name__ , p.label_ids )}
# Data collator
lowercase : List[str] =DataCollatorWithPadding(__magic_name__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase : Dict =Trainer(
model=__magic_name__ , args=__magic_name__ , train_dataset=__magic_name__ , eval_dataset=__magic_name__ , compute_metrics=__magic_name__ , data_collator=__magic_name__ , )
# 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_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase : Optional[Any] ={}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase : List[Any] =trainer.evaluate()
lowercase : Any =os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__magic_name__ , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __magic_name__ , __magic_name__ )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__magic_name__ )
return results
def _lowerCAmelCase ( __magic_name__ : Any ) -> Optional[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 92 | 0 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
def _lowerCamelCase ( UpperCAmelCase_ : str, UpperCAmelCase_ : Dict, UpperCAmelCase_ : str ) -> List[str]:
"""simple docstring"""
A__ = os.path.abspath(UpperCAmelCase_ )
logger.info(F"""Converting TensorFlow checkpoint from {tf_path}""" )
# Load weights from TF model
A__ = tf.train.list_variables(UpperCAmelCase_ )
A__ = []
A__ = []
A__ = []
for full_name, shape in init_vars:
# logger.info(f"Loading TF weight {name} with shape {shape}")
A__ = full_name.split("/" )
if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
logger.info(F"""Skipping non-model layer {full_name}""" )
continue
if "optimizer" in full_name:
logger.info(F"""Skipping optimization layer {full_name}""" )
continue
if name[0] == "model":
# ignore initial 'model'
A__ = name[1:]
# figure out how many levels deep the name is
A__ = 0
for _name in name:
if _name.startswith("layer_with_weights" ):
depth += 1
else:
break
layer_depth.append(UpperCAmelCase_ )
# read data
A__ = tf.train.load_variable(UpperCAmelCase_, UpperCAmelCase_ )
names.append("/".join(UpperCAmelCase_ ) )
arrays.append(UpperCAmelCase_ )
logger.info(F"""Read a total of {len(UpperCAmelCase_ ):,} layers""" )
# Sanity check
if len(set(UpperCAmelCase_ ) ) != 1:
raise ValueError(F"""Found layer names with different depths (layer depth {list(set(UpperCAmelCase_ ) )})""" )
A__ = list(set(UpperCAmelCase_ ) )[0]
if layer_depth != 1:
raise ValueError(
"The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
" heads." )
# convert layers
logger.info("Converting weights..." )
for full_name, array in zip(UpperCAmelCase_, UpperCAmelCase_ ):
A__ = full_name.split("/" )
A__ = model
A__ = []
for i, m_name in enumerate(UpperCAmelCase_ ):
if m_name == ".ATTRIBUTES":
# variable names end with .ATTRIBUTES/VARIABLE_VALUE
break
if m_name.startswith("layer_with_weights" ):
A__ = int(m_name.split("-" )[-1] )
if layer_num <= 2:
# embedding layers
# layer_num 0: word_embeddings
# layer_num 1: position_embeddings
# layer_num 2: token_type_embeddings
continue
elif layer_num == 3:
# embedding LayerNorm
trace.extend(["embeddings", "LayerNorm"] )
A__ = getattr(UpperCAmelCase_, "embeddings" )
A__ = getattr(UpperCAmelCase_, "LayerNorm" )
elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
# encoder layers
trace.extend(["encoder", "layer", str(layer_num - 4 )] )
A__ = getattr(UpperCAmelCase_, "encoder" )
A__ = getattr(UpperCAmelCase_, "layer" )
A__ = pointer[layer_num - 4]
elif layer_num == config.num_hidden_layers + 4:
# pooler layer
trace.extend(["pooler", "dense"] )
A__ = getattr(UpperCAmelCase_, "pooler" )
A__ = getattr(UpperCAmelCase_, "dense" )
elif m_name == "embeddings":
trace.append("embeddings" )
A__ = getattr(UpperCAmelCase_, "embeddings" )
if layer_num == 0:
trace.append("word_embeddings" )
A__ = getattr(UpperCAmelCase_, "word_embeddings" )
elif layer_num == 1:
trace.append("position_embeddings" )
A__ = getattr(UpperCAmelCase_, "position_embeddings" )
elif layer_num == 2:
trace.append("token_type_embeddings" )
A__ = getattr(UpperCAmelCase_, "token_type_embeddings" )
else:
raise ValueError(F"""Unknown embedding layer with name {full_name}""" )
trace.append("weight" )
A__ = getattr(UpperCAmelCase_, "weight" )
elif m_name == "_attention_layer":
# self-attention layer
trace.extend(["attention", "self"] )
A__ = getattr(UpperCAmelCase_, "attention" )
A__ = getattr(UpperCAmelCase_, "self" )
elif m_name == "_attention_layer_norm":
# output attention norm
trace.extend(["attention", "output", "LayerNorm"] )
A__ = getattr(UpperCAmelCase_, "attention" )
A__ = getattr(UpperCAmelCase_, "output" )
A__ = getattr(UpperCAmelCase_, "LayerNorm" )
elif m_name == "_attention_output_dense":
# output attention dense
trace.extend(["attention", "output", "dense"] )
A__ = getattr(UpperCAmelCase_, "attention" )
A__ = getattr(UpperCAmelCase_, "output" )
A__ = getattr(UpperCAmelCase_, "dense" )
elif m_name == "_output_dense":
# output dense
trace.extend(["output", "dense"] )
A__ = getattr(UpperCAmelCase_, "output" )
A__ = getattr(UpperCAmelCase_, "dense" )
elif m_name == "_output_layer_norm":
# output dense
trace.extend(["output", "LayerNorm"] )
A__ = getattr(UpperCAmelCase_, "output" )
A__ = getattr(UpperCAmelCase_, "LayerNorm" )
elif m_name == "_key_dense":
# attention key
trace.append("key" )
A__ = getattr(UpperCAmelCase_, "key" )
elif m_name == "_query_dense":
# attention query
trace.append("query" )
A__ = getattr(UpperCAmelCase_, "query" )
elif m_name == "_value_dense":
# attention value
trace.append("value" )
A__ = getattr(UpperCAmelCase_, "value" )
elif m_name == "_intermediate_dense":
# attention intermediate dense
trace.extend(["intermediate", "dense"] )
A__ = getattr(UpperCAmelCase_, "intermediate" )
A__ = getattr(UpperCAmelCase_, "dense" )
elif m_name == "_output_layer_norm":
# output layer norm
trace.append("output" )
A__ = getattr(UpperCAmelCase_, "output" )
# weights & biases
elif m_name in ["bias", "beta"]:
trace.append("bias" )
A__ = getattr(UpperCAmelCase_, "bias" )
elif m_name in ["kernel", "gamma"]:
trace.append("weight" )
A__ = getattr(UpperCAmelCase_, "weight" )
else:
logger.warning(F"""Ignored {m_name}""" )
# for certain layers reshape is necessary
A__ = ".".join(UpperCAmelCase_ )
if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)", UpperCAmelCase_ ) or re.match(
r"(\S+)\.attention\.output\.dense\.weight", UpperCAmelCase_ ):
A__ = array.reshape(pointer.data.shape )
if "kernel" in full_name:
A__ = array.transpose()
if pointer.shape == array.shape:
A__ = torch.from_numpy(UpperCAmelCase_ )
else:
raise ValueError(
F"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
F""" {array.shape}""" )
logger.info(F"""Successfully set variable {full_name} to PyTorch layer {trace}""" )
return model
def _lowerCamelCase ( UpperCAmelCase_ : Any, UpperCAmelCase_ : Union[str, Any], UpperCAmelCase_ : Any ) -> List[str]:
"""simple docstring"""
logger.info(F"""Loading model based on config from {config_path}...""" )
A__ = BertConfig.from_json_file(UpperCAmelCase_ )
A__ = BertModel(UpperCAmelCase_ )
# Load weights from checkpoint
logger.info(F"""Loading weights from checkpoint {tf_checkpoint_path}...""" )
load_tfa_weights_in_bert(UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ )
# Save pytorch-model
logger.info(F"""Saving PyTorch model to {pytorch_dump_path}...""" )
torch.save(model.state_dict(), UpperCAmelCase_ )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow 2.x checkpoint path."""
)
parser.add_argument(
"""--bert_config_file""",
type=str,
required=True,
help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
)
parser.add_argument(
"""--pytorch_dump_path""",
type=str,
required=True,
help="""Path to the output PyTorch model (must include filename).""",
)
UpperCamelCase = parser.parse_args()
convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 104 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
UpperCamelCase_ = logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_ = """cuda""" if torch.cuda.is_available() else """cpu"""
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : Tuple=100 , __magic_name__ : Optional[int]=" " ) -> List[str]:
lowercase : List[Any] =text.split(__magic_name__ )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(__magic_name__ ) , __magic_name__ )]
def _lowerCAmelCase ( __magic_name__ : dict ) -> dict:
lowercase , lowercase : int =[], []
for title, text in zip(documents['''title'''] , documents['''text'''] ):
if text is not None:
for passage in split_text(__magic_name__ ):
titles.append(title if title is not None else '''''' )
texts.append(__magic_name__ )
return {"title": titles, "text": texts}
def _lowerCAmelCase ( __magic_name__ : dict , __magic_name__ : DPRContextEncoder , __magic_name__ : DPRContextEncoderTokenizerFast ) -> dict:
lowercase : Dict =ctx_tokenizer(
documents['''title'''] , documents['''text'''] , truncation=__magic_name__ , padding='''longest''' , return_tensors='''pt''' )['''input_ids''']
lowercase : Optional[int] =ctx_encoder(input_ids.to(device=__magic_name__ ) , return_dict=__magic_name__ ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def _lowerCAmelCase ( __magic_name__ : "RagExampleArguments" , __magic_name__ : "ProcessingArguments" , __magic_name__ : "IndexHnswArguments" , ) -> str:
######################################
logger.info('''Step 1 - Create the dataset''' )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
lowercase : Tuple =load_dataset(
'''csv''' , data_files=[rag_example_args.csv_path] , split='''train''' , delimiter='''\t''' , column_names=['''title''', '''text'''] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
lowercase : Optional[int] =dataset.map(__magic_name__ , batched=__magic_name__ , num_proc=processing_args.num_proc )
# And compute the embeddings
lowercase : Any =DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=__magic_name__ )
lowercase : Any =DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
lowercase : Optional[int] =Features(
{'''text''': Value('''string''' ), '''title''': Value('''string''' ), '''embeddings''': Sequence(Value('''float32''' ) )} ) # optional, save as float32 instead of float64 to save space
lowercase : Optional[Any] =dataset.map(
partial(__magic_name__ , ctx_encoder=__magic_name__ , ctx_tokenizer=__magic_name__ ) , batched=__magic_name__ , batch_size=processing_args.batch_size , features=__magic_name__ , )
# And finally save your dataset
lowercase : Optional[Any] =os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset''' )
dataset.save_to_disk(__magic_name__ )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info('''Step 2 - Index the dataset''' )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
lowercase : Union[str, Any] =faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index('''embeddings''' , custom_index=__magic_name__ )
# And save the index
lowercase : Dict =os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset_hnsw_index.faiss''' )
dataset.get_index('''embeddings''' ).save(__magic_name__ )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=str(Path(lowercase__ ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv' ) , metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''} , )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'} , )
lowerCamelCase_ = field(
default='facebook/rag-sequence-nq' , metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''} , )
lowerCamelCase_ = field(
default='facebook/dpr-ctx_encoder-multiset-base' , metadata={
'help': (
'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
' \'facebook/dpr-ctx_encoder-multiset-base\''
)
} , )
lowerCamelCase_ = field(
default=str(Path(lowercase__ ).parent / 'test_run' / 'dummy-kb' ) , metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=lowercase__ , metadata={
'help': 'The number of processes to use to split the documents into passages. Default is single process.'
} , )
lowerCamelCase_ = field(
default=16 , metadata={
'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=7_68 , metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'} , )
lowerCamelCase_ = field(
default=1_28 , metadata={
'help': (
'The number of bi-directional links created for every new element during the HNSW index construction.'
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
UpperCamelCase_ = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
UpperCamelCase_ = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 92 | 0 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
def __UpperCAmelCase ( lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_ , config=lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = downstream_dict['projector.weight']
SCREAMING_SNAKE_CASE_ : Tuple = downstream_dict['projector.bias']
SCREAMING_SNAKE_CASE_ : List[str] = downstream_dict['model.post_net.linear.weight']
SCREAMING_SNAKE_CASE_ : Dict = downstream_dict['model.post_net.linear.bias']
return model
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_ , config=lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = downstream_dict['model.linear.weight']
SCREAMING_SNAKE_CASE_ : str = downstream_dict['model.linear.bias']
return model
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = UniSpeechSatForXVector.from_pretrained(lowerCamelCase_ , config=lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tuple = downstream_dict['connector.weight']
SCREAMING_SNAKE_CASE_ : Any = downstream_dict['connector.bias']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
SCREAMING_SNAKE_CASE_ : Any = downstream_dict[
F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
]
SCREAMING_SNAKE_CASE_ : Dict = downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']
SCREAMING_SNAKE_CASE_ : Dict = downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
SCREAMING_SNAKE_CASE_ : str = downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
SCREAMING_SNAKE_CASE_ : Dict = downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
SCREAMING_SNAKE_CASE_ : List[Any] = downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
SCREAMING_SNAKE_CASE_ : Optional[Any] = downstream_dict['objective.W']
return model
@torch.no_grad()
def __UpperCAmelCase ( lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : List[str] , lowerCamelCase_ : Any ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.load(lowerCamelCase_ , map_location='cpu' )
SCREAMING_SNAKE_CASE_ : Tuple = checkpoint['Downstream']
SCREAMING_SNAKE_CASE_ : List[Any] = UniSpeechSatConfig.from_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = WavaVecaFeatureExtractor.from_pretrained(
lowerCamelCase_ , return_attention_mask=lowerCamelCase_ , do_normalize=lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : int = hf_config.architectures[0]
if arch.endswith('ForSequenceClassification' ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = convert_classification(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
elif arch.endswith('ForAudioFrameClassification' ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = convert_diarization(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
elif arch.endswith('ForXVector' ):
SCREAMING_SNAKE_CASE_ : Any = convert_xvector(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
else:
raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}' )
if hf_config.use_weighted_layer_sum:
SCREAMING_SNAKE_CASE_ : str = checkpoint['Featurizer']['weights']
hf_feature_extractor.save_pretrained(lowerCamelCase_ )
hf_model.save_pretrained(lowerCamelCase_ )
if __name__ == "__main__":
UpperCamelCase__ : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
)
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
UpperCamelCase__ : List[str] = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 105 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
UpperCamelCase_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
UpperCamelCase_ = 128022
UpperCamelCase_ = 128028
@require_sentencepiece
class __SCREAMING_SNAKE_CASE ( lowercase__ , unittest.TestCase ):
lowerCamelCase_ = MaMaaaTokenizer
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = True
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
super().setUp()
lowercase : Dict =['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
lowercase : List[Any] =dict(zip(UpperCAmelCase__ , range(len(UpperCAmelCase__ ) ) ) )
lowercase : List[Any] =Path(self.tmpdirname )
save_json(UpperCAmelCase__ , save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(UpperCAmelCase__ , save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowercase : Tuple =MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase_ ( self : Any , **UpperCAmelCase__ : int ):
'''simple docstring'''
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Dict ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : Tuple ='''</s>'''
lowercase : Union[str, Any] =0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[Any] =self.get_tokenizer()
lowercase : Optional[Any] =list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''</s>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''<s>''' )
self.assertEqual(len(UpperCAmelCase__ ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip('''Skip this test while all models are still to be uploaded.''' )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Union[str, Any] =self.get_tokenizer()
lowercase : str =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(UpperCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [2, 3, 4, 5, 6] , )
lowercase : Optional[int] =tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(UpperCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
lowercase : Tuple =tokenizer.convert_tokens_to_string(UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , '''This is a test''' )
@slow
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
# fmt: off
lowercase : int ={'''input_ids''': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name='''facebook/m2m100_418M''' , revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e''' , )
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
lowerCamelCase_ = 'facebook/m2m100_418M'
lowerCamelCase_ = [
'In my opinion, there are two levels of response from the French government.',
'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
]
lowerCamelCase_ = [
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
]
# fmt: off
lowerCamelCase_ = [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]
@classmethod
def lowerCamelCase_ ( cls : Optional[Any] ):
'''simple docstring'''
lowercase : MaMaaaTokenizer =MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en''' , tgt_lang='''fr''' )
lowercase : Optional[int] =1
return cls
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
self.assertEqual(self.tokenizer.get_lang_id('''ar''' ) , 128006 )
self.assertEqual(self.tokenizer.get_lang_id('''en''' ) , 128022 )
self.assertEqual(self.tokenizer.get_lang_id('''ro''' ) , 128076 )
self.assertEqual(self.tokenizer.get_lang_id('''mr''' ) , 128063 )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : List[str] =self.tokenizer.get_vocab()
self.assertEqual(len(UpperCAmelCase__ ) , self.tokenizer.vocab_size )
self.assertEqual(vocab['''<unk>'''] , 3 )
self.assertIn(self.tokenizer.get_lang_token('''en''' ) , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : List[Any] ='''en'''
lowercase : Optional[Any] =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
self.assertIn(UpperCAmelCase__ , self.tokenizer.all_special_ids )
# fmt: off
lowercase : str =[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
# fmt: on
lowercase : Optional[Any] =self.tokenizer.decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ )
lowercase : Optional[Any] =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertNotIn(self.tokenizer.eos_token , UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Any =tempfile.mkdtemp()
lowercase : Tuple =self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(UpperCAmelCase__ )
lowercase : Union[str, Any] =MaMaaaTokenizer.from_pretrained(UpperCAmelCase__ )
self.assertDictEqual(new_tok.lang_token_to_id , UpperCAmelCase__ )
@require_torch
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[str] ='''en'''
lowercase : int ='''fr'''
lowercase : Union[str, Any] =self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase__ , return_tensors='''pt''' )
lowercase : str =shift_tokens_right(
batch['''labels'''] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
lowercase : int =batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Optional[int] ='''mr'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
lowercase : Union[str, Any] ='''zh'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : int ='''mr'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
lowercase : Optional[Any] ='''zh'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : Optional[Any] =self.tokenizer._build_translation_inputs('''A test''' , return_tensors='''pt''' , src_lang='''en''' , tgt_lang='''ar''' )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) , {
# en_XX, A, test, EOS
'''input_ids''': [[128022, 58, 4183, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 128006,
} , )
| 92 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class lowerCAmelCase__ ( _lowerCamelCase , unittest.TestCase ):
A_ : List[str] = ShapEPipeline
A_ : Dict = ['prompt']
A_ : Dict = ['prompt']
A_ : Union[str, Any] = [
'num_images_per_prompt',
'num_inference_steps',
'generator',
'latents',
'guidance_scale',
'frame_size',
'output_type',
'return_dict',
]
A_ : List[Any] = False
@property
def __UpperCamelCase ( self : int ) -> str:
return 32
@property
def __UpperCamelCase ( self : Tuple ) -> Optional[int]:
return 32
@property
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
return self.time_input_dim * 4
@property
def __UpperCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
return 8
@property
def __UpperCamelCase ( self : List[str] ) -> Dict:
A = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def __UpperCamelCase ( self : Optional[Any] ) -> Dict:
torch.manual_seed(0 )
A = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModelWithProjection(__UpperCamelCase )
@property
def __UpperCamelCase ( self : Dict ) -> List[Any]:
torch.manual_seed(0 )
A = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
A = PriorTransformer(**__UpperCamelCase )
return model
@property
def __UpperCamelCase ( self : Any ) -> str:
torch.manual_seed(0 )
A = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
A = ShapERenderer(**__UpperCamelCase )
return model
def __UpperCamelCase ( self : List[str] ) -> int:
A = self.dummy_prior
A = self.dummy_text_encoder
A = self.dummy_tokenizer
A = self.dummy_renderer
A = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1_024 , prediction_type='sample' , use_karras_sigmas=__UpperCamelCase , clip_sample=__UpperCamelCase , clip_sample_range=1.0 , )
A = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def __UpperCamelCase ( self : Optional[Any] , __UpperCamelCase : Tuple , __UpperCamelCase : Dict=0 ) -> Any:
if str(__UpperCamelCase ).startswith('mps' ):
A = torch.manual_seed(__UpperCamelCase )
else:
A = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
A = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]:
A = 'cpu'
A = self.get_dummy_components()
A = self.pipeline_class(**__UpperCamelCase )
A = pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
A = pipe(**self.get_dummy_inputs(__UpperCamelCase ) )
A = output.images[0]
A = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
A = np.array(
[
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
0.0_0_0_3_9_2_1_6,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : Tuple ) -> Optional[int]:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __UpperCamelCase ( self : List[Any] ) -> str:
A = torch_device == 'cpu'
A = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__UpperCamelCase , relax_max_difference=__UpperCamelCase , )
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
A = self.get_dummy_components()
A = self.pipeline_class(**__UpperCamelCase )
A = pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
A = 1
A = 2
A = self.get_dummy_inputs(__UpperCamelCase )
for key in inputs.keys():
if key in self.batch_params:
A = batch_size * [inputs[key]]
A = pipe(**__UpperCamelCase , num_images_per_prompt=__UpperCamelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
def __UpperCamelCase ( self : str ) -> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Any ) -> int:
A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
A = ShapEPipeline.from_pretrained('openai/shap-e' )
A = pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
A = torch.Generator(device=__UpperCamelCase ).manual_seed(0 )
A = pipe(
'a shark' , generator=__UpperCamelCase , guidance_scale=1_5.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__UpperCamelCase , __UpperCamelCase ) | 106 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int = 600851475143 ) -> int:
try:
lowercase : Any =int(__magic_name__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
lowercase : Optional[Any] =2
lowercase : Dict =0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
lowercase : Union[str, Any] =i
while n % i == 0:
lowercase : Optional[int] =n // i
i += 1
return int(__magic_name__ )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 92 | 0 |
'''simple docstring'''
from __future__ import annotations
_UpperCAmelCase : Optional[int] = []
def _SCREAMING_SNAKE_CASE ( __snake_case : list[list[int]] , __snake_case : int , __snake_case : int ):
for i in range(len(__snake_case ) ):
if board[row][i] == 1:
return False
for i in range(len(__snake_case ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(__snake_case , -1 , -1 ) , range(__snake_case , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(__snake_case , -1 , -1 ) , range(__snake_case , len(__snake_case ) ) ):
if board[i][j] == 1:
return False
return True
def _SCREAMING_SNAKE_CASE ( __snake_case : list[list[int]] , __snake_case : int ):
if row >= len(__snake_case ):
solution.append(__snake_case )
printboard(__snake_case )
print()
return True
for i in range(len(__snake_case ) ):
if is_safe(__snake_case , __snake_case , __snake_case ):
_A = 1
solve(__snake_case , row + 1 )
_A = 0
return False
def _SCREAMING_SNAKE_CASE ( __snake_case : list[list[int]] ):
for i in range(len(__snake_case ) ):
for j in range(len(__snake_case ) ):
if board[i][j] == 1:
print('Q' , end=' ' )
else:
print('.' , end=' ' )
print()
# n=int(input("The no. of queens"))
_UpperCAmelCase : Any = 8
_UpperCAmelCase : Dict = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))
| 107 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = 'speech_to_text_2'
lowerCamelCase_ = ['past_key_values']
lowerCamelCase_ = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : int , UpperCAmelCase__ : Dict=10000 , UpperCAmelCase__ : Optional[int]=6 , UpperCAmelCase__ : str=2048 , UpperCAmelCase__ : Optional[Any]=4 , UpperCAmelCase__ : Union[str, Any]=0.0 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Optional[Any]="relu" , UpperCAmelCase__ : List[str]=256 , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : List[Any]=0.0 , UpperCAmelCase__ : Optional[Any]=0.0 , UpperCAmelCase__ : Tuple=0.02 , UpperCAmelCase__ : int=2 , UpperCAmelCase__ : str=True , UpperCAmelCase__ : str=1 , UpperCAmelCase__ : Optional[Any]=0 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : List[Any]=1024 , **UpperCAmelCase__ : Dict , ):
'''simple docstring'''
lowercase : List[str] =vocab_size
lowercase : Optional[int] =d_model
lowercase : Optional[Any] =decoder_ffn_dim
lowercase : Any =decoder_layers
lowercase : Dict =decoder_attention_heads
lowercase : List[Any] =dropout
lowercase : List[Any] =attention_dropout
lowercase : Any =activation_dropout
lowercase : Optional[Any] =activation_function
lowercase : Optional[int] =init_std
lowercase : Dict =decoder_layerdrop
lowercase : Optional[int] =use_cache
lowercase : Optional[Any] =decoder_layers
lowercase : List[str] =scale_embedding # scale factor will be sqrt(d_model) if True
lowercase : str =max_target_positions
super().__init__(
pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , decoder_start_token_id=UpperCAmelCase__ , **UpperCAmelCase__ , )
| 92 | 0 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self : Union[str, Any] ) -> None:
"""simple docstring"""
_UpperCAmelCase = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
_UpperCAmelCase = Vector()
def lowerCamelCase ( self : Tuple ) -> None:
"""simple docstring"""
_UpperCAmelCase = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(lowerCamelCase ) , """(0,0,0,0,0,1)""" )
def lowerCamelCase ( self : Optional[int] ) -> None:
"""simple docstring"""
_UpperCAmelCase = Vector([1, 2, 3, 4] )
self.assertEqual(len(lowerCamelCase ) , 4 )
def lowerCamelCase ( self : Tuple ) -> None:
"""simple docstring"""
_UpperCAmelCase = Vector([1, 2] )
_UpperCAmelCase = Vector([1, 2, 3, 4, 5] )
_UpperCAmelCase = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
_UpperCAmelCase = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCamelCase ( self : Dict ) -> None:
"""simple docstring"""
_UpperCAmelCase = Vector([1, 2, 3] )
_UpperCAmelCase = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def lowerCamelCase ( self : Dict ) -> None:
"""simple docstring"""
_UpperCAmelCase = Vector([1, 2, 3] )
_UpperCAmelCase = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def lowerCamelCase ( self : int ) -> None:
"""simple docstring"""
_UpperCAmelCase = Vector([1, 2, 3] )
_UpperCAmelCase = Vector([2, -1, 4] ) # for test of dot product
_UpperCAmelCase = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , """(3.0,6.0,9.0)""" )
self.assertEqual((a * b) , 0 )
def lowerCamelCase ( self : Optional[int] ) -> None:
"""simple docstring"""
self.assertEqual(str(zero_vector(10 ) ).count("""0""" ) , 10 )
def lowerCamelCase ( self : Optional[Any] ) -> None:
"""simple docstring"""
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , """(0,1,0)""" )
def lowerCamelCase ( self : Any ) -> None:
"""simple docstring"""
_UpperCAmelCase = Vector([1, 2, 3] )
_UpperCAmelCase = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , lowerCamelCase , lowerCamelCase ) ) , """(3,4,7)""" )
def lowerCamelCase ( self : Union[str, Any] ) -> None:
"""simple docstring"""
_UpperCAmelCase = Vector([1, 0, 0, 0, 0, 0] )
_UpperCAmelCase = x.copy()
self.assertEqual(str(lowerCamelCase ) , str(lowerCamelCase ) )
def lowerCamelCase ( self : Tuple ) -> None:
"""simple docstring"""
_UpperCAmelCase = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(lowerCamelCase ) , """(0,1,0)""" )
def lowerCamelCase ( self : Union[str, Any] ) -> None:
"""simple docstring"""
_UpperCAmelCase = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n""" , str(lowerCamelCase ) )
def lowerCamelCase ( self : str ) -> None:
"""simple docstring"""
_UpperCAmelCase = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_UpperCAmelCase = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(lowerCamelCase , lowerCamelCase ) )
def lowerCamelCase ( self : str ) -> None:
"""simple docstring"""
_UpperCAmelCase = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_UpperCAmelCase = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(lowerCamelCase , lowerCamelCase ) )
def lowerCamelCase ( self : List[str] ) -> None:
"""simple docstring"""
_UpperCAmelCase = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCamelCase ( self : List[str] ) -> None:
"""simple docstring"""
_UpperCAmelCase = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
_UpperCAmelCase = Vector([1, 2, 3] )
self.assertEqual("""(14,32,50)""" , str(a * x ) )
self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n""" , str(a * 2 ) )
def lowerCamelCase ( self : Any ) -> None:
"""simple docstring"""
_UpperCAmelCase = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n""" , str(lowerCamelCase ) )
def lowerCamelCase ( self : int ) -> None:
"""simple docstring"""
_UpperCAmelCase = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCamelCase ( self : Optional[Any] ) -> None:
"""simple docstring"""
_UpperCAmelCase = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_UpperCAmelCase = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n""" , str(a + b ) )
def lowerCamelCase ( self : Tuple ) -> None:
"""simple docstring"""
_UpperCAmelCase = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_UpperCAmelCase = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n""" , str(a - b ) )
def lowerCamelCase ( self : List[str] ) -> None:
"""simple docstring"""
self.assertEqual(
"""|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n""" , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main() | 108 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self : str , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[str]=13 , UpperCAmelCase__ : Any=32 , UpperCAmelCase__ : List[str]=3 , UpperCAmelCase__ : Any=4 , UpperCAmelCase__ : List[str]=[10, 20, 30, 40] , UpperCAmelCase__ : Any=[2, 2, 3, 2] , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Optional[Any]=37 , UpperCAmelCase__ : Union[str, Any]="gelu" , UpperCAmelCase__ : Optional[Any]=10 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : Optional[int]=["stage2", "stage3", "stage4"] , UpperCAmelCase__ : Dict=[2, 3, 4] , UpperCAmelCase__ : Optional[int]=None , ):
'''simple docstring'''
lowercase : List[Any] =parent
lowercase : Tuple =batch_size
lowercase : List[str] =image_size
lowercase : List[Any] =num_channels
lowercase : Union[str, Any] =num_stages
lowercase : int =hidden_sizes
lowercase : Any =depths
lowercase : Tuple =is_training
lowercase : str =use_labels
lowercase : List[Any] =intermediate_size
lowercase : int =hidden_act
lowercase : Union[str, Any] =num_labels
lowercase : Optional[int] =initializer_range
lowercase : int =out_features
lowercase : List[str] =out_indices
lowercase : str =scope
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : str =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase : Dict =None
if self.use_labels:
lowercase : List[Any] =ids_tensor([self.batch_size] , self.num_labels )
lowercase : Dict =self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[Any] ):
'''simple docstring'''
lowercase : Dict =ConvNextVaModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[Any] =model(UpperCAmelCase__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase_ ( self : Any , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Dict =ConvNextVaForImageClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : str =model(UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Union[str, Any] =ConvNextVaBackbone(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[int] =model(UpperCAmelCase__ )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
lowercase : Optional[Any] =None
lowercase : str =ConvNextVaBackbone(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[Any] =model(UpperCAmelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Any =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase : str =config_and_inputs
lowercase : Any ={'''pixel_values''': pixel_values}
return config, inputs_dict
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : str =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase : List[str] =config_and_inputs
lowercase : Optional[Any] ={'''pixel_values''': pixel_values, '''labels''': labels}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase_ = (
{'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : Dict =ConvNextVaModelTester(self )
lowercase : str =ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=37 )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return
@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds''' )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings''' )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking''' )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase , lowercase : Union[str, Any] =self.model_tester.prepare_config_and_inputs_with_labels()
lowercase : Optional[int] =True
if model_class.__name__ in [
*get_values(UpperCAmelCase__ ),
*get_values(UpperCAmelCase__ ),
]:
continue
lowercase : Dict =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.train()
lowercase : Optional[Any] =self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
lowercase : List[Any] =model(**UpperCAmelCase__ ).loss
loss.backward()
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase , lowercase : Any =self.model_tester.prepare_config_and_inputs_with_labels()
lowercase : List[Any] =False
lowercase : Any =True
if (
model_class.__name__
in [*get_values(UpperCAmelCase__ ), *get_values(UpperCAmelCase__ )]
or not model_class.supports_gradient_checkpointing
):
continue
lowercase : Any =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.gradient_checkpointing_enable()
model.train()
lowercase : Optional[Any] =self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
lowercase : int =model(**UpperCAmelCase__ ).loss
loss.backward()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase , lowercase : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : Dict =model_class(UpperCAmelCase__ )
lowercase : Union[str, Any] =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase : int =[*signature.parameters.keys()]
lowercase : Optional[Any] =['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
def check_hidden_states_output(UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : Tuple ):
lowercase : int =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
lowercase : Any =model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase : Dict =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase : List[Any] =self.model_tester.num_stages
self.assertEqual(len(UpperCAmelCase__ ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowercase , lowercase : List[str] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : List[str] =True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase : Tuple =True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
lowercase : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__ )
@slow
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : List[Any] =ConvNextVaModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def _lowerCAmelCase ( ) -> List[Any]:
lowercase : Union[str, Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : Tuple =ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ).to(UpperCAmelCase__ )
lowercase : int =self.default_image_processor
lowercase : List[str] =prepare_img()
lowercase : List[Any] =preprocessor(images=UpperCAmelCase__ , return_tensors='''pt''' ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
lowercase : Dict =model(**UpperCAmelCase__ )
# verify the logits
lowercase : Optional[Any] =torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase__ )
lowercase : Tuple =torch.tensor([0.99_96, 0.19_66, -0.43_86] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase__ , atol=1E-4 ) )
| 92 | 0 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
return x + 2
class __a ( unittest.TestCase ):
def UpperCAmelCase__ ( self : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """x = 3"""
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{} ,state=lowerCamelCase )
assert result == 3
self.assertDictEqual(lowerCamelCase ,{"""x""": 3} )
__SCREAMING_SNAKE_CASE = """x = y"""
__SCREAMING_SNAKE_CASE = {"""y""": 5}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{} ,state=lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(lowerCamelCase ,{"""x""": 5, """y""": 5} )
def UpperCAmelCase__ ( self : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """y = add_two(x)"""
__SCREAMING_SNAKE_CASE = {"""x""": 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{"""add_two""": add_two} ,state=lowerCamelCase )
assert result == 5
self.assertDictEqual(lowerCamelCase ,{"""x""": 3, """y""": 5} )
# Won't work without the tool
with CaptureStdout() as out:
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{} ,state=lowerCamelCase )
assert result is None
assert "tried to execute add_two" in out.out
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """x = 3"""
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{} ,state=lowerCamelCase )
assert result == 3
self.assertDictEqual(lowerCamelCase ,{"""x""": 3} )
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """test_dict = {'x': x, 'y': add_two(x)}"""
__SCREAMING_SNAKE_CASE = {"""x""": 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{"""add_two""": add_two} ,state=lowerCamelCase )
self.assertDictEqual(lowerCamelCase ,{"""x""": 3, """y""": 5} )
self.assertDictEqual(lowerCamelCase ,{"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def UpperCAmelCase__ ( self : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """x = 3\ny = 5"""
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{} ,state=lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(lowerCamelCase ,{"""x""": 3, """y""": 5} )
def UpperCAmelCase__ ( self : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """text = f'This is x: {x}.'"""
__SCREAMING_SNAKE_CASE = {"""x""": 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{} ,state=lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(lowerCamelCase ,{"""x""": 3, """text""": """This is x: 3."""} )
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """if x <= 3:\n y = 2\nelse:\n y = 5"""
__SCREAMING_SNAKE_CASE = {"""x""": 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{} ,state=lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(lowerCamelCase ,{"""x""": 3, """y""": 2} )
__SCREAMING_SNAKE_CASE = {"""x""": 8}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{} ,state=lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(lowerCamelCase ,{"""x""": 8, """y""": 5} )
def UpperCAmelCase__ ( self : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """test_list = [x, add_two(x)]"""
__SCREAMING_SNAKE_CASE = {"""x""": 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{"""add_two""": add_two} ,state=lowerCamelCase )
self.assertListEqual(lowerCamelCase ,[3, 5] )
self.assertDictEqual(lowerCamelCase ,{"""x""": 3, """test_list""": [3, 5]} )
def UpperCAmelCase__ ( self : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """y = x"""
__SCREAMING_SNAKE_CASE = {"""x""": 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{} ,state=lowerCamelCase )
assert result == 3
self.assertDictEqual(lowerCamelCase ,{"""x""": 3, """y""": 3} )
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """test_list = [x, add_two(x)]\ntest_list[1]"""
__SCREAMING_SNAKE_CASE = {"""x""": 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{"""add_two""": add_two} ,state=lowerCamelCase )
assert result == 5
self.assertDictEqual(lowerCamelCase ,{"""x""": 3, """test_list""": [3, 5]} )
__SCREAMING_SNAKE_CASE = """test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"""
__SCREAMING_SNAKE_CASE = {"""x""": 3}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{"""add_two""": add_two} ,state=lowerCamelCase )
assert result == 5
self.assertDictEqual(lowerCamelCase ,{"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def UpperCAmelCase__ ( self : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """x = 0\nfor i in range(3):\n x = i"""
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(lowerCamelCase ,{"""range""": range} ,state=lowerCamelCase )
assert result == 2
self.assertDictEqual(lowerCamelCase ,{"""x""": 2, """i""": 2} )
| 109 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
UpperCamelCase_ = object()
# For specifying empty leaf dict `{}`
UpperCamelCase_ = object()
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : int ) -> Optional[int]:
lowercase : Optional[Any] =tuple((re.compile(x + '''$''' ) for x in qs) )
for i in range(len(__magic_name__ ) - len(__magic_name__ ) + 1 ):
lowercase : Union[str, Any] =[x.match(__magic_name__ ) for x, y in zip(__magic_name__ , ks[i:] )]
if matches and all(__magic_name__ ):
return True
return False
def _lowerCAmelCase ( __magic_name__ : Dict ) -> List[str]:
def replace(__magic_name__ : Optional[int] , __magic_name__ : Optional[Any] ):
for rule, replacement in rules:
if _match(__magic_name__ , __magic_name__ ):
return replacement
return val
return replace
def _lowerCAmelCase ( ) -> int:
return [
# embeddings
(("transformer", "wpe", "embedding"), P('''mp''' , __magic_name__ )),
(("transformer", "wte", "embedding"), P('''mp''' , __magic_name__ )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__magic_name__ , '''mp''' )),
(("attention", "out_proj", "kernel"), P('''mp''' , __magic_name__ )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(__magic_name__ , '''mp''' )),
(("mlp", "c_fc", "bias"), P('''mp''' )),
(("mlp", "c_proj", "kernel"), P('''mp''' , __magic_name__ )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def _lowerCAmelCase ( __magic_name__ : str ) -> int:
lowercase : int =_get_partition_rules()
lowercase : Tuple =_replacement_rules(__magic_name__ )
lowercase : Any ={k: _unmatched for k in flatten_dict(__magic_name__ )}
lowercase : Any ={k: replace(__magic_name__ , __magic_name__ ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(__magic_name__ ) )
| 92 | 0 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
UpperCAmelCase__ : Union[str, Any] = len(_snake_case )
UpperCAmelCase__ : Dict = len(_snake_case )
UpperCAmelCase__ : str = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
UpperCAmelCase__ : Any = True
for i in range(_snake_case ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
UpperCAmelCase__ : Union[str, Any] = True
if a[i].islower():
UpperCAmelCase__ : Any = True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 110 |
'''simple docstring'''
from collections import defaultdict
def _lowerCAmelCase ( __magic_name__ : int ) -> int:
lowercase : Optional[Any] =1
lowercase : Union[str, Any] =True
for v in tree[start]:
if v not in visited:
ret += dfs(__magic_name__ )
if ret % 2 == 0:
cuts.append(__magic_name__ )
return ret
def _lowerCAmelCase ( ) -> int:
dfs(1 )
if __name__ == "__main__":
UpperCamelCase_ , UpperCamelCase_ = 10, 9
UpperCamelCase_ = defaultdict(list)
UpperCamelCase_ = {}
UpperCamelCase_ = []
UpperCamelCase_ = 0
UpperCamelCase_ = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 92 | 0 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a : Optional[int] = '''▁'''
a : List[Any] = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class lowerCamelCase_ ( lowercase__ , unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase = BertGenerationTokenizer
__UpperCAmelCase = False
__UpperCAmelCase = True
def A ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
__lowercase = BertGenerationTokenizer(UpperCAmelCase__ , keep_accents=UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def A ( self ) -> Optional[Any]:
'''simple docstring'''
__lowercase = '''<s>'''
__lowercase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def A ( self ) -> List[Any]:
'''simple docstring'''
__lowercase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(UpperCAmelCase__ ) , 1_0_0_2 )
def A ( self ) -> Any:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_0 )
def A ( self ) -> Any:
'''simple docstring'''
__lowercase = BertGenerationTokenizer(UpperCAmelCase__ , keep_accents=UpperCAmelCase__ )
__lowercase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(UpperCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2] , )
__lowercase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
UpperCAmelCase__ , [
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 = tokenizer.convert_tokens_to_ids(UpperCAmelCase__ )
self.assertListEqual(
UpperCAmelCase__ , [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4] , )
__lowercase = tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
self.assertListEqual(
UpperCAmelCase__ , [
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>''',
'''.''',
] , )
@cached_property
def A ( self ) -> Dict:
'''simple docstring'''
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def A ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowercase = '''Hello World!'''
__lowercase = [1_8_5_3_6, 2_2_6_0, 1_0_1]
self.assertListEqual(UpperCAmelCase__ , self.big_tokenizer.encode(UpperCAmelCase__ ) )
@slow
def A ( self ) -> List[str]:
'''simple docstring'''
__lowercase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
__lowercase = [
8_7_1,
4_1_9,
3_5_8,
9_4_6,
9_9_1,
2_5_2_1,
4_5_2,
3_5_8,
1_3_5_7,
3_8_7,
7_7_5_1,
3_5_3_6,
1_1_2,
9_8_5,
4_5_6,
1_2_6,
8_6_5,
9_3_8,
5_4_0_0,
5_7_3_4,
4_5_8,
1_3_6_8,
4_6_7,
7_8_6,
2_4_6_2,
5_2_4_6,
1_1_5_9,
6_3_3,
8_6_5,
4_5_1_9,
4_5_7,
5_8_2,
8_5_2,
2_5_5_7,
4_2_7,
9_1_6,
5_0_8,
4_0_5,
3_4_3_2_4,
4_9_7,
3_9_1,
4_0_8,
1_1_3_4_2,
1_2_4_4,
3_8_5,
1_0_0,
9_3_8,
9_8_5,
4_5_6,
5_7_4,
3_6_2,
1_2_5_9_7,
3_2_0_0,
3_1_2_9,
1_1_7_2,
]
self.assertListEqual(UpperCAmelCase__ , self.big_tokenizer.encode(UpperCAmelCase__ ) )
@require_torch
@slow
def A ( self ) -> Optional[Any]:
'''simple docstring'''
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
__lowercase = list(self.big_tokenizer.get_vocab().keys() )[:1_0]
__lowercase = ''' '''.join(UpperCAmelCase__ )
__lowercase = self.big_tokenizer.encode_plus(UpperCAmelCase__ , return_tensors='''pt''' , return_token_type_ids=UpperCAmelCase__ )
__lowercase = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=UpperCAmelCase__ )
__lowercase = BertGenerationConfig()
__lowercase = BertGenerationEncoder(UpperCAmelCase__ )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**UpperCAmelCase__ )
model(**UpperCAmelCase__ )
@slow
def A ( self ) -> Dict:
'''simple docstring'''
__lowercase = {'''input_ids''': [[3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4], [4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 639 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
def _lowerCAmelCase ( __magic_name__ : List[str] ) -> Dict:
lowercase : List[str] =R'''\w+[.]\d+'''
lowercase : List[str] =re.findall(__magic_name__ , __magic_name__ )
for pat in pats:
lowercase : Optional[int] =key.replace(__magic_name__ , '''_'''.join(pat.split('''.''' ) ) )
return key
def _lowerCAmelCase ( __magic_name__ : List[str] , __magic_name__ : Union[str, Any] , __magic_name__ : Dict ) -> str:
lowercase : Dict =pt_tuple_key[:-1] + ('''scale''',)
if (
any('''norm''' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
lowercase : str =pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
lowercase : str =pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
lowercase : Dict =pt_tuple_key[:-1] + ('''embedding''',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowercase : Tuple =pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
lowercase : Tuple =pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowercase : str =pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight":
lowercase : Optional[Any] =pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowercase : Dict =pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowercase : Union[str, Any] =pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : Optional[Any] , __magic_name__ : Union[str, Any]=42 ) -> List[str]:
# Step 1: Convert pytorch tensor to numpy
lowercase : Optional[Any] ={k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
lowercase : str =flax_model.init_weights(PRNGKey(__magic_name__ ) )
lowercase : Dict =flatten_dict(__magic_name__ )
lowercase : Dict ={}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase : Dict =rename_key(__magic_name__ )
lowercase : Optional[int] =tuple(renamed_pt_key.split('''.''' ) )
# Correctly rename weight parameters
lowercase , lowercase : Any =rename_key_and_reshape_tensor(__magic_name__ , __magic_name__ , __magic_name__ )
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}.''' )
# also add unexpected weight so that warning is thrown
lowercase : Tuple =jnp.asarray(__magic_name__ )
return unflatten_dict(__magic_name__ )
| 92 | 0 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ,lowerCAmelCase_ : str = "cpu" ,lowerCAmelCase_ : Union[str, None] = None ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict =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' )
SCREAMING_SNAKE_CASE_ : List[str] =v.half()
if save_path is None: # overwrite src_path
SCREAMING_SNAKE_CASE_ : Union[str, Any] =src_path
torch.save(lowerCAmelCase_ ,lowerCAmelCase_ )
if __name__ == "__main__":
fire.Fire(convert)
| 220 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase_ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
UpperCamelCase_ = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
UpperCamelCase_ = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, float]:
lowercase : int =len([g for position, g in enumerate(__magic_name__ ) if g == main_target[position]] )
return (item, float(__magic_name__ ))
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, str]:
lowercase : Any =random.randint(0 , len(__magic_name__ ) - 1 )
lowercase : Tuple =parent_a[:random_slice] + parent_a[random_slice:]
lowercase : List[str] =parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : list[str] ) -> str:
lowercase : Union[str, Any] =list(__magic_name__ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
lowercase : Dict =random.choice(__magic_name__ )
return "".join(__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : tuple[str, float] , __magic_name__ : list[tuple[str, float]] , __magic_name__ : list[str] , ) -> list[str]:
lowercase : Any =[]
# Generate more children proportionally to the fitness score.
lowercase : Dict =int(parent_a[1] * 100 ) + 1
lowercase : List[str] =10 if child_n >= 10 else child_n
for _ in range(__magic_name__ ):
lowercase : List[str] =population_score[random.randint(0 , __magic_name__ )][0]
lowercase , lowercase : Dict =crossover(parent_a[0] , __magic_name__ )
# Append new string to the population list.
pop.append(mutate(__magic_name__ , __magic_name__ ) )
pop.append(mutate(__magic_name__ , __magic_name__ ) )
return pop
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : list[str] , __magic_name__ : bool = True ) -> tuple[int, int, str]:
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
lowercase : List[str] =f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
raise ValueError(__magic_name__ )
# Verify that the target contains no genes besides the ones inside genes variable.
lowercase : Optional[int] =sorted({c for c in target if c not in genes} )
if not_in_genes_list:
lowercase : Dict =f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
raise ValueError(__magic_name__ )
# Generate random starting population.
lowercase : int =[]
for _ in range(__magic_name__ ):
population.append(''''''.join([random.choice(__magic_name__ ) for i in range(len(__magic_name__ ) )] ) )
# Just some logs to know what the algorithms is doing.
lowercase , lowercase : Optional[int] =0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(__magic_name__ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
lowercase : List[str] =[evaluate(__magic_name__ , __magic_name__ ) for item in population]
# Check if there is a matching evolution.
lowercase : int =sorted(__magic_name__ , key=lambda __magic_name__ : x[1] , reverse=__magic_name__ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f'''\nGeneration: {generation}'''
f'''\nTotal Population:{total_population}'''
f'''\nBest score: {population_score[0][1]}'''
f'''\nBest string: {population_score[0][0]}''' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
lowercase : Any =population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(__magic_name__ )
# Normalize population score to be between 0 and 1.
lowercase : Dict =[
(item, score / len(__magic_name__ )) for item, score in population_score
]
# This is selection
for i in range(__magic_name__ ):
population.extend(select(population_score[int(__magic_name__ )] , __magic_name__ , __magic_name__ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(__magic_name__ ) > N_POPULATION:
break
if __name__ == "__main__":
UpperCamelCase_ = (
"""This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
)
UpperCamelCase_ = list(
""" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
"""nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
)
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = basic(target_str, genes_list)
print(
f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 92 | 0 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE_ ( lowercase__ , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase : Optional[Any] =GPTaTokenizer
__lowerCAmelCase : Optional[int] =GPTaTokenizerFast
__lowerCAmelCase : Optional[Any] =True
__lowerCAmelCase : Any ={'''add_prefix_space''': True}
__lowerCAmelCase : str =False
def UpperCamelCase__ ( self :Tuple):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_lowercase =[
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
'''<|endoftext|>''',
]
_lowercase =dict(zip(UpperCAmelCase__, range(len(UpperCAmelCase__))))
_lowercase =['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
_lowercase ={'''unk_token''': '''<unk>'''}
_lowercase =os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
_lowercase =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(UpperCAmelCase__) + '\n')
with open(self.merges_file, 'w', encoding='utf-8') as fp:
fp.write('\n'.join(UpperCAmelCase__))
def UpperCamelCase__ ( self :str, **snake_case :Dict):
"""simple docstring"""
kwargs.update(self.special_tokens_map)
return GPTaTokenizer.from_pretrained(self.tmpdirname, **UpperCAmelCase__)
def UpperCamelCase__ ( self :str, **snake_case :List[Any]):
"""simple docstring"""
kwargs.update(self.special_tokens_map)
return GPTaTokenizerFast.from_pretrained(self.tmpdirname, **UpperCAmelCase__)
def UpperCamelCase__ ( self :Any, snake_case :Tuple):
"""simple docstring"""
_lowercase ='''lower newer'''
_lowercase ='''lower newer'''
return input_text, output_text
def UpperCamelCase__ ( self :Dict):
"""simple docstring"""
_lowercase =GPTaTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
_lowercase ='''lower newer'''
_lowercase =['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
_lowercase =tokenizer.tokenize(UpperCAmelCase__, add_prefix_space=UpperCAmelCase__)
self.assertListEqual(UpperCAmelCase__, UpperCAmelCase__)
_lowercase =tokens + [tokenizer.unk_token]
_lowercase =[14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__), UpperCAmelCase__)
def UpperCamelCase__ ( self :Union[str, Any]):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_lowercase =self.get_tokenizer()
_lowercase =self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase__)
_lowercase ='''lower newer'''
# Testing tokenization
_lowercase =tokenizer.tokenize(UpperCAmelCase__, add_prefix_space=UpperCAmelCase__)
_lowercase =rust_tokenizer.tokenize(UpperCAmelCase__)
self.assertListEqual(UpperCAmelCase__, UpperCAmelCase__)
# Testing conversion to ids without special tokens
_lowercase =tokenizer.encode(UpperCAmelCase__, add_special_tokens=UpperCAmelCase__, add_prefix_space=UpperCAmelCase__)
_lowercase =rust_tokenizer.encode(UpperCAmelCase__, add_special_tokens=UpperCAmelCase__)
self.assertListEqual(UpperCAmelCase__, UpperCAmelCase__)
# Testing conversion to ids with special tokens
_lowercase =self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase__)
_lowercase =tokenizer.encode(UpperCAmelCase__, add_prefix_space=UpperCAmelCase__)
_lowercase =rust_tokenizer.encode(UpperCAmelCase__)
self.assertListEqual(UpperCAmelCase__, UpperCAmelCase__)
# Testing the unknown token
_lowercase =tokens + [rust_tokenizer.unk_token]
_lowercase =[14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCAmelCase__), UpperCAmelCase__)
def UpperCamelCase__ ( self :Tuple, *snake_case :List[str], **snake_case :int):
"""simple docstring"""
pass
def UpperCamelCase__ ( self :Any, snake_case :List[Any]=15):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
_lowercase =self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__, **UpperCAmelCase__)
# Simple input
_lowercase ='''This is a simple input'''
_lowercase =['''This is a simple input 1''', '''This is a simple input 2''']
_lowercase =('''This is a simple input''', '''This is a pair''')
_lowercase =[
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(UpperCAmelCase__, tokenizer_r.encode, UpperCAmelCase__, max_length=UpperCAmelCase__, padding='max_length')
# Simple input
self.assertRaises(UpperCAmelCase__, tokenizer_r.encode_plus, UpperCAmelCase__, max_length=UpperCAmelCase__, padding='max_length')
# Simple input
self.assertRaises(
UpperCAmelCase__, tokenizer_r.batch_encode_plus, UpperCAmelCase__, max_length=UpperCAmelCase__, padding='max_length', )
# Pair input
self.assertRaises(UpperCAmelCase__, tokenizer_r.encode, UpperCAmelCase__, max_length=UpperCAmelCase__, padding='max_length')
# Pair input
self.assertRaises(UpperCAmelCase__, tokenizer_r.encode_plus, UpperCAmelCase__, max_length=UpperCAmelCase__, padding='max_length')
# Pair input
self.assertRaises(
UpperCAmelCase__, tokenizer_r.batch_encode_plus, UpperCAmelCase__, max_length=UpperCAmelCase__, padding='max_length', )
def UpperCamelCase__ ( self :str):
"""simple docstring"""
_lowercase =GPTaTokenizer.from_pretrained(self.tmpdirname, pad_token='<pad>')
# Simple input
_lowercase ='''This is a simple input'''
_lowercase =['''This is a simple input looooooooong''', '''This is a simple input''']
_lowercase =('''This is a simple input''', '''This is a pair''')
_lowercase =[
('''This is a simple input loooooong''', '''This is a simple input'''),
('''This is a simple pair loooooong''', '''This is a simple pair'''),
]
_lowercase =tokenizer.pad_token_id
_lowercase =tokenizer(UpperCAmelCase__, padding='max_length', max_length=30, return_tensors='np')
_lowercase =tokenizer(UpperCAmelCase__, padding=UpperCAmelCase__, truncate=UpperCAmelCase__, return_tensors='np')
_lowercase =tokenizer(*UpperCAmelCase__, padding='max_length', max_length=60, return_tensors='np')
_lowercase =tokenizer(UpperCAmelCase__, padding=UpperCAmelCase__, truncate=UpperCAmelCase__, return_tensors='np')
# s
# test single string max_length padding
self.assertEqual(out_s['input_ids'].shape[-1], 30)
self.assertTrue(pad_token_id in out_s['input_ids'])
self.assertTrue(0 in out_s['attention_mask'])
# s2
# test automatic padding
self.assertEqual(out_sa['input_ids'].shape[-1], 33)
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['input_ids'][0])
self.assertFalse(0 in out_sa['attention_mask'][0])
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['input_ids'][1])
self.assertTrue(0 in out_sa['attention_mask'][1])
# p
# test single pair max_length padding
self.assertEqual(out_p['input_ids'].shape[-1], 60)
self.assertTrue(pad_token_id in out_p['input_ids'])
self.assertTrue(0 in out_p['attention_mask'])
# p2
# test automatic padding pair
self.assertEqual(out_pa['input_ids'].shape[-1], 52)
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['input_ids'][0])
self.assertFalse(0 in out_pa['attention_mask'][0])
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['input_ids'][1])
self.assertTrue(0 in out_pa['attention_mask'][1])
def UpperCamelCase__ ( self :Union[str, Any]):
"""simple docstring"""
_lowercase ='''$$$'''
_lowercase =GPTaTokenizer.from_pretrained(self.tmpdirname, bos_token=UpperCAmelCase__, add_bos_token=UpperCAmelCase__)
_lowercase ='''This is a simple input'''
_lowercase =['''This is a simple input 1''', '''This is a simple input 2''']
_lowercase =tokenizer.bos_token_id
_lowercase =tokenizer(UpperCAmelCase__)
_lowercase =tokenizer(UpperCAmelCase__)
self.assertEqual(out_s.input_ids[0], UpperCAmelCase__)
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))
_lowercase =tokenizer.decode(out_s.input_ids)
_lowercase =tokenizer.batch_decode(out_sa.input_ids)
self.assertEqual(decode_s.split()[0], UpperCAmelCase__)
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))
def UpperCamelCase__ ( self :Dict):
"""simple docstring"""
pass
def UpperCamelCase__ ( self :Optional[Any]):
"""simple docstring"""
_lowercase =[self.get_tokenizer(do_lower_case=UpperCAmelCase__, add_bos_token=UpperCAmelCase__)]
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}'''):
_lowercase ='''Encode this.'''
_lowercase ='''This one too please.'''
_lowercase =tokenizer.encode(UpperCAmelCase__, add_special_tokens=UpperCAmelCase__)
encoded_sequence += tokenizer.encode(UpperCAmelCase__, add_special_tokens=UpperCAmelCase__)
_lowercase =tokenizer.encode_plus(
UpperCAmelCase__, UpperCAmelCase__, add_special_tokens=UpperCAmelCase__, return_special_tokens_mask=UpperCAmelCase__, )
_lowercase =encoded_sequence_dict['''input_ids''']
_lowercase =encoded_sequence_dict['''special_tokens_mask''']
self.assertEqual(len(UpperCAmelCase__), len(UpperCAmelCase__))
_lowercase =[
(x if not special_tokens_mask[i] else None) for i, x in enumerate(UpperCAmelCase__)
]
_lowercase =[x for x in filtered_sequence if x is not None]
self.assertEqual(UpperCAmelCase__, UpperCAmelCase__)
@require_tokenizers
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self :Dict):
"""simple docstring"""
_lowercase =AutoTokenizer.from_pretrained('facebook/opt-350m', from_slow=UpperCAmelCase__)
_lowercase ='''A photo of a cat'''
_lowercase =tokenizer.encode(
UpperCAmelCase__, )
self.assertEqual(UpperCAmelCase__, [2, 250, 1345, 9, 10, 4758])
tokenizer.save_pretrained('test_opt')
_lowercase =AutoTokenizer.from_pretrained('./test_opt')
_lowercase =tokenizer.encode(
UpperCAmelCase__, )
self.assertEqual(UpperCAmelCase__, [2, 250, 1345, 9, 10, 4758])
def UpperCamelCase__ ( self :List[str]):
"""simple docstring"""
_lowercase =AutoTokenizer.from_pretrained('facebook/opt-350m', use_slow=UpperCAmelCase__)
_lowercase ='''A photo of a cat'''
_lowercase =tokenizer.encode(
UpperCAmelCase__, )
# Same as above
self.assertEqual(UpperCAmelCase__, [2, 250, 1345, 9, 10, 4758])
@unittest.skip('This test is failing because of a bug in the fast tokenizer')
def UpperCamelCase__ ( self :Optional[Any]):
"""simple docstring"""
_lowercase =AutoTokenizer.from_pretrained('facebook/opt-350m', from_slow=UpperCAmelCase__)
_lowercase ='''bos'''
_lowercase =tokenizer.get_vocab()['''bos''']
_lowercase ='''A photo of a cat'''
_lowercase =tokenizer.encode(
UpperCAmelCase__, )
# We changed the bos token
self.assertEqual(UpperCAmelCase__, [3_1957, 250, 1345, 9, 10, 4758])
tokenizer.save_pretrained('./tok')
_lowercase =AutoTokenizer.from_pretrained('./tok')
self.assertTrue(tokenizer.is_fast)
_lowercase =tokenizer.encode(
UpperCAmelCase__, )
self.assertEqual(UpperCAmelCase__, [3_1957, 250, 1345, 9, 10, 4758])
| 181 |
'''simple docstring'''
import datasets
UpperCamelCase_ = """\
@InProceedings{conneau2018xnli,
author = \"Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin\",
title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing\",
year = \"2018\",
publisher = \"Association for Computational Linguistics\",
location = \"Brussels, Belgium\",
}
"""
UpperCamelCase_ = """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""
UpperCamelCase_ = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: Predicted labels.
references: Ground truth labels.
Returns:
'accuracy': accuracy
Examples:
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> xnli_metric = datasets.load_metric(\"xnli\")
>>> results = xnli_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
"""
def _lowerCAmelCase ( __magic_name__ : Dict , __magic_name__ : Union[str, Any] ) -> Union[str, Any]:
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32''' ),
'''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32''' ),
} ) , codebase_urls=[] , reference_urls=[] , format='''numpy''' , )
def lowerCamelCase_ ( self : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[Any] ):
'''simple docstring'''
return {"accuracy": simple_accuracy(UpperCAmelCase__ , UpperCAmelCase__ )}
| 92 | 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
__UpperCamelCase : Tuple = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )
@require_torch
@require_tf
@slow
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self , _snake_case , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = True , ):
"""simple docstring"""
lowerCAmelCase = [file for file in os.listdir(UpperCAmelCase__ ) if os.path.isfile(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) )]
if identifier is not None:
lowerCAmelCase = [file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
for n_ in n_identifier:
lowerCAmelCase = [file for file in files if n_ not in file]
else:
lowerCAmelCase = [file for file in files if n_identifier not in file]
lowerCAmelCase = ignore_files or []
ignore_files.append('__init__.py' )
lowerCAmelCase = [file for file in files if file not in ignore_files]
for file in files:
# Open all files
print('Testing' , UpperCAmelCase__ )
if only_modules:
lowerCAmelCase = file.split('.' )[0]
try:
lowerCAmelCase = getattr(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase = doctest.DocTestSuite(UpperCAmelCase__ )
lowerCAmelCase = unittest.TextTestRunner().run(UpperCAmelCase__ )
self.assertIs(len(result.failures ) , 0 )
except AttributeError:
logger.info(F'{module_identifier} is not a module.' )
else:
lowerCAmelCase = doctest.testfile(str('..' / directory / file ) , optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed , 0 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Path('src/transformers' )
lowerCAmelCase = '''modeling'''
lowerCAmelCase = [
'''modeling_ctrl.py''',
'''modeling_tf_ctrl.py''',
]
self.analyze_directory(UpperCAmelCase__ , identifier=UpperCAmelCase__ , ignore_files=UpperCAmelCase__ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Path('src/transformers' )
lowerCAmelCase = '''tokenization'''
self.analyze_directory(UpperCAmelCase__ , identifier=UpperCAmelCase__ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Path('src/transformers' )
lowerCAmelCase = '''configuration'''
self.analyze_directory(UpperCAmelCase__ , identifier=UpperCAmelCase__ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Path('src/transformers' )
lowerCAmelCase = ['''configuration''', '''modeling''', '''tokenization''']
self.analyze_directory(UpperCAmelCase__ , n_identifier=UpperCAmelCase__ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Path('docs/source' )
lowerCAmelCase = ['''favicon.ico''']
self.analyze_directory(UpperCAmelCase__ , ignore_files=UpperCAmelCase__ , only_modules=UpperCAmelCase__ )
| 4 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[str] , UpperCAmelCase__ : Dict , ):
'''simple docstring'''
lowercase : Any =parent
lowercase : Optional[int] =13
lowercase : Union[str, Any] =7
lowercase : str =30
lowercase : Optional[int] =self.seq_length + self.mem_len
lowercase : Dict =15
lowercase : List[str] =True
lowercase : Optional[int] =True
lowercase : Tuple =99
lowercase : str =[10, 50, 80]
lowercase : List[Any] =32
lowercase : Optional[int] =32
lowercase : int =4
lowercase : Any =8
lowercase : List[Any] =128
lowercase : List[str] =2
lowercase : Tuple =2
lowercase : int =None
lowercase : Optional[int] =1
lowercase : int =0
lowercase : List[str] =3
lowercase : str =self.vocab_size - 1
lowercase : Tuple =0.01
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : str =None
if self.use_labels:
lowercase : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Union[str, Any] =TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
random.seed(self.seed )
tf.random.set_seed(self.seed )
def lowerCamelCase_ ( self : Union[str, Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[str] ):
'''simple docstring'''
lowercase : Tuple =TFTransfoXLModel(UpperCAmelCase__ )
lowercase , lowercase : Optional[Any] =model(UpperCAmelCase__ ).to_tuple()
lowercase : List[str] ={'''input_ids''': input_ids_a, '''mems''': mems_a}
lowercase , lowercase : Any =model(UpperCAmelCase__ ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def lowerCamelCase_ ( self : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : int =TFTransfoXLLMHeadModel(UpperCAmelCase__ )
lowercase , lowercase : Tuple =model(UpperCAmelCase__ ).to_tuple()
lowercase : Optional[Any] ={'''input_ids''': input_ids_a, '''labels''': lm_labels}
lowercase , lowercase : Optional[int] =model(UpperCAmelCase__ ).to_tuple()
lowercase , lowercase : List[str] =model([input_ids_a, mems_a] ).to_tuple()
lowercase : int ={'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}
lowercase , lowercase : str =model(UpperCAmelCase__ ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def lowerCamelCase_ ( self : str , UpperCAmelCase__ : int , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
lowercase : Optional[int] =TFTransfoXLForSequenceClassification(UpperCAmelCase__ )
lowercase : Union[str, Any] =model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[Any] =self.prepare_config_and_inputs()
((lowercase) , (lowercase) , (lowercase) , (lowercase)) : Optional[Any] =config_and_inputs
lowercase : Union[str, Any] ={'''input_ids''': input_ids_a}
return config, inputs_dict
@require_tf
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
lowerCamelCase_ = () if is_tf_available() else ()
lowerCamelCase_ = (
{
'feature-extraction': TFTransfoXLModel,
'text-classification': TFTransfoXLForSequenceClassification,
'text-generation': TFTransfoXLLMHeadModel,
'zero-shot': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def lowerCamelCase_ ( self : Dict , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int ):
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Any =TFTransfoXLModelTester(self )
lowercase : Optional[int] =ConfigTester(self , config_class=UpperCAmelCase__ , d_embed=37 )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
self.model_tester.set_seed()
lowercase : List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
self.model_tester.set_seed()
lowercase : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase , lowercase : Tuple =self.model_tester.prepare_config_and_inputs_for_common()
lowercase : int =[TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
lowercase : str =model_class(UpperCAmelCase__ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
lowercase : Union[str, Any] =model.get_output_embeddings()
assert isinstance(UpperCAmelCase__ , tf.keras.layers.Layer )
lowercase : Any =model.get_bias()
assert name is None
else:
lowercase : Optional[int] =model.get_output_embeddings()
assert x is None
lowercase : Optional[int] =model.get_bias()
assert name is None
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : int =TFTransfoXLModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.''' )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
pass
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@unittest.skip('''Skip test until #12651 is resolved.''' )
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : Optional[Any] =TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''' )
# fmt: off
lowercase : Tuple =tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
lowercase : Optional[int] =[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
lowercase : int =model.generate(UpperCAmelCase__ , max_length=200 , do_sample=UpperCAmelCase__ )
self.assertListEqual(output_ids[0].numpy().tolist() , UpperCAmelCase__ )
| 92 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
class _lowerCAmelCase ( lowercase__ , lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] ="maskformer-swin"
SCREAMING_SNAKE_CASE_ : Optional[int] ={
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : int , SCREAMING_SNAKE_CASE__ : Tuple=2_24 , SCREAMING_SNAKE_CASE__ : Optional[int]=4 , SCREAMING_SNAKE_CASE__ : List[Any]=3 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=96 , SCREAMING_SNAKE_CASE__ : int=[2, 2, 6, 2] , SCREAMING_SNAKE_CASE__ : str=[3, 6, 12, 24] , SCREAMING_SNAKE_CASE__ : Optional[Any]=7 , SCREAMING_SNAKE_CASE__ : List[str]=4.0 , SCREAMING_SNAKE_CASE__ : Any=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE__ : int=0.0 , SCREAMING_SNAKE_CASE__ : Tuple=0.1 , SCREAMING_SNAKE_CASE__ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE__ : Tuple=False , SCREAMING_SNAKE_CASE__ : int=0.02 , SCREAMING_SNAKE_CASE__ : int=1e-5 , SCREAMING_SNAKE_CASE__ : Dict=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=None , **SCREAMING_SNAKE_CASE__ : Optional[int] , ):
"""simple docstring"""
super().__init__(**UpperCAmelCase__ )
UpperCamelCase = image_size
UpperCamelCase = patch_size
UpperCamelCase = num_channels
UpperCamelCase = embed_dim
UpperCamelCase = depths
UpperCamelCase = len(UpperCAmelCase__ )
UpperCamelCase = num_heads
UpperCamelCase = window_size
UpperCamelCase = mlp_ratio
UpperCamelCase = qkv_bias
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = drop_path_rate
UpperCamelCase = hidden_act
UpperCamelCase = use_absolute_embeddings
UpperCamelCase = layer_norm_eps
UpperCamelCase = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
UpperCamelCase = int(embed_dim * 2 ** (len(UpperCAmelCase__ ) - 1) )
UpperCamelCase = ['''stem'''] + [F'stage{idx}' for idx in range(1 , len(UpperCAmelCase__ ) + 1 )]
UpperCamelCase = get_aligned_output_features_output_indices(
out_features=UpperCAmelCase__ , out_indices=UpperCAmelCase__ , stage_names=self.stage_names )
| 282 |
'''simple docstring'''
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 __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str=13 , UpperCAmelCase__ : Tuple=7 , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Optional[Any]=99 , UpperCAmelCase__ : Any=16 , UpperCAmelCase__ : Optional[Any]=36 , UpperCAmelCase__ : str=6 , UpperCAmelCase__ : Any=6 , UpperCAmelCase__ : Optional[int]=6 , UpperCAmelCase__ : Any=37 , UpperCAmelCase__ : List[str]="gelu" , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : Optional[int]=0.1 , UpperCAmelCase__ : List[str]=512 , UpperCAmelCase__ : List[str]=16 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : Union[str, Any]=3 , UpperCAmelCase__ : List[Any]=4 , UpperCAmelCase__ : Tuple=None , ):
'''simple docstring'''
lowercase : str =parent
lowercase : int =batch_size
lowercase : Any =seq_length
lowercase : int =is_training
lowercase : str =use_input_mask
lowercase : int =use_token_type_ids
lowercase : Dict =use_labels
lowercase : int =vocab_size
lowercase : str =embedding_size
lowercase : Union[str, Any] =hidden_size
lowercase : Tuple =num_hidden_layers
lowercase : Any =num_hidden_groups
lowercase : Union[str, Any] =num_attention_heads
lowercase : Any =intermediate_size
lowercase : Tuple =hidden_act
lowercase : Optional[int] =hidden_dropout_prob
lowercase : Union[str, Any] =attention_probs_dropout_prob
lowercase : List[Any] =max_position_embeddings
lowercase : int =type_vocab_size
lowercase : int =type_sequence_label_size
lowercase : Any =initializer_range
lowercase : List[Any] =num_labels
lowercase : int =num_choices
lowercase : Optional[int] =scope
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : int =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Optional[int] =None
if self.use_input_mask:
lowercase : List[str] =random_attention_mask([self.batch_size, self.seq_length] )
lowercase : Dict =None
if self.use_token_type_ids:
lowercase : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase : Tuple =None
lowercase : Any =None
lowercase : Dict =None
if self.use_labels:
lowercase : int =ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase : str =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase : Optional[Any] =ids_tensor([self.batch_size] , self.num_choices )
lowercase : Any =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase_ ( self : Union[str, Any] ):
'''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 lowerCamelCase_ ( self : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : str ):
'''simple docstring'''
lowercase : int =AlbertModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : int =model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
lowercase : Dict =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 lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple ):
'''simple docstring'''
lowercase : Tuple =AlbertForPreTraining(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : int =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 lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Tuple =AlbertForMaskedLM(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.vocab_size) )
def lowerCamelCase_ ( self : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
lowercase : List[str] =AlbertForQuestionAnswering(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : List[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 lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : str , UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
lowercase : Optional[Any] =self.num_labels
lowercase : Any =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 lowerCamelCase_ ( self : Optional[int] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict ):
'''simple docstring'''
lowercase : List[Any] =self.num_labels
lowercase : str =AlbertForTokenClassification(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : int =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 lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] ):
'''simple docstring'''
lowercase : Optional[int] =self.num_choices
lowercase : List[Any] =AlbertForMultipleChoice(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : List[Any] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : int =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : int =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : List[str] =model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Union[str, Any] =self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
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_torch
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase_ = (
{
'feature-extraction': AlbertModel,
'fill-mask': AlbertForMaskedLM,
'question-answering': AlbertForQuestionAnswering,
'text-classification': AlbertForSequenceClassification,
'token-classification': AlbertForTokenClassification,
'zero-shot': AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase_ = True
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int=False ):
'''simple docstring'''
lowercase : Optional[int] =super()._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
if return_labels:
if model_class in get_values(UpperCAmelCase__ ):
lowercase : Any =torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=UpperCAmelCase__ )
lowercase : Any =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
return inputs_dict
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : Tuple =AlbertModelTester(self )
lowercase : Optional[Any] =ConfigTester(self , config_class=UpperCAmelCase__ , hidden_size=37 )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
lowercase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
lowercase : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Optional[int] =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase : Tuple =type
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : str =AlbertModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : int =AlbertModel.from_pretrained('''albert-base-v2''' )
lowercase : Optional[int] =torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
lowercase : 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 : Union[str, Any] =torch.tensor(
[[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , UpperCAmelCase__ , atol=1E-4 ) )
| 92 | 0 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
__magic_name__ = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["""memory_attention""", """encoder_attn"""],
["""attention""", """attn"""],
["""/""", """."""],
[""".LayerNorm.gamma""", """_layer_norm.weight"""],
[""".LayerNorm.beta""", """_layer_norm.bias"""],
["""r.layer_""", """r.layers."""],
["""output_proj""", """out_proj"""],
["""ffn.dense_1.""", """fc2."""],
["""ffn.dense.""", """fc1."""],
["""ffn_layer_norm""", """final_layer_norm"""],
["""kernel""", """weight"""],
["""encoder_layer_norm.""", """encoder.layer_norm."""],
["""decoder_layer_norm.""", """decoder.layer_norm."""],
["""embeddings.weights""", """shared.weight"""],
]
def _A ( __lowercase ):
"""simple docstring"""
for pegasus_name, hf_name in PATTERNS:
lowerCamelCase__ = k.replace(__lowercase , __lowercase )
return k
def _A ( __lowercase , __lowercase ):
"""simple docstring"""
lowerCamelCase__ = DEFAULTS.copy()
cfg_kwargs.update(__lowercase )
lowerCamelCase__ = PegasusConfig(**__lowercase )
lowerCamelCase__ = PegasusForConditionalGeneration(__lowercase )
lowerCamelCase__ = torch_model.model.state_dict()
lowerCamelCase__ = {}
for k, v in tf_weights.items():
lowerCamelCase__ = rename_state_dict_key(__lowercase )
if new_k not in sd:
raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})""" )
if "dense" in k or "proj" in new_k:
lowerCamelCase__ = v.T
lowerCamelCase__ = torch.tensor(__lowercase , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
# make sure embedding.padding_idx is respected
lowerCamelCase__ = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
lowerCamelCase__ = mapping['''shared.weight''']
lowerCamelCase__ = mapping['''shared.weight''']
lowerCamelCase__ = {k: torch.zeros_like(__lowercase ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**__lowercase )
lowerCamelCase__ = torch_model.model.load_state_dict(__lowercase , strict=__lowercase )
lowerCamelCase__ = [
k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
]
assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
assert extra == [], f"""no matches found for the following tf keys {extra}"""
return torch_model
def _A ( __lowercase="./ckpt/aeslc/model.ckpt-32000" ):
"""simple docstring"""
lowerCamelCase__ = tf.train.list_variables(__lowercase )
lowerCamelCase__ = {}
lowerCamelCase__ = ['''Adafactor''', '''global_step''']
for name, shape in tqdm(__lowercase , desc="""converting tf checkpoint to dict""" ):
lowerCamelCase__ = any(pat in name for pat in ignore_name )
if skip_key:
continue
lowerCamelCase__ = tf.train.load_variable(__lowercase , __lowercase )
lowerCamelCase__ = array
return tf_weights
def _A ( __lowercase , __lowercase ):
"""simple docstring"""
lowerCamelCase__ = Path(__lowercase ).parent.name
lowerCamelCase__ = task_specific_params[f"""summarization_{dataset}"""]['''max_position_embeddings''']
lowerCamelCase__ = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=__lowercase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(__lowercase )
# convert model
lowerCamelCase__ = get_tf_weights_as_numpy(__lowercase )
lowerCamelCase__ = task_specific_params[f"""summarization_{dataset}"""]
if dataset == "large":
lowerCamelCase__ = task_specific_params
lowerCamelCase__ = convert_pegasus(__lowercase , __lowercase )
torch_model.save_pretrained(__lowercase )
lowerCamelCase__ = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(__lowercase , Path(__lowercase ) / """pytorch_model.bin""" )
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
__magic_name__ = parser.parse_args()
if args.save_dir is None:
__magic_name__ = Path(args.tf_ckpt_path).parent.name
__magic_name__ = os.path.join("""pegasus""", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 129 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int ):
'''simple docstring'''
if dst_width < 0 or dst_height < 0:
raise ValueError('''Destination width/height should be > 0''' )
lowercase : Union[str, Any] =img
lowercase : Union[str, Any] =img.shape[1]
lowercase : str =img.shape[0]
lowercase : Union[str, Any] =dst_width
lowercase : str =dst_height
lowercase : str =self.src_w / self.dst_w
lowercase : Optional[Any] =self.src_h / self.dst_h
lowercase : int =(
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 255
)
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
for i in range(self.dst_h ):
for j in range(self.dst_w ):
lowercase : List[Any] =self.img[self.get_y(UpperCAmelCase__ )][self.get_x(UpperCAmelCase__ )]
def lowerCamelCase_ ( self : Tuple , UpperCAmelCase__ : int ):
'''simple docstring'''
return int(self.ratio_x * x )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : int ):
'''simple docstring'''
return int(self.ratio_y * y )
if __name__ == "__main__":
UpperCamelCase_ , UpperCamelCase_ = 800, 600
UpperCamelCase_ = imread("""image_data/lena.jpg""", 1)
UpperCamelCase_ = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
)
waitKey(0)
destroyAllWindows()
| 92 | 0 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_A = ["small", "medium", "large"]
_A = "lm_head.decoder.weight"
_A = "lm_head.weight"
def lowerCamelCase__ ( __lowerCAmelCase : str , __lowerCAmelCase : str ):
"""simple docstring"""
lowerCAmelCase_ = torch.load(__lowerCAmelCase )
lowerCAmelCase_ = d.pop(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
torch.save(__lowerCAmelCase , os.path.join(__lowerCAmelCase , __lowerCAmelCase ) )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
parser.add_argument("--dialogpt_path", default=".", type=str)
_A = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
_A = os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
_A = f"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 290 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ : list[float] ) -> float:
lowercase : Any =0.0_0
lowercase : Tuple =0
for resistor in resistors:
if resistor <= 0:
lowercase : Dict =f'''Resistor at index {index} has a negative or zero value!'''
raise ValueError(__magic_name__ )
first_sum += 1 / float(__magic_name__ )
index += 1
return 1 / first_sum
def _lowerCAmelCase ( __magic_name__ : list[float] ) -> float:
lowercase : Optional[Any] =0.0_0
lowercase : int =0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowercase : Tuple =f'''Resistor at index {index} has a negative value!'''
raise ValueError(__magic_name__ )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92 | 0 |
'''simple docstring'''
import numpy as np
import datasets
A__ : Optional[int] = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
'''
A__ : Any = '''\
@article{de2000mahalanobis,
title={The mahalanobis distance},
author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
journal={Chemometrics and intelligent laboratory systems},
volume={50},
number={1},
pages={1--18},
year={2000},
publisher={Elsevier}
}
'''
A__ : int = '''
Args:
X: List of datapoints to be compared with the `reference_distribution`.
reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:
>>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")
>>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
>>> print(results)
{\'mahalanobis\': array([0.5])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class snake_case__ ( datasets.Metric ):
def A_ ( self : Any ) -> List[str]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'X': datasets.Sequence(datasets.Value('float' , id='sequence' ) , id='X' ),
} ) , )
def A_ ( self : str , __a : Optional[Any] , __a : str ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Optional[Any] = np.array(UpperCAmelCase__ )
__snake_case : List[str] = np.array(UpperCAmelCase__ )
# Assert that arrays are 2D
if len(X.shape ) != 2:
raise ValueError('Expected `X` to be a 2D vector' )
if len(reference_distribution.shape ) != 2:
raise ValueError('Expected `reference_distribution` to be a 2D vector' )
if reference_distribution.shape[0] < 2:
raise ValueError(
'Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension' )
# Get mahalanobis distance for each prediction
__snake_case : Tuple = X - np.mean(UpperCAmelCase__ )
__snake_case : int = np.cov(reference_distribution.T )
try:
__snake_case : Dict = np.linalg.inv(UpperCAmelCase__ )
except np.linalg.LinAlgError:
__snake_case : Optional[Any] = np.linalg.pinv(UpperCAmelCase__ )
__snake_case : int = np.dot(UpperCAmelCase__ , UpperCAmelCase__ )
__snake_case : Any = np.dot(UpperCAmelCase__ , X_minus_mu.T ).diagonal()
return {"mahalanobis": mahal_dist}
| 286 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
"""self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
"""self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
"""self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
"""self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
"""self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
"""self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
"""self_attn.rotary_emb""": """encoder.embed_positions""",
"""self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
"""conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
"""conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
"""conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
"""conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
"""conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
"""ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
"""ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
"""ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
"""ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
"""ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
"""ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
"""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""",
}
UpperCamelCase_ = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def _lowerCAmelCase ( __magic_name__ : Any , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Tuple ) -> str:
for attribute in key.split('''.''' ):
lowercase : Tuple =getattr(__magic_name__ , __magic_name__ )
if weight_type is not None:
lowercase : Optional[int] =getattr(__magic_name__ , __magic_name__ ).shape
else:
lowercase : List[Any] =hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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 : Any =value
elif weight_type == "weight_g":
lowercase : List[Any] =value
elif weight_type == "weight_v":
lowercase : Union[str, Any] =value
elif weight_type == "bias":
lowercase : Tuple =value
elif weight_type == "running_mean":
lowercase : Union[str, Any] =value
elif weight_type == "running_var":
lowercase : str =value
elif weight_type == "num_batches_tracked":
lowercase : Tuple =value
elif weight_type == "inv_freq":
lowercase : Optional[Any] =value
else:
lowercase : Tuple =value
logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def _lowerCAmelCase ( __magic_name__ : List[Any] , __magic_name__ : Union[str, Any] , __magic_name__ : str ) -> Union[str, Any]:
lowercase : Optional[int] =[]
lowercase : Tuple =fairseq_model.state_dict()
lowercase : List[Any] =hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
lowercase : Tuple =False
if "conv_layers" in name:
load_conv_layer(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , hf_model.config.feat_extract_norm == '''group''' , )
lowercase : List[Any] =True
else:
for key, mapped_key in MAPPING.items():
lowercase : Optional[int] ='''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
lowercase : Union[str, Any] =True
if "*" in mapped_key:
lowercase : Optional[int] =name.split(__magic_name__ )[0].split('''.''' )[-2]
lowercase : List[str] =mapped_key.replace('''*''' , __magic_name__ )
if "pos_bias_u" in name:
lowercase : Optional[Any] =None
elif "pos_bias_v" in name:
lowercase : Union[str, Any] =None
elif "weight_g" in name:
lowercase : Any ='''weight_g'''
elif "weight_v" in name:
lowercase : Tuple ='''weight_v'''
elif "bias" in name:
lowercase : Optional[int] ='''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase : Optional[int] ='''weight'''
elif "running_mean" in name:
lowercase : Union[str, Any] ='''running_mean'''
elif "inv_freq" in name:
lowercase : Any ='''inv_freq'''
elif "running_var" in name:
lowercase : Tuple ='''running_var'''
elif "num_batches_tracked" in name:
lowercase : Dict ='''num_batches_tracked'''
else:
lowercase : str =None
set_recursively(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
continue
if not is_used:
unused_weights.append(__magic_name__ )
logger.warning(f'''Unused weights: {unused_weights}''' )
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : Optional[Any] , __magic_name__ : List[Any] , __magic_name__ : Optional[Any] , __magic_name__ : Tuple ) -> int:
lowercase : Optional[Any] =full_name.split('''conv_layers.''' )[-1]
lowercase : Any =name.split('''.''' )
lowercase : List[str] =int(items[0] )
lowercase : Union[str, Any] =int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
lowercase : Union[str, Any] =value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
lowercase : Optional[Any] =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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
lowercase : Optional[int] =value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[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(__magic_name__ )
@torch.no_grad()
def _lowerCAmelCase ( __magic_name__ : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Tuple=None , __magic_name__ : Optional[Any]=None , __magic_name__ : Union[str, Any]=True ) -> Union[str, Any]:
if config_path is not None:
lowercase : Optional[Any] =WavaVecaConformerConfig.from_pretrained(__magic_name__ , hidden_act='''swish''' )
else:
lowercase : Optional[int] =WavaVecaConformerConfig()
if "rope" in checkpoint_path:
lowercase : Dict ='''rotary'''
if is_finetuned:
if dict_path:
lowercase : Optional[Any] =Dictionary.load(__magic_name__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowercase : str =target_dict.pad_index
lowercase : Union[str, Any] =target_dict.bos_index
lowercase : Any =target_dict.eos_index
lowercase : Tuple =len(target_dict.symbols )
lowercase : str =os.path.join(__magic_name__ , '''vocab.json''' )
if not os.path.isdir(__magic_name__ ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__magic_name__ ) )
return
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
lowercase : Dict =target_dict.indices
# fairseq has the <pad> and <s> switched
lowercase : str =0
lowercase : List[Any] =1
with open(__magic_name__ , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(__magic_name__ , __magic_name__ )
lowercase : List[str] =WavaVecaCTCTokenizer(
__magic_name__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=__magic_name__ , )
lowercase : Optional[int] =True if config.feat_extract_norm == '''layer''' else False
lowercase : str =WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__magic_name__ , return_attention_mask=__magic_name__ , )
lowercase : Tuple =WavaVecaProcessor(feature_extractor=__magic_name__ , tokenizer=__magic_name__ )
processor.save_pretrained(__magic_name__ )
lowercase : str =WavaVecaConformerForCTC(__magic_name__ )
else:
lowercase : Tuple =WavaVecaConformerForPreTraining(__magic_name__ )
if is_finetuned:
lowercase , lowercase , lowercase : Optional[int] =fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
lowercase : Dict =argparse.Namespace(task='''audio_pretraining''' )
lowercase : Optional[int] =fairseq.tasks.setup_task(__magic_name__ )
lowercase , lowercase , lowercase : str =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__magic_name__ )
lowercase : List[Any] =model[0].eval()
recursively_load_weights(__magic_name__ , __magic_name__ , not is_finetuned )
hf_wavavec.save_pretrained(__magic_name__ )
if __name__ == "__main__":
UpperCamelCase_ = 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_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCamelCase_ = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 92 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
snake_case : Tuple = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case : Optional[Any] = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
]
)
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = state_dict.pop(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = val
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
_SCREAMING_SNAKE_CASE = key.replace('backbone.0.body' ,'backbone.conv_encoder.model' )
_SCREAMING_SNAKE_CASE = value
else:
_SCREAMING_SNAKE_CASE = value
return new_state_dict
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ''''''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
_SCREAMING_SNAKE_CASE = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
_SCREAMING_SNAKE_CASE = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
_SCREAMING_SNAKE_CASE = in_proj_weight[:256, :]
_SCREAMING_SNAKE_CASE = in_proj_bias[:256]
_SCREAMING_SNAKE_CASE = in_proj_weight[256:512, :]
_SCREAMING_SNAKE_CASE = in_proj_bias[256:512]
_SCREAMING_SNAKE_CASE = in_proj_weight[-256:, :]
_SCREAMING_SNAKE_CASE = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
_SCREAMING_SNAKE_CASE = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
_SCREAMING_SNAKE_CASE = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
_SCREAMING_SNAKE_CASE = in_proj_weight[:256, :]
_SCREAMING_SNAKE_CASE = in_proj_bias[:256]
_SCREAMING_SNAKE_CASE = in_proj_weight[256:512, :]
_SCREAMING_SNAKE_CASE = in_proj_bias[256:512]
_SCREAMING_SNAKE_CASE = in_proj_weight[-256:, :]
_SCREAMING_SNAKE_CASE = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
_SCREAMING_SNAKE_CASE = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
_SCREAMING_SNAKE_CASE = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
_SCREAMING_SNAKE_CASE = in_proj_weight_cross_attn[:256, :]
_SCREAMING_SNAKE_CASE = in_proj_bias_cross_attn[:256]
_SCREAMING_SNAKE_CASE = in_proj_weight_cross_attn[256:512, :]
_SCREAMING_SNAKE_CASE = in_proj_bias_cross_attn[256:512]
_SCREAMING_SNAKE_CASE = in_proj_weight_cross_attn[-256:, :]
_SCREAMING_SNAKE_CASE = in_proj_bias_cross_attn[-256:]
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = image.size
_SCREAMING_SNAKE_CASE = max(UpperCAmelCase__ ,UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = 800 if '''detection''' in checkpoint_url else 1000
_SCREAMING_SNAKE_CASE = target_max_size / current_max_size
_SCREAMING_SNAKE_CASE = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = F.to_tensor(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = F.normalize(UpperCAmelCase__ ,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] )
return image
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
logger.info('Converting model...' )
# load original state dict
_SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(UpperCAmelCase__ ,map_location='cpu' )
# rename keys
for src, dest in rename_keys:
rename_key(UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = rename_backbone_keys(UpperCAmelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(UpperCAmelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_SCREAMING_SNAKE_CASE = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
_SCREAMING_SNAKE_CASE = state_dict.pop(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = val
# create HuggingFace model and load state dict
_SCREAMING_SNAKE_CASE = TableTransformerConfig(
backbone='resnet18' ,mask_loss_coefficient=1 ,dice_loss_coefficient=1 ,ce_loss_coefficient=1 ,bbox_loss_coefficient=5 ,giou_loss_coefficient=2 ,eos_coefficient=0.4 ,class_cost=1 ,bbox_cost=5 ,giou_cost=2 ,)
if "detection" in checkpoint_url:
_SCREAMING_SNAKE_CASE = 15
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = {0: '''table''', 1: '''table rotated'''}
_SCREAMING_SNAKE_CASE = idalabel
_SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
else:
_SCREAMING_SNAKE_CASE = 125
_SCREAMING_SNAKE_CASE = 6
_SCREAMING_SNAKE_CASE = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
_SCREAMING_SNAKE_CASE = idalabel
_SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE = DetrImageProcessor(
format='coco_detection' ,max_size=800 if 'detection' in checkpoint_url else 1000 )
_SCREAMING_SNAKE_CASE = TableTransformerForObjectDetection(UpperCAmelCase__ )
model.load_state_dict(UpperCAmelCase__ )
model.eval()
# verify our conversion
_SCREAMING_SNAKE_CASE = '''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
_SCREAMING_SNAKE_CASE = hf_hub_download(repo_id='nielsr/example-pdf' ,repo_type='dataset' ,filename=UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = Image.open(UpperCAmelCase__ ).convert('RGB' )
_SCREAMING_SNAKE_CASE = normalize(resize(UpperCAmelCase__ ,UpperCAmelCase__ ) ).unsqueeze(0 )
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ )
if "detection" in checkpoint_url:
_SCREAMING_SNAKE_CASE = (1, 15, 3)
_SCREAMING_SNAKE_CASE = torch.tensor(
[[-6.7_8_9_7, -16.9985, 6.7_9_3_7], [-8.0_1_8_6, -22.2192, 6.9_6_7_7], [-7.3_1_1_7, -21.0708, 7.4_0_5_5]] )
_SCREAMING_SNAKE_CASE = torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]] )
else:
_SCREAMING_SNAKE_CASE = (1, 125, 7)
_SCREAMING_SNAKE_CASE = torch.tensor(
[[-18.1430, -8.3_2_1_4, 4.8_2_7_4], [-18.4685, -7.1_3_6_1, -4.2_6_6_7], [-26.3693, -9.3_4_2_9, -4.9_9_6_2]] )
_SCREAMING_SNAKE_CASE = torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] ,UpperCAmelCase__ ,atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,UpperCAmelCase__ ,atol=1e-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
model.save_pretrained(UpperCAmelCase__ )
image_processor.save_pretrained(UpperCAmelCase__ )
if push_to_hub:
# Push model to HF hub
logger.info('Pushing model to the hub...' )
_SCREAMING_SNAKE_CASE = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(UpperCAmelCase__ )
image_processor.push_to_hub(UpperCAmelCase__ )
if __name__ == "__main__":
snake_case : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
type=str,
choices=[
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
],
help='URL of the Table Transformer checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
snake_case : Tuple = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 605 |
'''simple docstring'''
import math
import flax.linen as nn
import jax.numpy as jnp
def _lowerCAmelCase ( __magic_name__ : jnp.ndarray , __magic_name__ : int , __magic_name__ : float = 1 , __magic_name__ : float = 1 , __magic_name__ : float = 1.0E4 , __magic_name__ : bool = False , __magic_name__ : float = 1.0 , ) -> jnp.ndarray:
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
lowercase : int =float(embedding_dim // 2 )
lowercase : Optional[int] =math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
lowercase : Any =min_timescale * jnp.exp(jnp.arange(__magic_name__ , dtype=jnp.floataa ) * -log_timescale_increment )
lowercase : List[Any] =jnp.expand_dims(__magic_name__ , 1 ) * jnp.expand_dims(__magic_name__ , 0 )
# scale embeddings
lowercase : Tuple =scale * emb
if flip_sin_to_cos:
lowercase : Dict =jnp.concatenate([jnp.cos(__magic_name__ ), jnp.sin(__magic_name__ )] , axis=1 )
else:
lowercase : Any =jnp.concatenate([jnp.sin(__magic_name__ ), jnp.cos(__magic_name__ )] , axis=1 )
lowercase : List[str] =jnp.reshape(__magic_name__ , [jnp.shape(__magic_name__ )[0], embedding_dim] )
return signal
class __SCREAMING_SNAKE_CASE ( nn.Module ):
lowerCamelCase_ = 32
lowerCamelCase_ = jnp.floataa
@nn.compact
def __call__( self : Tuple , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : List[Any] =nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_1''' )(UpperCAmelCase__ )
lowercase : Any =nn.silu(UpperCAmelCase__ )
lowercase : int =nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_2''' )(UpperCAmelCase__ )
return temb
class __SCREAMING_SNAKE_CASE ( nn.Module ):
lowerCamelCase_ = 32
lowerCamelCase_ = False
lowerCamelCase_ = 1
@nn.compact
def __call__( self : int , UpperCAmelCase__ : str ):
'''simple docstring'''
return get_sinusoidal_embeddings(
UpperCAmelCase__ , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 92 | 0 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase__ ( lowercase__ ):
lowercase__ : Dict = (DPMSolverSDEScheduler,)
lowercase__ : List[Any] = 10
def lowercase_ ( self , **UpperCamelCase__ ):
'''simple docstring'''
A__ = {
'''num_train_timesteps''': 11_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''noise_sampler_seed''': 0,
}
config.update(**UpperCAmelCase__ )
return config
def lowercase_ ( self ):
'''simple docstring'''
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=UpperCAmelCase__ , beta_end=UpperCAmelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=UpperCAmelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config()
A__ = scheduler_class(**UpperCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
A__ = self.dummy_model()
A__ = self.dummy_sample_deter * scheduler.init_noise_sigma
A__ = sample.to(UpperCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
A__ = scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
A__ = model(UpperCAmelCase__ , UpperCAmelCase__ )
A__ = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
A__ = output.prev_sample
A__ = torch.sum(torch.abs(UpperCAmelCase__ ) )
A__ = torch.mean(torch.abs(UpperCAmelCase__ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75 ) < 1e-2
assert abs(result_mean.item() - 0.2178_7059_6456_5277 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06 ) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9068_9229_9652 ) < 1e-3
else:
assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62 ) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1e-3
def lowercase_ ( self ):
'''simple docstring'''
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config(prediction_type="v_prediction" )
A__ = scheduler_class(**UpperCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps )
A__ = self.dummy_model()
A__ = self.dummy_sample_deter * scheduler.init_noise_sigma
A__ = sample.to(UpperCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
A__ = scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
A__ = model(UpperCAmelCase__ , UpperCAmelCase__ )
A__ = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
A__ = output.prev_sample
A__ = torch.sum(torch.abs(UpperCAmelCase__ ) )
A__ = torch.mean(torch.abs(UpperCAmelCase__ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53 ) < 1e-2
assert abs(result_mean.item() - 0.1_6226_2890_1481_6284 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03 ) < 1e-2
assert abs(result_mean.item() - 0.1_6688_3260_0116_7297 ) < 1e-3
else:
assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25 ) < 1e-2
assert abs(result_mean.item() - 0.1560_5306_6253_6621 ) < 1e-3
def lowercase_ ( self ):
'''simple docstring'''
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config()
A__ = scheduler_class(**UpperCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=UpperCAmelCase__ )
A__ = self.dummy_model()
A__ = self.dummy_sample_deter.to(UpperCAmelCase__ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
A__ = scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
A__ = model(UpperCAmelCase__ , UpperCAmelCase__ )
A__ = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
A__ = output.prev_sample
A__ = torch.sum(torch.abs(UpperCAmelCase__ ) )
A__ = torch.mean(torch.abs(UpperCAmelCase__ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38 ) < 1e-2
assert abs(result_mean.item() - 0.2_1805_9346_0798_2635 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12 ) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9083_8241_5771 ) < 1e-3
else:
assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62 ) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1e-3
def lowercase_ ( self ):
'''simple docstring'''
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config()
A__ = scheduler_class(**UpperCAmelCase__ , use_karras_sigmas=UpperCAmelCase__ )
scheduler.set_timesteps(self.num_inference_steps , device=UpperCAmelCase__ )
A__ = self.dummy_model()
A__ = self.dummy_sample_deter.to(UpperCAmelCase__ ) * scheduler.init_noise_sigma
A__ = sample.to(UpperCAmelCase__ )
for t in scheduler.timesteps:
A__ = scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
A__ = model(UpperCAmelCase__ , UpperCAmelCase__ )
A__ = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
A__ = output.prev_sample
A__ = torch.sum(torch.abs(UpperCAmelCase__ ) )
A__ = torch.mean(torch.abs(UpperCAmelCase__ ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
else:
assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2 | 337 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase_ = {
"""facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = 'esm'
def __init__( self : Optional[Any] , UpperCAmelCase__ : str=None , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : Any=None , UpperCAmelCase__ : int=768 , UpperCAmelCase__ : Union[str, Any]=12 , UpperCAmelCase__ : List[Any]=12 , UpperCAmelCase__ : Optional[Any]=3072 , UpperCAmelCase__ : Dict=0.1 , UpperCAmelCase__ : int=0.1 , UpperCAmelCase__ : int=1026 , UpperCAmelCase__ : Tuple=0.02 , UpperCAmelCase__ : Optional[int]=1E-12 , UpperCAmelCase__ : List[Any]="absolute" , UpperCAmelCase__ : int=True , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : str=False , UpperCAmelCase__ : int=False , UpperCAmelCase__ : Optional[Any]=None , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : int , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCAmelCase__ , mask_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
lowercase : Any =vocab_size
lowercase : List[Any] =hidden_size
lowercase : Any =num_hidden_layers
lowercase : Optional[Any] =num_attention_heads
lowercase : Tuple =intermediate_size
lowercase : int =hidden_dropout_prob
lowercase : Dict =attention_probs_dropout_prob
lowercase : Optional[int] =max_position_embeddings
lowercase : Union[str, Any] =initializer_range
lowercase : Tuple =layer_norm_eps
lowercase : Union[str, Any] =position_embedding_type
lowercase : List[Any] =use_cache
lowercase : Dict =emb_layer_norm_before
lowercase : Optional[Any] =token_dropout
lowercase : Union[str, Any] =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
lowercase : Any =EsmFoldConfig()
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase : Optional[int] =EsmFoldConfig(**UpperCAmelCase__ )
lowercase : Union[str, Any] =esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
lowercase : int =get_default_vocab_list()
else:
lowercase : Tuple =vocab_list
else:
lowercase : Union[str, Any] =None
lowercase : Dict =None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , UpperCAmelCase__ ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : Union[str, Any] =super().to_dict()
if isinstance(self.esmfold_config , UpperCAmelCase__ ):
lowercase : Optional[Any] =self.esmfold_config.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = None
lowerCamelCase_ = True
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = 0
lowerCamelCase_ = True
lowerCamelCase_ = False
lowerCamelCase_ = 1_28
lowerCamelCase_ = None
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
if self.trunk is None:
lowercase : str =TrunkConfig()
elif isinstance(self.trunk , UpperCAmelCase__ ):
lowercase : int =TrunkConfig(**self.trunk )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : str =asdict(self )
lowercase : Union[str, Any] =self.trunk.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = 48
lowerCamelCase_ = 10_24
lowerCamelCase_ = 1_28
lowerCamelCase_ = 32
lowerCamelCase_ = 32
lowerCamelCase_ = 32
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = False
lowerCamelCase_ = 4
lowerCamelCase_ = 1_28
lowerCamelCase_ = None
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
if self.structure_module is None:
lowercase : Any =StructureModuleConfig()
elif isinstance(self.structure_module , UpperCAmelCase__ ):
lowercase : Union[str, Any] =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
lowercase : str =self.sequence_state_dim // self.sequence_head_width
lowercase : int =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : List[Any] =asdict(self )
lowercase : Any =self.structure_module.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = 3_84
lowerCamelCase_ = 1_28
lowerCamelCase_ = 16
lowerCamelCase_ = 1_28
lowerCamelCase_ = 12
lowerCamelCase_ = 4
lowerCamelCase_ = 8
lowerCamelCase_ = 0.1
lowerCamelCase_ = 8
lowerCamelCase_ = 1
lowerCamelCase_ = 2
lowerCamelCase_ = 7
lowerCamelCase_ = 10
lowerCamelCase_ = 1E-8
lowerCamelCase_ = 1E5
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return asdict(self )
def _lowerCAmelCase ( ) -> Optional[int]:
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 92 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
__A = logging.get_logger(__name__)
@dataclass
class snake_case ( lowercase__ ):
SCREAMING_SNAKE_CASE_ : Any = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self : Tuple , **UpperCamelCase__ : int)-> Any:
'''simple docstring'''
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
__lowerCAmelCase: Optional[int] = deprecated_arg[3:]
setattr(self , UpperCAmelCase__ , not kwargs.pop(UpperCAmelCase__))
logger.warning(
f"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"
f" {positive_arg}={kwargs[positive_arg]}")
__lowerCAmelCase: int = kwargs.pop("torchscript" , self.torchscript)
__lowerCAmelCase: List[str] = kwargs.pop("torch_xla_tpu_print_metrics" , self.torch_xla_tpu_print_metrics)
__lowerCAmelCase: int = kwargs.pop("fp16_opt_level" , self.fpaa_opt_level)
super().__init__(**UpperCAmelCase__)
SCREAMING_SNAKE_CASE_ : List[str] = field(default=lowercase__, metadata={"""help""": """Trace the models using torchscript"""} )
SCREAMING_SNAKE_CASE_ : str = field(default=lowercase__, metadata={"""help""": """Print Xla/PyTorch tpu metrics"""} )
SCREAMING_SNAKE_CASE_ : Any = field(
default="""O1""", metadata={
"""help""": (
"""For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. """
"""See details at https://nvidia.github.io/apex/amp.html"""
)
}, )
@cached_property
def lowercase_ ( self : List[Any])-> int:
'''simple docstring'''
requires_backends(self , ["torch"])
logger.info("PyTorch: setting up devices")
if not self.cuda:
__lowerCAmelCase: List[Any] = torch.device("cpu")
__lowerCAmelCase: int = 0
elif is_torch_tpu_available():
__lowerCAmelCase: Optional[Any] = xm.xla_device()
__lowerCAmelCase: int = 0
else:
__lowerCAmelCase: Optional[Any] = torch.device("cuda" if torch.cuda.is_available() else "cpu")
__lowerCAmelCase: str = torch.cuda.device_count()
return device, n_gpu
@property
def lowercase_ ( self : Tuple)-> Any:
'''simple docstring'''
return is_torch_tpu_available() and self.tpu
@property
def lowercase_ ( self : int)-> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ["torch"])
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def lowercase_ ( self : int)-> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ["torch"])
return self._setup_devices[0]
@property
def lowercase_ ( self : Union[str, Any])-> List[str]:
'''simple docstring'''
requires_backends(self , ["torch"])
return self._setup_devices[1]
@property
def lowercase_ ( self : Any)-> Dict:
'''simple docstring'''
return self.n_gpu > 0
| 346 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCamelCase_ = abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)
def _lowerCAmelCase ( __magic_name__ : int ) -> Tuple:
config.addinivalue_line(
'''markers''' , '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''' , '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''' , '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''' , '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''' , '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''' , '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def _lowerCAmelCase ( __magic_name__ : int ) -> Any:
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : Any ) -> Any:
from transformers.testing_utils import pytest_terminal_summary_main
lowercase : Optional[Any] =terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__magic_name__ , id=__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : Tuple , __magic_name__ : Union[str, Any] ) -> List[str]:
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowercase : Optional[int] =0
# Doctest custom flag to ignore output.
UpperCamelCase_ = doctest.register_optionflag("""IGNORE_RESULT""")
UpperCamelCase_ = doctest.OutputChecker
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
def lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[Any] ):
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
UpperCamelCase_ = CustomOutputChecker
UpperCamelCase_ = HfDoctestModule
UpperCamelCase_ = HfDocTestParser
| 92 | 0 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_=9_9 , snake_case_=1_3 , snake_case_=7 , snake_case_=9 , snake_case_=True , snake_case_=True , snake_case_=False , snake_case_=3_2 , snake_case_=5 , snake_case_=4 , snake_case_=3_7 , snake_case_=8 , snake_case_=0.1 , snake_case_=0.0_0_2 , snake_case_=1 , snake_case_=0 , snake_case_=0 , snake_case_=None , snake_case_=None , ) -> Optional[Any]:
'''simple docstring'''
__lowercase = parent
__lowercase = batch_size
__lowercase = encoder_seq_length
__lowercase = decoder_seq_length
# For common tests
__lowercase = self.decoder_seq_length
__lowercase = is_training
__lowercase = use_attention_mask
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = d_ff
__lowercase = relative_attention_num_buckets
__lowercase = dropout_rate
__lowercase = initializer_factor
__lowercase = eos_token_id
__lowercase = pad_token_id
__lowercase = decoder_start_token_id
__lowercase = None
__lowercase = decoder_layers
def A ( self ) -> List[str]:
'''simple docstring'''
return TaConfig.from_pretrained('''google/umt5-base''' )
def A ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , ) -> Optional[int]:
'''simple docstring'''
if attention_mask is None:
__lowercase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
__lowercase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
__lowercase = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=UpperCAmelCase__ )
if decoder_head_mask is None:
__lowercase = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=UpperCAmelCase__ )
if cross_attn_head_mask is None:
__lowercase = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=UpperCAmelCase__ )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def A ( self ) -> Any:
'''simple docstring'''
__lowercase = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
__lowercase = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
__lowercase = input_ids.clamp(self.pad_token_id + 1 )
__lowercase = decoder_input_ids.clamp(self.pad_token_id + 1 )
__lowercase = self.get_config()
__lowercase = config.num_attention_heads
__lowercase = self.prepare_inputs_dict(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return config, input_dict
def A ( self ) -> Any:
'''simple docstring'''
__lowercase = self.prepare_config_and_inputs()
return config, inputs_dict
def A ( self ) -> List[str]:
'''simple docstring'''
return TaConfig(
vocab_size=1_6_6 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def A ( self ) -> List[str]:
'''simple docstring'''
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def A ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ) -> Optional[Any]:
'''simple docstring'''
__lowercase = UMTaModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
__lowercase = model(
input_ids=UpperCAmelCase__ , decoder_input_ids=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , decoder_attention_mask=UpperCAmelCase__ , )
__lowercase = model(input_ids=UpperCAmelCase__ , decoder_input_ids=UpperCAmelCase__ )
__lowercase = result.last_hidden_state
__lowercase = result.past_key_values
__lowercase = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(UpperCAmelCase__ ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def A ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ) -> List[str]:
'''simple docstring'''
__lowercase = UMTaModel(config=UpperCAmelCase__ ).get_decoder().to(UpperCAmelCase__ ).eval()
# first forward pass
__lowercase = model(UpperCAmelCase__ , use_cache=UpperCAmelCase__ )
__lowercase = model(UpperCAmelCase__ )
__lowercase = model(UpperCAmelCase__ , use_cache=UpperCAmelCase__ )
self.parent.assertTrue(len(UpperCAmelCase__ ) == len(UpperCAmelCase__ ) )
self.parent.assertTrue(len(UpperCAmelCase__ ) == len(UpperCAmelCase__ ) + 1 )
__lowercase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__lowercase = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
__lowercase = torch.cat([input_ids, next_tokens] , dim=-1 )
__lowercase = model(UpperCAmelCase__ )['''last_hidden_state''']
__lowercase = model(UpperCAmelCase__ , past_key_values=UpperCAmelCase__ )['''last_hidden_state''']
# select random slice
__lowercase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__lowercase = output_from_no_past[:, -1, random_slice_idx].detach()
__lowercase = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCAmelCase__ , UpperCAmelCase__ , atol=1e-3 ) )
def A ( self , snake_case_ , snake_case_ , ) -> Dict:
'''simple docstring'''
__lowercase = UMTaModel(config=UpperCAmelCase__ ).to(UpperCAmelCase__ ).half().eval()
__lowercase = model(**UpperCAmelCase__ )['''last_hidden_state''']
self.parent.assertFalse(torch.isnan(UpperCAmelCase__ ).any().item() )
@require_torch
class lowerCamelCase_ ( lowercase__ , lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
__UpperCAmelCase = (UMTaForConditionalGeneration,) if is_torch_available() else ()
__UpperCAmelCase = (
{
"conversational": UMTaForConditionalGeneration,
"feature-extraction": UMTaModel,
"summarization": UMTaForConditionalGeneration,
"text2text-generation": UMTaForConditionalGeneration,
"translation": UMTaForConditionalGeneration,
"question-answering": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
__UpperCAmelCase = True
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = True
__UpperCAmelCase = True
# The small UMT5 model needs higher percentages for CPU/MP tests
__UpperCAmelCase = [0.8, 0.9]
def A ( self ) -> Dict:
'''simple docstring'''
__lowercase = UMTaModelTester(self )
@unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
def A ( self ) -> int:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
__lowercase = UMTaModel(config_and_inputs[0] ).to(UpperCAmelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
UpperCAmelCase__ , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F'{tmpdirname}/t5_test.onnx' , export_params=UpperCAmelCase__ , opset_version=9 , input_names=['''input_ids''', '''decoder_input_ids'''] , )
@unittest.skipIf(torch_device == '''cpu''' , '''Cant do half precision''' )
def A ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase__ )
def A ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowercase = ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
__lowercase = self.model_tester.prepare_config_and_inputs()
__lowercase = config_and_inputs[0]
__lowercase = UMTaForConditionalGeneration(UpperCAmelCase__ ).eval()
model.to(UpperCAmelCase__ )
__lowercase = {
'''head_mask''': torch.zeros(config.num_layers , config.num_heads , device=UpperCAmelCase__ ),
'''decoder_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=UpperCAmelCase__ ),
'''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers , config.num_heads , device=UpperCAmelCase__ ),
}
for attn_name, (name, mask) in zip(UpperCAmelCase__ , head_masking.items() ):
__lowercase = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
__lowercase = torch.ones(
config.num_decoder_layers , config.num_heads , device=UpperCAmelCase__ )
__lowercase = model.generate(
config_and_inputs[1]['''input_ids'''] , num_beams=1 , max_length=3 , output_attentions=UpperCAmelCase__ , return_dict_in_generate=UpperCAmelCase__ , **UpperCAmelCase__ , )
# We check the state of decoder_attentions and cross_attentions just from the last step
__lowercase = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.''' )
def A ( self ) -> List[Any]:
'''simple docstring'''
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
@unittest.skip(
'''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged''' )
def A ( self ) -> Any:
'''simple docstring'''
__lowercase = UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''' , return_dict=UpperCAmelCase__ ).to(UpperCAmelCase__ )
__lowercase = AutoTokenizer.from_pretrained('''google/umt5-small''' , use_fast=UpperCAmelCase__ , legacy=UpperCAmelCase__ )
__lowercase = [
'''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
'''No se como puedo <extra_id_0>.''',
'''This is the reason why we <extra_id_0> them.''',
'''The <extra_id_0> walks in <extra_id_1>, seats''',
'''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
]
__lowercase = tokenizer(UpperCAmelCase__ , return_tensors='''pt''' , padding=UpperCAmelCase__ ).input_ids
# fmt: off
__lowercase = torch.tensor(
[
[ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
] )
# fmt: on
torch.testing.assert_allclose(UpperCAmelCase__ , UpperCAmelCase__ )
__lowercase = model.generate(input_ids.to(UpperCAmelCase__ ) )
__lowercase = [
'''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
'''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
'''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
]
__lowercase = tokenizer.batch_decode(UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
| 639 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging
UpperCamelCase_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = ['pixel_values']
def __init__( self : List[Any] , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Union[int, float] = 1 / 255 , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : int = 8 , **UpperCAmelCase__ : str , ):
'''simple docstring'''
super().__init__(**UpperCAmelCase__ )
lowercase : Union[str, Any] =do_rescale
lowercase : List[Any] =rescale_factor
lowercase : Tuple =do_pad
lowercase : List[str] =pad_size
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : float , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : Optional[int] ):
'''simple docstring'''
return rescale(UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCamelCase_ ( self : Tuple , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None ):
'''simple docstring'''
lowercase , lowercase : Union[str, Any] =get_image_size(UpperCAmelCase__ )
lowercase : Tuple =(old_height // size + 1) * size - old_height
lowercase : Tuple =(old_width // size + 1) * size - old_width
return pad(UpperCAmelCase__ , ((0, pad_height), (0, pad_width)) , mode='''symmetric''' , data_format=UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : ImageInput , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[float] = None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[Union[str, TensorType]] = None , UpperCAmelCase__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **UpperCAmelCase__ : Optional[Any] , ):
'''simple docstring'''
lowercase : int =do_rescale if do_rescale is not None else self.do_rescale
lowercase : Tuple =rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase : int =do_pad if do_pad is not None else self.do_pad
lowercase : List[Any] =pad_size if pad_size is not None else self.pad_size
lowercase : Any =make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
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 : Dict =[to_numpy_array(UpperCAmelCase__ ) for image in images]
if do_rescale:
lowercase : Tuple =[self.rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ ) for image in images]
if do_pad:
lowercase : Union[str, Any] =[self.pad(UpperCAmelCase__ , size=UpperCAmelCase__ ) for image in images]
lowercase : Dict =[to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
lowercase : Any ={'''pixel_values''': images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 92 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__SCREAMING_SNAKE_CASE = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
__SCREAMING_SNAKE_CASE = {
'vocab_file': {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt',
},
'tokenizer_file': {
'unc-nlp/lxmert-base-uncased': (
'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'
),
},
}
__SCREAMING_SNAKE_CASE = {
'unc-nlp/lxmert-base-uncased': 512,
}
__SCREAMING_SNAKE_CASE = {
'unc-nlp/lxmert-base-uncased': {'do_lower_case': True},
}
class lowerCAmelCase_ ( lowercase__ ):
'''simple docstring'''
_lowercase = VOCAB_FILES_NAMES
_lowercase = PRETRAINED_VOCAB_FILES_MAP
_lowercase = PRETRAINED_INIT_CONFIGURATION
_lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase = LxmertTokenizer
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase="[UNK]" , __UpperCAmelCase="[SEP]" , __UpperCAmelCase="[PAD]" , __UpperCAmelCase="[CLS]" , __UpperCAmelCase="[MASK]" , __UpperCAmelCase=True , __UpperCAmelCase=None , **__UpperCAmelCase , ):
super().__init__(
UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , do_lower_case=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , tokenize_chinese_chars=UpperCAmelCase__ , strip_accents=UpperCAmelCase__ , **UpperCAmelCase__ , )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , UpperCAmelCase__ ) != do_lower_case
or normalizer_state.get('strip_accents' , UpperCAmelCase__ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , UpperCAmelCase__ ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE_ : Union[str, Any] =getattr(UpperCAmelCase__ , normalizer_state.pop('type' ) )
SCREAMING_SNAKE_CASE_ : int =do_lower_case
SCREAMING_SNAKE_CASE_ : Tuple =strip_accents
SCREAMING_SNAKE_CASE_ : Any =tokenize_chinese_chars
SCREAMING_SNAKE_CASE_ : Union[str, Any] =normalizer_class(**UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : int =do_lower_case
def __lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] =[self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
SCREAMING_SNAKE_CASE_ : List[Any] =[self.sep_token_id]
SCREAMING_SNAKE_CASE_ : List[str] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
SCREAMING_SNAKE_CASE_ : List[Any] =self._tokenizer.model.save(UpperCAmelCase__ , name=UpperCAmelCase__ )
return tuple(UpperCAmelCase__ )
| 220 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["""MBartTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["""MBartTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MBartForCausalLM""",
"""MBartForConditionalGeneration""",
"""MBartForQuestionAnswering""",
"""MBartForSequenceClassification""",
"""MBartModel""",
"""MBartPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""TFMBartForConditionalGeneration""",
"""TFMBartModel""",
"""TFMBartPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""FlaxMBartForConditionalGeneration""",
"""FlaxMBartForQuestionAnswering""",
"""FlaxMBartForSequenceClassification""",
"""FlaxMBartModel""",
"""FlaxMBartPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
def _snake_case (_snake_case : Optional[int]) -> Union[str, Any]:
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
3: [4],
4: [2, 3],
5: [2],
6: [0, 7],
7: [6],
8: [],
9: [1],
},
{
0: [4],
1: [6],
2: [],
3: [5, 6, 7],
4: [0, 6],
5: [3, 8, 9],
6: [1, 3, 4, 7],
7: [3, 6, 8, 9],
8: [5, 7],
9: [5, 7],
},
{
0: [1, 3],
1: [0, 2, 4],
2: [1, 3, 4],
3: [0, 2, 4],
4: [1, 2, 3],
},
][index]
def _snake_case (_snake_case : dict[int, list[int]]) -> list[tuple[int, int]]:
_lowercase =0
_lowercase =len(_snake_case) # No of vertices in graph
_lowercase =[0] * n
_lowercase =[False] * n
def dfs(_snake_case : Optional[Any] , _snake_case : Dict , _snake_case : int , _snake_case : Optional[int]):
_lowercase =True
_lowercase =id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(_snake_case , _snake_case , _snake_case , id_)
_lowercase =min(low[at] , low[to])
if id_ <= low[to]:
bridges.append((at, to) if at < to else (to, at))
else:
# This edge is a back edge and cannot be a bridge
_lowercase =min(low[at] , low[to])
_lowercase =[]
for i in range(_snake_case):
if not visited[i]:
dfs(_snake_case , -1 , _snake_case , id_)
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod()
| 181 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase_ = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class a ( lowercase__ , lowercase__ , lowercase__ , unittest.TestCase ):
snake_case__ = StableDiffusionControlNetImgaImgPipeline
snake_case__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
snake_case__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
snake_case__ = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''} )
snake_case__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def UpperCamelCase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
torch.manual_seed(0 )
lowerCAmelCase = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
lowerCAmelCase = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=UpperCAmelCase__ , set_alpha_to_one=UpperCAmelCase__ , )
torch.manual_seed(0 )
lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
lowerCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
lowerCAmelCase = CLIPTextModel(UpperCAmelCase__ )
lowerCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowerCAmelCase = {
'''unet''': unet,
'''controlnet''': controlnet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def UpperCamelCase__ ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(UpperCAmelCase__ ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(UpperCAmelCase__ )
else:
lowerCAmelCase = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
lowerCAmelCase = 2
lowerCAmelCase = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=UpperCAmelCase__ , device=torch.device(UpperCAmelCase__ ) , )
lowerCAmelCase = floats_tensor(control_image.shape , rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase = Image.fromarray(np.uinta(UpperCAmelCase__ ) ).convert('RGB' ).resize((64, 64) )
lowerCAmelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
'''image''': image,
'''control_image''': control_image,
}
return inputs
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class a ( lowercase__ , lowercase__ , unittest.TestCase ):
snake_case__ = StableDiffusionControlNetImgaImgPipeline
snake_case__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
snake_case__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
snake_case__ = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def UpperCamelCase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
torch.manual_seed(0 )
def init_weights(_snake_case ):
if isinstance(UpperCAmelCase__ , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
lowerCAmelCase = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(UpperCAmelCase__ )
torch.manual_seed(0 )
lowerCAmelCase = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(UpperCAmelCase__ )
torch.manual_seed(0 )
lowerCAmelCase = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=UpperCAmelCase__ , set_alpha_to_one=UpperCAmelCase__ , )
torch.manual_seed(0 )
lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
lowerCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
lowerCAmelCase = CLIPTextModel(UpperCAmelCase__ )
lowerCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowerCAmelCase = MultiControlNetModel([controlneta, controlneta] )
lowerCAmelCase = {
'''unet''': unet,
'''controlnet''': controlnet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def UpperCamelCase__ ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(UpperCAmelCase__ ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(UpperCAmelCase__ )
else:
lowerCAmelCase = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
lowerCAmelCase = 2
lowerCAmelCase = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=UpperCAmelCase__ , device=torch.device(UpperCAmelCase__ ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=UpperCAmelCase__ , device=torch.device(UpperCAmelCase__ ) , ),
]
lowerCAmelCase = floats_tensor(control_image[0].shape , rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase = Image.fromarray(np.uinta(UpperCAmelCase__ ) ).convert('RGB' ).resize((64, 64) )
lowerCAmelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
'''image''': image,
'''control_image''': control_image,
}
return inputs
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = self.pipeline_class(**UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
lowerCAmelCase = 10.0
lowerCAmelCase = 4
lowerCAmelCase = self.get_dummy_inputs(UpperCAmelCase__ )
lowerCAmelCase = steps
lowerCAmelCase = scale
lowerCAmelCase = pipe(**UpperCAmelCase__ )[0]
lowerCAmelCase = self.get_dummy_inputs(UpperCAmelCase__ )
lowerCAmelCase = steps
lowerCAmelCase = scale
lowerCAmelCase = pipe(**UpperCAmelCase__ , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
lowerCAmelCase = self.get_dummy_inputs(UpperCAmelCase__ )
lowerCAmelCase = steps
lowerCAmelCase = scale
lowerCAmelCase = pipe(**UpperCAmelCase__ , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
lowerCAmelCase = self.get_dummy_inputs(UpperCAmelCase__ )
lowerCAmelCase = steps
lowerCAmelCase = scale
lowerCAmelCase = pipe(**UpperCAmelCase__ , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = self.pipeline_class(**UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(UpperCAmelCase__ )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny' )
lowerCAmelCase = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , safety_checker=UpperCAmelCase__ , controlnet=UpperCAmelCase__ )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
lowerCAmelCase = torch.Generator(device='cpu' ).manual_seed(0 )
lowerCAmelCase = '''evil space-punk bird'''
lowerCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png' ).resize((5_12, 5_12) )
lowerCAmelCase = load_image(
'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png' ).resize((5_12, 5_12) )
lowerCAmelCase = pipe(
UpperCAmelCase__ , UpperCAmelCase__ , control_image=UpperCAmelCase__ , generator=UpperCAmelCase__ , output_type='np' , num_inference_steps=50 , strength=0.6 , )
lowerCAmelCase = output.images[0]
assert image.shape == (5_12, 5_12, 3)
lowerCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy' )
assert np.abs(expected_image - image ).max() < 9E-2
| 4 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
UpperCamelCase_ = logging.getLogger(__name__)
def _lowerCAmelCase ( __magic_name__ : List[Any] , __magic_name__ : str ) -> Union[str, Any]:
return (preds == labels).mean()
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys() )} )
lowerCamelCase_ = field(metadata={'help': 'Should contain the data files for the task.'} )
lowerCamelCase_ = field(
default=1_28 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _lowerCAmelCase ( ) -> 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 : Dict =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase , lowercase , lowercase : List[Any] =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.''' )
# 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''' , __magic_name__ )
# Set seed
set_seed(training_args.seed )
try:
lowercase : Any =processors[data_args.task_name]()
lowercase : Optional[int] =processor.get_labels()
lowercase : str =len(__magic_name__ )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase : List[str] =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__magic_name__ , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
lowercase : int =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 , )
lowercase : Any =AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__magic_name__ , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase : int =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__magic_name__ , task=data_args.task_name , 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 : Union[str, Any] =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__magic_name__ , task=data_args.task_name , 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 compute_metrics(__magic_name__ : EvalPrediction ) -> Dict:
lowercase : Dict =np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__magic_name__ , p.label_ids )}
# Data collator
lowercase : List[str] =DataCollatorWithPadding(__magic_name__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase : Dict =Trainer(
model=__magic_name__ , args=__magic_name__ , train_dataset=__magic_name__ , eval_dataset=__magic_name__ , compute_metrics=__magic_name__ , data_collator=__magic_name__ , )
# 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_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase : Optional[Any] ={}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase : List[Any] =trainer.evaluate()
lowercase : Any =os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__magic_name__ , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __magic_name__ , __magic_name__ )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__magic_name__ )
return results
def _lowerCAmelCase ( __magic_name__ : Any ) -> Optional[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 92 | 0 |
def __lowerCamelCase ( _lowercase = 600851475143 ) -> int:
try:
UpperCamelCase = int(_lowercase )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must be greater than or equal to one.' )
UpperCamelCase = 2
UpperCamelCase = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
UpperCamelCase = i
while n % i == 0:
UpperCamelCase = n // i
i += 1
return int(_lowercase )
if __name__ == "__main__":
print(F"{solution() = }")
| 282 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
UpperCamelCase_ = logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_ = """cuda""" if torch.cuda.is_available() else """cpu"""
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : Tuple=100 , __magic_name__ : Optional[int]=" " ) -> List[str]:
lowercase : List[Any] =text.split(__magic_name__ )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(__magic_name__ ) , __magic_name__ )]
def _lowerCAmelCase ( __magic_name__ : dict ) -> dict:
lowercase , lowercase : int =[], []
for title, text in zip(documents['''title'''] , documents['''text'''] ):
if text is not None:
for passage in split_text(__magic_name__ ):
titles.append(title if title is not None else '''''' )
texts.append(__magic_name__ )
return {"title": titles, "text": texts}
def _lowerCAmelCase ( __magic_name__ : dict , __magic_name__ : DPRContextEncoder , __magic_name__ : DPRContextEncoderTokenizerFast ) -> dict:
lowercase : Dict =ctx_tokenizer(
documents['''title'''] , documents['''text'''] , truncation=__magic_name__ , padding='''longest''' , return_tensors='''pt''' )['''input_ids''']
lowercase : Optional[int] =ctx_encoder(input_ids.to(device=__magic_name__ ) , return_dict=__magic_name__ ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def _lowerCAmelCase ( __magic_name__ : "RagExampleArguments" , __magic_name__ : "ProcessingArguments" , __magic_name__ : "IndexHnswArguments" , ) -> str:
######################################
logger.info('''Step 1 - Create the dataset''' )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
lowercase : Tuple =load_dataset(
'''csv''' , data_files=[rag_example_args.csv_path] , split='''train''' , delimiter='''\t''' , column_names=['''title''', '''text'''] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
lowercase : Optional[int] =dataset.map(__magic_name__ , batched=__magic_name__ , num_proc=processing_args.num_proc )
# And compute the embeddings
lowercase : Any =DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=__magic_name__ )
lowercase : Any =DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
lowercase : Optional[int] =Features(
{'''text''': Value('''string''' ), '''title''': Value('''string''' ), '''embeddings''': Sequence(Value('''float32''' ) )} ) # optional, save as float32 instead of float64 to save space
lowercase : Optional[Any] =dataset.map(
partial(__magic_name__ , ctx_encoder=__magic_name__ , ctx_tokenizer=__magic_name__ ) , batched=__magic_name__ , batch_size=processing_args.batch_size , features=__magic_name__ , )
# And finally save your dataset
lowercase : Optional[Any] =os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset''' )
dataset.save_to_disk(__magic_name__ )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info('''Step 2 - Index the dataset''' )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
lowercase : Union[str, Any] =faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index('''embeddings''' , custom_index=__magic_name__ )
# And save the index
lowercase : Dict =os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset_hnsw_index.faiss''' )
dataset.get_index('''embeddings''' ).save(__magic_name__ )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=str(Path(lowercase__ ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv' ) , metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''} , )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'} , )
lowerCamelCase_ = field(
default='facebook/rag-sequence-nq' , metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''} , )
lowerCamelCase_ = field(
default='facebook/dpr-ctx_encoder-multiset-base' , metadata={
'help': (
'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
' \'facebook/dpr-ctx_encoder-multiset-base\''
)
} , )
lowerCamelCase_ = field(
default=str(Path(lowercase__ ).parent / 'test_run' / 'dummy-kb' ) , metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=lowercase__ , metadata={
'help': 'The number of processes to use to split the documents into passages. Default is single process.'
} , )
lowerCamelCase_ = field(
default=16 , metadata={
'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=7_68 , metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'} , )
lowerCamelCase_ = field(
default=1_28 , metadata={
'help': (
'The number of bi-directional links created for every new element during the HNSW index construction.'
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
UpperCamelCase_ = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
UpperCamelCase_ = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 92 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
"""WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""WavLMForAudioFrameClassification""",
"""WavLMForCTC""",
"""WavLMForSequenceClassification""",
"""WavLMForXVector""",
"""WavLMModel""",
"""WavLMPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavlm import (
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 129 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
UpperCamelCase_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
UpperCamelCase_ = 128022
UpperCamelCase_ = 128028
@require_sentencepiece
class __SCREAMING_SNAKE_CASE ( lowercase__ , unittest.TestCase ):
lowerCamelCase_ = MaMaaaTokenizer
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = True
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
super().setUp()
lowercase : Dict =['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
lowercase : List[Any] =dict(zip(UpperCAmelCase__ , range(len(UpperCAmelCase__ ) ) ) )
lowercase : List[Any] =Path(self.tmpdirname )
save_json(UpperCAmelCase__ , save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(UpperCAmelCase__ , save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowercase : Tuple =MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase_ ( self : Any , **UpperCAmelCase__ : int ):
'''simple docstring'''
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Dict ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : Tuple ='''</s>'''
lowercase : Union[str, Any] =0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[Any] =self.get_tokenizer()
lowercase : Optional[Any] =list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''</s>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''<s>''' )
self.assertEqual(len(UpperCAmelCase__ ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip('''Skip this test while all models are still to be uploaded.''' )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Union[str, Any] =self.get_tokenizer()
lowercase : str =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(UpperCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [2, 3, 4, 5, 6] , )
lowercase : Optional[int] =tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(UpperCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
lowercase : Tuple =tokenizer.convert_tokens_to_string(UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , '''This is a test''' )
@slow
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
# fmt: off
lowercase : int ={'''input_ids''': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name='''facebook/m2m100_418M''' , revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e''' , )
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
lowerCamelCase_ = 'facebook/m2m100_418M'
lowerCamelCase_ = [
'In my opinion, there are two levels of response from the French government.',
'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
]
lowerCamelCase_ = [
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
]
# fmt: off
lowerCamelCase_ = [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]
@classmethod
def lowerCamelCase_ ( cls : Optional[Any] ):
'''simple docstring'''
lowercase : MaMaaaTokenizer =MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en''' , tgt_lang='''fr''' )
lowercase : Optional[int] =1
return cls
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
self.assertEqual(self.tokenizer.get_lang_id('''ar''' ) , 128006 )
self.assertEqual(self.tokenizer.get_lang_id('''en''' ) , 128022 )
self.assertEqual(self.tokenizer.get_lang_id('''ro''' ) , 128076 )
self.assertEqual(self.tokenizer.get_lang_id('''mr''' ) , 128063 )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : List[str] =self.tokenizer.get_vocab()
self.assertEqual(len(UpperCAmelCase__ ) , self.tokenizer.vocab_size )
self.assertEqual(vocab['''<unk>'''] , 3 )
self.assertIn(self.tokenizer.get_lang_token('''en''' ) , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : List[Any] ='''en'''
lowercase : Optional[Any] =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
self.assertIn(UpperCAmelCase__ , self.tokenizer.all_special_ids )
# fmt: off
lowercase : str =[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
# fmt: on
lowercase : Optional[Any] =self.tokenizer.decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ )
lowercase : Optional[Any] =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertNotIn(self.tokenizer.eos_token , UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Any =tempfile.mkdtemp()
lowercase : Tuple =self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(UpperCAmelCase__ )
lowercase : Union[str, Any] =MaMaaaTokenizer.from_pretrained(UpperCAmelCase__ )
self.assertDictEqual(new_tok.lang_token_to_id , UpperCAmelCase__ )
@require_torch
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[str] ='''en'''
lowercase : int ='''fr'''
lowercase : Union[str, Any] =self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase__ , return_tensors='''pt''' )
lowercase : str =shift_tokens_right(
batch['''labels'''] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
lowercase : int =batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Optional[int] ='''mr'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
lowercase : Union[str, Any] ='''zh'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : int ='''mr'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
lowercase : Optional[Any] ='''zh'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : Optional[Any] =self.tokenizer._build_translation_inputs('''A test''' , return_tensors='''pt''' , src_lang='''en''' , tgt_lang='''ar''' )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) , {
# en_XX, A, test, EOS
'''input_ids''': [[128022, 58, 4183, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 128006,
} , )
| 92 | 0 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowerCAmelCase :
def __init__( self ) -> Optional[int]:
lowerCAmelCase_ = ''''''
lowerCAmelCase_ = ''''''
lowerCAmelCase_ = []
lowerCAmelCase_ = 0
lowerCAmelCase_ = 256
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
def __a ( self , _UpperCamelCase ) -> Dict:
lowerCAmelCase_ = cva.imread(UpperCAmelCase__ , 0 )
lowerCAmelCase_ = copy.deepcopy(self.img )
lowerCAmelCase_ = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
lowerCAmelCase_ = np.sum(UpperCAmelCase__ )
for i in range(len(UpperCAmelCase__ ) ):
lowerCAmelCase_ = x[i] / self.k
self.sk += prk
lowerCAmelCase_ = (self.L - 1) * self.sk
if self.rem != 0:
lowerCAmelCase_ = int(last % last )
lowerCAmelCase_ = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(UpperCAmelCase__ )
lowerCAmelCase_ = int(np.ma.count(self.img ) / self.img[1].size )
lowerCAmelCase_ = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
lowerCAmelCase_ = self.img[j][i]
if num != self.last_list[num]:
lowerCAmelCase_ = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def __a ( self ) -> List[str]:
plt.hist(self.img.ravel() , 256 , [0, 256] )
def __a ( self ) -> Union[str, Any]:
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5_000 )
cva.destroyAllWindows()
if __name__ == "__main__":
_A = os.path.join(os.path.basename(__file__), "image_data/input.jpg")
_A = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 290 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int = 600851475143 ) -> int:
try:
lowercase : Any =int(__magic_name__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
lowercase : Optional[Any] =2
lowercase : Dict =0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
lowercase : Union[str, Any] =i
while n % i == 0:
lowercase : Optional[int] =n // i
i += 1
return int(__magic_name__ )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 92 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class snake_case__ :
def __init__( self : Dict , __a : Union[str, Any]=2 , __a : str=3 , __a : Optional[int]=64 , __a : Union[str, Any]=None ) -> str:
'''simple docstring'''
__snake_case : Optional[Any] = np.random.default_rng(UpperCAmelCase__ )
__snake_case : Union[str, Any] = length
__snake_case : List[Any] = rng.normal(size=(length,) ).astype(np.floataa )
__snake_case : Dict = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
return self.length
def __getitem__( self : Union[str, Any] , __a : Any ) -> Dict:
'''simple docstring'''
return {"x": self.x[i], "y": self.y[i]}
class snake_case__ ( torch.nn.Module ):
def __init__( self : Union[str, Any] , __a : List[Any]=0 , __a : List[Any]=0 , __a : str=False ) -> int:
'''simple docstring'''
super().__init__()
__snake_case : Any = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
__snake_case : List[str] = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
__snake_case : List[Any] = True
def A_ ( self : str , __a : Any=None ) -> Tuple:
'''simple docstring'''
if self.first_batch:
print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}''' )
__snake_case : Any = False
return x * self.a[0] + self.b[0]
class snake_case__ ( torch.nn.Module ):
def __init__( self : List[Any] , __a : Optional[Any]=0 , __a : int=0 , __a : Optional[Any]=False ) -> Optional[int]:
'''simple docstring'''
super().__init__()
__snake_case : Optional[int] = torch.nn.Parameter(torch.tensor(UpperCAmelCase__ ).float() )
__snake_case : int = torch.nn.Parameter(torch.tensor(UpperCAmelCase__ ).float() )
__snake_case : Tuple = True
def A_ ( self : List[Any] , __a : Optional[Any]=None ) -> Optional[Any]:
'''simple docstring'''
if self.first_batch:
print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}''' )
__snake_case : List[Any] = False
return x * self.a + self.b
def a_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : int = 16 ) -> Union[str, Any]:
from datasets import load_dataset
from transformers import AutoTokenizer
__snake_case : Dict = AutoTokenizer.from_pretrained('bert-base-cased' )
__snake_case : Optional[int] = {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
__snake_case : Dict = load_dataset('csv' ,data_files=_UpperCAmelCase )
__snake_case : int = datasets['''train'''].unique('label' )
__snake_case : List[str] = {v: i for i, v in enumerate(_UpperCAmelCase )}
def tokenize_function(_UpperCAmelCase : Dict ):
# max_length=None => use the model max length (it's actually the default)
__snake_case : Dict = tokenizer(
examples['sentence1'] ,examples['sentence2'] ,truncation=_UpperCAmelCase ,max_length=_UpperCAmelCase ,padding='max_length' )
if "label" in examples:
__snake_case : List[Any] = [label_to_id[l] for l in examples['''label''']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
__snake_case : Optional[int] = datasets.map(
_UpperCAmelCase ,batched=_UpperCAmelCase ,remove_columns=['sentence1', 'sentence2', 'label'] ,)
def collate_fn(_UpperCAmelCase : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_UpperCAmelCase ,padding='max_length' ,max_length=1_28 ,return_tensors='pt' )
return tokenizer.pad(_UpperCAmelCase ,padding='longest' ,return_tensors='pt' )
# Instantiate dataloaders.
__snake_case : Union[str, Any] = DataLoader(tokenized_datasets['train'] ,shuffle=_UpperCAmelCase ,collate_fn=_UpperCAmelCase ,batch_size=2 )
__snake_case : Tuple = DataLoader(tokenized_datasets['validation'] ,shuffle=_UpperCAmelCase ,collate_fn=_UpperCAmelCase ,batch_size=1 )
return train_dataloader, eval_dataloader
| 286 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = 'speech_to_text_2'
lowerCamelCase_ = ['past_key_values']
lowerCamelCase_ = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : int , UpperCAmelCase__ : Dict=10000 , UpperCAmelCase__ : Optional[int]=6 , UpperCAmelCase__ : str=2048 , UpperCAmelCase__ : Optional[Any]=4 , UpperCAmelCase__ : Union[str, Any]=0.0 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Optional[Any]="relu" , UpperCAmelCase__ : List[str]=256 , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : List[Any]=0.0 , UpperCAmelCase__ : Optional[Any]=0.0 , UpperCAmelCase__ : Tuple=0.02 , UpperCAmelCase__ : int=2 , UpperCAmelCase__ : str=True , UpperCAmelCase__ : str=1 , UpperCAmelCase__ : Optional[Any]=0 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : List[Any]=1024 , **UpperCAmelCase__ : Dict , ):
'''simple docstring'''
lowercase : List[str] =vocab_size
lowercase : Optional[int] =d_model
lowercase : Optional[Any] =decoder_ffn_dim
lowercase : Any =decoder_layers
lowercase : Dict =decoder_attention_heads
lowercase : List[Any] =dropout
lowercase : List[Any] =attention_dropout
lowercase : Any =activation_dropout
lowercase : Optional[Any] =activation_function
lowercase : Optional[int] =init_std
lowercase : Dict =decoder_layerdrop
lowercase : Optional[int] =use_cache
lowercase : Optional[Any] =decoder_layers
lowercase : List[str] =scale_embedding # scale factor will be sqrt(d_model) if True
lowercase : str =max_target_positions
super().__init__(
pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , decoder_start_token_id=UpperCAmelCase__ , **UpperCAmelCase__ , )
| 92 | 0 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
snake_case : Union[str, Any] = logging.get_logger(__name__)
snake_case : Any = TypeVar('DatasetType', Dataset, IterableDataset)
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ = None ,UpperCAmelCase__ = None ,UpperCAmelCase__ = None ,UpperCAmelCase__ = None ,UpperCAmelCase__ = "first_exhausted" ,):
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(UpperCAmelCase__ ):
if not isinstance(UpperCAmelCase__ ,(Dataset, IterableDataset) ):
if isinstance(UpperCAmelCase__ ,(DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
'is an empty dataset dictionary.' )
raise ValueError(
f'''Dataset at position {i} has at least one split: {list(UpperCAmelCase__ )}\n'''
f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCAmelCase__ ) )}\']''' )
raise ValueError(
f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCAmelCase__ ).__name__}.''' )
if i == 0:
_SCREAMING_SNAKE_CASE = (
(Dataset, IterableDataset) if isinstance(UpperCAmelCase__ ,UpperCAmelCase__ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCAmelCase__ ,UpperCAmelCase__ ):
raise ValueError(
f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.''' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,info=UpperCAmelCase__ ,split=UpperCAmelCase__ ,stopping_strategy=UpperCAmelCase__ )
else:
return _interleave_iterable_datasets(
UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,info=UpperCAmelCase__ ,split=UpperCAmelCase__ ,stopping_strategy=UpperCAmelCase__ )
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ = None ,UpperCAmelCase__ = None ,UpperCAmelCase__ = 0 ,):
"""simple docstring"""
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(UpperCAmelCase__ ):
if not isinstance(UpperCAmelCase__ ,(Dataset, IterableDataset) ):
if isinstance(UpperCAmelCase__ ,(DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
'is an empty dataset dictionary.' )
raise ValueError(
f'''Dataset at position {i} has at least one split: {list(UpperCAmelCase__ )}\n'''
f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCAmelCase__ ) )}\']''' )
raise ValueError(
f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCAmelCase__ ).__name__}.''' )
if i == 0:
_SCREAMING_SNAKE_CASE = (
(Dataset, IterableDataset) if isinstance(UpperCAmelCase__ ,UpperCAmelCase__ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCAmelCase__ ,UpperCAmelCase__ ):
raise ValueError(
f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(UpperCAmelCase__ ,info=UpperCAmelCase__ ,split=UpperCAmelCase__ ,axis=UpperCAmelCase__ )
else:
return _concatenate_iterable_datasets(UpperCAmelCase__ ,info=UpperCAmelCase__ ,split=UpperCAmelCase__ ,axis=UpperCAmelCase__ )
| 605 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self : str , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[str]=13 , UpperCAmelCase__ : Any=32 , UpperCAmelCase__ : List[str]=3 , UpperCAmelCase__ : Any=4 , UpperCAmelCase__ : List[str]=[10, 20, 30, 40] , UpperCAmelCase__ : Any=[2, 2, 3, 2] , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Optional[Any]=37 , UpperCAmelCase__ : Union[str, Any]="gelu" , UpperCAmelCase__ : Optional[Any]=10 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : Optional[int]=["stage2", "stage3", "stage4"] , UpperCAmelCase__ : Dict=[2, 3, 4] , UpperCAmelCase__ : Optional[int]=None , ):
'''simple docstring'''
lowercase : List[Any] =parent
lowercase : Tuple =batch_size
lowercase : List[str] =image_size
lowercase : List[Any] =num_channels
lowercase : Union[str, Any] =num_stages
lowercase : int =hidden_sizes
lowercase : Any =depths
lowercase : Tuple =is_training
lowercase : str =use_labels
lowercase : List[Any] =intermediate_size
lowercase : int =hidden_act
lowercase : Union[str, Any] =num_labels
lowercase : Optional[int] =initializer_range
lowercase : int =out_features
lowercase : List[str] =out_indices
lowercase : str =scope
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : str =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase : Dict =None
if self.use_labels:
lowercase : List[Any] =ids_tensor([self.batch_size] , self.num_labels )
lowercase : Dict =self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[Any] ):
'''simple docstring'''
lowercase : Dict =ConvNextVaModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[Any] =model(UpperCAmelCase__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase_ ( self : Any , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Dict =ConvNextVaForImageClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : str =model(UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Union[str, Any] =ConvNextVaBackbone(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[int] =model(UpperCAmelCase__ )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
lowercase : Optional[Any] =None
lowercase : str =ConvNextVaBackbone(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[Any] =model(UpperCAmelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Any =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase : str =config_and_inputs
lowercase : Any ={'''pixel_values''': pixel_values}
return config, inputs_dict
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : str =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase : List[str] =config_and_inputs
lowercase : Optional[Any] ={'''pixel_values''': pixel_values, '''labels''': labels}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase_ = (
{'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : Dict =ConvNextVaModelTester(self )
lowercase : str =ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=37 )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return
@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds''' )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings''' )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking''' )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase , lowercase : Union[str, Any] =self.model_tester.prepare_config_and_inputs_with_labels()
lowercase : Optional[int] =True
if model_class.__name__ in [
*get_values(UpperCAmelCase__ ),
*get_values(UpperCAmelCase__ ),
]:
continue
lowercase : Dict =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.train()
lowercase : Optional[Any] =self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
lowercase : List[Any] =model(**UpperCAmelCase__ ).loss
loss.backward()
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase , lowercase : Any =self.model_tester.prepare_config_and_inputs_with_labels()
lowercase : List[Any] =False
lowercase : Any =True
if (
model_class.__name__
in [*get_values(UpperCAmelCase__ ), *get_values(UpperCAmelCase__ )]
or not model_class.supports_gradient_checkpointing
):
continue
lowercase : Any =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.gradient_checkpointing_enable()
model.train()
lowercase : Optional[Any] =self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
lowercase : int =model(**UpperCAmelCase__ ).loss
loss.backward()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase , lowercase : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : Dict =model_class(UpperCAmelCase__ )
lowercase : Union[str, Any] =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase : int =[*signature.parameters.keys()]
lowercase : Optional[Any] =['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
def check_hidden_states_output(UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : Tuple ):
lowercase : int =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
lowercase : Any =model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase : Dict =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase : List[Any] =self.model_tester.num_stages
self.assertEqual(len(UpperCAmelCase__ ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowercase , lowercase : List[str] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : List[str] =True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase : Tuple =True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
lowercase : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__ )
@slow
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : List[Any] =ConvNextVaModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def _lowerCAmelCase ( ) -> List[Any]:
lowercase : Union[str, Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : Tuple =ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ).to(UpperCAmelCase__ )
lowercase : int =self.default_image_processor
lowercase : List[str] =prepare_img()
lowercase : List[Any] =preprocessor(images=UpperCAmelCase__ , return_tensors='''pt''' ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
lowercase : Dict =model(**UpperCAmelCase__ )
# verify the logits
lowercase : Optional[Any] =torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase__ )
lowercase : Tuple =torch.tensor([0.99_96, 0.19_66, -0.43_86] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase__ , atol=1E-4 ) )
| 92 | 0 |
"""simple docstring"""
def __a ( A = 4_000_000 ) -> int:
'''simple docstring'''
A__ = []
A__ = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(A )
A__ = b, a + b
return sum(A )
if __name__ == "__main__":
print(F'''{solution() = }''') | 337 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
UpperCamelCase_ = object()
# For specifying empty leaf dict `{}`
UpperCamelCase_ = object()
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : int ) -> Optional[int]:
lowercase : Optional[Any] =tuple((re.compile(x + '''$''' ) for x in qs) )
for i in range(len(__magic_name__ ) - len(__magic_name__ ) + 1 ):
lowercase : Union[str, Any] =[x.match(__magic_name__ ) for x, y in zip(__magic_name__ , ks[i:] )]
if matches and all(__magic_name__ ):
return True
return False
def _lowerCAmelCase ( __magic_name__ : Dict ) -> List[str]:
def replace(__magic_name__ : Optional[int] , __magic_name__ : Optional[Any] ):
for rule, replacement in rules:
if _match(__magic_name__ , __magic_name__ ):
return replacement
return val
return replace
def _lowerCAmelCase ( ) -> int:
return [
# embeddings
(("transformer", "wpe", "embedding"), P('''mp''' , __magic_name__ )),
(("transformer", "wte", "embedding"), P('''mp''' , __magic_name__ )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__magic_name__ , '''mp''' )),
(("attention", "out_proj", "kernel"), P('''mp''' , __magic_name__ )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(__magic_name__ , '''mp''' )),
(("mlp", "c_fc", "bias"), P('''mp''' )),
(("mlp", "c_proj", "kernel"), P('''mp''' , __magic_name__ )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def _lowerCAmelCase ( __magic_name__ : str ) -> int:
lowercase : int =_get_partition_rules()
lowercase : Tuple =_replacement_rules(__magic_name__ )
lowercase : Any ={k: _unmatched for k in flatten_dict(__magic_name__ )}
lowercase : Any ={k: replace(__magic_name__ , __magic_name__ ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(__magic_name__ ) )
| 92 | 0 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def a__ ( __SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus" ) -> dict:
__lowerCAmelCase: List[Any] = BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE ).text , "html.parser" )
__lowerCAmelCase: Tuple = soup.findAll("h1" )
__lowerCAmelCase: int = soup.findAll("div" , {"class": "maincounter-number"} )
keys += soup.findAll("span" , {"class": "panel-title"} )
values += soup.findAll("div" , {"class": "number-table-main"} )
return {key.text.strip(): value.text.strip() for key, value in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )}
if __name__ == "__main__":
print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 346 |
'''simple docstring'''
from collections import defaultdict
def _lowerCAmelCase ( __magic_name__ : int ) -> int:
lowercase : Optional[Any] =1
lowercase : Union[str, Any] =True
for v in tree[start]:
if v not in visited:
ret += dfs(__magic_name__ )
if ret % 2 == 0:
cuts.append(__magic_name__ )
return ret
def _lowerCAmelCase ( ) -> int:
dfs(1 )
if __name__ == "__main__":
UpperCamelCase_ , UpperCamelCase_ = 10, 9
UpperCamelCase_ = defaultdict(list)
UpperCamelCase_ = {}
UpperCamelCase_ = []
UpperCamelCase_ = 0
UpperCamelCase_ = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 92 | 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 transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def lowercase_ ( _UpperCamelCase , _UpperCamelCase=False , _UpperCamelCase=False , _UpperCamelCase=False ):
'''simple docstring'''
__lowercase = []
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 lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
__lowercase = '''vilt.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__lowercase = state_dict.pop(F'transformer.blocks.{i}.attn.qkv.weight' )
__lowercase = state_dict.pop(F'transformer.blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[
: config.hidden_size, :
]
__lowercase = in_proj_bias[: config.hidden_size]
__lowercase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__lowercase = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__lowercase = in_proj_weight[
-config.hidden_size :, :
]
__lowercase = in_proj_bias[-config.hidden_size :]
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(_UpperCamelCase , _UpperCamelCase )
def lowercase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowercase = dct.pop(_UpperCamelCase )
__lowercase = val
@torch.no_grad()
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowercase = ViltConfig(image_size=3_84 , patch_size=32 , tie_word_embeddings=_UpperCamelCase )
__lowercase = False
__lowercase = False
__lowercase = False
__lowercase = False
if "vqa" in checkpoint_url:
__lowercase = True
__lowercase = 31_29
__lowercase = '''huggingface/label-files'''
__lowercase = '''vqa2-id2label.json'''
__lowercase = json.load(open(hf_hub_download(_UpperCamelCase , _UpperCamelCase , repo_type='''dataset''' ) , '''r''' ) )
__lowercase = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
__lowercase = ViltForQuestionAnswering(_UpperCamelCase )
elif "nlvr" in checkpoint_url:
__lowercase = True
__lowercase = 2
__lowercase = {0: '''False''', 1: '''True'''}
__lowercase = {v: k for k, v in config.idalabel.items()}
__lowercase = 3
__lowercase = ViltForImagesAndTextClassification(_UpperCamelCase )
elif "irtr" in checkpoint_url:
__lowercase = True
__lowercase = ViltForImageAndTextRetrieval(_UpperCamelCase )
elif "mlm_itm" in checkpoint_url:
__lowercase = True
__lowercase = ViltForMaskedLM(_UpperCamelCase )
else:
raise ValueError('''Unknown model type''' )
# load state_dict of original model, remove and rename some keys
__lowercase = torch.hub.load_state_dict_from_url(_UpperCamelCase , map_location='''cpu''' )['''state_dict''']
__lowercase = create_rename_keys(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
for src, dest in rename_keys:
rename_key(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
read_in_q_k_v(_UpperCamelCase , _UpperCamelCase )
if mlm_model or irtr_model:
__lowercase = ['''itm_score.fc.weight''', '''itm_score.fc.bias''']
for k in ignore_keys:
state_dict.pop(_UpperCamelCase , _UpperCamelCase )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
__lowercase = model.load_state_dict(_UpperCamelCase , strict=_UpperCamelCase )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(_UpperCamelCase )
# Define processor
__lowercase = ViltImageProcessor(size=3_84 )
__lowercase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
__lowercase = ViltProcessor(_UpperCamelCase , _UpperCamelCase )
# Forward pass on example inputs (image + text)
if nlvr_model:
__lowercase = Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg''' , stream=_UpperCamelCase ).raw )
__lowercase = Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg''' , stream=_UpperCamelCase ).raw )
__lowercase = (
'''The left image contains twice the number of dogs as the right image, and at least two dogs in total are'''
''' standing.'''
)
__lowercase = processor(_UpperCamelCase , _UpperCamelCase , return_tensors='''pt''' )
__lowercase = processor(_UpperCamelCase , _UpperCamelCase , return_tensors='''pt''' )
__lowercase = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
__lowercase = Image.open(requests.get('''http://images.cocodataset.org/val2017/000000039769.jpg''' , stream=_UpperCamelCase ).raw )
if mlm_model:
__lowercase = '''a bunch of [MASK] laying on a [MASK].'''
else:
__lowercase = '''How many cats are there?'''
__lowercase = processor(_UpperCamelCase , _UpperCamelCase , return_tensors='''pt''' )
__lowercase = model(**_UpperCamelCase )
# Verify outputs
if mlm_model:
__lowercase = torch.Size([1, 11, 3_05_22] )
__lowercase = torch.tensor([-12.50_61, -12.51_23, -12.51_74] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , _UpperCamelCase , atol=1E-4 )
# verify masked token prediction equals "cats"
__lowercase = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
__lowercase = torch.Size([1, 31_29] )
__lowercase = torch.tensor([-15.94_95, -18.14_72, -10.30_41] )
assert torch.allclose(outputs.logits[0, :3] , _UpperCamelCase , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , _UpperCamelCase , atol=1E-4 )
# verify vqa prediction equals "2"
__lowercase = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
__lowercase = torch.Size([1, 2] )
__lowercase = torch.tensor([-2.8721, 2.1291] )
assert torch.allclose(outputs.logits[0, :3] , _UpperCamelCase , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(_UpperCamelCase ).mkdir(exist_ok=_UpperCamelCase )
print(F'Saving model and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(_UpperCamelCase )
processor.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a : Union[str, Any] = 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.'''
)
a : Any = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 639 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
def _lowerCAmelCase ( __magic_name__ : List[str] ) -> Dict:
lowercase : List[str] =R'''\w+[.]\d+'''
lowercase : List[str] =re.findall(__magic_name__ , __magic_name__ )
for pat in pats:
lowercase : Optional[int] =key.replace(__magic_name__ , '''_'''.join(pat.split('''.''' ) ) )
return key
def _lowerCAmelCase ( __magic_name__ : List[str] , __magic_name__ : Union[str, Any] , __magic_name__ : Dict ) -> str:
lowercase : Dict =pt_tuple_key[:-1] + ('''scale''',)
if (
any('''norm''' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
lowercase : str =pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
lowercase : str =pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
lowercase : Dict =pt_tuple_key[:-1] + ('''embedding''',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowercase : Tuple =pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
lowercase : Tuple =pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowercase : str =pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight":
lowercase : Optional[Any] =pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowercase : Dict =pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowercase : Union[str, Any] =pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : Optional[Any] , __magic_name__ : Union[str, Any]=42 ) -> List[str]:
# Step 1: Convert pytorch tensor to numpy
lowercase : Optional[Any] ={k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
lowercase : str =flax_model.init_weights(PRNGKey(__magic_name__ ) )
lowercase : Dict =flatten_dict(__magic_name__ )
lowercase : Dict ={}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase : Dict =rename_key(__magic_name__ )
lowercase : Optional[int] =tuple(renamed_pt_key.split('''.''' ) )
# Correctly rename weight parameters
lowercase , lowercase : Any =rename_key_and_reshape_tensor(__magic_name__ , __magic_name__ , __magic_name__ )
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}.''' )
# also add unexpected weight so that warning is thrown
lowercase : Tuple =jnp.asarray(__magic_name__ )
return unflatten_dict(__magic_name__ )
| 92 | 0 |
__SCREAMING_SNAKE_CASE = 'Alexander Joslin'
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict ={'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
SCREAMING_SNAKE_CASE_ : Stack[int] =Stack()
SCREAMING_SNAKE_CASE_ : Stack[str] =Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(lowerCAmelCase_ ) )
elif i in operators:
# RULE 2
operator_stack.push(lowerCAmelCase_ )
elif i == ")":
# RULE 4
SCREAMING_SNAKE_CASE_ : List[str] =operator_stack.peek()
operator_stack.pop()
SCREAMING_SNAKE_CASE_ : Dict =operand_stack.peek()
operand_stack.pop()
SCREAMING_SNAKE_CASE_ : Tuple =operand_stack.peek()
operand_stack.pop()
SCREAMING_SNAKE_CASE_ : Union[str, Any] =operators[opr](lowerCAmelCase_ ,lowerCAmelCase_ )
operand_stack.push(lowerCAmelCase_ )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = '(5 + ((4 * 2) * (2 + 3)))'
# answer = 45
print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")
| 220 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase_ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
UpperCamelCase_ = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
UpperCamelCase_ = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, float]:
lowercase : int =len([g for position, g in enumerate(__magic_name__ ) if g == main_target[position]] )
return (item, float(__magic_name__ ))
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, str]:
lowercase : Any =random.randint(0 , len(__magic_name__ ) - 1 )
lowercase : Tuple =parent_a[:random_slice] + parent_a[random_slice:]
lowercase : List[str] =parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : list[str] ) -> str:
lowercase : Union[str, Any] =list(__magic_name__ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
lowercase : Dict =random.choice(__magic_name__ )
return "".join(__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : tuple[str, float] , __magic_name__ : list[tuple[str, float]] , __magic_name__ : list[str] , ) -> list[str]:
lowercase : Any =[]
# Generate more children proportionally to the fitness score.
lowercase : Dict =int(parent_a[1] * 100 ) + 1
lowercase : List[str] =10 if child_n >= 10 else child_n
for _ in range(__magic_name__ ):
lowercase : List[str] =population_score[random.randint(0 , __magic_name__ )][0]
lowercase , lowercase : Dict =crossover(parent_a[0] , __magic_name__ )
# Append new string to the population list.
pop.append(mutate(__magic_name__ , __magic_name__ ) )
pop.append(mutate(__magic_name__ , __magic_name__ ) )
return pop
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : list[str] , __magic_name__ : bool = True ) -> tuple[int, int, str]:
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
lowercase : List[str] =f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
raise ValueError(__magic_name__ )
# Verify that the target contains no genes besides the ones inside genes variable.
lowercase : Optional[int] =sorted({c for c in target if c not in genes} )
if not_in_genes_list:
lowercase : Dict =f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
raise ValueError(__magic_name__ )
# Generate random starting population.
lowercase : int =[]
for _ in range(__magic_name__ ):
population.append(''''''.join([random.choice(__magic_name__ ) for i in range(len(__magic_name__ ) )] ) )
# Just some logs to know what the algorithms is doing.
lowercase , lowercase : Optional[int] =0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(__magic_name__ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
lowercase : List[str] =[evaluate(__magic_name__ , __magic_name__ ) for item in population]
# Check if there is a matching evolution.
lowercase : int =sorted(__magic_name__ , key=lambda __magic_name__ : x[1] , reverse=__magic_name__ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f'''\nGeneration: {generation}'''
f'''\nTotal Population:{total_population}'''
f'''\nBest score: {population_score[0][1]}'''
f'''\nBest string: {population_score[0][0]}''' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
lowercase : Any =population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(__magic_name__ )
# Normalize population score to be between 0 and 1.
lowercase : Dict =[
(item, score / len(__magic_name__ )) for item, score in population_score
]
# This is selection
for i in range(__magic_name__ ):
population.extend(select(population_score[int(__magic_name__ )] , __magic_name__ , __magic_name__ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(__magic_name__ ) > N_POPULATION:
break
if __name__ == "__main__":
UpperCamelCase_ = (
"""This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
)
UpperCamelCase_ = list(
""" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
"""nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
)
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = basic(target_str, genes_list)
print(
f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 92 | 0 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
__lowerCAmelCase : Optional[int] =4_2
__lowerCAmelCase : Union[str, Any] =None
__lowerCAmelCase : Dict =None
def _snake_case (_snake_case : TreeNode | None) -> bool:
# Validation
def is_valid_tree(_snake_case : TreeNode | None) -> bool:
if node is None:
return True
if not isinstance(_snake_case , _snake_case):
return False
try:
float(node.data)
except (TypeError, ValueError):
return False
return is_valid_tree(node.left) and is_valid_tree(node.right)
if not is_valid_tree(_snake_case):
raise ValueError(
'Each node should be type of TreeNode and data should be float.')
def is_binary_search_tree_recursive_check(
_snake_case : TreeNode | None , _snake_case : float , _snake_case : float) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , _snake_case , node.data)
and is_binary_search_tree_recursive_check(
node.right , node.data , _snake_case)
)
return is_binary_search_tree_recursive_check(_snake_case , -float('inf') , float('inf'))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 181 |
'''simple docstring'''
import datasets
UpperCamelCase_ = """\
@InProceedings{conneau2018xnli,
author = \"Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin\",
title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing\",
year = \"2018\",
publisher = \"Association for Computational Linguistics\",
location = \"Brussels, Belgium\",
}
"""
UpperCamelCase_ = """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""
UpperCamelCase_ = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: Predicted labels.
references: Ground truth labels.
Returns:
'accuracy': accuracy
Examples:
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> xnli_metric = datasets.load_metric(\"xnli\")
>>> results = xnli_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
"""
def _lowerCAmelCase ( __magic_name__ : Dict , __magic_name__ : Union[str, Any] ) -> Union[str, Any]:
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32''' ),
'''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32''' ),
} ) , codebase_urls=[] , reference_urls=[] , format='''numpy''' , )
def lowerCamelCase_ ( self : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[Any] ):
'''simple docstring'''
return {"accuracy": simple_accuracy(UpperCAmelCase__ , UpperCAmelCase__ )}
| 92 | 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
########################################################################
# 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
#
########################################################################
__UpperCamelCase : Union[str, Any] = 16
__UpperCamelCase : List[Any] = 32
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Accelerator , _UpperCAmelCase : int = 16 ):
lowerCAmelCase = AutoTokenizer.from_pretrained('bert-base-cased' )
lowerCAmelCase = load_dataset('glue' , 'mrpc' )
def tokenize_function(_UpperCAmelCase : Dict ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase )
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():
lowerCAmelCase = datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , 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
lowerCAmelCase = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_UpperCAmelCase : Optional[int] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowerCAmelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowerCAmelCase = 16
elif accelerator.mixed_precision != "no":
lowerCAmelCase = 8
else:
lowerCAmelCase = None
return tokenizer.pad(
_UpperCAmelCase , padding='longest' , max_length=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_tensors='pt' , )
# Instantiate dataloaders.
lowerCAmelCase = DataLoader(
tokenized_datasets['train'] , shuffle=_UpperCAmelCase , collate_fn=_UpperCAmelCase , batch_size=_UpperCAmelCase )
lowerCAmelCase = DataLoader(
tokenized_datasets['validation'] , shuffle=_UpperCAmelCase , collate_fn=_UpperCAmelCase , batch_size=_UpperCAmelCase )
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
__UpperCamelCase : Any = mocked_dataloaders # noqa: F811
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : str ):
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS' , _UpperCAmelCase ) == "1":
lowerCAmelCase = 2
# New Code #
lowerCAmelCase = int(args.gradient_accumulation_steps )
# Initialize accelerator
lowerCAmelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=_UpperCAmelCase )
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
lowerCAmelCase = config['''lr''']
lowerCAmelCase = int(config['num_epochs'] )
lowerCAmelCase = int(config['seed'] )
lowerCAmelCase = int(config['batch_size'] )
lowerCAmelCase = evaluate.load('glue' , 'mrpc' )
set_seed(_UpperCAmelCase )
lowerCAmelCase = get_dataloaders(_UpperCAmelCase , _UpperCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=_UpperCAmelCase )
# 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).
lowerCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
lowerCAmelCase = AdamW(params=model.parameters() , lr=_UpperCAmelCase )
# Instantiate scheduler
lowerCAmelCase = get_linear_schedule_with_warmup(
optimizer=_UpperCAmelCase , num_warmup_steps=100 , num_training_steps=(len(_UpperCAmelCase ) * 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.
lowerCAmelCase = accelerator.prepare(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# Now we train the model
for epoch in range(_UpperCAmelCase ):
model.train()
for step, batch in enumerate(_UpperCAmelCase ):
# 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(_UpperCAmelCase ):
lowerCAmelCase = model(**_UpperCAmelCase )
lowerCAmelCase = output.loss
accelerator.backward(_UpperCAmelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_UpperCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase = model(**_UpperCAmelCase )
lowerCAmelCase = outputs.logits.argmax(dim=-1 )
lowerCAmelCase = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=_UpperCAmelCase , references=_UpperCAmelCase , )
lowerCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , _UpperCAmelCase )
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=_UpperCAmelCase , default=_UpperCAmelCase , 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=_UpperCAmelCase , 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.' )
lowerCAmelCase = parser.parse_args()
lowerCAmelCase = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_UpperCAmelCase , _UpperCAmelCase )
if __name__ == "__main__":
main()
| 4 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[str] , UpperCAmelCase__ : Dict , ):
'''simple docstring'''
lowercase : Any =parent
lowercase : Optional[int] =13
lowercase : Union[str, Any] =7
lowercase : str =30
lowercase : Optional[int] =self.seq_length + self.mem_len
lowercase : Dict =15
lowercase : List[str] =True
lowercase : Optional[int] =True
lowercase : Tuple =99
lowercase : str =[10, 50, 80]
lowercase : List[Any] =32
lowercase : Optional[int] =32
lowercase : int =4
lowercase : Any =8
lowercase : List[Any] =128
lowercase : List[str] =2
lowercase : Tuple =2
lowercase : int =None
lowercase : Optional[int] =1
lowercase : int =0
lowercase : List[str] =3
lowercase : str =self.vocab_size - 1
lowercase : Tuple =0.01
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : str =None
if self.use_labels:
lowercase : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Union[str, Any] =TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
random.seed(self.seed )
tf.random.set_seed(self.seed )
def lowerCamelCase_ ( self : Union[str, Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[str] ):
'''simple docstring'''
lowercase : Tuple =TFTransfoXLModel(UpperCAmelCase__ )
lowercase , lowercase : Optional[Any] =model(UpperCAmelCase__ ).to_tuple()
lowercase : List[str] ={'''input_ids''': input_ids_a, '''mems''': mems_a}
lowercase , lowercase : Any =model(UpperCAmelCase__ ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def lowerCamelCase_ ( self : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : int =TFTransfoXLLMHeadModel(UpperCAmelCase__ )
lowercase , lowercase : Tuple =model(UpperCAmelCase__ ).to_tuple()
lowercase : Optional[Any] ={'''input_ids''': input_ids_a, '''labels''': lm_labels}
lowercase , lowercase : Optional[int] =model(UpperCAmelCase__ ).to_tuple()
lowercase , lowercase : List[str] =model([input_ids_a, mems_a] ).to_tuple()
lowercase : int ={'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}
lowercase , lowercase : str =model(UpperCAmelCase__ ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def lowerCamelCase_ ( self : str , UpperCAmelCase__ : int , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
lowercase : Optional[int] =TFTransfoXLForSequenceClassification(UpperCAmelCase__ )
lowercase : Union[str, Any] =model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[Any] =self.prepare_config_and_inputs()
((lowercase) , (lowercase) , (lowercase) , (lowercase)) : Optional[Any] =config_and_inputs
lowercase : Union[str, Any] ={'''input_ids''': input_ids_a}
return config, inputs_dict
@require_tf
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
lowerCamelCase_ = () if is_tf_available() else ()
lowerCamelCase_ = (
{
'feature-extraction': TFTransfoXLModel,
'text-classification': TFTransfoXLForSequenceClassification,
'text-generation': TFTransfoXLLMHeadModel,
'zero-shot': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def lowerCamelCase_ ( self : Dict , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int ):
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Any =TFTransfoXLModelTester(self )
lowercase : Optional[int] =ConfigTester(self , config_class=UpperCAmelCase__ , d_embed=37 )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
self.model_tester.set_seed()
lowercase : List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
self.model_tester.set_seed()
lowercase : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase , lowercase : Tuple =self.model_tester.prepare_config_and_inputs_for_common()
lowercase : int =[TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
lowercase : str =model_class(UpperCAmelCase__ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
lowercase : Union[str, Any] =model.get_output_embeddings()
assert isinstance(UpperCAmelCase__ , tf.keras.layers.Layer )
lowercase : Any =model.get_bias()
assert name is None
else:
lowercase : Optional[int] =model.get_output_embeddings()
assert x is None
lowercase : Optional[int] =model.get_bias()
assert name is None
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : int =TFTransfoXLModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.''' )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
pass
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@unittest.skip('''Skip test until #12651 is resolved.''' )
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : Optional[Any] =TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''' )
# fmt: off
lowercase : Tuple =tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
lowercase : Optional[int] =[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
lowercase : int =model.generate(UpperCAmelCase__ , max_length=200 , do_sample=UpperCAmelCase__ )
self.assertListEqual(output_ids[0].numpy().tolist() , UpperCAmelCase__ )
| 92 | 0 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> Any:
# load base model
UpperCamelCase = StableDiffusionPipeline.from_pretrained(_lowercase , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
UpperCamelCase = load_file(_lowercase )
UpperCamelCase = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
UpperCamelCase = key.split('.' )[0].split(LORA_PREFIX_TEXT_ENCODER + '_' )[-1].split('_' )
UpperCamelCase = pipeline.text_encoder
else:
UpperCamelCase = key.split('.' )[0].split(LORA_PREFIX_UNET + '_' )[-1].split('_' )
UpperCamelCase = pipeline.unet
# find the target layer
UpperCamelCase = layer_infos.pop(0 )
while len(_lowercase ) > -1:
try:
UpperCamelCase = curr_layer.__getattr__(_lowercase )
if len(_lowercase ) > 0:
UpperCamelCase = layer_infos.pop(0 )
elif len(_lowercase ) == 0:
break
except Exception:
if len(_lowercase ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
UpperCamelCase = layer_infos.pop(0 )
UpperCamelCase = []
if "lora_down" in key:
pair_keys.append(key.replace('lora_down' , 'lora_up' ) )
pair_keys.append(_lowercase )
else:
pair_keys.append(_lowercase )
pair_keys.append(key.replace('lora_up' , 'lora_down' ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
UpperCamelCase = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
UpperCamelCase = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(_lowercase , _lowercase ).unsqueeze(2 ).unsqueeze(3 )
else:
UpperCamelCase = state_dict[pair_keys[0]].to(torch.floataa )
UpperCamelCase = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(_lowercase , _lowercase )
# update visited list
for item in pair_keys:
visited.append(_lowercase )
return pipeline
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
)
parser.add_argument(
'''--lora_prefix_text_encoder''',
default='''lora_te''',
type=str,
help='''The prefix of text encoder weight in safetensors''',
)
parser.add_argument('''--alpha''', default=0.75, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
parser.add_argument(
'''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
)
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
_snake_case = parser.parse_args()
_snake_case = args.base_model_path
_snake_case = args.checkpoint_path
_snake_case = args.dump_path
_snake_case = args.lora_prefix_unet
_snake_case = args.lora_prefix_text_encoder
_snake_case = args.alpha
_snake_case = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
_snake_case = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 282 |
'''simple docstring'''
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 __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str=13 , UpperCAmelCase__ : Tuple=7 , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Optional[Any]=99 , UpperCAmelCase__ : Any=16 , UpperCAmelCase__ : Optional[Any]=36 , UpperCAmelCase__ : str=6 , UpperCAmelCase__ : Any=6 , UpperCAmelCase__ : Optional[int]=6 , UpperCAmelCase__ : Any=37 , UpperCAmelCase__ : List[str]="gelu" , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : Optional[int]=0.1 , UpperCAmelCase__ : List[str]=512 , UpperCAmelCase__ : List[str]=16 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : Union[str, Any]=3 , UpperCAmelCase__ : List[Any]=4 , UpperCAmelCase__ : Tuple=None , ):
'''simple docstring'''
lowercase : str =parent
lowercase : int =batch_size
lowercase : Any =seq_length
lowercase : int =is_training
lowercase : str =use_input_mask
lowercase : int =use_token_type_ids
lowercase : Dict =use_labels
lowercase : int =vocab_size
lowercase : str =embedding_size
lowercase : Union[str, Any] =hidden_size
lowercase : Tuple =num_hidden_layers
lowercase : Any =num_hidden_groups
lowercase : Union[str, Any] =num_attention_heads
lowercase : Any =intermediate_size
lowercase : Tuple =hidden_act
lowercase : Optional[int] =hidden_dropout_prob
lowercase : Union[str, Any] =attention_probs_dropout_prob
lowercase : List[Any] =max_position_embeddings
lowercase : int =type_vocab_size
lowercase : int =type_sequence_label_size
lowercase : Any =initializer_range
lowercase : List[Any] =num_labels
lowercase : int =num_choices
lowercase : Optional[int] =scope
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : int =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Optional[int] =None
if self.use_input_mask:
lowercase : List[str] =random_attention_mask([self.batch_size, self.seq_length] )
lowercase : Dict =None
if self.use_token_type_ids:
lowercase : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase : Tuple =None
lowercase : Any =None
lowercase : Dict =None
if self.use_labels:
lowercase : int =ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase : str =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase : Optional[Any] =ids_tensor([self.batch_size] , self.num_choices )
lowercase : Any =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase_ ( self : Union[str, Any] ):
'''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 lowerCamelCase_ ( self : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : str ):
'''simple docstring'''
lowercase : int =AlbertModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : int =model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
lowercase : Dict =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 lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple ):
'''simple docstring'''
lowercase : Tuple =AlbertForPreTraining(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : int =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 lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Tuple =AlbertForMaskedLM(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.vocab_size) )
def lowerCamelCase_ ( self : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
lowercase : List[str] =AlbertForQuestionAnswering(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : List[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 lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : str , UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
lowercase : Optional[Any] =self.num_labels
lowercase : Any =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 lowerCamelCase_ ( self : Optional[int] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict ):
'''simple docstring'''
lowercase : List[Any] =self.num_labels
lowercase : str =AlbertForTokenClassification(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : int =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 lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] ):
'''simple docstring'''
lowercase : Optional[int] =self.num_choices
lowercase : List[Any] =AlbertForMultipleChoice(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : List[Any] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : int =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : int =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : List[str] =model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Union[str, Any] =self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
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_torch
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase_ = (
{
'feature-extraction': AlbertModel,
'fill-mask': AlbertForMaskedLM,
'question-answering': AlbertForQuestionAnswering,
'text-classification': AlbertForSequenceClassification,
'token-classification': AlbertForTokenClassification,
'zero-shot': AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase_ = True
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int=False ):
'''simple docstring'''
lowercase : Optional[int] =super()._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
if return_labels:
if model_class in get_values(UpperCAmelCase__ ):
lowercase : Any =torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=UpperCAmelCase__ )
lowercase : Any =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
return inputs_dict
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : Tuple =AlbertModelTester(self )
lowercase : Optional[Any] =ConfigTester(self , config_class=UpperCAmelCase__ , hidden_size=37 )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
lowercase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
lowercase : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Optional[int] =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase : Tuple =type
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : str =AlbertModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : int =AlbertModel.from_pretrained('''albert-base-v2''' )
lowercase : Optional[int] =torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
lowercase : 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 : Union[str, Any] =torch.tensor(
[[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , UpperCAmelCase__ , atol=1E-4 ) )
| 92 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case = "ClapFeatureExtractor"
snake_case = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple ):
super().__init__(UpperCAmelCase__ , UpperCAmelCase__ )
def __call__( self : Tuple , SCREAMING_SNAKE_CASE_ : str=None , SCREAMING_SNAKE_CASE_ : Optional[Any]=None , SCREAMING_SNAKE_CASE_ : str=None , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
lowerCamelCase__ = kwargs.pop("""sampling_rate""" , UpperCAmelCase__ )
if text is None and audios is None:
raise ValueError("""You have to specify either text or audios. Both cannot be none.""" )
if text is not None:
lowerCamelCase__ = self.tokenizer(UpperCAmelCase__ , return_tensors=UpperCAmelCase__ , **UpperCAmelCase__ )
if audios is not None:
lowerCamelCase__ = self.feature_extractor(
UpperCAmelCase__ , sampling_rate=UpperCAmelCase__ , return_tensors=UpperCAmelCase__ , **UpperCAmelCase__ )
if text is not None and audios is not None:
lowerCamelCase__ = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCAmelCase__ ) , tensor_type=UpperCAmelCase__ )
def __UpperCAmelCase ( self : Tuple , *SCREAMING_SNAKE_CASE_ : Union[str, Any] , **SCREAMING_SNAKE_CASE_ : Tuple ):
return self.tokenizer.batch_decode(*UpperCAmelCase__ , **UpperCAmelCase__ )
def __UpperCAmelCase ( self : List[Any] , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
return self.tokenizer.decode(*UpperCAmelCase__ , **UpperCAmelCase__ )
@property
def __UpperCAmelCase ( self : List[str] ):
lowerCamelCase__ = self.tokenizer.model_input_names
lowerCamelCase__ = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 129 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int ):
'''simple docstring'''
if dst_width < 0 or dst_height < 0:
raise ValueError('''Destination width/height should be > 0''' )
lowercase : Union[str, Any] =img
lowercase : Union[str, Any] =img.shape[1]
lowercase : str =img.shape[0]
lowercase : Union[str, Any] =dst_width
lowercase : str =dst_height
lowercase : str =self.src_w / self.dst_w
lowercase : Optional[Any] =self.src_h / self.dst_h
lowercase : int =(
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 255
)
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
for i in range(self.dst_h ):
for j in range(self.dst_w ):
lowercase : List[Any] =self.img[self.get_y(UpperCAmelCase__ )][self.get_x(UpperCAmelCase__ )]
def lowerCamelCase_ ( self : Tuple , UpperCAmelCase__ : int ):
'''simple docstring'''
return int(self.ratio_x * x )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : int ):
'''simple docstring'''
return int(self.ratio_y * y )
if __name__ == "__main__":
UpperCamelCase_ , UpperCamelCase_ = 800, 600
UpperCamelCase_ = imread("""image_data/lena.jpg""", 1)
UpperCamelCase_ = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
)
waitKey(0)
destroyAllWindows()
| 92 | 0 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_A = None
_A = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_A = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class _lowerCAmelCase :
_lowercase =True
_lowercase =None
# Automatically constructed
_lowercase ='''PIL.Image.Image'''
_lowercase =pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
_lowercase =field(default='''Image''' , init=lowercase__ , repr=lowercase__ )
def __call__( self ) -> Optional[Any]:
return self.pa_type
def __a ( self , _UpperCamelCase ) -> Tuple:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install \'Pillow\'." )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
lowerCAmelCase_ = np.array(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return {"path": value, "bytes": None}
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return {"path": None, "bytes": value}
elif isinstance(UpperCAmelCase__ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(UpperCAmelCase__ )
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(UpperCAmelCase__ )
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
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f"""An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.""" )
def __a ( self , _UpperCamelCase , _UpperCamelCase=None ) -> List[Any]:
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead." )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support decoding images, please install \'Pillow\'." )
if token_per_repo_id is None:
lowerCAmelCase_ = {}
lowerCAmelCase_ = value['''path'''], value['''bytes''']
if bytes_ is None:
if path is None:
raise ValueError(f"""An image should have one of \'path\' or \'bytes\' but both are None in {value}.""" )
else:
if is_local_path(UpperCAmelCase__ ):
lowerCAmelCase_ = PIL.Image.open(UpperCAmelCase__ )
else:
lowerCAmelCase_ = path.split("::" )[-1]
try:
lowerCAmelCase_ = string_to_dict(UpperCAmelCase__ , config.HUB_DATASETS_URL )['''repo_id''']
lowerCAmelCase_ = token_per_repo_id.get(UpperCAmelCase__ )
except ValueError:
lowerCAmelCase_ = None
with xopen(UpperCAmelCase__ , "rb" , use_auth_token=UpperCAmelCase__ ) as f:
lowerCAmelCase_ = BytesIO(f.read() )
lowerCAmelCase_ = PIL.Image.open(bytes_ )
else:
lowerCAmelCase_ = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def __a ( self ) -> Union[str, Any]:
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("binary" ),
"path": Value("string" ),
}
)
def __a ( self , _UpperCamelCase ) -> List[Any]:
if pa.types.is_string(storage.type ):
lowerCAmelCase_ = pa.array([None] * len(UpperCAmelCase__ ) , type=pa.binary() )
lowerCAmelCase_ = pa.StructArray.from_arrays([bytes_array, storage] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowerCAmelCase_ = pa.array([None] * len(UpperCAmelCase__ ) , type=pa.string() )
lowerCAmelCase_ = pa.StructArray.from_arrays([storage, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
lowerCAmelCase_ = storage.field("bytes" )
else:
lowerCAmelCase_ = pa.array([None] * len(UpperCAmelCase__ ) , type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
lowerCAmelCase_ = storage.field("path" )
else:
lowerCAmelCase_ = pa.array([None] * len(UpperCAmelCase__ ) , type=pa.string() )
lowerCAmelCase_ = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowerCAmelCase_ = pa.array(
[encode_np_array(np.array(UpperCAmelCase__ ) )["bytes"] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowerCAmelCase_ = pa.array([None] * len(UpperCAmelCase__ ) , type=pa.string() )
lowerCAmelCase_ = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(UpperCAmelCase__ , self.pa_type )
def __a ( self , _UpperCamelCase ) -> Optional[Any]:
@no_op_if_value_is_null
def path_to_bytes(_UpperCamelCase ):
with xopen(UpperCAmelCase__ , "rb" ) as f:
lowerCAmelCase_ = f.read()
return bytes_
lowerCAmelCase_ = 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() , )
lowerCAmelCase_ = pa.array(
[os.path.basename(UpperCAmelCase__ ) if path is not None else None for path in storage.field("path" ).to_pylist()] , type=pa.string() , )
lowerCAmelCase_ = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(UpperCAmelCase__ , self.pa_type )
def lowerCamelCase__ ( ):
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install \'Pillow\'." )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowerCAmelCase_ = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase__ ( __lowerCAmelCase : "PIL.Image.Image" ):
"""simple docstring"""
lowerCAmelCase_ = BytesIO()
if image.format in list_image_compression_formats():
lowerCAmelCase_ = image.format
else:
lowerCAmelCase_ = '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
image.save(__lowerCAmelCase , format=__lowerCAmelCase )
return buffer.getvalue()
def lowerCamelCase__ ( __lowerCAmelCase : "PIL.Image.Image" ):
"""simple docstring"""
if hasattr(__lowerCAmelCase , "filename" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(__lowerCAmelCase )}
def lowerCamelCase__ ( __lowerCAmelCase : np.ndarray ):
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install \'Pillow\'." )
lowerCAmelCase_ = array.dtype
lowerCAmelCase_ = dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
lowerCAmelCase_ = dtype.kind
lowerCAmelCase_ = dtype.itemsize
lowerCAmelCase_ = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowerCAmelCase_ = np.dtype("|u1" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.""" )
if dtype is not dest_dtype:
warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'""" )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowerCAmelCase_ = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowerCAmelCase_ = dtype_byteorder + dtype_kind + str(__lowerCAmelCase )
lowerCAmelCase_ = np.dtype(__lowerCAmelCase )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'""" )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}""" )
lowerCAmelCase_ = PIL.Image.fromarray(array.astype(__lowerCAmelCase ) )
return {"path": None, "bytes": image_to_bytes(__lowerCAmelCase )}
def lowerCamelCase__ ( __lowerCAmelCase : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ):
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install \'Pillow\'." )
if objs:
lowerCAmelCase_ = first_non_null_value(__lowerCAmelCase )
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(__lowerCAmelCase , np.ndarray ):
lowerCAmelCase_ = no_op_if_value_is_null(__lowerCAmelCase )
return [obj_to_image_dict_func(__lowerCAmelCase ) for obj in objs]
elif isinstance(__lowerCAmelCase , PIL.Image.Image ):
lowerCAmelCase_ = no_op_if_value_is_null(__lowerCAmelCase )
return [obj_to_image_dict_func(__lowerCAmelCase ) for obj in objs]
else:
return objs
else:
return objs
| 290 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ : list[float] ) -> float:
lowercase : Any =0.0_0
lowercase : Tuple =0
for resistor in resistors:
if resistor <= 0:
lowercase : Dict =f'''Resistor at index {index} has a negative or zero value!'''
raise ValueError(__magic_name__ )
first_sum += 1 / float(__magic_name__ )
index += 1
return 1 / first_sum
def _lowerCAmelCase ( __magic_name__ : list[float] ) -> float:
lowercase : Optional[Any] =0.0_0
lowercase : int =0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowercase : Tuple =f'''Resistor at index {index} has a negative value!'''
raise ValueError(__magic_name__ )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A__ : Tuple = logging.get_logger(__name__)
A__ : Optional[int] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ : List[str] = {
'''tokenizer_file''': {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
},
}
A__ : List[Any] = {
'''gpt-neox-20b''': 2_0_4_8,
}
class snake_case__ ( lowercase__ ):
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = ['''input_ids''', '''attention_mask''']
def __init__( self : Union[str, Any] , __a : List[str]=None , __a : Optional[int]=None , __a : str=None , __a : Optional[Any]="<|endoftext|>" , __a : str="<|endoftext|>" , __a : Optional[Any]="<|endoftext|>" , __a : int=False , **__a : int , ) -> Tuple:
'''simple docstring'''
super().__init__(
UpperCAmelCase__ , UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , bos_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , add_prefix_space=UpperCAmelCase__ , **UpperCAmelCase__ , )
__snake_case : Tuple = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , UpperCAmelCase__ ) != add_prefix_space:
__snake_case : Any = getattr(UpperCAmelCase__ , pre_tok_state.pop('type' ) )
__snake_case : List[str] = add_prefix_space
__snake_case : List[str] = pre_tok_class(**UpperCAmelCase__ )
__snake_case : Optional[int] = add_prefix_space
def A_ ( self : int , __a : str , __a : Optional[str] = None ) -> Tuple:
'''simple docstring'''
__snake_case : int = self._tokenizer.model.save(UpperCAmelCase__ , name=UpperCAmelCase__ )
return tuple(UpperCAmelCase__ )
def A_ ( self : Union[str, Any] , __a : "Conversation" ) -> Tuple:
'''simple docstring'''
__snake_case : Dict = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ ) + [self.eos_token_id] )
if len(UpperCAmelCase__ ) > self.model_max_length:
__snake_case : Optional[Any] = input_ids[-self.model_max_length :]
return input_ids
| 286 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
"""self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
"""self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
"""self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
"""self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
"""self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
"""self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
"""self_attn.rotary_emb""": """encoder.embed_positions""",
"""self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
"""conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
"""conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
"""conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
"""conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
"""conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
"""ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
"""ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
"""ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
"""ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
"""ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
"""ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
"""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""",
}
UpperCamelCase_ = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def _lowerCAmelCase ( __magic_name__ : Any , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Tuple ) -> str:
for attribute in key.split('''.''' ):
lowercase : Tuple =getattr(__magic_name__ , __magic_name__ )
if weight_type is not None:
lowercase : Optional[int] =getattr(__magic_name__ , __magic_name__ ).shape
else:
lowercase : List[Any] =hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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 : Any =value
elif weight_type == "weight_g":
lowercase : List[Any] =value
elif weight_type == "weight_v":
lowercase : Union[str, Any] =value
elif weight_type == "bias":
lowercase : Tuple =value
elif weight_type == "running_mean":
lowercase : Union[str, Any] =value
elif weight_type == "running_var":
lowercase : str =value
elif weight_type == "num_batches_tracked":
lowercase : Tuple =value
elif weight_type == "inv_freq":
lowercase : Optional[Any] =value
else:
lowercase : Tuple =value
logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def _lowerCAmelCase ( __magic_name__ : List[Any] , __magic_name__ : Union[str, Any] , __magic_name__ : str ) -> Union[str, Any]:
lowercase : Optional[int] =[]
lowercase : Tuple =fairseq_model.state_dict()
lowercase : List[Any] =hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
lowercase : Tuple =False
if "conv_layers" in name:
load_conv_layer(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , hf_model.config.feat_extract_norm == '''group''' , )
lowercase : List[Any] =True
else:
for key, mapped_key in MAPPING.items():
lowercase : Optional[int] ='''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
lowercase : Union[str, Any] =True
if "*" in mapped_key:
lowercase : Optional[int] =name.split(__magic_name__ )[0].split('''.''' )[-2]
lowercase : List[str] =mapped_key.replace('''*''' , __magic_name__ )
if "pos_bias_u" in name:
lowercase : Optional[Any] =None
elif "pos_bias_v" in name:
lowercase : Union[str, Any] =None
elif "weight_g" in name:
lowercase : Any ='''weight_g'''
elif "weight_v" in name:
lowercase : Tuple ='''weight_v'''
elif "bias" in name:
lowercase : Optional[int] ='''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase : Optional[int] ='''weight'''
elif "running_mean" in name:
lowercase : Union[str, Any] ='''running_mean'''
elif "inv_freq" in name:
lowercase : Any ='''inv_freq'''
elif "running_var" in name:
lowercase : Tuple ='''running_var'''
elif "num_batches_tracked" in name:
lowercase : Dict ='''num_batches_tracked'''
else:
lowercase : str =None
set_recursively(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
continue
if not is_used:
unused_weights.append(__magic_name__ )
logger.warning(f'''Unused weights: {unused_weights}''' )
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : Optional[Any] , __magic_name__ : List[Any] , __magic_name__ : Optional[Any] , __magic_name__ : Tuple ) -> int:
lowercase : Optional[Any] =full_name.split('''conv_layers.''' )[-1]
lowercase : Any =name.split('''.''' )
lowercase : List[str] =int(items[0] )
lowercase : Union[str, Any] =int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
lowercase : Union[str, Any] =value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
lowercase : Optional[Any] =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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
lowercase : Optional[int] =value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[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(__magic_name__ )
@torch.no_grad()
def _lowerCAmelCase ( __magic_name__ : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Tuple=None , __magic_name__ : Optional[Any]=None , __magic_name__ : Union[str, Any]=True ) -> Union[str, Any]:
if config_path is not None:
lowercase : Optional[Any] =WavaVecaConformerConfig.from_pretrained(__magic_name__ , hidden_act='''swish''' )
else:
lowercase : Optional[int] =WavaVecaConformerConfig()
if "rope" in checkpoint_path:
lowercase : Dict ='''rotary'''
if is_finetuned:
if dict_path:
lowercase : Optional[Any] =Dictionary.load(__magic_name__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowercase : str =target_dict.pad_index
lowercase : Union[str, Any] =target_dict.bos_index
lowercase : Any =target_dict.eos_index
lowercase : Tuple =len(target_dict.symbols )
lowercase : str =os.path.join(__magic_name__ , '''vocab.json''' )
if not os.path.isdir(__magic_name__ ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__magic_name__ ) )
return
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
lowercase : Dict =target_dict.indices
# fairseq has the <pad> and <s> switched
lowercase : str =0
lowercase : List[Any] =1
with open(__magic_name__ , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(__magic_name__ , __magic_name__ )
lowercase : List[str] =WavaVecaCTCTokenizer(
__magic_name__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=__magic_name__ , )
lowercase : Optional[int] =True if config.feat_extract_norm == '''layer''' else False
lowercase : str =WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__magic_name__ , return_attention_mask=__magic_name__ , )
lowercase : Tuple =WavaVecaProcessor(feature_extractor=__magic_name__ , tokenizer=__magic_name__ )
processor.save_pretrained(__magic_name__ )
lowercase : str =WavaVecaConformerForCTC(__magic_name__ )
else:
lowercase : Tuple =WavaVecaConformerForPreTraining(__magic_name__ )
if is_finetuned:
lowercase , lowercase , lowercase : Optional[int] =fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
lowercase : Dict =argparse.Namespace(task='''audio_pretraining''' )
lowercase : Optional[int] =fairseq.tasks.setup_task(__magic_name__ )
lowercase , lowercase , lowercase : str =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__magic_name__ )
lowercase : List[Any] =model[0].eval()
recursively_load_weights(__magic_name__ , __magic_name__ , not is_finetuned )
hf_wavavec.save_pretrained(__magic_name__ )
if __name__ == "__main__":
UpperCamelCase_ = 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_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCamelCase_ = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 92 | 0 |
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
snake_case : Tuple = abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
config.addinivalue_line(
'markers' ,'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested' )
config.addinivalue_line(
'markers' ,'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested' )
config.addinivalue_line('markers' ,'is_pipeline_test: mark test to run only when pipelines are tested' )
config.addinivalue_line('markers' ,'is_staging_test: mark test to run only in the staging environment' )
config.addinivalue_line('markers' ,'accelerate_tests: mark test that require accelerate' )
config.addinivalue_line('markers' ,'tool_tests: mark the tool tests that are run on their specific schedule' )
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(UpperCAmelCase__ )
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
from transformers.testing_utils import pytest_terminal_summary_main
_SCREAMING_SNAKE_CASE = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(UpperCAmelCase__ ,id=UpperCAmelCase__ )
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if exitstatus == 5:
_SCREAMING_SNAKE_CASE = 0
# Doctest custom flag to ignore output.
snake_case : List[str] = doctest.register_optionflag('IGNORE_RESULT')
snake_case : Any = doctest.OutputChecker
class __lowercase ( lowercase__ ):
"""simple docstring"""
def __magic_name__ ( self , A_ , A_ , A_ )-> int:
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
snake_case : Union[str, Any] = CustomOutputChecker
snake_case : List[Any] = HfDoctestModule
snake_case : Optional[int] = HfDocTestParser
| 605 |
'''simple docstring'''
import math
import flax.linen as nn
import jax.numpy as jnp
def _lowerCAmelCase ( __magic_name__ : jnp.ndarray , __magic_name__ : int , __magic_name__ : float = 1 , __magic_name__ : float = 1 , __magic_name__ : float = 1.0E4 , __magic_name__ : bool = False , __magic_name__ : float = 1.0 , ) -> jnp.ndarray:
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
lowercase : int =float(embedding_dim // 2 )
lowercase : Optional[int] =math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
lowercase : Any =min_timescale * jnp.exp(jnp.arange(__magic_name__ , dtype=jnp.floataa ) * -log_timescale_increment )
lowercase : List[Any] =jnp.expand_dims(__magic_name__ , 1 ) * jnp.expand_dims(__magic_name__ , 0 )
# scale embeddings
lowercase : Tuple =scale * emb
if flip_sin_to_cos:
lowercase : Dict =jnp.concatenate([jnp.cos(__magic_name__ ), jnp.sin(__magic_name__ )] , axis=1 )
else:
lowercase : Any =jnp.concatenate([jnp.sin(__magic_name__ ), jnp.cos(__magic_name__ )] , axis=1 )
lowercase : List[str] =jnp.reshape(__magic_name__ , [jnp.shape(__magic_name__ )[0], embedding_dim] )
return signal
class __SCREAMING_SNAKE_CASE ( nn.Module ):
lowerCamelCase_ = 32
lowerCamelCase_ = jnp.floataa
@nn.compact
def __call__( self : Tuple , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : List[Any] =nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_1''' )(UpperCAmelCase__ )
lowercase : Any =nn.silu(UpperCAmelCase__ )
lowercase : int =nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_2''' )(UpperCAmelCase__ )
return temb
class __SCREAMING_SNAKE_CASE ( nn.Module ):
lowerCamelCase_ = 32
lowerCamelCase_ = False
lowerCamelCase_ = 1
@nn.compact
def __call__( self : int , UpperCAmelCase__ : str ):
'''simple docstring'''
return get_sinusoidal_embeddings(
UpperCAmelCase__ , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 92 | 0 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
__UpperCAmelCase ="""\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew and
Dorr, Bonnie and
Schwartz, Rich and
Micciulla, Linnea and
Makhoul, John\",
booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
month = aug # \" 8-12\",
year = \"2006\",
address = \"Cambridge, Massachusetts, USA\",
publisher = \"Association for Machine Translation in the Americas\",
url = \"https://aclanthology.org/2006.amta-papers.25\",
pages = \"223--231\",
}
@inproceedings{post-2018-call,
title = \"A Call for Clarity in Reporting {BLEU} Scores\",
author = \"Post, Matt\",
booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
month = oct,
year = \"2018\",
address = \"Belgium, Brussels\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/W18-6319\",
pages = \"186--191\",
}
"""
__UpperCAmelCase ="""\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.
The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534
See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""
__UpperCAmelCase ="""
Produces TER scores alongside the number of edits and reference length.
Args:
predictions (list of str): The system stream (a sequence of segments).
references (list of list of str): A list of one or more reference streams (each a sequence of segments).
normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
Only applies if `normalized = True`. Defaults to `False`.
case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.
Returns:
'score' (float): TER score (num_edits / sum_ref_lengths * 100)
'num_edits' (int): The cumulative number of edits
'ref_length' (float): The cumulative average reference length
Examples:
Example 1:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\",
... \"What did the TER metric user say to the developer?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
... [\"Your jokes are...\", \"...TERrible\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... case_sensitive=True)
>>> print(results)
{'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}
Example 2:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... case_sensitive=True)
>>> print(results)
{'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}
Example 3:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... normalized=True,
... case_sensitive=True)
>>> print(results)
{'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}
Example 4:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... ignore_punct=True,
... case_sensitive=False)
>>> print(results)
{'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}
Example 5:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\",
... \"What did the TER metric user say to the developer?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
... [\"Your jokes are...\", \"...TERrible\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... ignore_punct=True,
... case_sensitive=False)
>>> print(results)
{'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase__ ( datasets.Metric ):
def lowercase_ ( self ):
'''simple docstring'''
if version.parse(scb.__version__ ) < version.parse("1.4.12" ):
raise ImportWarning(
"To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n"
"You can install it with `pip install \"sacrebleu>=1.4.12\"`." )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="http://www.cs.umd.edu/~snover/tercom/" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Sequence(datasets.Value("string" , id="sequence" ) , id="references" ),
} ) , codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"] , reference_urls=[
"https://github.com/jhclark/tercom",
] , )
def lowercase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = False , ):
'''simple docstring'''
A__ = len(references[0] )
if any(len(UpperCAmelCase__ ) != references_per_prediction for refs in references ):
raise ValueError("Sacrebleu requires the same number of references for each prediction" )
A__ = [[refs[i] for refs in references] for i in range(UpperCAmelCase__ )]
A__ = TER(
normalized=UpperCAmelCase__ , no_punct=UpperCAmelCase__ , asian_support=UpperCAmelCase__ , case_sensitive=UpperCAmelCase__ , )
A__ = sb_ter.corpus_score(UpperCAmelCase__ , UpperCAmelCase__ )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length} | 337 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase_ = {
"""facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = 'esm'
def __init__( self : Optional[Any] , UpperCAmelCase__ : str=None , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : Any=None , UpperCAmelCase__ : int=768 , UpperCAmelCase__ : Union[str, Any]=12 , UpperCAmelCase__ : List[Any]=12 , UpperCAmelCase__ : Optional[Any]=3072 , UpperCAmelCase__ : Dict=0.1 , UpperCAmelCase__ : int=0.1 , UpperCAmelCase__ : int=1026 , UpperCAmelCase__ : Tuple=0.02 , UpperCAmelCase__ : Optional[int]=1E-12 , UpperCAmelCase__ : List[Any]="absolute" , UpperCAmelCase__ : int=True , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : str=False , UpperCAmelCase__ : int=False , UpperCAmelCase__ : Optional[Any]=None , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : int , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCAmelCase__ , mask_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
lowercase : Any =vocab_size
lowercase : List[Any] =hidden_size
lowercase : Any =num_hidden_layers
lowercase : Optional[Any] =num_attention_heads
lowercase : Tuple =intermediate_size
lowercase : int =hidden_dropout_prob
lowercase : Dict =attention_probs_dropout_prob
lowercase : Optional[int] =max_position_embeddings
lowercase : Union[str, Any] =initializer_range
lowercase : Tuple =layer_norm_eps
lowercase : Union[str, Any] =position_embedding_type
lowercase : List[Any] =use_cache
lowercase : Dict =emb_layer_norm_before
lowercase : Optional[Any] =token_dropout
lowercase : Union[str, Any] =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
lowercase : Any =EsmFoldConfig()
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase : Optional[int] =EsmFoldConfig(**UpperCAmelCase__ )
lowercase : Union[str, Any] =esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
lowercase : int =get_default_vocab_list()
else:
lowercase : Tuple =vocab_list
else:
lowercase : Union[str, Any] =None
lowercase : Dict =None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , UpperCAmelCase__ ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : Union[str, Any] =super().to_dict()
if isinstance(self.esmfold_config , UpperCAmelCase__ ):
lowercase : Optional[Any] =self.esmfold_config.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = None
lowerCamelCase_ = True
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = 0
lowerCamelCase_ = True
lowerCamelCase_ = False
lowerCamelCase_ = 1_28
lowerCamelCase_ = None
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
if self.trunk is None:
lowercase : str =TrunkConfig()
elif isinstance(self.trunk , UpperCAmelCase__ ):
lowercase : int =TrunkConfig(**self.trunk )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : str =asdict(self )
lowercase : Union[str, Any] =self.trunk.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = 48
lowerCamelCase_ = 10_24
lowerCamelCase_ = 1_28
lowerCamelCase_ = 32
lowerCamelCase_ = 32
lowerCamelCase_ = 32
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = False
lowerCamelCase_ = 4
lowerCamelCase_ = 1_28
lowerCamelCase_ = None
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
if self.structure_module is None:
lowercase : Any =StructureModuleConfig()
elif isinstance(self.structure_module , UpperCAmelCase__ ):
lowercase : Union[str, Any] =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
lowercase : str =self.sequence_state_dim // self.sequence_head_width
lowercase : int =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : List[Any] =asdict(self )
lowercase : Any =self.structure_module.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = 3_84
lowerCamelCase_ = 1_28
lowerCamelCase_ = 16
lowerCamelCase_ = 1_28
lowerCamelCase_ = 12
lowerCamelCase_ = 4
lowerCamelCase_ = 8
lowerCamelCase_ = 0.1
lowerCamelCase_ = 8
lowerCamelCase_ = 1
lowerCamelCase_ = 2
lowerCamelCase_ = 7
lowerCamelCase_ = 10
lowerCamelCase_ = 1E-8
lowerCamelCase_ = 1E5
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return asdict(self )
def _lowerCAmelCase ( ) -> Optional[int]:
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 92 | 0 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
__A = pd.read_csv(
"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
"position_salaries.csv"
)
__A = dataset.iloc[:, 1:2].values
__A = dataset.iloc[:, 2].values
__A , __A , __A , __A = train_test_split(X, y, test_size=0.2, random_state=0)
__A = PolynomialFeatures(degree=4)
__A = poly_reg.fit_transform(X)
__A = LinearRegression()
pol_reg.fit(X_poly, y)
def a__ ( ) -> str:
plt.scatter(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , color="red" )
plt.plot(__SCREAMING_SNAKE_CASE , pol_reg.predict(poly_reg.fit_transform(__SCREAMING_SNAKE_CASE ) ) , color="blue" )
plt.title("Truth or Bluff (Linear Regression)" )
plt.xlabel("Position level" )
plt.ylabel("Salary" )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 346 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCamelCase_ = abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)
def _lowerCAmelCase ( __magic_name__ : int ) -> Tuple:
config.addinivalue_line(
'''markers''' , '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''' , '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''' , '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''' , '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''' , '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''' , '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def _lowerCAmelCase ( __magic_name__ : int ) -> Any:
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : Any ) -> Any:
from transformers.testing_utils import pytest_terminal_summary_main
lowercase : Optional[Any] =terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__magic_name__ , id=__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : Tuple , __magic_name__ : Union[str, Any] ) -> List[str]:
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowercase : Optional[int] =0
# Doctest custom flag to ignore output.
UpperCamelCase_ = doctest.register_optionflag("""IGNORE_RESULT""")
UpperCamelCase_ = doctest.OutputChecker
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
def lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[Any] ):
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
UpperCamelCase_ = CustomOutputChecker
UpperCamelCase_ = HfDoctestModule
UpperCamelCase_ = HfDocTestParser
| 92 | 0 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
a : Any = logging.getLogger(__name__)
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = git.Repo(search_parent_directories=_UpperCamelCase )
__lowercase = {
'''repo_id''': str(_UpperCamelCase ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
}
with open(os.path.join(_UpperCamelCase , '''git_log.json''' ) , '''w''' ) as f:
json.dump(_UpperCamelCase , _UpperCamelCase , indent=4 )
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if params.n_gpu <= 0:
__lowercase = 0
__lowercase = -1
__lowercase = True
__lowercase = False
return
assert torch.cuda.is_available()
logger.info('''Initializing GPUs''' )
if params.n_gpu > 1:
assert params.local_rank != -1
__lowercase = int(os.environ['''WORLD_SIZE'''] )
__lowercase = int(os.environ['''N_GPU_NODE'''] )
__lowercase = int(os.environ['''RANK'''] )
# number of nodes / node ID
__lowercase = params.world_size // params.n_gpu_per_node
__lowercase = params.global_rank // params.n_gpu_per_node
__lowercase = True
assert params.n_nodes == int(os.environ['''N_NODES'''] )
assert params.node_id == int(os.environ['''NODE_RANK'''] )
# local job (single GPU)
else:
assert params.local_rank == -1
__lowercase = 1
__lowercase = 0
__lowercase = 0
__lowercase = 0
__lowercase = 1
__lowercase = 1
__lowercase = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
__lowercase = params.node_id == 0 and params.local_rank == 0
__lowercase = params.n_nodes > 1
# summary
__lowercase = F'--- Global rank: {params.global_rank} - '
logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes )
logger.info(PREFIX + '''Node ID : %i''' % params.node_id )
logger.info(PREFIX + '''Local rank : %i''' % params.local_rank )
logger.info(PREFIX + '''World size : %i''' % params.world_size )
logger.info(PREFIX + '''GPUs per node : %i''' % params.n_gpu_per_node )
logger.info(PREFIX + '''Master : %s''' % str(params.is_master ) )
logger.info(PREFIX + '''Multi-node : %s''' % str(params.multi_node ) )
logger.info(PREFIX + '''Multi-GPU : %s''' % str(params.multi_gpu ) )
logger.info(PREFIX + '''Hostname : %s''' % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info('''Initializing PyTorch distributed''' )
torch.distributed.init_process_group(
init_method='''env://''' , backend='''nccl''' , )
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 639 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging
UpperCamelCase_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = ['pixel_values']
def __init__( self : List[Any] , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Union[int, float] = 1 / 255 , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : int = 8 , **UpperCAmelCase__ : str , ):
'''simple docstring'''
super().__init__(**UpperCAmelCase__ )
lowercase : Union[str, Any] =do_rescale
lowercase : List[Any] =rescale_factor
lowercase : Tuple =do_pad
lowercase : List[str] =pad_size
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : float , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : Optional[int] ):
'''simple docstring'''
return rescale(UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCamelCase_ ( self : Tuple , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None ):
'''simple docstring'''
lowercase , lowercase : Union[str, Any] =get_image_size(UpperCAmelCase__ )
lowercase : Tuple =(old_height // size + 1) * size - old_height
lowercase : Tuple =(old_width // size + 1) * size - old_width
return pad(UpperCAmelCase__ , ((0, pad_height), (0, pad_width)) , mode='''symmetric''' , data_format=UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : ImageInput , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[float] = None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[Union[str, TensorType]] = None , UpperCAmelCase__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **UpperCAmelCase__ : Optional[Any] , ):
'''simple docstring'''
lowercase : int =do_rescale if do_rescale is not None else self.do_rescale
lowercase : Tuple =rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase : int =do_pad if do_pad is not None else self.do_pad
lowercase : List[Any] =pad_size if pad_size is not None else self.pad_size
lowercase : Any =make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
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 : Dict =[to_numpy_array(UpperCAmelCase__ ) for image in images]
if do_rescale:
lowercase : Tuple =[self.rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ ) for image in images]
if do_pad:
lowercase : Union[str, Any] =[self.pad(UpperCAmelCase__ , size=UpperCAmelCase__ ) for image in images]
lowercase : Dict =[to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
lowercase : Any ={'''pixel_values''': images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 92 | 0 |
from __future__ import annotations
from collections.abc import MutableSequence
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ):
if len(UpperCAmelCase__ ) != degree + 1:
raise ValueError(
'The number of coefficients should be equal to the degree + 1.' )
SCREAMING_SNAKE_CASE_ : list[float] =list(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =degree
def __add__( self , __UpperCAmelCase ):
if self.degree > polynomial_a.degree:
SCREAMING_SNAKE_CASE_ : Optional[int] =self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , UpperCAmelCase__ )
else:
SCREAMING_SNAKE_CASE_ : Dict =polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , UpperCAmelCase__ )
def __sub__( self , __UpperCAmelCase ):
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self ):
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ : list[float] =[0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , UpperCAmelCase__ )
def __lowerCamelCase ( self , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ : int | float =0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] =''''''
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(UpperCAmelCase__ )
return polynomial
def __repr__( self ):
return self.__str__()
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : list[float] =[0] * self.degree
for i in range(self.degree ):
SCREAMING_SNAKE_CASE_ : Tuple =self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , UpperCAmelCase__ )
def __lowerCamelCase ( self , __UpperCAmelCase = 0 ):
SCREAMING_SNAKE_CASE_ : list[float] =[0] * (self.degree + 2)
SCREAMING_SNAKE_CASE_ : str =constant
for i in range(self.degree + 1 ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] =self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , UpperCAmelCase__ )
def __eq__( self , __UpperCAmelCase ):
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self , __UpperCAmelCase ):
return not self.__eq__(UpperCAmelCase__ )
| 220 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["""MBartTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["""MBartTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MBartForCausalLM""",
"""MBartForConditionalGeneration""",
"""MBartForQuestionAnswering""",
"""MBartForSequenceClassification""",
"""MBartModel""",
"""MBartPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""TFMBartForConditionalGeneration""",
"""TFMBartModel""",
"""TFMBartPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""FlaxMBartForConditionalGeneration""",
"""FlaxMBartForQuestionAnswering""",
"""FlaxMBartForSequenceClassification""",
"""FlaxMBartModel""",
"""FlaxMBartPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ["MBartTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ["MBartTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
"MBartForCausalLM",
"MBartForConditionalGeneration",
"MBartForQuestionAnswering",
"MBartForSequenceClassification",
"MBartModel",
"MBartPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"TFMBartForConditionalGeneration",
"TFMBartModel",
"TFMBartPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"FlaxMBartForConditionalGeneration",
"FlaxMBartForQuestionAnswering",
"FlaxMBartForSequenceClassification",
"FlaxMBartModel",
"FlaxMBartPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 181 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase_ = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
__UpperCamelCase : Optional[Any] = True
from torch.cuda.amp import autocast
__UpperCamelCase : Any = logging.getLogger(__name__)
@dataclass
class a :
snake_case__ = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
snake_case__ = field(
default=lowercase__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
snake_case__ = field(
default=lowercase__ , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''} )
snake_case__ = field(
default=lowercase__ , metadata={'''help''': '''Whether to log verbose messages or not.'''} , )
snake_case__ = field(
default=2.0 , metadata={'''help''': '''Maximum temperature for gumbel softmax.'''} )
snake_case__ = field(
default=0.5 , metadata={'''help''': '''Minimum temperature for gumbel softmax.'''} )
snake_case__ = field(
default=0.99_99_95 , metadata={'''help''': '''Decay of gumbel temperature during training.'''} )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : ModelArguments , _UpperCAmelCase : TrainingArguments ):
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
lowerCAmelCase = logging.WARNING
if model_args.verbose_logging:
lowerCAmelCase = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
lowerCAmelCase = logging.INFO
logger.setLevel(_UpperCAmelCase )
@dataclass
class a :
snake_case__ = field(
default=lowercase__ , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
snake_case__ = field(
default=lowercase__ , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
snake_case__ = field(
default='''train''' , metadata={
'''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
} , )
snake_case__ = field(
default='''validation''' , metadata={
'''help''': (
'''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
)
} , )
snake_case__ = field(
default='''file''' , metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''} , )
snake_case__ = field(
default=lowercase__ , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
snake_case__ = field(
default=1 , metadata={
'''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
} , )
snake_case__ = field(
default=lowercase__ , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
snake_case__ = field(
default=2_0.0 , metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''} )
@dataclass
class a :
snake_case__ = 4_2
snake_case__ = 4_2
snake_case__ = '''longest'''
snake_case__ = None
snake_case__ = None
def __call__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.feature_extractor.pad(
UpperCAmelCase__ , max_length=self.max_length , padding=self.padding , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='pt' , )
lowerCAmelCase = self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1] )
lowerCAmelCase = batch['''input_values'''].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
lowerCAmelCase = self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1 ) ).to(
torch.long )
lowerCAmelCase = torch.zeros(
(batch_size, mask_indices_seq_length) , dtype=torch.long , device=batch['input_values'].device )
# these two operations makes sure that all values
# before the output lengths indices are attended to
lowerCAmelCase = 1
lowerCAmelCase = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
lowerCAmelCase = _compute_mask_indices(
(batch_size, mask_indices_seq_length) , self.model.config.mask_time_prob , self.model.config.mask_time_length , attention_mask=UpperCAmelCase__ , min_masks=2 , )
return batch
class a ( lowercase__ ):
def __init__( self , *_snake_case , _snake_case=1 , _snake_case=0 , _snake_case=1.0 , **_snake_case ):
"""simple docstring"""
super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__ )
lowerCAmelCase = 0
lowerCAmelCase = max_gumbel_temp
lowerCAmelCase = min_gumbel_temp
lowerCAmelCase = gumbel_temp_decay
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
model.train()
lowerCAmelCase = self._prepare_inputs(UpperCAmelCase__ )
if self.use_amp:
with autocast():
lowerCAmelCase = self.compute_loss(UpperCAmelCase__ , UpperCAmelCase__ )
else:
lowerCAmelCase = self.compute_loss(UpperCAmelCase__ , UpperCAmelCase__ )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
lowerCAmelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
lowerCAmelCase = loss.sum() / (inputs['''mask_time_indices''']).sum()
else:
raise ValueError(F'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']' )
if self.args.gradient_accumulation_steps > 1:
lowerCAmelCase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(UpperCAmelCase__ ).backward()
elif self.use_apex:
with amp.scale_loss(UpperCAmelCase__ , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(UpperCAmelCase__ )
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp ) )
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp ) )
return loss.detach()
def _SCREAMING_SNAKE_CASE ():
# 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.
lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowerCAmelCase = parser.parse_args_into_dataclasses()
configure_logger(_UpperCAmelCase , _UpperCAmelCase )
# Downloading and loading a dataset from the hub.
lowerCAmelCase = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
lowerCAmelCase = DatasetDict()
lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'{data_args.train_split_name}[:{data_args.validation_split_percentage}%]' , cache_dir=model_args.cache_dir , )
lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'{data_args.train_split_name}[{data_args.validation_split_percentage}%:]' , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
lowerCAmelCase = DatasetDict()
lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split='validation' , cache_dir=model_args.cache_dir , )
lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'{data_args.train_split_name}' , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
lowerCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=_UpperCAmelCase )
def prepare_dataset(_UpperCAmelCase : Optional[Any] ):
# check that all files have the correct sampling rate
lowerCAmelCase = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
lowerCAmelCase = datasets.map(
_UpperCAmelCase , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets['train'].column_names )
# filter audio files that are too long
lowerCAmelCase = vectorized_datasets.filter(
lambda _UpperCAmelCase : len(data['speech'] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(_UpperCAmelCase : Tuple ):
return feature_extractor(batch['speech'] , sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
lowerCAmelCase = vectorized_datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets['train'].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
lowerCAmelCase = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
'PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'
' ``config.feat_extract_norm=\'layer\'' )
lowerCAmelCase = WavaVecaForPreTraining(_UpperCAmelCase )
lowerCAmelCase = DataCollatorForWavaVecaPretraining(model=_UpperCAmelCase , feature_extractor=_UpperCAmelCase )
lowerCAmelCase = WavaVecaPreTrainer(
model=_UpperCAmelCase , data_collator=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=vectorized_datasets['train'] , eval_dataset=vectorized_datasets['validation'] , tokenizer=_UpperCAmelCase , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main()
| 4 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
UpperCamelCase_ = logging.getLogger(__name__)
def _lowerCAmelCase ( __magic_name__ : List[Any] , __magic_name__ : str ) -> Union[str, Any]:
return (preds == labels).mean()
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys() )} )
lowerCamelCase_ = field(metadata={'help': 'Should contain the data files for the task.'} )
lowerCamelCase_ = field(
default=1_28 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _lowerCAmelCase ( ) -> 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 : Dict =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase , lowercase , lowercase : List[Any] =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.''' )
# 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''' , __magic_name__ )
# Set seed
set_seed(training_args.seed )
try:
lowercase : Any =processors[data_args.task_name]()
lowercase : Optional[int] =processor.get_labels()
lowercase : str =len(__magic_name__ )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase : List[str] =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__magic_name__ , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
lowercase : int =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 , )
lowercase : Any =AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__magic_name__ , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase : int =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__magic_name__ , task=data_args.task_name , 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 : Union[str, Any] =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__magic_name__ , task=data_args.task_name , 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 compute_metrics(__magic_name__ : EvalPrediction ) -> Dict:
lowercase : Dict =np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__magic_name__ , p.label_ids )}
# Data collator
lowercase : List[str] =DataCollatorWithPadding(__magic_name__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase : Dict =Trainer(
model=__magic_name__ , args=__magic_name__ , train_dataset=__magic_name__ , eval_dataset=__magic_name__ , compute_metrics=__magic_name__ , data_collator=__magic_name__ , )
# 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_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase : Optional[Any] ={}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase : List[Any] =trainer.evaluate()
lowercase : Any =os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__magic_name__ , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __magic_name__ , __magic_name__ )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__magic_name__ )
return results
def _lowerCAmelCase ( __magic_name__ : Any ) -> Optional[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 92 | 0 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
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 (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Tuple=13 , SCREAMING_SNAKE_CASE__ : Optional[int]=30 , SCREAMING_SNAKE_CASE__ : Tuple=2 , SCREAMING_SNAKE_CASE__ : List[Any]=3 , SCREAMING_SNAKE_CASE__ : Optional[int]=True , SCREAMING_SNAKE_CASE__ : int=True , SCREAMING_SNAKE_CASE__ : Any=32 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=5 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=4 , SCREAMING_SNAKE_CASE__ : str=37 , SCREAMING_SNAKE_CASE__ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE__ : int=0.1 , SCREAMING_SNAKE_CASE__ : List[str]=0.1 , SCREAMING_SNAKE_CASE__ : int=10 , SCREAMING_SNAKE_CASE__ : Tuple=0.02 , SCREAMING_SNAKE_CASE__ : Tuple=3 , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : List[str]=2 , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = image_size
UpperCamelCase = patch_size
UpperCamelCase = num_channels
UpperCamelCase = is_training
UpperCamelCase = use_labels
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = scope
UpperCamelCase = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
UpperCamelCase = (image_size // patch_size) ** 2
UpperCamelCase = num_patches + 2
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
return DeiTConfig(
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 , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowerCAmelCase ( self : str , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[Any] ):
"""simple docstring"""
UpperCamelCase = DeiTModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase = model(UpperCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self : List[str] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = DeiTForMaskedImageModeling(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase = model(UpperCAmelCase__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
UpperCamelCase = 1
UpperCamelCase = DeiTForMaskedImageModeling(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase = model(UpperCAmelCase__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowerCAmelCase ( self : str , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict ):
"""simple docstring"""
UpperCamelCase = self.type_sequence_label_size
UpperCamelCase = DeiTForImageClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase = model(UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCamelCase = 1
UpperCamelCase = DeiTForImageClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase = model(UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowerCAmelCase ( self : Dict ):
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
UpperCamelCase
) = config_and_inputs
UpperCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( lowercase__ , lowercase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] =(
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ : int =(
{
"feature-extraction": DeiTModel,
"image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ : Optional[Any] =False
SCREAMING_SNAKE_CASE_ : str =False
SCREAMING_SNAKE_CASE_ : Optional[int] =False
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = DeiTModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=37 )
def __lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds' )
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(UpperCAmelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase__ , nn.Linear ) )
def __lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(UpperCAmelCase__ )
UpperCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase = [*signature.parameters.keys()]
UpperCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCAmelCase__ )
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase__ )
def __lowerCAmelCase ( self : Any ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__ )
def __lowerCAmelCase ( self : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Any=False ):
"""simple docstring"""
UpperCamelCase = super()._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
if not self.model_tester.is_training:
return
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(UpperCAmelCase__ )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
UpperCamelCase = model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.train()
UpperCamelCase = self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
UpperCamelCase = model(**UpperCAmelCase__ ).loss
loss.backward()
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
UpperCamelCase = False
UpperCamelCase = True
for model_class in self.all_model_classes:
if model_class in get_values(UpperCAmelCase__ ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
UpperCamelCase = model_class(UpperCAmelCase__ )
model.gradient_checkpointing_enable()
model.to(UpperCAmelCase__ )
model.train()
UpperCamelCase = self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
UpperCamelCase = model(**UpperCAmelCase__ ).loss
loss.backward()
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = [
{'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
{'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
{'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(UpperCAmelCase__ ),
*get_values(UpperCAmelCase__ ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}' ):
UpperCamelCase = problem_type['''title''']
UpperCamelCase = problem_type['''num_labels''']
UpperCamelCase = model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.train()
UpperCamelCase = self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
if problem_type["num_labels"] > 1:
UpperCamelCase = inputs['''labels'''].unsqueeze(1 ).repeat(1 , problem_type['num_labels'] )
UpperCamelCase = inputs['''labels'''].to(problem_type['dtype'] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=UpperCAmelCase__ ) as warning_list:
UpperCamelCase = model(**UpperCAmelCase__ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F'Something is going wrong in the regression problem: intercepted {w.message}' )
loss.backward()
@slow
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = DeiTModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def __lowerCamelCase ( ) -> List[str]:
UpperCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224' )
if is_vision_available()
else None
)
@slow
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224' ).to(
UpperCAmelCase__ )
UpperCamelCase = self.default_image_processor
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(images=UpperCAmelCase__ , return_tensors='pt' ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
UpperCamelCase = model(**UpperCAmelCase__ )
# verify the logits
UpperCamelCase = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase__ )
UpperCamelCase = torch.tensor([-1.0266, 0.1912, -1.2861] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase__ , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = DeiTModel.from_pretrained(
'facebook/deit-base-distilled-patch16-224' , torch_dtype=torch.floataa , device_map='auto' )
UpperCamelCase = self.default_image_processor
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(images=UpperCAmelCase__ , return_tensors='pt' )
UpperCamelCase = inputs.pixel_values.to(UpperCAmelCase__ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
UpperCamelCase = model(UpperCAmelCase__ )
| 282 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
UpperCamelCase_ = logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_ = """cuda""" if torch.cuda.is_available() else """cpu"""
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : Tuple=100 , __magic_name__ : Optional[int]=" " ) -> List[str]:
lowercase : List[Any] =text.split(__magic_name__ )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(__magic_name__ ) , __magic_name__ )]
def _lowerCAmelCase ( __magic_name__ : dict ) -> dict:
lowercase , lowercase : int =[], []
for title, text in zip(documents['''title'''] , documents['''text'''] ):
if text is not None:
for passage in split_text(__magic_name__ ):
titles.append(title if title is not None else '''''' )
texts.append(__magic_name__ )
return {"title": titles, "text": texts}
def _lowerCAmelCase ( __magic_name__ : dict , __magic_name__ : DPRContextEncoder , __magic_name__ : DPRContextEncoderTokenizerFast ) -> dict:
lowercase : Dict =ctx_tokenizer(
documents['''title'''] , documents['''text'''] , truncation=__magic_name__ , padding='''longest''' , return_tensors='''pt''' )['''input_ids''']
lowercase : Optional[int] =ctx_encoder(input_ids.to(device=__magic_name__ ) , return_dict=__magic_name__ ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def _lowerCAmelCase ( __magic_name__ : "RagExampleArguments" , __magic_name__ : "ProcessingArguments" , __magic_name__ : "IndexHnswArguments" , ) -> str:
######################################
logger.info('''Step 1 - Create the dataset''' )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
lowercase : Tuple =load_dataset(
'''csv''' , data_files=[rag_example_args.csv_path] , split='''train''' , delimiter='''\t''' , column_names=['''title''', '''text'''] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
lowercase : Optional[int] =dataset.map(__magic_name__ , batched=__magic_name__ , num_proc=processing_args.num_proc )
# And compute the embeddings
lowercase : Any =DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=__magic_name__ )
lowercase : Any =DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
lowercase : Optional[int] =Features(
{'''text''': Value('''string''' ), '''title''': Value('''string''' ), '''embeddings''': Sequence(Value('''float32''' ) )} ) # optional, save as float32 instead of float64 to save space
lowercase : Optional[Any] =dataset.map(
partial(__magic_name__ , ctx_encoder=__magic_name__ , ctx_tokenizer=__magic_name__ ) , batched=__magic_name__ , batch_size=processing_args.batch_size , features=__magic_name__ , )
# And finally save your dataset
lowercase : Optional[Any] =os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset''' )
dataset.save_to_disk(__magic_name__ )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info('''Step 2 - Index the dataset''' )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
lowercase : Union[str, Any] =faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index('''embeddings''' , custom_index=__magic_name__ )
# And save the index
lowercase : Dict =os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset_hnsw_index.faiss''' )
dataset.get_index('''embeddings''' ).save(__magic_name__ )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=str(Path(lowercase__ ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv' ) , metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''} , )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'} , )
lowerCamelCase_ = field(
default='facebook/rag-sequence-nq' , metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''} , )
lowerCamelCase_ = field(
default='facebook/dpr-ctx_encoder-multiset-base' , metadata={
'help': (
'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
' \'facebook/dpr-ctx_encoder-multiset-base\''
)
} , )
lowerCamelCase_ = field(
default=str(Path(lowercase__ ).parent / 'test_run' / 'dummy-kb' ) , metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=lowercase__ , metadata={
'help': 'The number of processes to use to split the documents into passages. Default is single process.'
} , )
lowerCamelCase_ = field(
default=16 , metadata={
'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=7_68 , metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'} , )
lowerCamelCase_ = field(
default=1_28 , metadata={
'help': (
'The number of bi-directional links created for every new element during the HNSW index construction.'
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
UpperCamelCase_ = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
UpperCamelCase_ = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 92 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {
"""configuration_layoutlmv3""": [
"""LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""LayoutLMv3Config""",
"""LayoutLMv3OnnxConfig""",
],
"""processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
"""tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ["""LayoutLMv3TokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
"""LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LayoutLMv3ForQuestionAnswering""",
"""LayoutLMv3ForSequenceClassification""",
"""LayoutLMv3ForTokenClassification""",
"""LayoutLMv3Model""",
"""LayoutLMv3PreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
"""TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFLayoutLMv3ForQuestionAnswering""",
"""TFLayoutLMv3ForSequenceClassification""",
"""TFLayoutLMv3ForTokenClassification""",
"""TFLayoutLMv3Model""",
"""TFLayoutLMv3PreTrainedModel""",
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ["""LayoutLMv3FeatureExtractor"""]
__magic_name__ = ["""LayoutLMv3ImageProcessor"""]
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 129 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
UpperCamelCase_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
UpperCamelCase_ = 128022
UpperCamelCase_ = 128028
@require_sentencepiece
class __SCREAMING_SNAKE_CASE ( lowercase__ , unittest.TestCase ):
lowerCamelCase_ = MaMaaaTokenizer
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = True
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
super().setUp()
lowercase : Dict =['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
lowercase : List[Any] =dict(zip(UpperCAmelCase__ , range(len(UpperCAmelCase__ ) ) ) )
lowercase : List[Any] =Path(self.tmpdirname )
save_json(UpperCAmelCase__ , save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(UpperCAmelCase__ , save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowercase : Tuple =MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase_ ( self : Any , **UpperCAmelCase__ : int ):
'''simple docstring'''
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Dict ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : Tuple ='''</s>'''
lowercase : Union[str, Any] =0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[Any] =self.get_tokenizer()
lowercase : Optional[Any] =list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''</s>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''<s>''' )
self.assertEqual(len(UpperCAmelCase__ ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip('''Skip this test while all models are still to be uploaded.''' )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Union[str, Any] =self.get_tokenizer()
lowercase : str =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(UpperCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [2, 3, 4, 5, 6] , )
lowercase : Optional[int] =tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(UpperCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
lowercase : Tuple =tokenizer.convert_tokens_to_string(UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , '''This is a test''' )
@slow
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
# fmt: off
lowercase : int ={'''input_ids''': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name='''facebook/m2m100_418M''' , revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e''' , )
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
lowerCamelCase_ = 'facebook/m2m100_418M'
lowerCamelCase_ = [
'In my opinion, there are two levels of response from the French government.',
'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
]
lowerCamelCase_ = [
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
]
# fmt: off
lowerCamelCase_ = [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]
@classmethod
def lowerCamelCase_ ( cls : Optional[Any] ):
'''simple docstring'''
lowercase : MaMaaaTokenizer =MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en''' , tgt_lang='''fr''' )
lowercase : Optional[int] =1
return cls
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
self.assertEqual(self.tokenizer.get_lang_id('''ar''' ) , 128006 )
self.assertEqual(self.tokenizer.get_lang_id('''en''' ) , 128022 )
self.assertEqual(self.tokenizer.get_lang_id('''ro''' ) , 128076 )
self.assertEqual(self.tokenizer.get_lang_id('''mr''' ) , 128063 )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : List[str] =self.tokenizer.get_vocab()
self.assertEqual(len(UpperCAmelCase__ ) , self.tokenizer.vocab_size )
self.assertEqual(vocab['''<unk>'''] , 3 )
self.assertIn(self.tokenizer.get_lang_token('''en''' ) , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : List[Any] ='''en'''
lowercase : Optional[Any] =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
self.assertIn(UpperCAmelCase__ , self.tokenizer.all_special_ids )
# fmt: off
lowercase : str =[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
# fmt: on
lowercase : Optional[Any] =self.tokenizer.decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ )
lowercase : Optional[Any] =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertNotIn(self.tokenizer.eos_token , UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Any =tempfile.mkdtemp()
lowercase : Tuple =self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(UpperCAmelCase__ )
lowercase : Union[str, Any] =MaMaaaTokenizer.from_pretrained(UpperCAmelCase__ )
self.assertDictEqual(new_tok.lang_token_to_id , UpperCAmelCase__ )
@require_torch
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[str] ='''en'''
lowercase : int ='''fr'''
lowercase : Union[str, Any] =self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase__ , return_tensors='''pt''' )
lowercase : str =shift_tokens_right(
batch['''labels'''] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
lowercase : int =batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Optional[int] ='''mr'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
lowercase : Union[str, Any] ='''zh'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : int ='''mr'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
lowercase : Optional[Any] ='''zh'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : Optional[Any] =self.tokenizer._build_translation_inputs('''A test''' , return_tensors='''pt''' , src_lang='''en''' , tgt_lang='''ar''' )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) , {
# en_XX, A, test, EOS
'''input_ids''': [[128022, 58, 4183, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 128006,
} , )
| 92 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class _lowerCAmelCase ( unittest.TestCase ):
def __a ( self ) -> Tuple:
lowerCAmelCase_ = inspect.getfile(accelerate.test_utils )
lowerCAmelCase_ = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["scripts", "external_deps", "test_metrics.py"] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
lowerCAmelCase_ = test_metrics
@require_cpu
def __a ( self ) -> List[Any]:
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def __a ( self ) -> List[str]:
debug_launcher(self.test_metrics.main )
@require_single_gpu
def __a ( self ) -> Dict:
self.test_metrics.main()
@require_multi_gpu
def __a ( self ) -> str:
print(f"""Found {torch.cuda.device_count()} devices.""" )
lowerCAmelCase_ = ['''torchrun''', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(UpperCAmelCase__ , env=os.environ.copy() )
| 290 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int = 600851475143 ) -> int:
try:
lowercase : Any =int(__magic_name__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
lowercase : Optional[Any] =2
lowercase : Dict =0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
lowercase : Union[str, Any] =i
while n % i == 0:
lowercase : Optional[int] =n // i
i += 1
return int(__magic_name__ )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 92 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ : str = {
'''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Any = ['''RemBertTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Dict = ['''RemBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Union[str, Any] = [
'''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RemBertForCausalLM''',
'''RemBertForMaskedLM''',
'''RemBertForMultipleChoice''',
'''RemBertForQuestionAnswering''',
'''RemBertForSequenceClassification''',
'''RemBertForTokenClassification''',
'''RemBertLayer''',
'''RemBertModel''',
'''RemBertPreTrainedModel''',
'''load_tf_weights_in_rembert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Any = [
'''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRemBertForCausalLM''',
'''TFRemBertForMaskedLM''',
'''TFRemBertForMultipleChoice''',
'''TFRemBertForQuestionAnswering''',
'''TFRemBertForSequenceClassification''',
'''TFRemBertForTokenClassification''',
'''TFRemBertLayer''',
'''TFRemBertModel''',
'''TFRemBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
A__ : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 286 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = 'speech_to_text_2'
lowerCamelCase_ = ['past_key_values']
lowerCamelCase_ = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : int , UpperCAmelCase__ : Dict=10000 , UpperCAmelCase__ : Optional[int]=6 , UpperCAmelCase__ : str=2048 , UpperCAmelCase__ : Optional[Any]=4 , UpperCAmelCase__ : Union[str, Any]=0.0 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Optional[Any]="relu" , UpperCAmelCase__ : List[str]=256 , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : List[Any]=0.0 , UpperCAmelCase__ : Optional[Any]=0.0 , UpperCAmelCase__ : Tuple=0.02 , UpperCAmelCase__ : int=2 , UpperCAmelCase__ : str=True , UpperCAmelCase__ : str=1 , UpperCAmelCase__ : Optional[Any]=0 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : List[Any]=1024 , **UpperCAmelCase__ : Dict , ):
'''simple docstring'''
lowercase : List[str] =vocab_size
lowercase : Optional[int] =d_model
lowercase : Optional[Any] =decoder_ffn_dim
lowercase : Any =decoder_layers
lowercase : Dict =decoder_attention_heads
lowercase : List[Any] =dropout
lowercase : List[Any] =attention_dropout
lowercase : Any =activation_dropout
lowercase : Optional[Any] =activation_function
lowercase : Optional[int] =init_std
lowercase : Dict =decoder_layerdrop
lowercase : Optional[int] =use_cache
lowercase : Optional[Any] =decoder_layers
lowercase : List[str] =scale_embedding # scale factor will be sqrt(d_model) if True
lowercase : str =max_target_positions
super().__init__(
pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , decoder_start_token_id=UpperCAmelCase__ , **UpperCAmelCase__ , )
| 92 | 0 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_SCREAMING_SNAKE_CASE = ''''''
_SCREAMING_SNAKE_CASE = ''''''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(UpperCAmelCase__ ) - 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
_SCREAMING_SNAKE_CASE = 0, 0
# length[i] shows the length of palindromic substring with center i
_SCREAMING_SNAKE_CASE = [1 for i in range(len(UpperCAmelCase__ ) )]
# for each character in new_string find corresponding palindromic string
_SCREAMING_SNAKE_CASE = 0
for j in range(len(UpperCAmelCase__ ) ):
_SCREAMING_SNAKE_CASE = 1 if j > r else min(length[l + r - j] // 2 ,r - j + 1 )
while (
j - k >= 0
and j + k < len(UpperCAmelCase__ )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
_SCREAMING_SNAKE_CASE = 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:
_SCREAMING_SNAKE_CASE = j - k + 1 # noqa: E741
_SCREAMING_SNAKE_CASE = j + k - 1
# update max_length and start position
if max_length < length[j]:
_SCREAMING_SNAKE_CASE = length[j]
_SCREAMING_SNAKE_CASE = j
# create that string
_SCREAMING_SNAKE_CASE = 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()
| 605 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self : str , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[str]=13 , UpperCAmelCase__ : Any=32 , UpperCAmelCase__ : List[str]=3 , UpperCAmelCase__ : Any=4 , UpperCAmelCase__ : List[str]=[10, 20, 30, 40] , UpperCAmelCase__ : Any=[2, 2, 3, 2] , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Optional[Any]=37 , UpperCAmelCase__ : Union[str, Any]="gelu" , UpperCAmelCase__ : Optional[Any]=10 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : Optional[int]=["stage2", "stage3", "stage4"] , UpperCAmelCase__ : Dict=[2, 3, 4] , UpperCAmelCase__ : Optional[int]=None , ):
'''simple docstring'''
lowercase : List[Any] =parent
lowercase : Tuple =batch_size
lowercase : List[str] =image_size
lowercase : List[Any] =num_channels
lowercase : Union[str, Any] =num_stages
lowercase : int =hidden_sizes
lowercase : Any =depths
lowercase : Tuple =is_training
lowercase : str =use_labels
lowercase : List[Any] =intermediate_size
lowercase : int =hidden_act
lowercase : Union[str, Any] =num_labels
lowercase : Optional[int] =initializer_range
lowercase : int =out_features
lowercase : List[str] =out_indices
lowercase : str =scope
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : str =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase : Dict =None
if self.use_labels:
lowercase : List[Any] =ids_tensor([self.batch_size] , self.num_labels )
lowercase : Dict =self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[Any] ):
'''simple docstring'''
lowercase : Dict =ConvNextVaModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[Any] =model(UpperCAmelCase__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase_ ( self : Any , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Dict =ConvNextVaForImageClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : str =model(UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Union[str, Any] =ConvNextVaBackbone(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[int] =model(UpperCAmelCase__ )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
lowercase : Optional[Any] =None
lowercase : str =ConvNextVaBackbone(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[Any] =model(UpperCAmelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Any =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase : str =config_and_inputs
lowercase : Any ={'''pixel_values''': pixel_values}
return config, inputs_dict
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : str =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase : List[str] =config_and_inputs
lowercase : Optional[Any] ={'''pixel_values''': pixel_values, '''labels''': labels}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase_ = (
{'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : Dict =ConvNextVaModelTester(self )
lowercase : str =ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=37 )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return
@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds''' )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings''' )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking''' )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase , lowercase : Union[str, Any] =self.model_tester.prepare_config_and_inputs_with_labels()
lowercase : Optional[int] =True
if model_class.__name__ in [
*get_values(UpperCAmelCase__ ),
*get_values(UpperCAmelCase__ ),
]:
continue
lowercase : Dict =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.train()
lowercase : Optional[Any] =self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
lowercase : List[Any] =model(**UpperCAmelCase__ ).loss
loss.backward()
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase , lowercase : Any =self.model_tester.prepare_config_and_inputs_with_labels()
lowercase : List[Any] =False
lowercase : Any =True
if (
model_class.__name__
in [*get_values(UpperCAmelCase__ ), *get_values(UpperCAmelCase__ )]
or not model_class.supports_gradient_checkpointing
):
continue
lowercase : Any =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.gradient_checkpointing_enable()
model.train()
lowercase : Optional[Any] =self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
lowercase : int =model(**UpperCAmelCase__ ).loss
loss.backward()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase , lowercase : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : Dict =model_class(UpperCAmelCase__ )
lowercase : Union[str, Any] =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase : int =[*signature.parameters.keys()]
lowercase : Optional[Any] =['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
def check_hidden_states_output(UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : Tuple ):
lowercase : int =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
lowercase : Any =model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase : Dict =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase : List[Any] =self.model_tester.num_stages
self.assertEqual(len(UpperCAmelCase__ ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowercase , lowercase : List[str] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : List[str] =True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase : Tuple =True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
lowercase : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__ )
@slow
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : List[Any] =ConvNextVaModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def _lowerCAmelCase ( ) -> List[Any]:
lowercase : Union[str, Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : Tuple =ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ).to(UpperCAmelCase__ )
lowercase : int =self.default_image_processor
lowercase : List[str] =prepare_img()
lowercase : List[Any] =preprocessor(images=UpperCAmelCase__ , return_tensors='''pt''' ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
lowercase : Dict =model(**UpperCAmelCase__ )
# verify the logits
lowercase : Optional[Any] =torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase__ )
lowercase : Tuple =torch.tensor([0.99_96, 0.19_66, -0.43_86] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase__ , atol=1E-4 ) )
| 92 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class lowerCAmelCase__ ( lowercase__ ):
lowercase__ : Optional[Any] = """fnet"""
def __init__( self , UpperCamelCase__=3_20_00 , UpperCamelCase__=7_68 , UpperCamelCase__=12 , UpperCamelCase__=30_72 , UpperCamelCase__="gelu_new" , UpperCamelCase__=0.1 , UpperCamelCase__=5_12 , UpperCamelCase__=4 , UpperCamelCase__=0.02 , UpperCamelCase__=1e-12 , UpperCamelCase__=False , UpperCamelCase__=5_12 , UpperCamelCase__=3 , UpperCamelCase__=1 , UpperCamelCase__=2 , **UpperCamelCase__ , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = num_hidden_layers
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = initializer_range
A__ = type_vocab_size
A__ = layer_norm_eps
A__ = use_tpu_fourier_optimizations
A__ = tpu_short_seq_length | 337 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
UpperCamelCase_ = object()
# For specifying empty leaf dict `{}`
UpperCamelCase_ = object()
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : int ) -> Optional[int]:
lowercase : Optional[Any] =tuple((re.compile(x + '''$''' ) for x in qs) )
for i in range(len(__magic_name__ ) - len(__magic_name__ ) + 1 ):
lowercase : Union[str, Any] =[x.match(__magic_name__ ) for x, y in zip(__magic_name__ , ks[i:] )]
if matches and all(__magic_name__ ):
return True
return False
def _lowerCAmelCase ( __magic_name__ : Dict ) -> List[str]:
def replace(__magic_name__ : Optional[int] , __magic_name__ : Optional[Any] ):
for rule, replacement in rules:
if _match(__magic_name__ , __magic_name__ ):
return replacement
return val
return replace
def _lowerCAmelCase ( ) -> int:
return [
# embeddings
(("transformer", "wpe", "embedding"), P('''mp''' , __magic_name__ )),
(("transformer", "wte", "embedding"), P('''mp''' , __magic_name__ )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__magic_name__ , '''mp''' )),
(("attention", "out_proj", "kernel"), P('''mp''' , __magic_name__ )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(__magic_name__ , '''mp''' )),
(("mlp", "c_fc", "bias"), P('''mp''' )),
(("mlp", "c_proj", "kernel"), P('''mp''' , __magic_name__ )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def _lowerCAmelCase ( __magic_name__ : str ) -> int:
lowercase : int =_get_partition_rules()
lowercase : Tuple =_replacement_rules(__magic_name__ )
lowercase : Any ={k: _unmatched for k in flatten_dict(__magic_name__ )}
lowercase : Any ={k: replace(__magic_name__ , __magic_name__ ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(__magic_name__ ) )
| 92 | 0 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __SCREAMING_SNAKE_CASE ) -> float:
__lowerCAmelCase: Any = 0.00
__lowerCAmelCase: Tuple = 0
for resistor in resistors:
if resistor <= 0:
__lowerCAmelCase: Dict = F"Resistor at index {index} has a negative or zero value!"
raise ValueError(__SCREAMING_SNAKE_CASE )
first_sum += 1 / float(__SCREAMING_SNAKE_CASE )
index += 1
return 1 / first_sum
def a__ ( __SCREAMING_SNAKE_CASE ) -> float:
__lowerCAmelCase: Optional[Any] = 0.00
__lowerCAmelCase: int = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
__lowerCAmelCase: Tuple = F"Resistor at index {index} has a negative value!"
raise ValueError(__SCREAMING_SNAKE_CASE )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 346 |
'''simple docstring'''
from collections import defaultdict
def _lowerCAmelCase ( __magic_name__ : int ) -> int:
lowercase : Optional[Any] =1
lowercase : Union[str, Any] =True
for v in tree[start]:
if v not in visited:
ret += dfs(__magic_name__ )
if ret % 2 == 0:
cuts.append(__magic_name__ )
return ret
def _lowerCAmelCase ( ) -> int:
dfs(1 )
if __name__ == "__main__":
UpperCamelCase_ , UpperCamelCase_ = 10, 9
UpperCamelCase_ = defaultdict(list)
UpperCamelCase_ = {}
UpperCamelCase_ = []
UpperCamelCase_ = 0
UpperCamelCase_ = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 92 | 0 |
import math
import sys
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = ''''''
try:
with open(_UpperCamelCase , '''rb''' ) as binary_file:
__lowercase = binary_file.read()
for dat in data:
__lowercase = F'{dat:08b}'
result += curr_byte
return result
except OSError:
print('''File not accessible''' )
sys.exit()
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = {'''0''': '''0''', '''1''': '''1'''}
__lowercase = '''''', ''''''
__lowercase = len(_UpperCamelCase )
for i in range(len(_UpperCamelCase ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
__lowercase = lexicon[curr_string]
result += last_match_id
__lowercase = last_match_id + '''0'''
if math.loga(_UpperCamelCase ).is_integer():
__lowercase = {}
for curr_key in list(_UpperCamelCase ):
__lowercase = lexicon.pop(_UpperCamelCase )
__lowercase = new_lex
__lowercase = last_match_id + '''1'''
index += 1
__lowercase = ''''''
return result
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowercase = 8
try:
with open(_UpperCamelCase , '''wb''' ) as opened_file:
__lowercase = [
to_write[i : i + byte_length]
for i in range(0 , len(_UpperCamelCase ) , _UpperCamelCase )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('''10000000''' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(_UpperCamelCase , 2 ).to_bytes(1 , byteorder='''big''' ) )
except OSError:
print('''File not accessible''' )
sys.exit()
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
__lowercase = data_bits[counter:]
__lowercase = data_bits[counter + 1 :]
return data_bits
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowercase = read_file_binary(_UpperCamelCase )
__lowercase = remove_prefix(_UpperCamelCase )
__lowercase = decompress_data(_UpperCamelCase )
write_file_binary(_UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 639 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
def _lowerCAmelCase ( __magic_name__ : List[str] ) -> Dict:
lowercase : List[str] =R'''\w+[.]\d+'''
lowercase : List[str] =re.findall(__magic_name__ , __magic_name__ )
for pat in pats:
lowercase : Optional[int] =key.replace(__magic_name__ , '''_'''.join(pat.split('''.''' ) ) )
return key
def _lowerCAmelCase ( __magic_name__ : List[str] , __magic_name__ : Union[str, Any] , __magic_name__ : Dict ) -> str:
lowercase : Dict =pt_tuple_key[:-1] + ('''scale''',)
if (
any('''norm''' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
lowercase : str =pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
lowercase : str =pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
lowercase : Dict =pt_tuple_key[:-1] + ('''embedding''',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowercase : Tuple =pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
lowercase : Tuple =pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowercase : str =pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight":
lowercase : Optional[Any] =pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowercase : Dict =pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowercase : Union[str, Any] =pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : Optional[Any] , __magic_name__ : Union[str, Any]=42 ) -> List[str]:
# Step 1: Convert pytorch tensor to numpy
lowercase : Optional[Any] ={k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
lowercase : str =flax_model.init_weights(PRNGKey(__magic_name__ ) )
lowercase : Dict =flatten_dict(__magic_name__ )
lowercase : Dict ={}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase : Dict =rename_key(__magic_name__ )
lowercase : Optional[int] =tuple(renamed_pt_key.split('''.''' ) )
# Correctly rename weight parameters
lowercase , lowercase : Any =rename_key_and_reshape_tensor(__magic_name__ , __magic_name__ , __magic_name__ )
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}.''' )
# also add unexpected weight so that warning is thrown
lowercase : Tuple =jnp.asarray(__magic_name__ )
return unflatten_dict(__magic_name__ )
| 92 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Any ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict =SwinConfig(image_size=192 )
if "base" in model_name:
SCREAMING_SNAKE_CASE_ : Any =6
SCREAMING_SNAKE_CASE_ : Dict =128
SCREAMING_SNAKE_CASE_ : List[Any] =(2, 2, 18, 2)
SCREAMING_SNAKE_CASE_ : Optional[int] =(4, 8, 16, 32)
elif "large" in model_name:
SCREAMING_SNAKE_CASE_ : int =12
SCREAMING_SNAKE_CASE_ : str =192
SCREAMING_SNAKE_CASE_ : int =(2, 2, 18, 2)
SCREAMING_SNAKE_CASE_ : Optional[int] =(6, 12, 24, 48)
else:
raise ValueError('Model not supported, only supports base and large variants' )
SCREAMING_SNAKE_CASE_ : List[str] =window_size
SCREAMING_SNAKE_CASE_ : Dict =embed_dim
SCREAMING_SNAKE_CASE_ : Optional[int] =depths
SCREAMING_SNAKE_CASE_ : Union[str, Any] =num_heads
return config
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : int ) -> Dict:
"""simple docstring"""
if "encoder.mask_token" in name:
SCREAMING_SNAKE_CASE_ : Any =name.replace('encoder.mask_token' ,'embeddings.mask_token' )
if "encoder.patch_embed.proj" in name:
SCREAMING_SNAKE_CASE_ : Dict =name.replace('encoder.patch_embed.proj' ,'embeddings.patch_embeddings.projection' )
if "encoder.patch_embed.norm" in name:
SCREAMING_SNAKE_CASE_ : Dict =name.replace('encoder.patch_embed.norm' ,'embeddings.norm' )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE_ : Any =name.replace('attn.proj' ,'attention.output.dense' )
if "attn" in name:
SCREAMING_SNAKE_CASE_ : List[str] =name.replace('attn' ,'attention.self' )
if "norm1" in name:
SCREAMING_SNAKE_CASE_ : Optional[Any] =name.replace('norm1' ,'layernorm_before' )
if "norm2" in name:
SCREAMING_SNAKE_CASE_ : Dict =name.replace('norm2' ,'layernorm_after' )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE_ : List[Any] =name.replace('mlp.fc1' ,'intermediate.dense' )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE_ : Optional[Any] =name.replace('mlp.fc2' ,'output.dense' )
if name == "encoder.norm.weight":
SCREAMING_SNAKE_CASE_ : List[str] ='''layernorm.weight'''
if name == "encoder.norm.bias":
SCREAMING_SNAKE_CASE_ : List[str] ='''layernorm.bias'''
if "decoder" in name:
pass
else:
SCREAMING_SNAKE_CASE_ : Any ='''swin.''' + name
return name
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Optional[int] ,lowerCAmelCase_ : Any ) -> Optional[int]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE_ : str =orig_state_dict.pop(lowerCAmelCase_ )
if "attn_mask" in key:
pass
elif "qkv" in key:
SCREAMING_SNAKE_CASE_ : List[str] =key.split('.' )
SCREAMING_SNAKE_CASE_ : Any =int(key_split[2] )
SCREAMING_SNAKE_CASE_ : List[str] =int(key_split[4] )
SCREAMING_SNAKE_CASE_ : int =model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE_ : Dict =val[:dim, :]
SCREAMING_SNAKE_CASE_ : Optional[int] =val[
dim : dim * 2, :
]
SCREAMING_SNAKE_CASE_ : str =val[-dim:, :]
else:
SCREAMING_SNAKE_CASE_ : Any =val[
:dim
]
SCREAMING_SNAKE_CASE_ : Optional[int] =val[
dim : dim * 2
]
SCREAMING_SNAKE_CASE_ : Any =val[
-dim:
]
else:
SCREAMING_SNAKE_CASE_ : Any =val
return orig_state_dict
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : List[Any] ,lowerCAmelCase_ : Any ,lowerCAmelCase_ : Optional[int] ,lowerCAmelCase_ : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple =torch.load(lowerCAmelCase_ ,map_location='cpu' )['''model''']
SCREAMING_SNAKE_CASE_ : Union[str, Any] =get_swin_config(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : int =SwinForMaskedImageModeling(lowerCAmelCase_ )
model.eval()
SCREAMING_SNAKE_CASE_ : List[Any] =convert_state_dict(lowerCAmelCase_ ,lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : List[Any] ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
SCREAMING_SNAKE_CASE_ : Optional[Any] =ViTImageProcessor(size={'height': 192, 'width': 192} )
SCREAMING_SNAKE_CASE_ : List[str] =Image.open(requests.get(lowerCAmelCase_ ,stream=lowerCAmelCase_ ).raw )
SCREAMING_SNAKE_CASE_ : str =image_processor(images=lowerCAmelCase_ ,return_tensors='pt' )
with torch.no_grad():
SCREAMING_SNAKE_CASE_ : List[str] =model(**lowerCAmelCase_ ).logits
print(outputs.keys() )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowerCAmelCase_ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowerCAmelCase_ )
if push_to_hub:
print(F"""Pushing model and image processor for {model_name} to hub""" )
model.push_to_hub(F"""microsoft/{model_name}""" )
image_processor.push_to_hub(F"""microsoft/{model_name}""" )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='swin-base-simmim-window6-192',
type=str,
choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
help='Name of the Swin SimMIM model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
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 or not to push the converted model to the 🤗 hub.'
)
__SCREAMING_SNAKE_CASE = parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 220 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase_ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
UpperCamelCase_ = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
UpperCamelCase_ = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, float]:
lowercase : int =len([g for position, g in enumerate(__magic_name__ ) if g == main_target[position]] )
return (item, float(__magic_name__ ))
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, str]:
lowercase : Any =random.randint(0 , len(__magic_name__ ) - 1 )
lowercase : Tuple =parent_a[:random_slice] + parent_a[random_slice:]
lowercase : List[str] =parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : list[str] ) -> str:
lowercase : Union[str, Any] =list(__magic_name__ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
lowercase : Dict =random.choice(__magic_name__ )
return "".join(__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : tuple[str, float] , __magic_name__ : list[tuple[str, float]] , __magic_name__ : list[str] , ) -> list[str]:
lowercase : Any =[]
# Generate more children proportionally to the fitness score.
lowercase : Dict =int(parent_a[1] * 100 ) + 1
lowercase : List[str] =10 if child_n >= 10 else child_n
for _ in range(__magic_name__ ):
lowercase : List[str] =population_score[random.randint(0 , __magic_name__ )][0]
lowercase , lowercase : Dict =crossover(parent_a[0] , __magic_name__ )
# Append new string to the population list.
pop.append(mutate(__magic_name__ , __magic_name__ ) )
pop.append(mutate(__magic_name__ , __magic_name__ ) )
return pop
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : list[str] , __magic_name__ : bool = True ) -> tuple[int, int, str]:
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
lowercase : List[str] =f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
raise ValueError(__magic_name__ )
# Verify that the target contains no genes besides the ones inside genes variable.
lowercase : Optional[int] =sorted({c for c in target if c not in genes} )
if not_in_genes_list:
lowercase : Dict =f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
raise ValueError(__magic_name__ )
# Generate random starting population.
lowercase : int =[]
for _ in range(__magic_name__ ):
population.append(''''''.join([random.choice(__magic_name__ ) for i in range(len(__magic_name__ ) )] ) )
# Just some logs to know what the algorithms is doing.
lowercase , lowercase : Optional[int] =0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(__magic_name__ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
lowercase : List[str] =[evaluate(__magic_name__ , __magic_name__ ) for item in population]
# Check if there is a matching evolution.
lowercase : int =sorted(__magic_name__ , key=lambda __magic_name__ : x[1] , reverse=__magic_name__ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f'''\nGeneration: {generation}'''
f'''\nTotal Population:{total_population}'''
f'''\nBest score: {population_score[0][1]}'''
f'''\nBest string: {population_score[0][0]}''' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
lowercase : Any =population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(__magic_name__ )
# Normalize population score to be between 0 and 1.
lowercase : Dict =[
(item, score / len(__magic_name__ )) for item, score in population_score
]
# This is selection
for i in range(__magic_name__ ):
population.extend(select(population_score[int(__magic_name__ )] , __magic_name__ , __magic_name__ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(__magic_name__ ) > N_POPULATION:
break
if __name__ == "__main__":
UpperCamelCase_ = (
"""This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
)
UpperCamelCase_ = list(
""" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
"""nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
)
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = basic(target_str, genes_list)
print(
f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 92 | 0 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.basicConfig(level=logging.DEBUG)
_SCREAMING_SNAKE_CASE = logging.getLogger()
_SCREAMING_SNAKE_CASE = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE_ ( lowercase__ ):
"""simple docstring"""
def UpperCamelCase__ ( self :str, snake_case :Optional[Any]):
"""simple docstring"""
os.makedirs(UpperCAmelCase__, exist_ok=UpperCAmelCase__)
_lowercase ={'''source''': '''What is love ?''', '''target''': '''life'''}
_lowercase ={'''train''': 12, '''val''': 2, '''test''': 2}
for split in ["train", "test", "val"]:
for field in ["source", "target"]:
_lowercase ='''\n'''.join([contents[field]] * n_lines[split])
with open(os.path.join(UpperCAmelCase__, f'''{split}.{field}'''), 'w') as f:
f.write(UpperCAmelCase__)
def UpperCamelCase__ ( self :Union[str, Any], snake_case :int, snake_case :str = "pytorch"):
"""simple docstring"""
_lowercase =self.get_auto_remove_tmp_dir()
_lowercase =os.path.join(UpperCAmelCase__, 'output')
_lowercase =os.path.join(UpperCAmelCase__, 'data')
self._create_dummy_data(data_dir=UpperCAmelCase__)
_lowercase =f'''
--data_dir {data_dir} \
--output_dir {output_dir} \
--model_name_or_path facebook/rag-sequence-base \
--model_type rag_sequence \
--do_train \
--do_predict \
--n_val -1 \
--val_check_interval 1.0 \
--train_batch_size 2 \
--eval_batch_size 1 \
--max_source_length 25 \
--max_target_length 25 \
--val_max_target_length 25 \
--test_max_target_length 25 \
--label_smoothing 0.1 \
--dropout 0.1 \
--attention_dropout 0.1 \
--weight_decay 0.001 \
--adam_epsilon 1e-08 \
--max_grad_norm 0.1 \
--lr_scheduler polynomial \
--learning_rate 3e-04 \
--num_train_epochs 1 \
--warmup_steps 4 \
--gradient_accumulation_steps 1 \
--distributed-port 8787 \
--use_dummy_dataset 1 \
--distributed_retriever {distributed_retriever} \
'''.split()
if gpus > 0:
testargs.append(f'''--gpus={gpus}''')
if is_apex_available():
testargs.append('--fp16')
else:
testargs.append('--gpus=0')
testargs.append('--distributed_backend=ddp_cpu')
testargs.append('--num_processes=2')
_lowercase =[sys.executable, str(Path(finetune_rag.__file__).resolve())] + testargs
execute_subprocess_async(UpperCAmelCase__, env=self.get_env())
_lowercase =os.path.join(UpperCAmelCase__, 'metrics.json')
with open(UpperCAmelCase__) as f:
_lowercase =json.load(UpperCAmelCase__)
return result
@require_torch_gpu
def UpperCamelCase__ ( self :Optional[int]):
"""simple docstring"""
_lowercase =self._run_finetune(gpus=1)
self.assertGreaterEqual(result['test'][0]['test_avg_em'], 0.2)
@require_torch_multi_gpu
def UpperCamelCase__ ( self :List[Any]):
"""simple docstring"""
_lowercase =self._run_finetune(gpus=2)
self.assertGreaterEqual(result['test'][0]['test_avg_em'], 0.2)
@require_torch_gpu
@require_ray
def UpperCamelCase__ ( self :Dict):
"""simple docstring"""
_lowercase =self._run_finetune(gpus=1, distributed_retriever='ray')
self.assertGreaterEqual(result['test'][0]['test_avg_em'], 0.2)
@require_torch_multi_gpu
@require_ray
def UpperCamelCase__ ( self :int):
"""simple docstring"""
_lowercase =self._run_finetune(gpus=1, distributed_retriever='ray')
self.assertGreaterEqual(result['test'][0]['test_avg_em'], 0.2)
| 181 |
'''simple docstring'''
import datasets
UpperCamelCase_ = """\
@InProceedings{conneau2018xnli,
author = \"Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin\",
title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing\",
year = \"2018\",
publisher = \"Association for Computational Linguistics\",
location = \"Brussels, Belgium\",
}
"""
UpperCamelCase_ = """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""
UpperCamelCase_ = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: Predicted labels.
references: Ground truth labels.
Returns:
'accuracy': accuracy
Examples:
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> xnli_metric = datasets.load_metric(\"xnli\")
>>> results = xnli_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
"""
def _lowerCAmelCase ( __magic_name__ : Dict , __magic_name__ : Union[str, Any] ) -> Union[str, Any]:
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32''' ),
'''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32''' ),
} ) , codebase_urls=[] , reference_urls=[] , format='''numpy''' , )
def lowerCamelCase_ ( self : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[Any] ):
'''simple docstring'''
return {"accuracy": simple_accuracy(UpperCAmelCase__ , UpperCAmelCase__ )}
| 92 | 0 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[list[int]] ):
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(_UpperCAmelCase ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(_UpperCAmelCase ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[str] , UpperCAmelCase__ : Dict , ):
'''simple docstring'''
lowercase : Any =parent
lowercase : Optional[int] =13
lowercase : Union[str, Any] =7
lowercase : str =30
lowercase : Optional[int] =self.seq_length + self.mem_len
lowercase : Dict =15
lowercase : List[str] =True
lowercase : Optional[int] =True
lowercase : Tuple =99
lowercase : str =[10, 50, 80]
lowercase : List[Any] =32
lowercase : Optional[int] =32
lowercase : int =4
lowercase : Any =8
lowercase : List[Any] =128
lowercase : List[str] =2
lowercase : Tuple =2
lowercase : int =None
lowercase : Optional[int] =1
lowercase : int =0
lowercase : List[str] =3
lowercase : str =self.vocab_size - 1
lowercase : Tuple =0.01
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : str =None
if self.use_labels:
lowercase : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Union[str, Any] =TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
random.seed(self.seed )
tf.random.set_seed(self.seed )
def lowerCamelCase_ ( self : Union[str, Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[str] ):
'''simple docstring'''
lowercase : Tuple =TFTransfoXLModel(UpperCAmelCase__ )
lowercase , lowercase : Optional[Any] =model(UpperCAmelCase__ ).to_tuple()
lowercase : List[str] ={'''input_ids''': input_ids_a, '''mems''': mems_a}
lowercase , lowercase : Any =model(UpperCAmelCase__ ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def lowerCamelCase_ ( self : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : int =TFTransfoXLLMHeadModel(UpperCAmelCase__ )
lowercase , lowercase : Tuple =model(UpperCAmelCase__ ).to_tuple()
lowercase : Optional[Any] ={'''input_ids''': input_ids_a, '''labels''': lm_labels}
lowercase , lowercase : Optional[int] =model(UpperCAmelCase__ ).to_tuple()
lowercase , lowercase : List[str] =model([input_ids_a, mems_a] ).to_tuple()
lowercase : int ={'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}
lowercase , lowercase : str =model(UpperCAmelCase__ ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def lowerCamelCase_ ( self : str , UpperCAmelCase__ : int , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
lowercase : Optional[int] =TFTransfoXLForSequenceClassification(UpperCAmelCase__ )
lowercase : Union[str, Any] =model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[Any] =self.prepare_config_and_inputs()
((lowercase) , (lowercase) , (lowercase) , (lowercase)) : Optional[Any] =config_and_inputs
lowercase : Union[str, Any] ={'''input_ids''': input_ids_a}
return config, inputs_dict
@require_tf
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
lowerCamelCase_ = () if is_tf_available() else ()
lowerCamelCase_ = (
{
'feature-extraction': TFTransfoXLModel,
'text-classification': TFTransfoXLForSequenceClassification,
'text-generation': TFTransfoXLLMHeadModel,
'zero-shot': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def lowerCamelCase_ ( self : Dict , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int ):
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Any =TFTransfoXLModelTester(self )
lowercase : Optional[int] =ConfigTester(self , config_class=UpperCAmelCase__ , d_embed=37 )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
self.model_tester.set_seed()
lowercase : List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
self.model_tester.set_seed()
lowercase : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase , lowercase : Tuple =self.model_tester.prepare_config_and_inputs_for_common()
lowercase : int =[TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
lowercase : str =model_class(UpperCAmelCase__ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
lowercase : Union[str, Any] =model.get_output_embeddings()
assert isinstance(UpperCAmelCase__ , tf.keras.layers.Layer )
lowercase : Any =model.get_bias()
assert name is None
else:
lowercase : Optional[int] =model.get_output_embeddings()
assert x is None
lowercase : Optional[int] =model.get_bias()
assert name is None
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : int =TFTransfoXLModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.''' )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
pass
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@unittest.skip('''Skip test until #12651 is resolved.''' )
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : Optional[Any] =TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''' )
# fmt: off
lowercase : Tuple =tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
lowercase : Optional[int] =[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
lowercase : int =model.generate(UpperCAmelCase__ , max_length=200 , do_sample=UpperCAmelCase__ )
self.assertListEqual(output_ids[0].numpy().tolist() , UpperCAmelCase__ )
| 92 | 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 _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : str=13 , SCREAMING_SNAKE_CASE__ : Tuple=7 , SCREAMING_SNAKE_CASE__ : Dict=True , SCREAMING_SNAKE_CASE__ : Optional[int]=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : Tuple=True , SCREAMING_SNAKE_CASE__ : Optional[Any]=99 , SCREAMING_SNAKE_CASE__ : Any=16 , SCREAMING_SNAKE_CASE__ : Optional[Any]=36 , SCREAMING_SNAKE_CASE__ : str=6 , SCREAMING_SNAKE_CASE__ : Any=6 , SCREAMING_SNAKE_CASE__ : Optional[int]=6 , SCREAMING_SNAKE_CASE__ : Any=37 , SCREAMING_SNAKE_CASE__ : List[str]="gelu" , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE__ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE__ : List[str]=5_12 , SCREAMING_SNAKE_CASE__ : List[str]=16 , SCREAMING_SNAKE_CASE__ : Optional[int]=2 , SCREAMING_SNAKE_CASE__ : Any=0.02 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=3 , SCREAMING_SNAKE_CASE__ : List[Any]=4 , SCREAMING_SNAKE_CASE__ : Tuple=None , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_input_mask
UpperCamelCase = use_token_type_ids
UpperCamelCase = use_labels
UpperCamelCase = vocab_size
UpperCamelCase = embedding_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_hidden_groups
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = num_labels
UpperCamelCase = num_choices
UpperCamelCase = scope
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = None
if self.use_token_type_ids:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self : Union[str, Any] ):
"""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 __lowerCAmelCase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : str ):
"""simple docstring"""
UpperCamelCase = AlbertModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
UpperCamelCase = model(UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
UpperCamelCase = 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 __lowerCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple ):
"""simple docstring"""
UpperCamelCase = AlbertForPreTraining(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase = 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 __lowerCAmelCase ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
UpperCamelCase = AlbertForMaskedLM(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase = 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 __lowerCAmelCase ( self : Any , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = AlbertForQuestionAnswering(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase = 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 __lowerCAmelCase ( self : List[str] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = AlbertForSequenceClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict ):
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = AlbertForTokenClassification(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase = 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 __lowerCAmelCase ( self : List[str] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str] ):
"""simple docstring"""
UpperCamelCase = self.num_choices
UpperCamelCase = AlbertForMultipleChoice(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
UpperCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase = model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
UpperCamelCase
) = config_and_inputs
UpperCamelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( lowercase__ , lowercase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple =(
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ : Any =(
{
"feature-extraction": AlbertModel,
"fill-mask": AlbertForMaskedLM,
"question-answering": AlbertForQuestionAnswering,
"text-classification": AlbertForSequenceClassification,
"token-classification": AlbertForTokenClassification,
"zero-shot": AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ : int =True
def __lowerCAmelCase ( self : List[str] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int=False ):
"""simple docstring"""
UpperCamelCase = super()._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
if return_labels:
if model_class in get_values(UpperCAmelCase__ ):
UpperCamelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=UpperCAmelCase__ )
UpperCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
return inputs_dict
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = AlbertModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=UpperCAmelCase__ , hidden_size=37 )
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__ )
def __lowerCAmelCase ( self : Dict ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__ )
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__ )
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__ )
def __lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__ )
def __lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase = type
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
@slow
def __lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = AlbertModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = AlbertModel.from_pretrained('albert-base-v2' )
UpperCamelCase = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
UpperCamelCase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCamelCase = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ )[0]
UpperCamelCase = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , UpperCAmelCase__ )
UpperCamelCase = 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 ) )
| 282 |
'''simple docstring'''
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 __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str=13 , UpperCAmelCase__ : Tuple=7 , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Optional[Any]=99 , UpperCAmelCase__ : Any=16 , UpperCAmelCase__ : Optional[Any]=36 , UpperCAmelCase__ : str=6 , UpperCAmelCase__ : Any=6 , UpperCAmelCase__ : Optional[int]=6 , UpperCAmelCase__ : Any=37 , UpperCAmelCase__ : List[str]="gelu" , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : Optional[int]=0.1 , UpperCAmelCase__ : List[str]=512 , UpperCAmelCase__ : List[str]=16 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : Union[str, Any]=3 , UpperCAmelCase__ : List[Any]=4 , UpperCAmelCase__ : Tuple=None , ):
'''simple docstring'''
lowercase : str =parent
lowercase : int =batch_size
lowercase : Any =seq_length
lowercase : int =is_training
lowercase : str =use_input_mask
lowercase : int =use_token_type_ids
lowercase : Dict =use_labels
lowercase : int =vocab_size
lowercase : str =embedding_size
lowercase : Union[str, Any] =hidden_size
lowercase : Tuple =num_hidden_layers
lowercase : Any =num_hidden_groups
lowercase : Union[str, Any] =num_attention_heads
lowercase : Any =intermediate_size
lowercase : Tuple =hidden_act
lowercase : Optional[int] =hidden_dropout_prob
lowercase : Union[str, Any] =attention_probs_dropout_prob
lowercase : List[Any] =max_position_embeddings
lowercase : int =type_vocab_size
lowercase : int =type_sequence_label_size
lowercase : Any =initializer_range
lowercase : List[Any] =num_labels
lowercase : int =num_choices
lowercase : Optional[int] =scope
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : int =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Optional[int] =None
if self.use_input_mask:
lowercase : List[str] =random_attention_mask([self.batch_size, self.seq_length] )
lowercase : Dict =None
if self.use_token_type_ids:
lowercase : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase : Tuple =None
lowercase : Any =None
lowercase : Dict =None
if self.use_labels:
lowercase : int =ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase : str =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase : Optional[Any] =ids_tensor([self.batch_size] , self.num_choices )
lowercase : Any =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase_ ( self : Union[str, Any] ):
'''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 lowerCamelCase_ ( self : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : str ):
'''simple docstring'''
lowercase : int =AlbertModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : int =model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
lowercase : Dict =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 lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple ):
'''simple docstring'''
lowercase : Tuple =AlbertForPreTraining(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : int =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 lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Tuple =AlbertForMaskedLM(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.vocab_size) )
def lowerCamelCase_ ( self : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
lowercase : List[str] =AlbertForQuestionAnswering(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : List[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 lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : str , UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
lowercase : Optional[Any] =self.num_labels
lowercase : Any =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 lowerCamelCase_ ( self : Optional[int] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict ):
'''simple docstring'''
lowercase : List[Any] =self.num_labels
lowercase : str =AlbertForTokenClassification(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : int =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 lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] ):
'''simple docstring'''
lowercase : Optional[int] =self.num_choices
lowercase : List[Any] =AlbertForMultipleChoice(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : List[Any] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : int =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : int =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : List[str] =model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Union[str, Any] =self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
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_torch
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase_ = (
{
'feature-extraction': AlbertModel,
'fill-mask': AlbertForMaskedLM,
'question-answering': AlbertForQuestionAnswering,
'text-classification': AlbertForSequenceClassification,
'token-classification': AlbertForTokenClassification,
'zero-shot': AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase_ = True
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int=False ):
'''simple docstring'''
lowercase : Optional[int] =super()._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
if return_labels:
if model_class in get_values(UpperCAmelCase__ ):
lowercase : Any =torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=UpperCAmelCase__ )
lowercase : Any =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
return inputs_dict
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : Tuple =AlbertModelTester(self )
lowercase : Optional[Any] =ConfigTester(self , config_class=UpperCAmelCase__ , hidden_size=37 )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
lowercase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
lowercase : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Optional[int] =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase : Tuple =type
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : str =AlbertModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : int =AlbertModel.from_pretrained('''albert-base-v2''' )
lowercase : Optional[int] =torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
lowercase : 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 : Union[str, Any] =torch.tensor(
[[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , UpperCAmelCase__ , atol=1E-4 ) )
| 92 | 0 |
"""simple docstring"""
from __future__ import annotations
from statistics import mean
def _A ( __lowercase , __lowercase , __lowercase ):
"""simple docstring"""
lowerCamelCase__ = [0] * no_of_processes
lowerCamelCase__ = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(__lowercase ):
lowerCamelCase__ = burst_time[i]
lowerCamelCase__ = []
lowerCamelCase__ = 0
lowerCamelCase__ = 0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
lowerCamelCase__ = []
lowerCamelCase__ = -1
for i in range(__lowercase ):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(__lowercase )
if len(__lowercase ) > 0:
lowerCamelCase__ = ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
lowerCamelCase__ = i
total_time += burst_time[target_process]
completed += 1
lowerCamelCase__ = 0
lowerCamelCase__ = (
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def _A ( __lowercase , __lowercase , __lowercase ):
"""simple docstring"""
lowerCamelCase__ = [0] * no_of_processes
for i in range(__lowercase ):
lowerCamelCase__ = burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print("""[TEST CASE 01]""")
__magic_name__ = 4
__magic_name__ = [2, 5, 3, 7]
__magic_name__ = [0, 0, 0, 0]
__magic_name__ = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
__magic_name__ = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
for i, process_id in enumerate(list(range(1, 5))):
print(
F'{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'
F'{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'
)
print(F'\nAverage waiting time = {mean(waiting_time):.5f}')
print(F'Average turnaround time = {mean(turn_around_time):.5f}')
| 129 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int ):
'''simple docstring'''
if dst_width < 0 or dst_height < 0:
raise ValueError('''Destination width/height should be > 0''' )
lowercase : Union[str, Any] =img
lowercase : Union[str, Any] =img.shape[1]
lowercase : str =img.shape[0]
lowercase : Union[str, Any] =dst_width
lowercase : str =dst_height
lowercase : str =self.src_w / self.dst_w
lowercase : Optional[Any] =self.src_h / self.dst_h
lowercase : int =(
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 255
)
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
for i in range(self.dst_h ):
for j in range(self.dst_w ):
lowercase : List[Any] =self.img[self.get_y(UpperCAmelCase__ )][self.get_x(UpperCAmelCase__ )]
def lowerCamelCase_ ( self : Tuple , UpperCAmelCase__ : int ):
'''simple docstring'''
return int(self.ratio_x * x )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : int ):
'''simple docstring'''
return int(self.ratio_y * y )
if __name__ == "__main__":
UpperCamelCase_ , UpperCamelCase_ = 800, 600
UpperCamelCase_ = imread("""image_data/lena.jpg""", 1)
UpperCamelCase_ = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
)
waitKey(0)
destroyAllWindows()
| 92 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _lowerCAmelCase ( unittest.TestCase ):
def __init__( self , _UpperCamelCase , _UpperCamelCase=7 , _UpperCamelCase=3 , _UpperCamelCase=18 , _UpperCamelCase=30 , _UpperCamelCase=400 , _UpperCamelCase=True , _UpperCamelCase=None , _UpperCamelCase=True , _UpperCamelCase=None , _UpperCamelCase=True , _UpperCamelCase=[0.5, 0.5, 0.5] , _UpperCamelCase=[0.5, 0.5, 0.5] , ) -> Optional[int]:
lowerCAmelCase_ = size if size is not None else {'''shortest_edge''': 18}
lowerCAmelCase_ = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = num_channels
lowerCAmelCase_ = image_size
lowerCAmelCase_ = min_resolution
lowerCAmelCase_ = max_resolution
lowerCAmelCase_ = do_resize
lowerCAmelCase_ = size
lowerCAmelCase_ = do_center_crop
lowerCAmelCase_ = crop_size
lowerCAmelCase_ = do_normalize
lowerCAmelCase_ = image_mean
lowerCAmelCase_ = image_std
def __a ( self ) -> Dict:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class _lowerCAmelCase ( lowercase__ , unittest.TestCase ):
_lowercase =LevitImageProcessor if is_vision_available() else None
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = LevitImageProcessingTester(self )
@property
def __a ( self ) -> List[Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def __a ( self ) -> str:
lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase__ , "image_mean" ) )
self.assertTrue(hasattr(UpperCAmelCase__ , "image_std" ) )
self.assertTrue(hasattr(UpperCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(UpperCAmelCase__ , "do_resize" ) )
self.assertTrue(hasattr(UpperCAmelCase__ , "do_center_crop" ) )
self.assertTrue(hasattr(UpperCAmelCase__ , "size" ) )
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
lowerCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def __a ( self ) -> Dict:
pass
def __a ( self ) -> int:
lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , Image.Image )
# Test not batched input
lowerCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
lowerCAmelCase_ = image_processing(UpperCAmelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def __a ( self ) -> List[Any]:
lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , numpify=UpperCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , np.ndarray )
# Test not batched input
lowerCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
lowerCAmelCase_ = image_processing(UpperCAmelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def __a ( self ) -> Dict:
lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , torchify=UpperCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , torch.Tensor )
# Test not batched input
lowerCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
lowerCAmelCase_ = image_processing(UpperCAmelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 290 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ : list[float] ) -> float:
lowercase : Any =0.0_0
lowercase : Tuple =0
for resistor in resistors:
if resistor <= 0:
lowercase : Dict =f'''Resistor at index {index} has a negative or zero value!'''
raise ValueError(__magic_name__ )
first_sum += 1 / float(__magic_name__ )
index += 1
return 1 / first_sum
def _lowerCAmelCase ( __magic_name__ : list[float] ) -> float:
lowercase : Optional[Any] =0.0_0
lowercase : int =0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowercase : Tuple =f'''Resistor at index {index} has a negative value!'''
raise ValueError(__magic_name__ )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92 | 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> str:
if number > 0:
raise ValueError('input must be a negative integer' )
__snake_case : Dict = len(bin(_UpperCAmelCase )[3:] )
__snake_case : List[Any] = bin(abs(_UpperCAmelCase ) - (1 << binary_number_length) )[3:]
__snake_case : List[str] = (
(
'''1'''
+ '''0''' * (binary_number_length - len(_UpperCAmelCase ))
+ twos_complement_number
)
if number < 0
else '''0'''
)
return "0b" + twos_complement_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 286 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
"""self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
"""self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
"""self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
"""self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
"""self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
"""self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
"""self_attn.rotary_emb""": """encoder.embed_positions""",
"""self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
"""conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
"""conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
"""conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
"""conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
"""conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
"""ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
"""ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
"""ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
"""ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
"""ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
"""ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
"""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""",
}
UpperCamelCase_ = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def _lowerCAmelCase ( __magic_name__ : Any , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Tuple ) -> str:
for attribute in key.split('''.''' ):
lowercase : Tuple =getattr(__magic_name__ , __magic_name__ )
if weight_type is not None:
lowercase : Optional[int] =getattr(__magic_name__ , __magic_name__ ).shape
else:
lowercase : List[Any] =hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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 : Any =value
elif weight_type == "weight_g":
lowercase : List[Any] =value
elif weight_type == "weight_v":
lowercase : Union[str, Any] =value
elif weight_type == "bias":
lowercase : Tuple =value
elif weight_type == "running_mean":
lowercase : Union[str, Any] =value
elif weight_type == "running_var":
lowercase : str =value
elif weight_type == "num_batches_tracked":
lowercase : Tuple =value
elif weight_type == "inv_freq":
lowercase : Optional[Any] =value
else:
lowercase : Tuple =value
logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def _lowerCAmelCase ( __magic_name__ : List[Any] , __magic_name__ : Union[str, Any] , __magic_name__ : str ) -> Union[str, Any]:
lowercase : Optional[int] =[]
lowercase : Tuple =fairseq_model.state_dict()
lowercase : List[Any] =hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
lowercase : Tuple =False
if "conv_layers" in name:
load_conv_layer(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , hf_model.config.feat_extract_norm == '''group''' , )
lowercase : List[Any] =True
else:
for key, mapped_key in MAPPING.items():
lowercase : Optional[int] ='''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
lowercase : Union[str, Any] =True
if "*" in mapped_key:
lowercase : Optional[int] =name.split(__magic_name__ )[0].split('''.''' )[-2]
lowercase : List[str] =mapped_key.replace('''*''' , __magic_name__ )
if "pos_bias_u" in name:
lowercase : Optional[Any] =None
elif "pos_bias_v" in name:
lowercase : Union[str, Any] =None
elif "weight_g" in name:
lowercase : Any ='''weight_g'''
elif "weight_v" in name:
lowercase : Tuple ='''weight_v'''
elif "bias" in name:
lowercase : Optional[int] ='''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase : Optional[int] ='''weight'''
elif "running_mean" in name:
lowercase : Union[str, Any] ='''running_mean'''
elif "inv_freq" in name:
lowercase : Any ='''inv_freq'''
elif "running_var" in name:
lowercase : Tuple ='''running_var'''
elif "num_batches_tracked" in name:
lowercase : Dict ='''num_batches_tracked'''
else:
lowercase : str =None
set_recursively(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
continue
if not is_used:
unused_weights.append(__magic_name__ )
logger.warning(f'''Unused weights: {unused_weights}''' )
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : Optional[Any] , __magic_name__ : List[Any] , __magic_name__ : Optional[Any] , __magic_name__ : Tuple ) -> int:
lowercase : Optional[Any] =full_name.split('''conv_layers.''' )[-1]
lowercase : Any =name.split('''.''' )
lowercase : List[str] =int(items[0] )
lowercase : Union[str, Any] =int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
lowercase : Union[str, Any] =value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
lowercase : Optional[Any] =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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
lowercase : Optional[int] =value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[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(__magic_name__ )
@torch.no_grad()
def _lowerCAmelCase ( __magic_name__ : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Tuple=None , __magic_name__ : Optional[Any]=None , __magic_name__ : Union[str, Any]=True ) -> Union[str, Any]:
if config_path is not None:
lowercase : Optional[Any] =WavaVecaConformerConfig.from_pretrained(__magic_name__ , hidden_act='''swish''' )
else:
lowercase : Optional[int] =WavaVecaConformerConfig()
if "rope" in checkpoint_path:
lowercase : Dict ='''rotary'''
if is_finetuned:
if dict_path:
lowercase : Optional[Any] =Dictionary.load(__magic_name__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowercase : str =target_dict.pad_index
lowercase : Union[str, Any] =target_dict.bos_index
lowercase : Any =target_dict.eos_index
lowercase : Tuple =len(target_dict.symbols )
lowercase : str =os.path.join(__magic_name__ , '''vocab.json''' )
if not os.path.isdir(__magic_name__ ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__magic_name__ ) )
return
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
lowercase : Dict =target_dict.indices
# fairseq has the <pad> and <s> switched
lowercase : str =0
lowercase : List[Any] =1
with open(__magic_name__ , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(__magic_name__ , __magic_name__ )
lowercase : List[str] =WavaVecaCTCTokenizer(
__magic_name__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=__magic_name__ , )
lowercase : Optional[int] =True if config.feat_extract_norm == '''layer''' else False
lowercase : str =WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__magic_name__ , return_attention_mask=__magic_name__ , )
lowercase : Tuple =WavaVecaProcessor(feature_extractor=__magic_name__ , tokenizer=__magic_name__ )
processor.save_pretrained(__magic_name__ )
lowercase : str =WavaVecaConformerForCTC(__magic_name__ )
else:
lowercase : Tuple =WavaVecaConformerForPreTraining(__magic_name__ )
if is_finetuned:
lowercase , lowercase , lowercase : Optional[int] =fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
lowercase : Dict =argparse.Namespace(task='''audio_pretraining''' )
lowercase : Optional[int] =fairseq.tasks.setup_task(__magic_name__ )
lowercase , lowercase , lowercase : str =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__magic_name__ )
lowercase : List[Any] =model[0].eval()
recursively_load_weights(__magic_name__ , __magic_name__ , not is_finetuned )
hf_wavavec.save_pretrained(__magic_name__ )
if __name__ == "__main__":
UpperCamelCase_ = 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_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCamelCase_ = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 92 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
snake_case : Tuple = TypeVar('T')
snake_case : List[Any] = TypeVar('U')
class __lowercase ( Generic[T, U] ):
"""simple docstring"""
def __init__( self , A_ , A_ )-> Optional[int]:
_SCREAMING_SNAKE_CASE = key
_SCREAMING_SNAKE_CASE = val
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
def __repr__( self )-> Optional[int]:
return (
F'''Node: key: {self.key}, val: {self.val}, '''
F'''has next: {bool(self.next )}, has prev: {bool(self.prev )}'''
)
class __lowercase ( Generic[T, U] ):
"""simple docstring"""
def __init__( self )-> int:
_SCREAMING_SNAKE_CASE = DoubleLinkedListNode(UpperCAmelCase__ , UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = DoubleLinkedListNode(UpperCAmelCase__ , UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = self.rear, self.head
def __repr__( self )-> Tuple:
_SCREAMING_SNAKE_CASE = ['''DoubleLinkedList''']
_SCREAMING_SNAKE_CASE = self.head
while node.next is not None:
rep.append(str(UpperCAmelCase__ ) )
_SCREAMING_SNAKE_CASE = node.next
rep.append(str(self.rear ) )
return ",\n ".join(UpperCAmelCase__ )
def __magic_name__ ( self , A_ )-> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
_SCREAMING_SNAKE_CASE = node
_SCREAMING_SNAKE_CASE = previous
_SCREAMING_SNAKE_CASE = node
_SCREAMING_SNAKE_CASE = self.rear
def __magic_name__ ( self , A_ )-> int:
if node.prev is None or node.next is None:
return None
_SCREAMING_SNAKE_CASE = node.next
_SCREAMING_SNAKE_CASE = node.prev
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
return node
class __lowercase ( Generic[T, U] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = {}
def __init__( self , A_ )-> Any:
_SCREAMING_SNAKE_CASE = DoubleLinkedList()
_SCREAMING_SNAKE_CASE = capacity
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = {}
def __repr__( self )-> Optional[int]:
return (
F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
F'''capacity={self.capacity}, current size={self.num_keys})'''
)
def __contains__( self , A_ )-> List[str]:
return key in self.cache
def __magic_name__ ( self , A_ )-> Optional[Any]:
# Note: pythonic interface would throw KeyError rather than return None
if key in self.cache:
self.hits += 1
_SCREAMING_SNAKE_CASE = self.cache[key]
_SCREAMING_SNAKE_CASE = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(UpperCAmelCase__ )
return node.val
self.miss += 1
return None
def __magic_name__ ( self , A_ , A_ )-> Any:
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
_SCREAMING_SNAKE_CASE = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(UpperCAmelCase__ ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
_SCREAMING_SNAKE_CASE = DoubleLinkedListNode(UpperCAmelCase__ , UpperCAmelCase__ )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
_SCREAMING_SNAKE_CASE = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
_SCREAMING_SNAKE_CASE = value
self.list.add(UpperCAmelCase__ )
@classmethod
def __magic_name__ ( cls , A_ = 128 )-> Optional[int]:
def cache_decorator_inner(A_ ) -> Callable[..., U]:
def cache_decorator_wrapper(*A_ ) -> U:
if func not in cls.decorator_function_to_instance_map:
_SCREAMING_SNAKE_CASE = LRUCache(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
_SCREAMING_SNAKE_CASE = func(*UpperCAmelCase__ )
cls.decorator_function_to_instance_map[func].put(args[0] , UpperCAmelCase__ )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(UpperCAmelCase__ , 'cache_info' , UpperCAmelCase__ ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 605 |
'''simple docstring'''
import math
import flax.linen as nn
import jax.numpy as jnp
def _lowerCAmelCase ( __magic_name__ : jnp.ndarray , __magic_name__ : int , __magic_name__ : float = 1 , __magic_name__ : float = 1 , __magic_name__ : float = 1.0E4 , __magic_name__ : bool = False , __magic_name__ : float = 1.0 , ) -> jnp.ndarray:
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
lowercase : int =float(embedding_dim // 2 )
lowercase : Optional[int] =math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
lowercase : Any =min_timescale * jnp.exp(jnp.arange(__magic_name__ , dtype=jnp.floataa ) * -log_timescale_increment )
lowercase : List[Any] =jnp.expand_dims(__magic_name__ , 1 ) * jnp.expand_dims(__magic_name__ , 0 )
# scale embeddings
lowercase : Tuple =scale * emb
if flip_sin_to_cos:
lowercase : Dict =jnp.concatenate([jnp.cos(__magic_name__ ), jnp.sin(__magic_name__ )] , axis=1 )
else:
lowercase : Any =jnp.concatenate([jnp.sin(__magic_name__ ), jnp.cos(__magic_name__ )] , axis=1 )
lowercase : List[str] =jnp.reshape(__magic_name__ , [jnp.shape(__magic_name__ )[0], embedding_dim] )
return signal
class __SCREAMING_SNAKE_CASE ( nn.Module ):
lowerCamelCase_ = 32
lowerCamelCase_ = jnp.floataa
@nn.compact
def __call__( self : Tuple , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : List[Any] =nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_1''' )(UpperCAmelCase__ )
lowercase : Any =nn.silu(UpperCAmelCase__ )
lowercase : int =nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_2''' )(UpperCAmelCase__ )
return temb
class __SCREAMING_SNAKE_CASE ( nn.Module ):
lowerCamelCase_ = 32
lowerCamelCase_ = False
lowerCamelCase_ = 1
@nn.compact
def __call__( self : int , UpperCAmelCase__ : str ):
'''simple docstring'''
return get_sinusoidal_embeddings(
UpperCAmelCase__ , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 92 | 0 |
"""simple docstring"""
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
__UpperCAmelCase =logging.get_logger(__name__)
enable_full_determinism()
class lowerCAmelCase__ ( lowercase__ , lowercase__ , unittest.TestCase ):
lowercase__ : Tuple = UNetaDModel
lowercase__ : Union[str, Any] = """sample"""
@property
def lowercase_ ( self ):
'''simple docstring'''
A__ = 4
A__ = 3
A__ = (32, 32)
A__ = floats_tensor((batch_size, num_channels) + sizes ).to(UpperCAmelCase__ )
A__ = torch.tensor([10] ).to(UpperCAmelCase__ )
return {"sample": noise, "timestep": time_step}
@property
def lowercase_ ( self ):
'''simple docstring'''
return (3, 32, 32)
@property
def lowercase_ ( self ):
'''simple docstring'''
return (3, 32, 32)
def lowercase_ ( self ):
'''simple docstring'''
A__ = {
'''block_out_channels''': (32, 64),
'''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
'''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
'''attention_head_dim''': 3,
'''out_channels''': 3,
'''in_channels''': 3,
'''layers_per_block''': 2,
'''sample_size''': 32,
}
A__ = self.dummy_input
return init_dict, inputs_dict
class lowerCAmelCase__ ( lowercase__ , lowercase__ , unittest.TestCase ):
lowercase__ : Union[str, Any] = UNetaDModel
lowercase__ : Optional[int] = """sample"""
@property
def lowercase_ ( self ):
'''simple docstring'''
A__ = 4
A__ = 4
A__ = (32, 32)
A__ = floats_tensor((batch_size, num_channels) + sizes ).to(UpperCAmelCase__ )
A__ = torch.tensor([10] ).to(UpperCAmelCase__ )
return {"sample": noise, "timestep": time_step}
@property
def lowercase_ ( self ):
'''simple docstring'''
return (4, 32, 32)
@property
def lowercase_ ( self ):
'''simple docstring'''
return (4, 32, 32)
def lowercase_ ( self ):
'''simple docstring'''
A__ = {
'''sample_size''': 32,
'''in_channels''': 4,
'''out_channels''': 4,
'''layers_per_block''': 2,
'''block_out_channels''': (32, 64),
'''attention_head_dim''': 32,
'''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
'''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
}
A__ = self.dummy_input
return init_dict, inputs_dict
def lowercase_ ( self ):
'''simple docstring'''
A__ = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" , output_loading_info=UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(UpperCAmelCase__ )
A__ = model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != "cuda" , "This test is supposed to run on GPU" )
def lowercase_ ( self ):
'''simple docstring'''
A__ = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" , output_loading_info=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
A__ = model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != "cuda" , "This test is supposed to run on GPU" )
def lowercase_ ( self ):
'''simple docstring'''
A__ = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" , output_loading_info=UpperCAmelCase__ )
model_accelerate.to(UpperCAmelCase__ )
model_accelerate.eval()
A__ = torch.randn(
1 , model_accelerate.config.in_channels , model_accelerate.config.sample_size , model_accelerate.config.sample_size , generator=torch.manual_seed(0 ) , )
A__ = noise.to(UpperCAmelCase__ )
A__ = torch.tensor([10] * noise.shape[0] ).to(UpperCAmelCase__ )
A__ = model_accelerate(UpperCAmelCase__ , UpperCAmelCase__ )['''sample''']
# two models don't need to stay in the device at the same time
del model_accelerate
torch.cuda.empty_cache()
gc.collect()
A__ = UNetaDModel.from_pretrained(
"fusing/unet-ldm-dummy-update" , output_loading_info=UpperCAmelCase__ , low_cpu_mem_usage=UpperCAmelCase__ )
model_normal_load.to(UpperCAmelCase__ )
model_normal_load.eval()
A__ = model_normal_load(UpperCAmelCase__ , UpperCAmelCase__ )['''sample''']
assert torch_all_close(UpperCAmelCase__ , UpperCAmelCase__ , rtol=1e-3 )
def lowercase_ ( self ):
'''simple docstring'''
A__ = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" )
model.eval()
model.to(UpperCAmelCase__ )
A__ = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
A__ = noise.to(UpperCAmelCase__ )
A__ = torch.tensor([10] * noise.shape[0] ).to(UpperCAmelCase__ )
with torch.no_grad():
A__ = model(UpperCAmelCase__ , UpperCAmelCase__ ).sample
A__ = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
A__ = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800] )
# fmt: on
self.assertTrue(torch_all_close(UpperCAmelCase__ , UpperCAmelCase__ , rtol=1e-3 ) )
class lowerCAmelCase__ ( lowercase__ , lowercase__ , unittest.TestCase ):
lowercase__ : Optional[int] = UNetaDModel
lowercase__ : str = """sample"""
@property
def lowercase_ ( self , UpperCamelCase__=(32, 32) ):
'''simple docstring'''
A__ = 4
A__ = 3
A__ = floats_tensor((batch_size, num_channels) + sizes ).to(UpperCAmelCase__ )
A__ = torch.tensor(batch_size * [10] ).to(dtype=torch.intaa , device=UpperCAmelCase__ )
return {"sample": noise, "timestep": time_step}
@property
def lowercase_ ( self ):
'''simple docstring'''
return (3, 32, 32)
@property
def lowercase_ ( self ):
'''simple docstring'''
return (3, 32, 32)
def lowercase_ ( self ):
'''simple docstring'''
A__ = {
'''block_out_channels''': [32, 64, 64, 64],
'''in_channels''': 3,
'''layers_per_block''': 1,
'''out_channels''': 3,
'''time_embedding_type''': '''fourier''',
'''norm_eps''': 1e-6,
'''mid_block_scale_factor''': math.sqrt(2.0 ),
'''norm_num_groups''': None,
'''down_block_types''': [
'''SkipDownBlock2D''',
'''AttnSkipDownBlock2D''',
'''SkipDownBlock2D''',
'''SkipDownBlock2D''',
],
'''up_block_types''': [
'''SkipUpBlock2D''',
'''SkipUpBlock2D''',
'''AttnSkipUpBlock2D''',
'''SkipUpBlock2D''',
],
}
A__ = self.dummy_input
return init_dict, inputs_dict
@slow
def lowercase_ ( self ):
'''simple docstring'''
A__ = UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" , output_loading_info=UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(UpperCAmelCase__ )
A__ = self.dummy_input
A__ = floats_tensor((4, 3) + (2_56, 2_56) ).to(UpperCAmelCase__ )
A__ = noise
A__ = model(**UpperCAmelCase__ )
assert image is not None, "Make sure output is not None"
@slow
def lowercase_ ( self ):
'''simple docstring'''
A__ = UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" )
model.to(UpperCAmelCase__ )
A__ = 4
A__ = 3
A__ = (2_56, 2_56)
A__ = torch.ones((batch_size, num_channels) + sizes ).to(UpperCAmelCase__ )
A__ = torch.tensor(batch_size * [1e-4] ).to(UpperCAmelCase__ )
with torch.no_grad():
A__ = model(UpperCAmelCase__ , UpperCAmelCase__ ).sample
A__ = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
A__ = torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08] )
# fmt: on
self.assertTrue(torch_all_close(UpperCAmelCase__ , UpperCAmelCase__ , rtol=1e-2 ) )
def lowercase_ ( self ):
'''simple docstring'''
A__ = UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update" )
model.to(UpperCAmelCase__ )
A__ = 4
A__ = 3
A__ = (32, 32)
A__ = torch.ones((batch_size, num_channels) + sizes ).to(UpperCAmelCase__ )
A__ = torch.tensor(batch_size * [1e-4] ).to(UpperCAmelCase__ )
with torch.no_grad():
A__ = model(UpperCAmelCase__ , UpperCAmelCase__ ).sample
A__ = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
A__ = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256] )
# fmt: on
self.assertTrue(torch_all_close(UpperCAmelCase__ , UpperCAmelCase__ , rtol=1e-2 ) )
def lowercase_ ( self ):
'''simple docstring'''
pass | 337 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase_ = {
"""facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = 'esm'
def __init__( self : Optional[Any] , UpperCAmelCase__ : str=None , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : Any=None , UpperCAmelCase__ : int=768 , UpperCAmelCase__ : Union[str, Any]=12 , UpperCAmelCase__ : List[Any]=12 , UpperCAmelCase__ : Optional[Any]=3072 , UpperCAmelCase__ : Dict=0.1 , UpperCAmelCase__ : int=0.1 , UpperCAmelCase__ : int=1026 , UpperCAmelCase__ : Tuple=0.02 , UpperCAmelCase__ : Optional[int]=1E-12 , UpperCAmelCase__ : List[Any]="absolute" , UpperCAmelCase__ : int=True , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : str=False , UpperCAmelCase__ : int=False , UpperCAmelCase__ : Optional[Any]=None , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : int , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCAmelCase__ , mask_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
lowercase : Any =vocab_size
lowercase : List[Any] =hidden_size
lowercase : Any =num_hidden_layers
lowercase : Optional[Any] =num_attention_heads
lowercase : Tuple =intermediate_size
lowercase : int =hidden_dropout_prob
lowercase : Dict =attention_probs_dropout_prob
lowercase : Optional[int] =max_position_embeddings
lowercase : Union[str, Any] =initializer_range
lowercase : Tuple =layer_norm_eps
lowercase : Union[str, Any] =position_embedding_type
lowercase : List[Any] =use_cache
lowercase : Dict =emb_layer_norm_before
lowercase : Optional[Any] =token_dropout
lowercase : Union[str, Any] =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
lowercase : Any =EsmFoldConfig()
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase : Optional[int] =EsmFoldConfig(**UpperCAmelCase__ )
lowercase : Union[str, Any] =esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
lowercase : int =get_default_vocab_list()
else:
lowercase : Tuple =vocab_list
else:
lowercase : Union[str, Any] =None
lowercase : Dict =None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , UpperCAmelCase__ ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : Union[str, Any] =super().to_dict()
if isinstance(self.esmfold_config , UpperCAmelCase__ ):
lowercase : Optional[Any] =self.esmfold_config.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = None
lowerCamelCase_ = True
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = 0
lowerCamelCase_ = True
lowerCamelCase_ = False
lowerCamelCase_ = 1_28
lowerCamelCase_ = None
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
if self.trunk is None:
lowercase : str =TrunkConfig()
elif isinstance(self.trunk , UpperCAmelCase__ ):
lowercase : int =TrunkConfig(**self.trunk )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : str =asdict(self )
lowercase : Union[str, Any] =self.trunk.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = 48
lowerCamelCase_ = 10_24
lowerCamelCase_ = 1_28
lowerCamelCase_ = 32
lowerCamelCase_ = 32
lowerCamelCase_ = 32
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = False
lowerCamelCase_ = 4
lowerCamelCase_ = 1_28
lowerCamelCase_ = None
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
if self.structure_module is None:
lowercase : Any =StructureModuleConfig()
elif isinstance(self.structure_module , UpperCAmelCase__ ):
lowercase : Union[str, Any] =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
lowercase : str =self.sequence_state_dim // self.sequence_head_width
lowercase : int =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : List[Any] =asdict(self )
lowercase : Any =self.structure_module.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = 3_84
lowerCamelCase_ = 1_28
lowerCamelCase_ = 16
lowerCamelCase_ = 1_28
lowerCamelCase_ = 12
lowerCamelCase_ = 4
lowerCamelCase_ = 8
lowerCamelCase_ = 0.1
lowerCamelCase_ = 8
lowerCamelCase_ = 1
lowerCamelCase_ = 2
lowerCamelCase_ = 7
lowerCamelCase_ = 10
lowerCamelCase_ = 1E-8
lowerCamelCase_ = 1E5
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return asdict(self )
def _lowerCAmelCase ( ) -> Optional[int]:
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 92 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A = logging.get_logger(__name__)
class snake_case ( lowercase__ ):
def __init__( self : List[Any] , *UpperCamelCase__ : Any , **UpperCamelCase__ : int)-> Union[str, Any]:
'''simple docstring'''
warnings.warn(
"The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use MobileViTImageProcessor instead." , UpperCAmelCase__ , )
super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__)
| 346 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCamelCase_ = abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)
def _lowerCAmelCase ( __magic_name__ : int ) -> Tuple:
config.addinivalue_line(
'''markers''' , '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''' , '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''' , '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''' , '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''' , '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''' , '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def _lowerCAmelCase ( __magic_name__ : int ) -> Any:
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : Any ) -> Any:
from transformers.testing_utils import pytest_terminal_summary_main
lowercase : Optional[Any] =terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__magic_name__ , id=__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : Tuple , __magic_name__ : Union[str, Any] ) -> List[str]:
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowercase : Optional[int] =0
# Doctest custom flag to ignore output.
UpperCamelCase_ = doctest.register_optionflag("""IGNORE_RESULT""")
UpperCamelCase_ = doctest.OutputChecker
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
def lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[Any] ):
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
UpperCamelCase_ = CustomOutputChecker
UpperCamelCase_ = HfDoctestModule
UpperCamelCase_ = HfDocTestParser
| 92 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Dict = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Tuple = [
'''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OPTForCausalLM''',
'''OPTModel''',
'''OPTPreTrainedModel''',
'''OPTForSequenceClassification''',
'''OPTForQuestionAnswering''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : str = ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Union[str, Any] = [
'''FlaxOPTForCausalLM''',
'''FlaxOPTModel''',
'''FlaxOPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
a : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 639 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging
UpperCamelCase_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = ['pixel_values']
def __init__( self : List[Any] , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Union[int, float] = 1 / 255 , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : int = 8 , **UpperCAmelCase__ : str , ):
'''simple docstring'''
super().__init__(**UpperCAmelCase__ )
lowercase : Union[str, Any] =do_rescale
lowercase : List[Any] =rescale_factor
lowercase : Tuple =do_pad
lowercase : List[str] =pad_size
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : float , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : Optional[int] ):
'''simple docstring'''
return rescale(UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCamelCase_ ( self : Tuple , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None ):
'''simple docstring'''
lowercase , lowercase : Union[str, Any] =get_image_size(UpperCAmelCase__ )
lowercase : Tuple =(old_height // size + 1) * size - old_height
lowercase : Tuple =(old_width // size + 1) * size - old_width
return pad(UpperCAmelCase__ , ((0, pad_height), (0, pad_width)) , mode='''symmetric''' , data_format=UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : ImageInput , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[float] = None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[Union[str, TensorType]] = None , UpperCAmelCase__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **UpperCAmelCase__ : Optional[Any] , ):
'''simple docstring'''
lowercase : int =do_rescale if do_rescale is not None else self.do_rescale
lowercase : Tuple =rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase : int =do_pad if do_pad is not None else self.do_pad
lowercase : List[Any] =pad_size if pad_size is not None else self.pad_size
lowercase : Any =make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
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 : Dict =[to_numpy_array(UpperCAmelCase__ ) for image in images]
if do_rescale:
lowercase : Tuple =[self.rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ ) for image in images]
if do_pad:
lowercase : Union[str, Any] =[self.pad(UpperCAmelCase__ , size=UpperCAmelCase__ ) for image in images]
lowercase : Dict =[to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
lowercase : Any ={'''pixel_values''': images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 92 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : Optional[int] =tempfile.mkdtemp()
# fmt: off
SCREAMING_SNAKE_CASE_ : Union[str, Any] =['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
SCREAMING_SNAKE_CASE_ : int =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE_ : Tuple ={
'''do_resize''': True,
'''size''': {'''height''': 18, '''width''': 18},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
SCREAMING_SNAKE_CASE_ : Optional[Any] =os.path.join(self.tmpdirname , UpperCAmelCase__ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(UpperCAmelCase__ , UpperCAmelCase__ )
def __lowerCamelCase ( self , **__UpperCAmelCase ):
return BertTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def __lowerCamelCase ( self , **__UpperCAmelCase ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def __lowerCamelCase ( self ):
shutil.rmtree(self.tmpdirname )
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : Dict =[np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE_ : Optional[Any] =[Image.fromarray(np.moveaxis(UpperCAmelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : List[Any] =self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : Tuple =self.get_image_processor()
SCREAMING_SNAKE_CASE_ : Any =VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ : List[str] =VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCAmelCase__ )
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : str =VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ : List[str] =self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
SCREAMING_SNAKE_CASE_ : Optional[Any] =self.get_image_processor(do_normalize=UpperCAmelCase__ , padding_value=1.0 )
SCREAMING_SNAKE_CASE_ : Tuple =VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=UpperCAmelCase__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCAmelCase__ )
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : List[str] =self.get_image_processor()
SCREAMING_SNAKE_CASE_ : List[str] =self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : Optional[int] =VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Optional[int] =self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ : Optional[Any] =image_processor(UpperCAmelCase__ , return_tensors='np' )
SCREAMING_SNAKE_CASE_ : List[str] =processor(images=UpperCAmelCase__ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] =self.get_image_processor()
SCREAMING_SNAKE_CASE_ : str =self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : List[str] =VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Optional[Any] ='''lower newer'''
SCREAMING_SNAKE_CASE_ : List[str] =processor(text=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : List[Any] =tokenizer(UpperCAmelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : List[Any] =self.get_image_processor()
SCREAMING_SNAKE_CASE_ : List[str] =self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : Any =VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : str ='''lower newer'''
SCREAMING_SNAKE_CASE_ : Union[str, Any] =self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ : Tuple =processor(text=UpperCAmelCase__ , images=UpperCAmelCase__ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with self.assertRaises(UpperCAmelCase__ ):
processor()
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : Optional[Any] =self.get_image_processor()
SCREAMING_SNAKE_CASE_ : Union[str, Any] =self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : Dict =VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Any =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE_ : Union[str, Any] =processor.batch_decode(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Optional[int] =tokenizer.batch_decode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] =self.get_image_processor()
SCREAMING_SNAKE_CASE_ : Tuple =self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : Union[str, Any] =VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] ='''lower newer'''
SCREAMING_SNAKE_CASE_ : List[str] =self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ : Any =processor(text=UpperCAmelCase__ , images=UpperCAmelCase__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 220 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["""MBartTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["""MBartTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MBartForCausalLM""",
"""MBartForConditionalGeneration""",
"""MBartForQuestionAnswering""",
"""MBartForSequenceClassification""",
"""MBartModel""",
"""MBartPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""TFMBartForConditionalGeneration""",
"""TFMBartModel""",
"""TFMBartPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""FlaxMBartForConditionalGeneration""",
"""FlaxMBartForQuestionAnswering""",
"""FlaxMBartForSequenceClassification""",
"""FlaxMBartModel""",
"""FlaxMBartPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_roc_bert": ["RoCBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoCBertForCausalLM",
"RoCBertForMaskedLM",
"RoCBertForMultipleChoice",
"RoCBertForPreTraining",
"RoCBertForQuestionAnswering",
"RoCBertForSequenceClassification",
"RoCBertForTokenClassification",
"RoCBertLayer",
"RoCBertModel",
"RoCBertPreTrainedModel",
"load_tf_weights_in_roc_bert",
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 181 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase_ = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
snake_case__ = CLIPConfig
snake_case__ = ['''CLIPEncoderLayer''']
def __init__( self , _snake_case ):
"""simple docstring"""
super().__init__(UpperCAmelCase__ )
lowerCAmelCase = CLIPVisionModelWithProjection(config.vision_config )
lowerCAmelCase = nn.Linear(config.vision_config.projection_dim , 1 )
lowerCAmelCase = nn.Linear(config.vision_config.projection_dim , 1 )
@torch.no_grad()
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case=0.5 , _snake_case=0.5 ):
"""simple docstring"""
lowerCAmelCase = self.vision_model(UpperCAmelCase__ )[0]
lowerCAmelCase = self.p_head(UpperCAmelCase__ )
lowerCAmelCase = nsfw_detected.flatten()
lowerCAmelCase = nsfw_detected > p_threshold
lowerCAmelCase = nsfw_detected.tolist()
if any(UpperCAmelCase__ ):
logger.warning(
'Potential NSFW content was detected in one or more images. A black image will be returned instead.'
' Try again with a different prompt and/or seed.' )
for idx, nsfw_detected_ in enumerate(UpperCAmelCase__ ):
if nsfw_detected_:
lowerCAmelCase = np.zeros(images[idx].shape )
lowerCAmelCase = self.w_head(UpperCAmelCase__ )
lowerCAmelCase = watermark_detected.flatten()
lowerCAmelCase = watermark_detected > w_threshold
lowerCAmelCase = watermark_detected.tolist()
if any(UpperCAmelCase__ ):
logger.warning(
'Potential watermarked content was detected in one or more images. A black image will be returned instead.'
' Try again with a different prompt and/or seed.' )
for idx, watermark_detected_ in enumerate(UpperCAmelCase__ ):
if watermark_detected_:
lowerCAmelCase = np.zeros(images[idx].shape )
return images, nsfw_detected, watermark_detected
| 4 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
UpperCamelCase_ = logging.getLogger(__name__)
def _lowerCAmelCase ( __magic_name__ : List[Any] , __magic_name__ : str ) -> Union[str, Any]:
return (preds == labels).mean()
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys() )} )
lowerCamelCase_ = field(metadata={'help': 'Should contain the data files for the task.'} )
lowerCamelCase_ = field(
default=1_28 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _lowerCAmelCase ( ) -> 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 : Dict =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase , lowercase , lowercase : List[Any] =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.''' )
# 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''' , __magic_name__ )
# Set seed
set_seed(training_args.seed )
try:
lowercase : Any =processors[data_args.task_name]()
lowercase : Optional[int] =processor.get_labels()
lowercase : str =len(__magic_name__ )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase : List[str] =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__magic_name__ , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
lowercase : int =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 , )
lowercase : Any =AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__magic_name__ , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase : int =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__magic_name__ , task=data_args.task_name , 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 : Union[str, Any] =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__magic_name__ , task=data_args.task_name , 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 compute_metrics(__magic_name__ : EvalPrediction ) -> Dict:
lowercase : Dict =np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__magic_name__ , p.label_ids )}
# Data collator
lowercase : List[str] =DataCollatorWithPadding(__magic_name__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase : Dict =Trainer(
model=__magic_name__ , args=__magic_name__ , train_dataset=__magic_name__ , eval_dataset=__magic_name__ , compute_metrics=__magic_name__ , data_collator=__magic_name__ , )
# 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_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase : Optional[Any] ={}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase : List[Any] =trainer.evaluate()
lowercase : Any =os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__magic_name__ , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __magic_name__ , __magic_name__ )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__magic_name__ )
return results
def _lowerCAmelCase ( __magic_name__ : Any ) -> Optional[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 92 | 0 |
import numpy as np
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any]=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=None , SCREAMING_SNAKE_CASE__ : str=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=None , SCREAMING_SNAKE_CASE__ : List[Any]=None ):
"""simple docstring"""
self.set_matricies(red=UpperCAmelCase__ , green=UpperCAmelCase__ , blue=UpperCAmelCase__ , red_edge=UpperCAmelCase__ , nir=UpperCAmelCase__ )
def __lowerCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : str=None , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : Dict=None , SCREAMING_SNAKE_CASE__ : List[Any]=None ):
"""simple docstring"""
if red is not None:
UpperCamelCase = red
if green is not None:
UpperCamelCase = green
if blue is not None:
UpperCamelCase = blue
if red_edge is not None:
UpperCamelCase = red_edge
if nir is not None:
UpperCamelCase = nir
return True
def __lowerCAmelCase ( self : Tuple , SCREAMING_SNAKE_CASE__ : int="" , SCREAMING_SNAKE_CASE__ : Optional[Any]=None , SCREAMING_SNAKE_CASE__ : int=None , SCREAMING_SNAKE_CASE__ : int=None , SCREAMING_SNAKE_CASE__ : Optional[int]=None , SCREAMING_SNAKE_CASE__ : Union[str, Any]=None ):
"""simple docstring"""
self.set_matricies(red=UpperCAmelCase__ , green=UpperCAmelCase__ , blue=UpperCAmelCase__ , red_edge=UpperCAmelCase__ , nir=UpperCAmelCase__ )
UpperCamelCase = {
'''ARVI2''': self.arvaa,
'''CCCI''': self.ccci,
'''CVI''': self.cvi,
'''GLI''': self.gli,
'''NDVI''': self.ndvi,
'''BNDVI''': self.bndvi,
'''redEdgeNDVI''': self.red_edge_ndvi,
'''GNDVI''': self.gndvi,
'''GBNDVI''': self.gbndvi,
'''GRNDVI''': self.grndvi,
'''RBNDVI''': self.rbndvi,
'''PNDVI''': self.pndvi,
'''ATSAVI''': self.atsavi,
'''BWDRVI''': self.bwdrvi,
'''CIgreen''': self.ci_green,
'''CIrededge''': self.ci_rededge,
'''CI''': self.ci,
'''CTVI''': self.ctvi,
'''GDVI''': self.gdvi,
'''EVI''': self.evi,
'''GEMI''': self.gemi,
'''GOSAVI''': self.gosavi,
'''GSAVI''': self.gsavi,
'''Hue''': self.hue,
'''IVI''': self.ivi,
'''IPVI''': self.ipvi,
'''I''': self.i,
'''RVI''': self.rvi,
'''MRVI''': self.mrvi,
'''MSAVI''': self.m_savi,
'''NormG''': self.norm_g,
'''NormNIR''': self.norm_nir,
'''NormR''': self.norm_r,
'''NGRDI''': self.ngrdi,
'''RI''': self.ri,
'''S''': self.s,
'''IF''': self._if,
'''DVI''': self.dvi,
'''TVI''': self.tvi,
'''NDRE''': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('Index not in the list!' )
return False
def __lowerCAmelCase ( self : Any ):
"""simple docstring"""
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def __lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
return self.nir * (self.red / (self.green**2))
def __lowerCAmelCase ( self : Dict ):
"""simple docstring"""
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
return (self.nir - self.red) / (self.nir + self.red)
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
return (self.nir - self.blue) / (self.nir + self.blue)
def __lowerCAmelCase ( self : Any ):
"""simple docstring"""
return (self.redEdge - self.red) / (self.redEdge + self.red)
def __lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
return (self.nir - self.green) / (self.nir + self.green)
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def __lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def __lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def __lowerCAmelCase ( self : Dict ):
"""simple docstring"""
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def __lowerCAmelCase ( self : str , SCREAMING_SNAKE_CASE__ : List[Any]=0.08 , SCREAMING_SNAKE_CASE__ : Tuple=1.22 , SCREAMING_SNAKE_CASE__ : List[str]=0.03 ):
"""simple docstring"""
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def __lowerCAmelCase ( self : Dict ):
"""simple docstring"""
return (self.nir / self.green) - 1
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
return (self.nir / self.redEdge) - 1
def __lowerCAmelCase ( self : Dict ):
"""simple docstring"""
return (self.red - self.blue) / self.red
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
return self.nir - self.green
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
UpperCamelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def __lowerCAmelCase ( self : str , SCREAMING_SNAKE_CASE__ : List[Any]=0.16 ):
"""simple docstring"""
return (self.nir - self.green) / (self.nir + self.green + y)
def __lowerCAmelCase ( self : List[Any] , SCREAMING_SNAKE_CASE__ : int=0.5 ):
"""simple docstring"""
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def __lowerCAmelCase ( self : int , SCREAMING_SNAKE_CASE__ : Optional[int]=None , SCREAMING_SNAKE_CASE__ : int=None ):
"""simple docstring"""
return (self.nir - b) / (a * self.red)
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def __lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
return (self.red + self.green + self.blue) / 30.5
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
return self.nir / self.red
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
return (self.rvi() - 1) / (self.rvi() + 1)
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def __lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
return self.green / (self.nir + self.red + self.green)
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
return self.nir / (self.nir + self.red + self.green)
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
return self.red / (self.nir + self.red + self.green)
def __lowerCAmelCase ( self : Any ):
"""simple docstring"""
return (self.green - self.red) / (self.green + self.red)
def __lowerCAmelCase ( self : Any ):
"""simple docstring"""
return (self.red - self.green) / (self.red + self.green)
def __lowerCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
UpperCamelCase = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def __lowerCAmelCase ( self : Dict ):
"""simple docstring"""
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
return self.nir / self.red
def __lowerCAmelCase ( self : int ):
"""simple docstring"""
return (self.ndvi() + 0.5) ** (1 / 2)
def __lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 282 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
UpperCamelCase_ = logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_ = """cuda""" if torch.cuda.is_available() else """cpu"""
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : Tuple=100 , __magic_name__ : Optional[int]=" " ) -> List[str]:
lowercase : List[Any] =text.split(__magic_name__ )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(__magic_name__ ) , __magic_name__ )]
def _lowerCAmelCase ( __magic_name__ : dict ) -> dict:
lowercase , lowercase : int =[], []
for title, text in zip(documents['''title'''] , documents['''text'''] ):
if text is not None:
for passage in split_text(__magic_name__ ):
titles.append(title if title is not None else '''''' )
texts.append(__magic_name__ )
return {"title": titles, "text": texts}
def _lowerCAmelCase ( __magic_name__ : dict , __magic_name__ : DPRContextEncoder , __magic_name__ : DPRContextEncoderTokenizerFast ) -> dict:
lowercase : Dict =ctx_tokenizer(
documents['''title'''] , documents['''text'''] , truncation=__magic_name__ , padding='''longest''' , return_tensors='''pt''' )['''input_ids''']
lowercase : Optional[int] =ctx_encoder(input_ids.to(device=__magic_name__ ) , return_dict=__magic_name__ ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def _lowerCAmelCase ( __magic_name__ : "RagExampleArguments" , __magic_name__ : "ProcessingArguments" , __magic_name__ : "IndexHnswArguments" , ) -> str:
######################################
logger.info('''Step 1 - Create the dataset''' )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
lowercase : Tuple =load_dataset(
'''csv''' , data_files=[rag_example_args.csv_path] , split='''train''' , delimiter='''\t''' , column_names=['''title''', '''text'''] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
lowercase : Optional[int] =dataset.map(__magic_name__ , batched=__magic_name__ , num_proc=processing_args.num_proc )
# And compute the embeddings
lowercase : Any =DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=__magic_name__ )
lowercase : Any =DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
lowercase : Optional[int] =Features(
{'''text''': Value('''string''' ), '''title''': Value('''string''' ), '''embeddings''': Sequence(Value('''float32''' ) )} ) # optional, save as float32 instead of float64 to save space
lowercase : Optional[Any] =dataset.map(
partial(__magic_name__ , ctx_encoder=__magic_name__ , ctx_tokenizer=__magic_name__ ) , batched=__magic_name__ , batch_size=processing_args.batch_size , features=__magic_name__ , )
# And finally save your dataset
lowercase : Optional[Any] =os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset''' )
dataset.save_to_disk(__magic_name__ )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info('''Step 2 - Index the dataset''' )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
lowercase : Union[str, Any] =faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index('''embeddings''' , custom_index=__magic_name__ )
# And save the index
lowercase : Dict =os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset_hnsw_index.faiss''' )
dataset.get_index('''embeddings''' ).save(__magic_name__ )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=str(Path(lowercase__ ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv' ) , metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''} , )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'} , )
lowerCamelCase_ = field(
default='facebook/rag-sequence-nq' , metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''} , )
lowerCamelCase_ = field(
default='facebook/dpr-ctx_encoder-multiset-base' , metadata={
'help': (
'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
' \'facebook/dpr-ctx_encoder-multiset-base\''
)
} , )
lowerCamelCase_ = field(
default=str(Path(lowercase__ ).parent / 'test_run' / 'dummy-kb' ) , metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=lowercase__ , metadata={
'help': 'The number of processes to use to split the documents into passages. Default is single process.'
} , )
lowerCamelCase_ = field(
default=16 , metadata={
'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=7_68 , metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'} , )
lowerCamelCase_ = field(
default=1_28 , metadata={
'help': (
'The number of bi-directional links created for every new element during the HNSW index construction.'
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
UpperCamelCase_ = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
UpperCamelCase_ = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 92 | 0 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__magic_name__ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : uuid.UUID = None , SCREAMING_SNAKE_CASE_ : str=None , SCREAMING_SNAKE_CASE_ : int=None ):
if not conversation_id:
lowerCamelCase__ = uuid.uuida()
if past_user_inputs is None:
lowerCamelCase__ = []
if generated_responses is None:
lowerCamelCase__ = []
lowerCamelCase__ = conversation_id
lowerCamelCase__ = past_user_inputs
lowerCamelCase__ = generated_responses
lowerCamelCase__ = text
def __eq__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tuple ):
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def __UpperCAmelCase ( self : str , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : bool = False ):
if self.new_user_input:
if overwrite:
logger.warning(
f"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
f"""with: \"{text}\".""" )
lowerCamelCase__ = text
else:
logger.warning(
f"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
f"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input""" )
else:
lowerCamelCase__ = text
def __UpperCAmelCase ( self : List[Any] ):
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
lowerCamelCase__ = None
def __UpperCAmelCase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : str ):
self.generated_responses.append(UpperCAmelCase__ )
def __UpperCAmelCase ( self : str ):
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self : int ):
lowerCamelCase__ = f"""Conversation id: {self.uuid} \n"""
for is_user, text in self.iter_texts():
lowerCamelCase__ = '''user''' if is_user else '''bot'''
output += f"""{name} >> {text} \n"""
return output
@add_end_docstrings(
lowercase__ , r"\n min_length_for_response (`int`, *optional*, defaults to 32):\n The minimum length (in number of tokens) for a response.\n minimum_tokens (`int`, *optional*, defaults to 10):\n The minimum length of tokens to leave for a response.\n " , )
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def __init__( self : Union[str, Any] , *SCREAMING_SNAKE_CASE_ : Any , **SCREAMING_SNAKE_CASE_ : str ):
super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__ )
if self.tokenizer.pad_token_id is None:
lowerCamelCase__ = self.tokenizer.eos_token
def __UpperCAmelCase ( self : Tuple , SCREAMING_SNAKE_CASE_ : Tuple=None , SCREAMING_SNAKE_CASE_ : Optional[Any]=None , SCREAMING_SNAKE_CASE_ : Any=None , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
lowerCamelCase__ = {}
lowerCamelCase__ = {}
lowerCamelCase__ = {}
if min_length_for_response is not None:
lowerCamelCase__ = min_length_for_response
if minimum_tokens is not None:
lowerCamelCase__ = minimum_tokens
if "max_length" in generate_kwargs:
lowerCamelCase__ = generate_kwargs['''max_length''']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
lowerCamelCase__ = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(UpperCAmelCase__ )
return preprocess_params, forward_params, postprocess_params
def __call__( self : str , SCREAMING_SNAKE_CASE_ : Union[Conversation, List[Conversation]] , SCREAMING_SNAKE_CASE_ : Any=0 , **SCREAMING_SNAKE_CASE_ : Any ):
lowerCamelCase__ = super().__call__(UpperCAmelCase__ , num_workers=UpperCAmelCase__ , **UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and len(UpperCAmelCase__ ) == 1:
return outputs[0]
return outputs
def __UpperCAmelCase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Conversation , SCREAMING_SNAKE_CASE_ : Any=32 ):
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError("""ConversationalPipeline, expects Conversation as inputs""" )
if conversation.new_user_input is None:
raise ValueError(
f"""Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. """
"""Add user inputs with the conversation\'s `add_user_input` method""" )
if hasattr(self.tokenizer , """_build_conversation_input_ids""" ):
lowerCamelCase__ = self.tokenizer._build_conversation_input_ids(UpperCAmelCase__ )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
lowerCamelCase__ = self._legacy_parse_and_tokenize(UpperCAmelCase__ )
if self.framework == "pt":
lowerCamelCase__ = torch.LongTensor([input_ids] )
elif self.framework == "tf":
lowerCamelCase__ = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def __UpperCAmelCase ( self : List[str] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : int=10 , **SCREAMING_SNAKE_CASE_ : Tuple ):
lowerCamelCase__ = generate_kwargs.get("""max_length""" , self.model.config.max_length )
lowerCamelCase__ = model_inputs['''input_ids'''].shape[1]
if max_length - minimum_tokens < n:
logger.warning(f"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})""" )
lowerCamelCase__ = max_length - minimum_tokens
lowerCamelCase__ = model_inputs['''input_ids'''][:, -trim:]
if "attention_mask" in model_inputs:
lowerCamelCase__ = model_inputs['''attention_mask'''][:, -trim:]
lowerCamelCase__ = model_inputs.pop("""conversation""" )
lowerCamelCase__ = max_length
lowerCamelCase__ = self.model.generate(**UpperCAmelCase__ , **UpperCAmelCase__ )
if self.model.config.is_encoder_decoder:
lowerCamelCase__ = 1
else:
lowerCamelCase__ = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def __UpperCAmelCase ( self : Dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any]=True ):
lowerCamelCase__ = model_outputs['''output_ids''']
lowerCamelCase__ = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=UpperCAmelCase__ , clean_up_tokenization_spaces=UpperCAmelCase__ , )
lowerCamelCase__ = model_outputs['''conversation''']
conversation.mark_processed()
conversation.append_response(UpperCAmelCase__ )
return conversation
def __UpperCAmelCase ( self : Any , SCREAMING_SNAKE_CASE_ : Conversation ):
lowerCamelCase__ = self.tokenizer.eos_token_id
lowerCamelCase__ = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ ) )
if len(UpperCAmelCase__ ) > self.tokenizer.model_max_length:
lowerCamelCase__ = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 129 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
UpperCamelCase_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
UpperCamelCase_ = 128022
UpperCamelCase_ = 128028
@require_sentencepiece
class __SCREAMING_SNAKE_CASE ( lowercase__ , unittest.TestCase ):
lowerCamelCase_ = MaMaaaTokenizer
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = True
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
super().setUp()
lowercase : Dict =['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
lowercase : List[Any] =dict(zip(UpperCAmelCase__ , range(len(UpperCAmelCase__ ) ) ) )
lowercase : List[Any] =Path(self.tmpdirname )
save_json(UpperCAmelCase__ , save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(UpperCAmelCase__ , save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowercase : Tuple =MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase_ ( self : Any , **UpperCAmelCase__ : int ):
'''simple docstring'''
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Dict ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : Tuple ='''</s>'''
lowercase : Union[str, Any] =0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[Any] =self.get_tokenizer()
lowercase : Optional[Any] =list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''</s>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''<s>''' )
self.assertEqual(len(UpperCAmelCase__ ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip('''Skip this test while all models are still to be uploaded.''' )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Union[str, Any] =self.get_tokenizer()
lowercase : str =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(UpperCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [2, 3, 4, 5, 6] , )
lowercase : Optional[int] =tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(UpperCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
lowercase : Tuple =tokenizer.convert_tokens_to_string(UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , '''This is a test''' )
@slow
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
# fmt: off
lowercase : int ={'''input_ids''': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name='''facebook/m2m100_418M''' , revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e''' , )
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
lowerCamelCase_ = 'facebook/m2m100_418M'
lowerCamelCase_ = [
'In my opinion, there are two levels of response from the French government.',
'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
]
lowerCamelCase_ = [
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
]
# fmt: off
lowerCamelCase_ = [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]
@classmethod
def lowerCamelCase_ ( cls : Optional[Any] ):
'''simple docstring'''
lowercase : MaMaaaTokenizer =MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en''' , tgt_lang='''fr''' )
lowercase : Optional[int] =1
return cls
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
self.assertEqual(self.tokenizer.get_lang_id('''ar''' ) , 128006 )
self.assertEqual(self.tokenizer.get_lang_id('''en''' ) , 128022 )
self.assertEqual(self.tokenizer.get_lang_id('''ro''' ) , 128076 )
self.assertEqual(self.tokenizer.get_lang_id('''mr''' ) , 128063 )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : List[str] =self.tokenizer.get_vocab()
self.assertEqual(len(UpperCAmelCase__ ) , self.tokenizer.vocab_size )
self.assertEqual(vocab['''<unk>'''] , 3 )
self.assertIn(self.tokenizer.get_lang_token('''en''' ) , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : List[Any] ='''en'''
lowercase : Optional[Any] =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
self.assertIn(UpperCAmelCase__ , self.tokenizer.all_special_ids )
# fmt: off
lowercase : str =[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
# fmt: on
lowercase : Optional[Any] =self.tokenizer.decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ )
lowercase : Optional[Any] =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertNotIn(self.tokenizer.eos_token , UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Any =tempfile.mkdtemp()
lowercase : Tuple =self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(UpperCAmelCase__ )
lowercase : Union[str, Any] =MaMaaaTokenizer.from_pretrained(UpperCAmelCase__ )
self.assertDictEqual(new_tok.lang_token_to_id , UpperCAmelCase__ )
@require_torch
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[str] ='''en'''
lowercase : int ='''fr'''
lowercase : Union[str, Any] =self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase__ , return_tensors='''pt''' )
lowercase : str =shift_tokens_right(
batch['''labels'''] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
lowercase : int =batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Optional[int] ='''mr'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
lowercase : Union[str, Any] ='''zh'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : int ='''mr'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
lowercase : Optional[Any] ='''zh'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : Optional[Any] =self.tokenizer._build_translation_inputs('''A test''' , return_tensors='''pt''' , src_lang='''en''' , tgt_lang='''ar''' )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) , {
# en_XX, A, test, EOS
'''input_ids''': [[128022, 58, 4183, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 128006,
} , )
| 92 | 0 |
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=lowercase__ ):
_lowercase =['''torch''', '''transformers''', '''onnx''']
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ) -> List[Any]:
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __a ( cls , *_UpperCamelCase , **_UpperCamelCase ) -> Optional[Any]:
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __a ( cls , *_UpperCamelCase , **_UpperCamelCase ) -> int:
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _lowerCAmelCase ( metaclass=lowercase__ ):
_lowercase =['''torch''', '''transformers''', '''onnx''']
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ) -> str:
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __a ( cls , *_UpperCamelCase , **_UpperCamelCase ) -> Union[str, Any]:
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __a ( cls , *_UpperCamelCase , **_UpperCamelCase ) -> List[Any]:
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _lowerCAmelCase ( metaclass=lowercase__ ):
_lowercase =['''torch''', '''transformers''', '''onnx''']
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ) -> List[str]:
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __a ( cls , *_UpperCamelCase , **_UpperCamelCase ) -> List[str]:
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __a ( cls , *_UpperCamelCase , **_UpperCamelCase ) -> List[str]:
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _lowerCAmelCase ( metaclass=lowercase__ ):
_lowercase =['''torch''', '''transformers''', '''onnx''']
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ) -> Any:
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __a ( cls , *_UpperCamelCase , **_UpperCamelCase ) -> List[Any]:
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __a ( cls , *_UpperCamelCase , **_UpperCamelCase ) -> int:
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _lowerCAmelCase ( metaclass=lowercase__ ):
_lowercase =['''torch''', '''transformers''', '''onnx''']
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ) -> Optional[Any]:
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __a ( cls , *_UpperCamelCase , **_UpperCamelCase ) -> str:
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __a ( cls , *_UpperCamelCase , **_UpperCamelCase ) -> int:
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _lowerCAmelCase ( metaclass=lowercase__ ):
_lowercase =['''torch''', '''transformers''', '''onnx''']
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ) -> str:
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __a ( cls , *_UpperCamelCase , **_UpperCamelCase ) -> Any:
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __a ( cls , *_UpperCamelCase , **_UpperCamelCase ) -> Union[str, Any]:
requires_backends(cls , ["torch", "transformers", "onnx"] )
| 290 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int = 600851475143 ) -> int:
try:
lowercase : Any =int(__magic_name__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
lowercase : Optional[Any] =2
lowercase : Dict =0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
lowercase : Union[str, Any] =i
while n % i == 0:
lowercase : Optional[int] =n // i
i += 1
return int(__magic_name__ )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 92 | 0 |
'''simple docstring'''
from __future__ import annotations
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : list[str] | None = None ) -> list[list[str]]:
__snake_case : int = word_bank or []
# create a table
__snake_case : int = len(_UpperCAmelCase ) + 1
__snake_case : list[list[list[str]]] = []
for _ in range(_UpperCAmelCase ):
table.append([] )
# seed value
__snake_case : Union[str, Any] = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(_UpperCAmelCase ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(_UpperCAmelCase )] == word:
__snake_case : list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(_UpperCAmelCase )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(_UpperCAmelCase )]:
combination.reverse()
return table[len(_UpperCAmelCase )]
if __name__ == "__main__":
print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
print(
all_construct(
'''hexagonosaurus''',
['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
)
)
| 286 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = 'speech_to_text_2'
lowerCamelCase_ = ['past_key_values']
lowerCamelCase_ = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : int , UpperCAmelCase__ : Dict=10000 , UpperCAmelCase__ : Optional[int]=6 , UpperCAmelCase__ : str=2048 , UpperCAmelCase__ : Optional[Any]=4 , UpperCAmelCase__ : Union[str, Any]=0.0 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Optional[Any]="relu" , UpperCAmelCase__ : List[str]=256 , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : List[Any]=0.0 , UpperCAmelCase__ : Optional[Any]=0.0 , UpperCAmelCase__ : Tuple=0.02 , UpperCAmelCase__ : int=2 , UpperCAmelCase__ : str=True , UpperCAmelCase__ : str=1 , UpperCAmelCase__ : Optional[Any]=0 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : List[Any]=1024 , **UpperCAmelCase__ : Dict , ):
'''simple docstring'''
lowercase : List[str] =vocab_size
lowercase : Optional[int] =d_model
lowercase : Optional[Any] =decoder_ffn_dim
lowercase : Any =decoder_layers
lowercase : Dict =decoder_attention_heads
lowercase : List[Any] =dropout
lowercase : List[Any] =attention_dropout
lowercase : Any =activation_dropout
lowercase : Optional[Any] =activation_function
lowercase : Optional[int] =init_std
lowercase : Dict =decoder_layerdrop
lowercase : Optional[int] =use_cache
lowercase : Optional[Any] =decoder_layers
lowercase : List[str] =scale_embedding # scale factor will be sqrt(d_model) if True
lowercase : str =max_target_positions
super().__init__(
pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , decoder_start_token_id=UpperCAmelCase__ , **UpperCAmelCase__ , )
| 92 | 0 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if depth < 0:
raise ValueError('Depth cannot be less than 0' )
if len(UpperCAmelCase__ ) == 0:
raise ValueError('Scores cannot be empty' )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 ,node_index * 2 ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ) ,minimax(depth + 1 ,node_index * 2 + 1 ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ) ,)
return min(
minimax(depth + 1 ,node_index * 2 ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ) ,minimax(depth + 1 ,node_index * 2 + 1 ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ) ,)
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [90, 23, 6, 33, 21, 65, 123, 3_4423]
_SCREAMING_SNAKE_CASE = math.log(len(UpperCAmelCase__ ) ,2 )
print('Optimal value : ' ,end='' )
print(minimax(0 ,0 ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 605 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self : str , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[str]=13 , UpperCAmelCase__ : Any=32 , UpperCAmelCase__ : List[str]=3 , UpperCAmelCase__ : Any=4 , UpperCAmelCase__ : List[str]=[10, 20, 30, 40] , UpperCAmelCase__ : Any=[2, 2, 3, 2] , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Optional[Any]=37 , UpperCAmelCase__ : Union[str, Any]="gelu" , UpperCAmelCase__ : Optional[Any]=10 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : Optional[int]=["stage2", "stage3", "stage4"] , UpperCAmelCase__ : Dict=[2, 3, 4] , UpperCAmelCase__ : Optional[int]=None , ):
'''simple docstring'''
lowercase : List[Any] =parent
lowercase : Tuple =batch_size
lowercase : List[str] =image_size
lowercase : List[Any] =num_channels
lowercase : Union[str, Any] =num_stages
lowercase : int =hidden_sizes
lowercase : Any =depths
lowercase : Tuple =is_training
lowercase : str =use_labels
lowercase : List[Any] =intermediate_size
lowercase : int =hidden_act
lowercase : Union[str, Any] =num_labels
lowercase : Optional[int] =initializer_range
lowercase : int =out_features
lowercase : List[str] =out_indices
lowercase : str =scope
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : str =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase : Dict =None
if self.use_labels:
lowercase : List[Any] =ids_tensor([self.batch_size] , self.num_labels )
lowercase : Dict =self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[Any] ):
'''simple docstring'''
lowercase : Dict =ConvNextVaModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[Any] =model(UpperCAmelCase__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase_ ( self : Any , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Dict =ConvNextVaForImageClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : str =model(UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Union[str, Any] =ConvNextVaBackbone(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[int] =model(UpperCAmelCase__ )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
lowercase : Optional[Any] =None
lowercase : str =ConvNextVaBackbone(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[Any] =model(UpperCAmelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Any =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase : str =config_and_inputs
lowercase : Any ={'''pixel_values''': pixel_values}
return config, inputs_dict
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : str =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase : List[str] =config_and_inputs
lowercase : Optional[Any] ={'''pixel_values''': pixel_values, '''labels''': labels}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase_ = (
{'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : Dict =ConvNextVaModelTester(self )
lowercase : str =ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=37 )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return
@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds''' )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings''' )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking''' )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase , lowercase : Union[str, Any] =self.model_tester.prepare_config_and_inputs_with_labels()
lowercase : Optional[int] =True
if model_class.__name__ in [
*get_values(UpperCAmelCase__ ),
*get_values(UpperCAmelCase__ ),
]:
continue
lowercase : Dict =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.train()
lowercase : Optional[Any] =self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
lowercase : List[Any] =model(**UpperCAmelCase__ ).loss
loss.backward()
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase , lowercase : Any =self.model_tester.prepare_config_and_inputs_with_labels()
lowercase : List[Any] =False
lowercase : Any =True
if (
model_class.__name__
in [*get_values(UpperCAmelCase__ ), *get_values(UpperCAmelCase__ )]
or not model_class.supports_gradient_checkpointing
):
continue
lowercase : Any =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.gradient_checkpointing_enable()
model.train()
lowercase : Optional[Any] =self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
lowercase : int =model(**UpperCAmelCase__ ).loss
loss.backward()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase , lowercase : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : Dict =model_class(UpperCAmelCase__ )
lowercase : Union[str, Any] =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase : int =[*signature.parameters.keys()]
lowercase : Optional[Any] =['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
def check_hidden_states_output(UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : Tuple ):
lowercase : int =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
lowercase : Any =model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase : Dict =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase : List[Any] =self.model_tester.num_stages
self.assertEqual(len(UpperCAmelCase__ ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowercase , lowercase : List[str] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : List[str] =True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase : Tuple =True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
lowercase : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__ )
@slow
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : List[Any] =ConvNextVaModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def _lowerCAmelCase ( ) -> List[Any]:
lowercase : Union[str, Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : Tuple =ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ).to(UpperCAmelCase__ )
lowercase : int =self.default_image_processor
lowercase : List[str] =prepare_img()
lowercase : List[Any] =preprocessor(images=UpperCAmelCase__ , return_tensors='''pt''' ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
lowercase : Dict =model(**UpperCAmelCase__ )
# verify the logits
lowercase : Optional[Any] =torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase__ )
lowercase : Tuple =torch.tensor([0.99_96, 0.19_66, -0.43_86] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase__ , atol=1E-4 ) )
| 92 | 0 |
"""simple docstring"""
import unittest
import numpy as np
def __a ( A , A , A , A = None , ) -> np.ndarray:
'''simple docstring'''
A__ = np.shape(A )
A__ = np.shape(A )
A__ = np.shape(A )
if shape_a[0] != shape_b[0]:
A__ = (
'''Expected the same number of rows for A and B. '''
f"""Instead found A of size {shape_a} and B of size {shape_b}"""
)
raise ValueError(A )
if shape_b[1] != shape_c[1]:
A__ = (
'''Expected the same number of columns for B and C. '''
f"""Instead found B of size {shape_b} and C of size {shape_c}"""
)
raise ValueError(A )
A__ = pseudo_inv
if a_inv is None:
try:
A__ = np.linalg.inv(A )
except np.linalg.LinAlgError:
raise ValueError(
"Input matrix A is not invertible. Cannot compute Schur complement." )
return mat_c - mat_b.T @ a_inv @ mat_b
class lowerCAmelCase__ ( unittest.TestCase ):
def lowercase_ ( self ):
'''simple docstring'''
A__ = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
A__ = np.array([[0, 3], [3, 0], [2, 3]] )
A__ = np.array([[2, 1], [6, 3]] )
A__ = schur_complement(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
A__ = np.block([[a, b], [b.T, c]] )
A__ = np.linalg.det(UpperCAmelCase__ )
A__ = np.linalg.det(UpperCAmelCase__ )
A__ = np.linalg.det(UpperCAmelCase__ )
self.assertAlmostEqual(UpperCAmelCase__ , det_a * det_s )
def lowercase_ ( self ):
'''simple docstring'''
A__ = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
A__ = np.array([[0, 3], [3, 0], [2, 3]] )
A__ = np.array([[2, 1], [6, 3]] )
with self.assertRaises(UpperCAmelCase__ ):
schur_complement(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
A__ = np.array([[0, 3], [3, 0], [2, 3]] )
A__ = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(UpperCAmelCase__ ):
schur_complement(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main() | 337 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
UpperCamelCase_ = object()
# For specifying empty leaf dict `{}`
UpperCamelCase_ = object()
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : int ) -> Optional[int]:
lowercase : Optional[Any] =tuple((re.compile(x + '''$''' ) for x in qs) )
for i in range(len(__magic_name__ ) - len(__magic_name__ ) + 1 ):
lowercase : Union[str, Any] =[x.match(__magic_name__ ) for x, y in zip(__magic_name__ , ks[i:] )]
if matches and all(__magic_name__ ):
return True
return False
def _lowerCAmelCase ( __magic_name__ : Dict ) -> List[str]:
def replace(__magic_name__ : Optional[int] , __magic_name__ : Optional[Any] ):
for rule, replacement in rules:
if _match(__magic_name__ , __magic_name__ ):
return replacement
return val
return replace
def _lowerCAmelCase ( ) -> int:
return [
# embeddings
(("transformer", "wpe", "embedding"), P('''mp''' , __magic_name__ )),
(("transformer", "wte", "embedding"), P('''mp''' , __magic_name__ )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__magic_name__ , '''mp''' )),
(("attention", "out_proj", "kernel"), P('''mp''' , __magic_name__ )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(__magic_name__ , '''mp''' )),
(("mlp", "c_fc", "bias"), P('''mp''' )),
(("mlp", "c_proj", "kernel"), P('''mp''' , __magic_name__ )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def _lowerCAmelCase ( __magic_name__ : str ) -> int:
lowercase : int =_get_partition_rules()
lowercase : Tuple =_replacement_rules(__magic_name__ )
lowercase : Any ={k: _unmatched for k in flatten_dict(__magic_name__ )}
lowercase : Any ={k: replace(__magic_name__ , __magic_name__ ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(__magic_name__ ) )
| 92 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Any=7 , UpperCamelCase__ : Union[str, Any]=3 , UpperCamelCase__ : Tuple=1_8 , UpperCamelCase__ : List[str]=3_0 , UpperCamelCase__ : Union[str, Any]=4_0_0 , UpperCamelCase__ : Optional[int]=True , UpperCamelCase__ : Any=None , UpperCamelCase__ : Any=True , )-> Any:
'''simple docstring'''
__lowerCAmelCase: Union[str, Any] = size if size is not None else {'''height''': 1_8, '''width''': 1_8}
__lowerCAmelCase: Tuple = parent
__lowerCAmelCase: str = batch_size
__lowerCAmelCase: List[str] = num_channels
__lowerCAmelCase: int = image_size
__lowerCAmelCase: int = min_resolution
__lowerCAmelCase: Optional[Any] = max_resolution
__lowerCAmelCase: Any = do_resize
__lowerCAmelCase: int = size
__lowerCAmelCase: int = do_normalize
def lowercase_ ( self : int)-> Union[str, Any]:
'''simple docstring'''
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
[-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
]),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class snake_case ( lowercase__, unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Any = ImageGPTImageProcessor if is_vision_available() else None
def lowercase_ ( self : int)-> int:
'''simple docstring'''
__lowerCAmelCase: Union[str, Any] = ImageGPTImageProcessingTester(self)
@property
def lowercase_ ( self : List[Any])-> Tuple:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase_ ( self : int)-> Tuple:
'''simple docstring'''
__lowerCAmelCase: Optional[Any] = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(UpperCAmelCase__ , "clusters"))
self.assertTrue(hasattr(UpperCAmelCase__ , "do_resize"))
self.assertTrue(hasattr(UpperCAmelCase__ , "size"))
self.assertTrue(hasattr(UpperCAmelCase__ , "do_normalize"))
def lowercase_ ( self : Optional[Any])-> Tuple:
'''simple docstring'''
__lowerCAmelCase: Any = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {"height": 1_8, "width": 1_8})
__lowerCAmelCase: Any = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2)
self.assertEqual(image_processor.size , {"height": 4_2, "width": 4_2})
def lowercase_ ( self : Optional[Any])-> Optional[int]:
'''simple docstring'''
__lowerCAmelCase: List[str] = self.image_processing_class(**self.image_processor_dict)
__lowerCAmelCase: List[str] = json.loads(image_processor.to_json_string())
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(UpperCAmelCase__ , obj[key]))
else:
self.assertEqual(obj[key] , UpperCAmelCase__)
def lowercase_ ( self : Tuple)-> Any:
'''simple docstring'''
__lowerCAmelCase: str = self.image_processing_class(**self.image_processor_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCAmelCase: Any = os.path.join(UpperCAmelCase__ , "image_processor.json")
image_processor_first.to_json_file(UpperCAmelCase__)
__lowerCAmelCase: Optional[Any] = self.image_processing_class.from_json_file(UpperCAmelCase__).to_dict()
__lowerCAmelCase: Tuple = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(UpperCAmelCase__ , image_processor_second[key]))
else:
self.assertEqual(image_processor_first[key] , UpperCAmelCase__)
def lowercase_ ( self : List[str])-> Optional[int]:
'''simple docstring'''
__lowerCAmelCase: Optional[int] = self.image_processing_class(**self.image_processor_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(UpperCAmelCase__)
__lowerCAmelCase: Tuple = self.image_processing_class.from_pretrained(UpperCAmelCase__).to_dict()
__lowerCAmelCase: Tuple = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(UpperCAmelCase__ , image_processor_second[key]))
else:
self.assertEqual(image_processor_first[key] , UpperCAmelCase__)
@unittest.skip("ImageGPT requires clusters at initialization")
def lowercase_ ( self : Optional[Any])-> Dict:
'''simple docstring'''
pass
def a__ ( ) -> Dict:
__lowerCAmelCase: List[Any] = load_dataset("hf-internal-testing/fixtures_image_utils" , split="test" )
__lowerCAmelCase: Optional[int] = Image.open(dataset[4]["file"] )
__lowerCAmelCase: Optional[Any] = Image.open(dataset[5]["file"] )
__lowerCAmelCase: Optional[Any] = [imagea, imagea]
return images
@require_vision
@require_torch
class snake_case ( unittest.TestCase ):
@slow
def lowercase_ ( self : Optional[int])-> Dict:
'''simple docstring'''
__lowerCAmelCase: Union[str, Any] = ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small")
__lowerCAmelCase: int = prepare_images()
# test non-batched
__lowerCAmelCase: int = image_processing(images[0] , return_tensors="pt")
self.assertIsInstance(encoding.input_ids , torch.LongTensor)
self.assertEqual(encoding.input_ids.shape , (1, 1_0_2_4))
__lowerCAmelCase: Tuple = [3_0_6, 1_9_1, 1_9_1]
self.assertEqual(encoding.input_ids[0, :3].tolist() , UpperCAmelCase__)
# test batched
__lowerCAmelCase: Optional[int] = image_processing(UpperCAmelCase__ , return_tensors="pt")
self.assertIsInstance(encoding.input_ids , torch.LongTensor)
self.assertEqual(encoding.input_ids.shape , (2, 1_0_2_4))
__lowerCAmelCase: str = [3_0_3, 1_3, 1_3]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , UpperCAmelCase__)
| 346 |
'''simple docstring'''
from collections import defaultdict
def _lowerCAmelCase ( __magic_name__ : int ) -> int:
lowercase : Optional[Any] =1
lowercase : Union[str, Any] =True
for v in tree[start]:
if v not in visited:
ret += dfs(__magic_name__ )
if ret % 2 == 0:
cuts.append(__magic_name__ )
return ret
def _lowerCAmelCase ( ) -> int:
dfs(1 )
if __name__ == "__main__":
UpperCamelCase_ , UpperCamelCase_ = 10, 9
UpperCamelCase_ = defaultdict(list)
UpperCamelCase_ = {}
UpperCamelCase_ = []
UpperCamelCase_ = 0
UpperCamelCase_ = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 92 | 0 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
__lowercase = sorted(string.lower() )
return len(_UpperCamelCase ) == len(set(_UpperCamelCase ) )
if __name__ == "__main__":
a : str = input('''Enter a string ''').strip()
a : int = is_isogram(input_str)
print(f'''{input_str} is {'an' if isogram else 'not an'} isogram.''')
| 639 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
def _lowerCAmelCase ( __magic_name__ : List[str] ) -> Dict:
lowercase : List[str] =R'''\w+[.]\d+'''
lowercase : List[str] =re.findall(__magic_name__ , __magic_name__ )
for pat in pats:
lowercase : Optional[int] =key.replace(__magic_name__ , '''_'''.join(pat.split('''.''' ) ) )
return key
def _lowerCAmelCase ( __magic_name__ : List[str] , __magic_name__ : Union[str, Any] , __magic_name__ : Dict ) -> str:
lowercase : Dict =pt_tuple_key[:-1] + ('''scale''',)
if (
any('''norm''' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
lowercase : str =pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
lowercase : str =pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
lowercase : Dict =pt_tuple_key[:-1] + ('''embedding''',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowercase : Tuple =pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
lowercase : Tuple =pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowercase : str =pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight":
lowercase : Optional[Any] =pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowercase : Dict =pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowercase : Union[str, Any] =pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : Optional[Any] , __magic_name__ : Union[str, Any]=42 ) -> List[str]:
# Step 1: Convert pytorch tensor to numpy
lowercase : Optional[Any] ={k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
lowercase : str =flax_model.init_weights(PRNGKey(__magic_name__ ) )
lowercase : Dict =flatten_dict(__magic_name__ )
lowercase : Dict ={}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase : Dict =rename_key(__magic_name__ )
lowercase : Optional[int] =tuple(renamed_pt_key.split('''.''' ) )
# Correctly rename weight parameters
lowercase , lowercase : Any =rename_key_and_reshape_tensor(__magic_name__ , __magic_name__ , __magic_name__ )
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}.''' )
# also add unexpected weight so that warning is thrown
lowercase : Tuple =jnp.asarray(__magic_name__ )
return unflatten_dict(__magic_name__ )
| 92 | 0 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__SCREAMING_SNAKE_CASE = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowerCAmelCase_ ( datasets.BuilderConfig ):
'''simple docstring'''
_lowercase = 10_000
_lowercase = None
_lowercase = None
class lowerCAmelCase_ ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
_lowercase = ParquetConfig
def __lowerCamelCase ( self ):
return datasets.DatasetInfo(features=self.config.features )
def __lowerCamelCase ( self , __UpperCAmelCase ):
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}""" )
SCREAMING_SNAKE_CASE_ : List[str] =dl_manager.download_and_extract(self.config.data_files )
if isinstance(UpperCAmelCase__ , (str, list, tuple) ):
SCREAMING_SNAKE_CASE_ : Dict =data_files
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE_ : Tuple =[files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
SCREAMING_SNAKE_CASE_ : Optional[int] =[dl_manager.iter_files(UpperCAmelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'files': files} )]
SCREAMING_SNAKE_CASE_ : int =[]
for split_name, files in data_files.items():
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE_ : List[Any] =[files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
SCREAMING_SNAKE_CASE_ : Optional[int] =[dl_manager.iter_files(UpperCAmelCase__ ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(UpperCAmelCase__ ):
with open(UpperCAmelCase__ , 'rb' ) as f:
SCREAMING_SNAKE_CASE_ : Any =datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase__ ) )
break
splits.append(datasets.SplitGenerator(name=UpperCAmelCase__ , gen_kwargs={'files': files} ) )
return splits
def __lowerCamelCase ( self , __UpperCAmelCase ):
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
SCREAMING_SNAKE_CASE_ : Dict =table_cast(UpperCAmelCase__ , self.info.features.arrow_schema )
return pa_table
def __lowerCamelCase ( self , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ : Optional[Any] =self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
F"""Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'""" )
for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase__ ) ):
with open(UpperCAmelCase__ , 'rb' ) as f:
SCREAMING_SNAKE_CASE_ : Dict =pq.ParquetFile(UpperCAmelCase__ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
SCREAMING_SNAKE_CASE_ : int =pa.Table.from_batches([record_batch] )
# 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 F"""{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
| 220 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase_ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
UpperCamelCase_ = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
UpperCamelCase_ = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, float]:
lowercase : int =len([g for position, g in enumerate(__magic_name__ ) if g == main_target[position]] )
return (item, float(__magic_name__ ))
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, str]:
lowercase : Any =random.randint(0 , len(__magic_name__ ) - 1 )
lowercase : Tuple =parent_a[:random_slice] + parent_a[random_slice:]
lowercase : List[str] =parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : list[str] ) -> str:
lowercase : Union[str, Any] =list(__magic_name__ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
lowercase : Dict =random.choice(__magic_name__ )
return "".join(__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : tuple[str, float] , __magic_name__ : list[tuple[str, float]] , __magic_name__ : list[str] , ) -> list[str]:
lowercase : Any =[]
# Generate more children proportionally to the fitness score.
lowercase : Dict =int(parent_a[1] * 100 ) + 1
lowercase : List[str] =10 if child_n >= 10 else child_n
for _ in range(__magic_name__ ):
lowercase : List[str] =population_score[random.randint(0 , __magic_name__ )][0]
lowercase , lowercase : Dict =crossover(parent_a[0] , __magic_name__ )
# Append new string to the population list.
pop.append(mutate(__magic_name__ , __magic_name__ ) )
pop.append(mutate(__magic_name__ , __magic_name__ ) )
return pop
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : list[str] , __magic_name__ : bool = True ) -> tuple[int, int, str]:
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
lowercase : List[str] =f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
raise ValueError(__magic_name__ )
# Verify that the target contains no genes besides the ones inside genes variable.
lowercase : Optional[int] =sorted({c for c in target if c not in genes} )
if not_in_genes_list:
lowercase : Dict =f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
raise ValueError(__magic_name__ )
# Generate random starting population.
lowercase : int =[]
for _ in range(__magic_name__ ):
population.append(''''''.join([random.choice(__magic_name__ ) for i in range(len(__magic_name__ ) )] ) )
# Just some logs to know what the algorithms is doing.
lowercase , lowercase : Optional[int] =0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(__magic_name__ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
lowercase : List[str] =[evaluate(__magic_name__ , __magic_name__ ) for item in population]
# Check if there is a matching evolution.
lowercase : int =sorted(__magic_name__ , key=lambda __magic_name__ : x[1] , reverse=__magic_name__ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f'''\nGeneration: {generation}'''
f'''\nTotal Population:{total_population}'''
f'''\nBest score: {population_score[0][1]}'''
f'''\nBest string: {population_score[0][0]}''' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
lowercase : Any =population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(__magic_name__ )
# Normalize population score to be between 0 and 1.
lowercase : Dict =[
(item, score / len(__magic_name__ )) for item, score in population_score
]
# This is selection
for i in range(__magic_name__ ):
population.extend(select(population_score[int(__magic_name__ )] , __magic_name__ , __magic_name__ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(__magic_name__ ) > N_POPULATION:
break
if __name__ == "__main__":
UpperCamelCase_ = (
"""This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
)
UpperCamelCase_ = list(
""" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
"""nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
)
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = basic(target_str, genes_list)
print(
f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 92 | 0 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
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
# 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/text-classification/requirements.txt")
_SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
__lowerCAmelCase : Optional[Any] =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 : List[Any] =field(
default=lowercase__ , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
__lowerCAmelCase : Any =field(
default=lowercase__ , metadata={
'''help''': (
'''Whether to pad all samples to `max_seq_length`. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
)
} , )
__lowerCAmelCase : Union[str, Any] =field(
default=lowercase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
__lowerCAmelCase : List[str] =field(
default=lowercase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
__lowerCAmelCase : List[Any] =field(
default=lowercase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
@dataclass
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
__lowerCAmelCase : Optional[int] =field(
default=lowercase__ , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
__lowerCAmelCase : Tuple =field(
default=lowercase__ , metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''} )
__lowerCAmelCase : Tuple =field(
default=lowercase__ , metadata={'''help''': '''Train language if it is different from the evaluation language.'''} )
__lowerCAmelCase : Tuple =field(
default=lowercase__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__lowerCAmelCase : Dict =field(
default=lowercase__ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
__lowerCAmelCase : Optional[int] =field(
default=lowercase__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
__lowerCAmelCase : Tuple =field(
default=lowercase__ , metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''} , )
__lowerCAmelCase : Optional[int] =field(
default=lowercase__ , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
__lowerCAmelCase : Optional[int] =field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
__lowerCAmelCase : Optional[int] =field(
default=lowercase__ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
__lowerCAmelCase : List[Any] =field(
default=lowercase__ , metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} , )
def _snake_case () -> 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 =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
_lowercase =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_xnli' , _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 =training_args.get_process_log_level()
logger.setLevel(_snake_case)
datasets.utils.logging.set_verbosity(_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 =None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
_lowercase =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:
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.')
# Set seed before initializing model.
set_seed(training_args.seed)
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
_lowercase =load_dataset(
'xnli' , model_args.language , split='train' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
_lowercase =load_dataset(
'xnli' , model_args.train_language , split='train' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
_lowercase =train_dataset.features['''label'''].names
if training_args.do_eval:
_lowercase =load_dataset(
'xnli' , model_args.language , split='validation' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
_lowercase =eval_dataset.features['''label'''].names
if training_args.do_predict:
_lowercase =load_dataset(
'xnli' , model_args.language , split='test' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
_lowercase =predict_dataset.features['''label'''].names
# Labels
_lowercase =len(_snake_case)
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowercase =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_snake_case , idalabel={str(_snake_case): label for i, label in enumerate(_snake_case)} , labelaid={label: i for i, label in enumerate(_snake_case)} , finetuning_task='xnli' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_lowercase =AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_lowercase =AutoModelForSequenceClassification.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 , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
_lowercase ='''max_length'''
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_lowercase =False
def preprocess_function(_snake_case : List[str]):
# Tokenize the texts
return tokenizer(
examples['premise'] , examples['hypothesis'] , padding=_snake_case , max_length=data_args.max_seq_length , truncation=_snake_case , )
if training_args.do_train:
if data_args.max_train_samples is not None:
_lowercase =min(len(_snake_case) , data_args.max_train_samples)
_lowercase =train_dataset.select(range(_snake_case))
with training_args.main_process_first(desc='train dataset map pre-processing'):
_lowercase =train_dataset.map(
_snake_case , batched=_snake_case , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on train dataset' , )
# Log a few random samples from the training set:
for index in random.sample(range(len(_snake_case)) , 3):
logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.''')
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_lowercase =min(len(_snake_case) , data_args.max_eval_samples)
_lowercase =eval_dataset.select(range(_snake_case))
with training_args.main_process_first(desc='validation dataset map pre-processing'):
_lowercase =eval_dataset.map(
_snake_case , batched=_snake_case , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on validation dataset' , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
_lowercase =min(len(_snake_case) , data_args.max_predict_samples)
_lowercase =predict_dataset.select(range(_snake_case))
with training_args.main_process_first(desc='prediction dataset map pre-processing'):
_lowercase =predict_dataset.map(
_snake_case , batched=_snake_case , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on prediction dataset' , )
# Get the metric function
_lowercase =evaluate.load('xnli')
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_snake_case : EvalPrediction):
_lowercase =p.predictions[0] if isinstance(p.predictions , _snake_case) else p.predictions
_lowercase =np.argmax(_snake_case , axis=1)
return metric.compute(predictions=_snake_case , references=p.label_ids)
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_lowercase =default_data_collator
elif training_args.fpaa:
_lowercase =DataCollatorWithPadding(_snake_case , pad_to_multiple_of=8)
else:
_lowercase =None
# Initialize our Trainer
_lowercase =Trainer(
model=_snake_case , args=_snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_snake_case , tokenizer=_snake_case , data_collator=_snake_case , )
# Training
if training_args.do_train:
_lowercase =None
if training_args.resume_from_checkpoint is not None:
_lowercase =training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_lowercase =last_checkpoint
_lowercase =trainer.train(resume_from_checkpoint=_snake_case)
_lowercase =train_result.metrics
_lowercase =(
data_args.max_train_samples if data_args.max_train_samples is not None else len(_snake_case)
)
_lowercase =min(_snake_case , len(_snake_case))
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _snake_case)
trainer.save_metrics('train' , _snake_case)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***')
_lowercase =trainer.evaluate(eval_dataset=_snake_case)
_lowercase =data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_snake_case)
_lowercase =min(_snake_case , len(_snake_case))
trainer.log_metrics('eval' , _snake_case)
trainer.save_metrics('eval' , _snake_case)
# Prediction
if training_args.do_predict:
logger.info('*** Predict ***')
_lowercase =trainer.predict(_snake_case , metric_key_prefix='predict')
_lowercase =(
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_snake_case)
)
_lowercase =min(_snake_case , len(_snake_case))
trainer.log_metrics('predict' , _snake_case)
trainer.save_metrics('predict' , _snake_case)
_lowercase =np.argmax(_snake_case , axis=1)
_lowercase =os.path.join(training_args.output_dir , 'predictions.txt')
if trainer.is_world_process_zero():
with open(_snake_case , 'w') as writer:
writer.write('index\tprediction\n')
for index, item in enumerate(_snake_case):
_lowercase =label_list[item]
writer.write(f'''{index}\t{item}\n''')
if __name__ == "__main__":
main()
| 181 |
'''simple docstring'''
import datasets
UpperCamelCase_ = """\
@InProceedings{conneau2018xnli,
author = \"Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin\",
title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing\",
year = \"2018\",
publisher = \"Association for Computational Linguistics\",
location = \"Brussels, Belgium\",
}
"""
UpperCamelCase_ = """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""
UpperCamelCase_ = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: Predicted labels.
references: Ground truth labels.
Returns:
'accuracy': accuracy
Examples:
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> xnli_metric = datasets.load_metric(\"xnli\")
>>> results = xnli_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
"""
def _lowerCAmelCase ( __magic_name__ : Dict , __magic_name__ : Union[str, Any] ) -> Union[str, Any]:
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32''' ),
'''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32''' ),
} ) , codebase_urls=[] , reference_urls=[] , format='''numpy''' , )
def lowerCamelCase_ ( self : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[Any] ):
'''simple docstring'''
return {"accuracy": simple_accuracy(UpperCAmelCase__ , UpperCAmelCase__ )}
| 92 | 0 |
"""simple docstring"""
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
__UpperCamelCase : Tuple = open # noqa: we just need to have a builtin inside this module to test it properly
| 4 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[str] , UpperCAmelCase__ : Dict , ):
'''simple docstring'''
lowercase : Any =parent
lowercase : Optional[int] =13
lowercase : Union[str, Any] =7
lowercase : str =30
lowercase : Optional[int] =self.seq_length + self.mem_len
lowercase : Dict =15
lowercase : List[str] =True
lowercase : Optional[int] =True
lowercase : Tuple =99
lowercase : str =[10, 50, 80]
lowercase : List[Any] =32
lowercase : Optional[int] =32
lowercase : int =4
lowercase : Any =8
lowercase : List[Any] =128
lowercase : List[str] =2
lowercase : Tuple =2
lowercase : int =None
lowercase : Optional[int] =1
lowercase : int =0
lowercase : List[str] =3
lowercase : str =self.vocab_size - 1
lowercase : Tuple =0.01
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : str =None
if self.use_labels:
lowercase : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Union[str, Any] =TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
random.seed(self.seed )
tf.random.set_seed(self.seed )
def lowerCamelCase_ ( self : Union[str, Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[str] ):
'''simple docstring'''
lowercase : Tuple =TFTransfoXLModel(UpperCAmelCase__ )
lowercase , lowercase : Optional[Any] =model(UpperCAmelCase__ ).to_tuple()
lowercase : List[str] ={'''input_ids''': input_ids_a, '''mems''': mems_a}
lowercase , lowercase : Any =model(UpperCAmelCase__ ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def lowerCamelCase_ ( self : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : int =TFTransfoXLLMHeadModel(UpperCAmelCase__ )
lowercase , lowercase : Tuple =model(UpperCAmelCase__ ).to_tuple()
lowercase : Optional[Any] ={'''input_ids''': input_ids_a, '''labels''': lm_labels}
lowercase , lowercase : Optional[int] =model(UpperCAmelCase__ ).to_tuple()
lowercase , lowercase : List[str] =model([input_ids_a, mems_a] ).to_tuple()
lowercase : int ={'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}
lowercase , lowercase : str =model(UpperCAmelCase__ ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def lowerCamelCase_ ( self : str , UpperCAmelCase__ : int , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
lowercase : Optional[int] =TFTransfoXLForSequenceClassification(UpperCAmelCase__ )
lowercase : Union[str, Any] =model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[Any] =self.prepare_config_and_inputs()
((lowercase) , (lowercase) , (lowercase) , (lowercase)) : Optional[Any] =config_and_inputs
lowercase : Union[str, Any] ={'''input_ids''': input_ids_a}
return config, inputs_dict
@require_tf
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
lowerCamelCase_ = () if is_tf_available() else ()
lowerCamelCase_ = (
{
'feature-extraction': TFTransfoXLModel,
'text-classification': TFTransfoXLForSequenceClassification,
'text-generation': TFTransfoXLLMHeadModel,
'zero-shot': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def lowerCamelCase_ ( self : Dict , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int ):
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Any =TFTransfoXLModelTester(self )
lowercase : Optional[int] =ConfigTester(self , config_class=UpperCAmelCase__ , d_embed=37 )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
self.model_tester.set_seed()
lowercase : List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
self.model_tester.set_seed()
lowercase : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase , lowercase : Tuple =self.model_tester.prepare_config_and_inputs_for_common()
lowercase : int =[TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
lowercase : str =model_class(UpperCAmelCase__ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
lowercase : Union[str, Any] =model.get_output_embeddings()
assert isinstance(UpperCAmelCase__ , tf.keras.layers.Layer )
lowercase : Any =model.get_bias()
assert name is None
else:
lowercase : Optional[int] =model.get_output_embeddings()
assert x is None
lowercase : Optional[int] =model.get_bias()
assert name is None
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : int =TFTransfoXLModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.''' )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
pass
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@unittest.skip('''Skip test until #12651 is resolved.''' )
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : Optional[Any] =TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''' )
# fmt: off
lowercase : Tuple =tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
lowercase : Optional[int] =[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
lowercase : int =model.generate(UpperCAmelCase__ , max_length=200 , do_sample=UpperCAmelCase__ )
self.assertListEqual(output_ids[0].numpy().tolist() , UpperCAmelCase__ )
| 92 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_snake_case = False
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : List[Any] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' , torch_dtype=torch.floataa )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
UpperCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
UpperCamelCase = torch.manual_seed(0 )
UpperCamelCase = pipe.dual_guided(
prompt='first prompt' , image=UpperCAmelCase__ , text_to_image_strength=0.75 , generator=UpperCAmelCase__ , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(UpperCAmelCase__ )
UpperCamelCase = VersatileDiffusionPipeline.from_pretrained(UpperCAmelCase__ , torch_dtype=torch.floataa )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
UpperCamelCase = generator.manual_seed(0 )
UpperCamelCase = pipe.dual_guided(
prompt='first prompt' , image=UpperCAmelCase__ , text_to_image_strength=0.75 , generator=UpperCAmelCase__ , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def __lowerCAmelCase ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' , torch_dtype=torch.floataa )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
UpperCamelCase = '''cyberpunk 2077'''
UpperCamelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
UpperCamelCase = torch.manual_seed(0 )
UpperCamelCase = pipe.dual_guided(
prompt=UpperCAmelCase__ , image=UpperCAmelCase__ , text_to_image_strength=0.75 , generator=UpperCAmelCase__ , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' , ).images
UpperCamelCase = image[0, 2_53:2_56, 2_53:2_56, -1]
assert image.shape == (1, 5_12, 5_12, 3)
UpperCamelCase = np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
UpperCamelCase = '''A painting of a squirrel eating a burger '''
UpperCamelCase = torch.manual_seed(0 )
UpperCamelCase = pipe.text_to_image(
prompt=UpperCAmelCase__ , generator=UpperCAmelCase__ , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' ).images
UpperCamelCase = image[0, 2_53:2_56, 2_53:2_56, -1]
assert image.shape == (1, 5_12, 5_12, 3)
UpperCamelCase = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
UpperCamelCase = pipe.image_variation(UpperCAmelCase__ , generator=UpperCAmelCase__ , output_type='numpy' ).images
UpperCamelCase = image[0, 2_53:2_56, 2_53:2_56, -1]
assert image.shape == (1, 5_12, 5_12, 3)
UpperCamelCase = np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 282 |
'''simple docstring'''
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 __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str=13 , UpperCAmelCase__ : Tuple=7 , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Optional[Any]=99 , UpperCAmelCase__ : Any=16 , UpperCAmelCase__ : Optional[Any]=36 , UpperCAmelCase__ : str=6 , UpperCAmelCase__ : Any=6 , UpperCAmelCase__ : Optional[int]=6 , UpperCAmelCase__ : Any=37 , UpperCAmelCase__ : List[str]="gelu" , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : Optional[int]=0.1 , UpperCAmelCase__ : List[str]=512 , UpperCAmelCase__ : List[str]=16 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : Union[str, Any]=3 , UpperCAmelCase__ : List[Any]=4 , UpperCAmelCase__ : Tuple=None , ):
'''simple docstring'''
lowercase : str =parent
lowercase : int =batch_size
lowercase : Any =seq_length
lowercase : int =is_training
lowercase : str =use_input_mask
lowercase : int =use_token_type_ids
lowercase : Dict =use_labels
lowercase : int =vocab_size
lowercase : str =embedding_size
lowercase : Union[str, Any] =hidden_size
lowercase : Tuple =num_hidden_layers
lowercase : Any =num_hidden_groups
lowercase : Union[str, Any] =num_attention_heads
lowercase : Any =intermediate_size
lowercase : Tuple =hidden_act
lowercase : Optional[int] =hidden_dropout_prob
lowercase : Union[str, Any] =attention_probs_dropout_prob
lowercase : List[Any] =max_position_embeddings
lowercase : int =type_vocab_size
lowercase : int =type_sequence_label_size
lowercase : Any =initializer_range
lowercase : List[Any] =num_labels
lowercase : int =num_choices
lowercase : Optional[int] =scope
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : int =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Optional[int] =None
if self.use_input_mask:
lowercase : List[str] =random_attention_mask([self.batch_size, self.seq_length] )
lowercase : Dict =None
if self.use_token_type_ids:
lowercase : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase : Tuple =None
lowercase : Any =None
lowercase : Dict =None
if self.use_labels:
lowercase : int =ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase : str =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase : Optional[Any] =ids_tensor([self.batch_size] , self.num_choices )
lowercase : Any =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase_ ( self : Union[str, Any] ):
'''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 lowerCamelCase_ ( self : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : str ):
'''simple docstring'''
lowercase : int =AlbertModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : int =model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
lowercase : Dict =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 lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple ):
'''simple docstring'''
lowercase : Tuple =AlbertForPreTraining(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : int =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 lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Tuple =AlbertForMaskedLM(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.vocab_size) )
def lowerCamelCase_ ( self : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict , UpperCAmelCase__ : str , UpperCAmelCase__ : str , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
lowercase : List[str] =AlbertForQuestionAnswering(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : List[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 lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : str , UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
lowercase : Optional[Any] =self.num_labels
lowercase : Any =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 lowerCamelCase_ ( self : Optional[int] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict ):
'''simple docstring'''
lowercase : List[Any] =self.num_labels
lowercase : str =AlbertForTokenClassification(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : int =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 lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] ):
'''simple docstring'''
lowercase : Optional[int] =self.num_choices
lowercase : List[Any] =AlbertForMultipleChoice(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : List[Any] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : int =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : int =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase : List[str] =model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Union[str, Any] =self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
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_torch
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase_ = (
{
'feature-extraction': AlbertModel,
'fill-mask': AlbertForMaskedLM,
'question-answering': AlbertForQuestionAnswering,
'text-classification': AlbertForSequenceClassification,
'token-classification': AlbertForTokenClassification,
'zero-shot': AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase_ = True
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int=False ):
'''simple docstring'''
lowercase : Optional[int] =super()._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
if return_labels:
if model_class in get_values(UpperCAmelCase__ ):
lowercase : Any =torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=UpperCAmelCase__ )
lowercase : Any =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
return inputs_dict
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : Tuple =AlbertModelTester(self )
lowercase : Optional[Any] =ConfigTester(self , config_class=UpperCAmelCase__ , hidden_size=37 )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
lowercase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
lowercase : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Optional[int] =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase : Tuple =type
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : str =AlbertModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : int =AlbertModel.from_pretrained('''albert-base-v2''' )
lowercase : Optional[int] =torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
lowercase : 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 : Union[str, Any] =torch.tensor(
[[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , UpperCAmelCase__ , atol=1E-4 ) )
| 92 | 0 |
"""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
__magic_name__ = logging.get_logger(__name__)
# General docstring
__magic_name__ = """RegNetConfig"""
# Base docstring
__magic_name__ = """facebook/regnet-y-040"""
__magic_name__ = [1, 10_88, 7, 7]
# Image classification docstring
__magic_name__ = """facebook/regnet-y-040"""
__magic_name__ = """tabby, tabby cat"""
__magic_name__ = [
"""facebook/regnet-y-040""",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 3 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : Optional[str] = "relu" , ):
super().__init__()
lowerCamelCase__ = nn.Convad(
UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=UpperCAmelCase__ , stride=UpperCAmelCase__ , padding=kernel_size // 2 , groups=UpperCAmelCase__ , bias=UpperCAmelCase__ , )
lowerCamelCase__ = nn.BatchNormad(UpperCAmelCase__ )
lowerCamelCase__ = ACTaFN[activation] if activation is not None else nn.Identity()
def __UpperCAmelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Any ):
lowerCamelCase__ = self.convolution(UpperCAmelCase__ )
lowerCamelCase__ = self.normalization(UpperCAmelCase__ )
lowerCamelCase__ = self.activation(UpperCAmelCase__ )
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig ):
super().__init__()
lowerCamelCase__ = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
lowerCamelCase__ = config.num_channels
def __UpperCAmelCase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : List[str] ):
lowerCamelCase__ = 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.""" )
lowerCamelCase__ = self.embedder(UpperCAmelCase__ )
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 2 ):
super().__init__()
lowerCamelCase__ = nn.Convad(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , stride=UpperCAmelCase__ , bias=UpperCAmelCase__ )
lowerCamelCase__ = nn.BatchNormad(UpperCAmelCase__ )
def __UpperCAmelCase ( self : Tuple , SCREAMING_SNAKE_CASE_ : Tensor ):
lowerCamelCase__ = self.convolution(UpperCAmelCase__ )
lowerCamelCase__ = self.normalization(UpperCAmelCase__ )
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
super().__init__()
lowerCamelCase__ = nn.AdaptiveAvgPoolad((1, 1) )
lowerCamelCase__ = nn.Sequential(
nn.Convad(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 ) , nn.ReLU() , nn.Convad(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 ) , nn.Sigmoid() , )
def __UpperCAmelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any] ):
lowerCamelCase__ = self.pooler(UpperCAmelCase__ )
lowerCamelCase__ = self.attention(UpperCAmelCase__ )
lowerCamelCase__ = hidden_state * attention
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : str , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 1 ):
super().__init__()
lowerCamelCase__ = in_channels != out_channels or stride != 1
lowerCamelCase__ = max(1 , out_channels // config.groups_width )
lowerCamelCase__ = (
RegNetShortCut(UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ ) if should_apply_shortcut else nn.Identity()
)
lowerCamelCase__ = nn.Sequential(
RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ , groups=UpperCAmelCase__ , activation=config.hidden_act ) , RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , activation=UpperCAmelCase__ ) , )
lowerCamelCase__ = ACTaFN[config.hidden_act]
def __UpperCAmelCase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
lowerCamelCase__ = hidden_state
lowerCamelCase__ = self.layer(UpperCAmelCase__ )
lowerCamelCase__ = self.shortcut(UpperCAmelCase__ )
hidden_state += residual
lowerCamelCase__ = self.activation(UpperCAmelCase__ )
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 1 ):
super().__init__()
lowerCamelCase__ = in_channels != out_channels or stride != 1
lowerCamelCase__ = max(1 , out_channels // config.groups_width )
lowerCamelCase__ = (
RegNetShortCut(UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ ) if should_apply_shortcut else nn.Identity()
)
lowerCamelCase__ = nn.Sequential(
RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ , groups=UpperCAmelCase__ , activation=config.hidden_act ) , RegNetSELayer(UpperCAmelCase__ , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , activation=UpperCAmelCase__ ) , )
lowerCamelCase__ = ACTaFN[config.hidden_act]
def __UpperCAmelCase ( self : Any , SCREAMING_SNAKE_CASE_ : Tuple ):
lowerCamelCase__ = hidden_state
lowerCamelCase__ = self.layer(UpperCAmelCase__ )
lowerCamelCase__ = self.shortcut(UpperCAmelCase__ )
hidden_state += residual
lowerCamelCase__ = self.activation(UpperCAmelCase__ )
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : RegNetConfig , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 2 , ):
super().__init__()
lowerCamelCase__ = RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer
lowerCamelCase__ = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ , ) , *[layer(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) for _ in range(depth - 1 )] , )
def __UpperCAmelCase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
lowerCamelCase__ = self.layers(UpperCAmelCase__ )
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : int , SCREAMING_SNAKE_CASE_ : RegNetConfig ):
super().__init__()
lowerCamelCase__ = 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(
UpperCAmelCase__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
lowerCamelCase__ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(UpperCAmelCase__ , config.depths[1:] ):
self.stages.append(RegNetStage(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , depth=UpperCAmelCase__ ) )
def __UpperCAmelCase ( self : List[str] , SCREAMING_SNAKE_CASE_ : Tensor , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : bool = True ):
lowerCamelCase__ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowerCamelCase__ = hidden_states + (hidden_state,)
lowerCamelCase__ = stage_module(UpperCAmelCase__ )
if output_hidden_states:
lowerCamelCase__ = 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=UpperCAmelCase__ , hidden_states=UpperCAmelCase__ )
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case = RegNetConfig
snake_case = "regnet"
snake_case = "pixel_values"
snake_case = True
def __UpperCAmelCase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tuple ):
if isinstance(UpperCAmelCase__ , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="""fan_out""" , nonlinearity="""relu""" )
elif isinstance(UpperCAmelCase__ , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def __UpperCAmelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[int]=False ):
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
lowerCamelCase__ = value
__magic_name__ = R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
__magic_name__ = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." , lowercase__ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : List[Any] ):
super().__init__(UpperCAmelCase__ )
lowerCamelCase__ = config
lowerCamelCase__ = RegNetEmbeddings(UpperCAmelCase__ )
lowerCamelCase__ = RegNetEncoder(UpperCAmelCase__ )
lowerCamelCase__ = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=UpperCAmelCase__ , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __UpperCAmelCase ( self : List[str] , SCREAMING_SNAKE_CASE_ : Tensor , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None ):
lowerCamelCase__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCamelCase__ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase__ = self.embedder(UpperCAmelCase__ )
lowerCamelCase__ = self.encoder(
UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__ , return_dict=UpperCAmelCase__ )
lowerCamelCase__ = encoder_outputs[0]
lowerCamelCase__ = self.pooler(UpperCAmelCase__ )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=UpperCAmelCase__ , pooler_output=UpperCAmelCase__ , 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 " , lowercase__ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def __init__( self : str , SCREAMING_SNAKE_CASE_ : Any ):
super().__init__(UpperCAmelCase__ )
lowerCamelCase__ = config.num_labels
lowerCamelCase__ = RegNetModel(UpperCAmelCase__ )
# classification head
lowerCamelCase__ = 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(UpperCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=UpperCAmelCase__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __UpperCAmelCase ( self : str , SCREAMING_SNAKE_CASE_ : Optional[torch.FloatTensor] = None , SCREAMING_SNAKE_CASE_ : Optional[torch.LongTensor] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , ):
lowerCamelCase__ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase__ = self.regnet(UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__ , return_dict=UpperCAmelCase__ )
lowerCamelCase__ = outputs.pooler_output if return_dict else outputs[1]
lowerCamelCase__ = self.classifier(UpperCAmelCase__ )
lowerCamelCase__ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowerCamelCase__ = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowerCamelCase__ = '''single_label_classification'''
else:
lowerCamelCase__ = '''multi_label_classification'''
if self.config.problem_type == "regression":
lowerCamelCase__ = MSELoss()
if self.num_labels == 1:
lowerCamelCase__ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowerCamelCase__ = loss_fct(UpperCAmelCase__ , UpperCAmelCase__ )
elif self.config.problem_type == "single_label_classification":
lowerCamelCase__ = CrossEntropyLoss()
lowerCamelCase__ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowerCamelCase__ = BCEWithLogitsLoss()
lowerCamelCase__ = loss_fct(UpperCAmelCase__ , UpperCAmelCase__ )
if not return_dict:
lowerCamelCase__ = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=UpperCAmelCase__ , logits=UpperCAmelCase__ , hidden_states=outputs.hidden_states )
| 129 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int ):
'''simple docstring'''
if dst_width < 0 or dst_height < 0:
raise ValueError('''Destination width/height should be > 0''' )
lowercase : Union[str, Any] =img
lowercase : Union[str, Any] =img.shape[1]
lowercase : str =img.shape[0]
lowercase : Union[str, Any] =dst_width
lowercase : str =dst_height
lowercase : str =self.src_w / self.dst_w
lowercase : Optional[Any] =self.src_h / self.dst_h
lowercase : int =(
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 255
)
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
for i in range(self.dst_h ):
for j in range(self.dst_w ):
lowercase : List[Any] =self.img[self.get_y(UpperCAmelCase__ )][self.get_x(UpperCAmelCase__ )]
def lowerCamelCase_ ( self : Tuple , UpperCAmelCase__ : int ):
'''simple docstring'''
return int(self.ratio_x * x )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : int ):
'''simple docstring'''
return int(self.ratio_y * y )
if __name__ == "__main__":
UpperCamelCase_ , UpperCamelCase_ = 800, 600
UpperCamelCase_ = imread("""image_data/lena.jpg""", 1)
UpperCamelCase_ = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
)
waitKey(0)
destroyAllWindows()
| 92 | 0 |
def lowerCamelCase__ ( __lowerCAmelCase : int ):
"""simple docstring"""
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase_ = F"""Input value of [number={number}] must be an integer"""
raise TypeError(__lowerCAmelCase )
if number < 1:
lowerCAmelCase_ = F"""Input value of [number={number}] must be > 0"""
raise ValueError(__lowerCAmelCase )
lowerCAmelCase_ = 1
for i in range(1 , __lowerCAmelCase ):
current_number *= 4 * i - 2
current_number //= i + 1
return current_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ : list[float] ) -> float:
lowercase : Any =0.0_0
lowercase : Tuple =0
for resistor in resistors:
if resistor <= 0:
lowercase : Dict =f'''Resistor at index {index} has a negative or zero value!'''
raise ValueError(__magic_name__ )
first_sum += 1 / float(__magic_name__ )
index += 1
return 1 / first_sum
def _lowerCAmelCase ( __magic_name__ : list[float] ) -> float:
lowercase : Optional[Any] =0.0_0
lowercase : int =0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowercase : Tuple =f'''Resistor at index {index} has a negative value!'''
raise ValueError(__magic_name__ )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92 | 0 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
A__ : Optional[int] = logging.get_logger(__name__)
A__ : Any = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
A__ : Tuple = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
A__ : Union[str, Any] = {
'''facebook/blenderbot_small-90M''': 5_1_2,
}
class snake_case__ ( lowercase__ ):
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = BlenderbotSmallTokenizer
def __init__( self : Tuple , __a : Union[str, Any]=None , __a : Optional[int]=None , __a : Optional[Any]="<|endoftext|>" , __a : Dict="<|endoftext|>" , __a : List[str]="<|endoftext|>" , __a : List[Any]=False , __a : Optional[Any]=True , **__a : Optional[int] , ) -> Optional[int]:
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=UpperCAmelCase__ , merges=UpperCAmelCase__ , add_prefix_space=UpperCAmelCase__ , trim_offsets=UpperCAmelCase__ , ) , bos_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , **UpperCAmelCase__ , )
__snake_case : int = add_prefix_space
def A_ ( self : Any , __a : str , __a : Tuple=None ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Any = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def A_ ( self : Union[str, Any] , __a : List[int] , __a : Optional[List[int]] = None ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[Any] = [self.sep_token_id]
__snake_case : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 286 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
"""self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
"""self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
"""self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
"""self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
"""self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
"""self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
"""self_attn.rotary_emb""": """encoder.embed_positions""",
"""self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
"""conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
"""conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
"""conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
"""conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
"""conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
"""ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
"""ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
"""ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
"""ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
"""ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
"""ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
"""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""",
}
UpperCamelCase_ = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def _lowerCAmelCase ( __magic_name__ : Any , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Tuple ) -> str:
for attribute in key.split('''.''' ):
lowercase : Tuple =getattr(__magic_name__ , __magic_name__ )
if weight_type is not None:
lowercase : Optional[int] =getattr(__magic_name__ , __magic_name__ ).shape
else:
lowercase : List[Any] =hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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 : Any =value
elif weight_type == "weight_g":
lowercase : List[Any] =value
elif weight_type == "weight_v":
lowercase : Union[str, Any] =value
elif weight_type == "bias":
lowercase : Tuple =value
elif weight_type == "running_mean":
lowercase : Union[str, Any] =value
elif weight_type == "running_var":
lowercase : str =value
elif weight_type == "num_batches_tracked":
lowercase : Tuple =value
elif weight_type == "inv_freq":
lowercase : Optional[Any] =value
else:
lowercase : Tuple =value
logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def _lowerCAmelCase ( __magic_name__ : List[Any] , __magic_name__ : Union[str, Any] , __magic_name__ : str ) -> Union[str, Any]:
lowercase : Optional[int] =[]
lowercase : Tuple =fairseq_model.state_dict()
lowercase : List[Any] =hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
lowercase : Tuple =False
if "conv_layers" in name:
load_conv_layer(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , hf_model.config.feat_extract_norm == '''group''' , )
lowercase : List[Any] =True
else:
for key, mapped_key in MAPPING.items():
lowercase : Optional[int] ='''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
lowercase : Union[str, Any] =True
if "*" in mapped_key:
lowercase : Optional[int] =name.split(__magic_name__ )[0].split('''.''' )[-2]
lowercase : List[str] =mapped_key.replace('''*''' , __magic_name__ )
if "pos_bias_u" in name:
lowercase : Optional[Any] =None
elif "pos_bias_v" in name:
lowercase : Union[str, Any] =None
elif "weight_g" in name:
lowercase : Any ='''weight_g'''
elif "weight_v" in name:
lowercase : Tuple ='''weight_v'''
elif "bias" in name:
lowercase : Optional[int] ='''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase : Optional[int] ='''weight'''
elif "running_mean" in name:
lowercase : Union[str, Any] ='''running_mean'''
elif "inv_freq" in name:
lowercase : Any ='''inv_freq'''
elif "running_var" in name:
lowercase : Tuple ='''running_var'''
elif "num_batches_tracked" in name:
lowercase : Dict ='''num_batches_tracked'''
else:
lowercase : str =None
set_recursively(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
continue
if not is_used:
unused_weights.append(__magic_name__ )
logger.warning(f'''Unused weights: {unused_weights}''' )
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : Optional[Any] , __magic_name__ : List[Any] , __magic_name__ : Optional[Any] , __magic_name__ : Tuple ) -> int:
lowercase : Optional[Any] =full_name.split('''conv_layers.''' )[-1]
lowercase : Any =name.split('''.''' )
lowercase : List[str] =int(items[0] )
lowercase : Union[str, Any] =int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
lowercase : Union[str, Any] =value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
lowercase : Optional[Any] =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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
lowercase : Optional[int] =value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[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(__magic_name__ )
@torch.no_grad()
def _lowerCAmelCase ( __magic_name__ : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Tuple=None , __magic_name__ : Optional[Any]=None , __magic_name__ : Union[str, Any]=True ) -> Union[str, Any]:
if config_path is not None:
lowercase : Optional[Any] =WavaVecaConformerConfig.from_pretrained(__magic_name__ , hidden_act='''swish''' )
else:
lowercase : Optional[int] =WavaVecaConformerConfig()
if "rope" in checkpoint_path:
lowercase : Dict ='''rotary'''
if is_finetuned:
if dict_path:
lowercase : Optional[Any] =Dictionary.load(__magic_name__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowercase : str =target_dict.pad_index
lowercase : Union[str, Any] =target_dict.bos_index
lowercase : Any =target_dict.eos_index
lowercase : Tuple =len(target_dict.symbols )
lowercase : str =os.path.join(__magic_name__ , '''vocab.json''' )
if not os.path.isdir(__magic_name__ ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__magic_name__ ) )
return
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
lowercase : Dict =target_dict.indices
# fairseq has the <pad> and <s> switched
lowercase : str =0
lowercase : List[Any] =1
with open(__magic_name__ , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(__magic_name__ , __magic_name__ )
lowercase : List[str] =WavaVecaCTCTokenizer(
__magic_name__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=__magic_name__ , )
lowercase : Optional[int] =True if config.feat_extract_norm == '''layer''' else False
lowercase : str =WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__magic_name__ , return_attention_mask=__magic_name__ , )
lowercase : Tuple =WavaVecaProcessor(feature_extractor=__magic_name__ , tokenizer=__magic_name__ )
processor.save_pretrained(__magic_name__ )
lowercase : str =WavaVecaConformerForCTC(__magic_name__ )
else:
lowercase : Tuple =WavaVecaConformerForPreTraining(__magic_name__ )
if is_finetuned:
lowercase , lowercase , lowercase : Optional[int] =fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
lowercase : Dict =argparse.Namespace(task='''audio_pretraining''' )
lowercase : Optional[int] =fairseq.tasks.setup_task(__magic_name__ )
lowercase , lowercase , lowercase : str =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__magic_name__ )
lowercase : List[Any] =model[0].eval()
recursively_load_weights(__magic_name__ , __magic_name__ , not is_finetuned )
hf_wavavec.save_pretrained(__magic_name__ )
if __name__ == "__main__":
UpperCamelCase_ = 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_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCamelCase_ = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 92 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : List[Any] = logging.get_logger(__name__)
snake_case : str = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class __lowercase ( lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = "switch_transformers"
SCREAMING_SNAKE_CASE : int = ["past_key_values"]
SCREAMING_SNAKE_CASE : Any = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self , A_=32128 , A_=768 , A_=64 , A_=2048 , A_=64 , A_=12 , A_=3 , A_=12 , A_=3 , A_=12 , A_=8 , A_=False , A_=0.01 , A_="float32" , A_=False , A_=32 , A_=128 , A_=0.1 , A_=1e-6 , A_=0.001 , A_=0.001 , A_=1.0 , A_="relu" , A_=True , A_=False , A_=True , A_=0 , A_=1 , **A_ , )-> Optional[Any]:
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = d_model
_SCREAMING_SNAKE_CASE = d_kv
_SCREAMING_SNAKE_CASE = d_ff
_SCREAMING_SNAKE_CASE = num_sparse_encoder_layers
_SCREAMING_SNAKE_CASE = num_layers
_SCREAMING_SNAKE_CASE = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
_SCREAMING_SNAKE_CASE = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
_SCREAMING_SNAKE_CASE = self.num_layers // self.num_sparse_encoder_layers
else:
_SCREAMING_SNAKE_CASE = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
_SCREAMING_SNAKE_CASE = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
_SCREAMING_SNAKE_CASE = self.num_decoder_layers # HACK: this will create 0 sparse layers
_SCREAMING_SNAKE_CASE = num_heads
_SCREAMING_SNAKE_CASE = num_experts
_SCREAMING_SNAKE_CASE = expert_capacity
_SCREAMING_SNAKE_CASE = router_bias
_SCREAMING_SNAKE_CASE = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}''' )
_SCREAMING_SNAKE_CASE = router_dtype
_SCREAMING_SNAKE_CASE = router_ignore_padding_tokens
_SCREAMING_SNAKE_CASE = relative_attention_num_buckets
_SCREAMING_SNAKE_CASE = relative_attention_max_distance
_SCREAMING_SNAKE_CASE = dropout_rate
_SCREAMING_SNAKE_CASE = layer_norm_epsilon
_SCREAMING_SNAKE_CASE = initializer_factor
_SCREAMING_SNAKE_CASE = feed_forward_proj
_SCREAMING_SNAKE_CASE = use_cache
_SCREAMING_SNAKE_CASE = add_router_probs
_SCREAMING_SNAKE_CASE = router_z_loss_coef
_SCREAMING_SNAKE_CASE = router_aux_loss_coef
_SCREAMING_SNAKE_CASE = self.feed_forward_proj.split('-' )
_SCREAMING_SNAKE_CASE = act_info[-1]
_SCREAMING_SNAKE_CASE = act_info[0] == '''gated'''
if len(UpperCAmelCase__ ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase__ ) > 2:
raise ValueError(
F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
_SCREAMING_SNAKE_CASE = '''gelu_new'''
super().__init__(
pad_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , is_encoder_decoder=UpperCAmelCase__ , **UpperCAmelCase__ , )
| 605 |
'''simple docstring'''
import math
import flax.linen as nn
import jax.numpy as jnp
def _lowerCAmelCase ( __magic_name__ : jnp.ndarray , __magic_name__ : int , __magic_name__ : float = 1 , __magic_name__ : float = 1 , __magic_name__ : float = 1.0E4 , __magic_name__ : bool = False , __magic_name__ : float = 1.0 , ) -> jnp.ndarray:
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
lowercase : int =float(embedding_dim // 2 )
lowercase : Optional[int] =math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
lowercase : Any =min_timescale * jnp.exp(jnp.arange(__magic_name__ , dtype=jnp.floataa ) * -log_timescale_increment )
lowercase : List[Any] =jnp.expand_dims(__magic_name__ , 1 ) * jnp.expand_dims(__magic_name__ , 0 )
# scale embeddings
lowercase : Tuple =scale * emb
if flip_sin_to_cos:
lowercase : Dict =jnp.concatenate([jnp.cos(__magic_name__ ), jnp.sin(__magic_name__ )] , axis=1 )
else:
lowercase : Any =jnp.concatenate([jnp.sin(__magic_name__ ), jnp.cos(__magic_name__ )] , axis=1 )
lowercase : List[str] =jnp.reshape(__magic_name__ , [jnp.shape(__magic_name__ )[0], embedding_dim] )
return signal
class __SCREAMING_SNAKE_CASE ( nn.Module ):
lowerCamelCase_ = 32
lowerCamelCase_ = jnp.floataa
@nn.compact
def __call__( self : Tuple , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : List[Any] =nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_1''' )(UpperCAmelCase__ )
lowercase : Any =nn.silu(UpperCAmelCase__ )
lowercase : int =nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_2''' )(UpperCAmelCase__ )
return temb
class __SCREAMING_SNAKE_CASE ( nn.Module ):
lowerCamelCase_ = 32
lowerCamelCase_ = False
lowerCamelCase_ = 1
@nn.compact
def __call__( self : int , UpperCAmelCase__ : str ):
'''simple docstring'''
return get_sinusoidal_embeddings(
UpperCAmelCase__ , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 92 | 0 |
"""simple docstring"""
class lowerCAmelCase__ :
def __init__( self ):
'''simple docstring'''
A__ = ''''''
A__ = ''''''
A__ = []
def lowercase_ ( self , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
A__ = self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
A__ = self.__min_dist_top_down_dp(UpperCAmelCase__ , n - 1 )
A__ = self.__min_dist_top_down_dp(m - 1 , UpperCAmelCase__ )
A__ = self.__min_dist_top_down_dp(m - 1 , n - 1 )
A__ = 1 + min(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return self.dp[m][n]
def lowercase_ ( self , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
A__ = worda
A__ = worda
A__ = [[-1 for _ in range(len(UpperCAmelCase__ ) )] for _ in range(len(UpperCAmelCase__ ) )]
return self.__min_dist_top_down_dp(len(UpperCAmelCase__ ) - 1 , len(UpperCAmelCase__ ) - 1 )
def lowercase_ ( self , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
A__ = worda
A__ = worda
A__ = len(UpperCAmelCase__ )
A__ = len(UpperCAmelCase__ )
A__ = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
A__ = j
elif j == 0: # second string is empty
A__ = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
A__ = self.dp[i - 1][j - 1]
else:
A__ = self.dp[i][j - 1]
A__ = self.dp[i - 1][j]
A__ = self.dp[i - 1][j - 1]
A__ = 1 + min(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return self.dp[m][n]
if __name__ == "__main__":
__UpperCAmelCase =EditDistance()
print("""****************** Testing Edit Distance DP Algorithm ******************""")
print()
__UpperCAmelCase =input("""Enter the first string: """).strip()
__UpperCAmelCase =input("""Enter the second string: """).strip()
print()
print(F'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
print(F'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
print()
print("""*************** End of Testing Edit Distance DP Algorithm ***************""") | 337 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase_ = {
"""facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = 'esm'
def __init__( self : Optional[Any] , UpperCAmelCase__ : str=None , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : Any=None , UpperCAmelCase__ : int=768 , UpperCAmelCase__ : Union[str, Any]=12 , UpperCAmelCase__ : List[Any]=12 , UpperCAmelCase__ : Optional[Any]=3072 , UpperCAmelCase__ : Dict=0.1 , UpperCAmelCase__ : int=0.1 , UpperCAmelCase__ : int=1026 , UpperCAmelCase__ : Tuple=0.02 , UpperCAmelCase__ : Optional[int]=1E-12 , UpperCAmelCase__ : List[Any]="absolute" , UpperCAmelCase__ : int=True , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : str=False , UpperCAmelCase__ : int=False , UpperCAmelCase__ : Optional[Any]=None , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : int , ):
'''simple docstring'''
super().__init__(pad_token_id=UpperCAmelCase__ , mask_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
lowercase : Any =vocab_size
lowercase : List[Any] =hidden_size
lowercase : Any =num_hidden_layers
lowercase : Optional[Any] =num_attention_heads
lowercase : Tuple =intermediate_size
lowercase : int =hidden_dropout_prob
lowercase : Dict =attention_probs_dropout_prob
lowercase : Optional[int] =max_position_embeddings
lowercase : Union[str, Any] =initializer_range
lowercase : Tuple =layer_norm_eps
lowercase : Union[str, Any] =position_embedding_type
lowercase : List[Any] =use_cache
lowercase : Dict =emb_layer_norm_before
lowercase : Optional[Any] =token_dropout
lowercase : Union[str, Any] =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
lowercase : Any =EsmFoldConfig()
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase : Optional[int] =EsmFoldConfig(**UpperCAmelCase__ )
lowercase : Union[str, Any] =esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
lowercase : int =get_default_vocab_list()
else:
lowercase : Tuple =vocab_list
else:
lowercase : Union[str, Any] =None
lowercase : Dict =None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , UpperCAmelCase__ ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : Union[str, Any] =super().to_dict()
if isinstance(self.esmfold_config , UpperCAmelCase__ ):
lowercase : Optional[Any] =self.esmfold_config.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = None
lowerCamelCase_ = True
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = 0
lowerCamelCase_ = True
lowerCamelCase_ = False
lowerCamelCase_ = 1_28
lowerCamelCase_ = None
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
if self.trunk is None:
lowercase : str =TrunkConfig()
elif isinstance(self.trunk , UpperCAmelCase__ ):
lowercase : int =TrunkConfig(**self.trunk )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : str =asdict(self )
lowercase : Union[str, Any] =self.trunk.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = 48
lowerCamelCase_ = 10_24
lowerCamelCase_ = 1_28
lowerCamelCase_ = 32
lowerCamelCase_ = 32
lowerCamelCase_ = 32
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = False
lowerCamelCase_ = 4
lowerCamelCase_ = 1_28
lowerCamelCase_ = None
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
if self.structure_module is None:
lowercase : Any =StructureModuleConfig()
elif isinstance(self.structure_module , UpperCAmelCase__ ):
lowercase : Union[str, Any] =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
lowercase : str =self.sequence_state_dim // self.sequence_head_width
lowercase : int =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : List[Any] =asdict(self )
lowercase : Any =self.structure_module.to_dict()
return output
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = 3_84
lowerCamelCase_ = 1_28
lowerCamelCase_ = 16
lowerCamelCase_ = 1_28
lowerCamelCase_ = 12
lowerCamelCase_ = 4
lowerCamelCase_ = 8
lowerCamelCase_ = 0.1
lowerCamelCase_ = 8
lowerCamelCase_ = 1
lowerCamelCase_ = 2
lowerCamelCase_ = 7
lowerCamelCase_ = 10
lowerCamelCase_ = 1E-8
lowerCamelCase_ = 1E5
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return asdict(self )
def _lowerCAmelCase ( ) -> Optional[int]:
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 92 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class snake_case ( lowercase__ ):
def __init__( self : str)-> Optional[Any]:
'''simple docstring'''
self.test()
def lowercase_ ( self : Tuple)-> Optional[int]:
'''simple docstring'''
__lowerCAmelCase: int = 0
__lowerCAmelCase: List[Any] = False
while not completed:
if counter == 1:
self.reset()
__lowerCAmelCase: Dict = self.advance()
if not self.does_advance(UpperCAmelCase__):
raise Exception(
"Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.")
__lowerCAmelCase: str = self.update(UpperCAmelCase__)
counter += 1
if counter > 1_0_0_0_0:
raise Exception("update() does not fulfill the constraint.")
if self.remaining() != 0:
raise Exception("Custom Constraint is not defined correctly.")
@abstractmethod
def lowercase_ ( self : Any)-> Any:
'''simple docstring'''
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")
@abstractmethod
def lowercase_ ( self : List[str] , UpperCamelCase__ : int)-> List[str]:
'''simple docstring'''
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")
@abstractmethod
def lowercase_ ( self : Union[str, Any] , UpperCamelCase__ : int)-> Any:
'''simple docstring'''
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")
@abstractmethod
def lowercase_ ( self : Optional[int])-> List[Any]:
'''simple docstring'''
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")
@abstractmethod
def lowercase_ ( self : List[str])-> Optional[Any]:
'''simple docstring'''
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")
@abstractmethod
def lowercase_ ( self : Optional[int] , UpperCamelCase__ : Optional[Any]=False)-> Dict:
'''simple docstring'''
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")
class snake_case ( lowercase__ ):
def __init__( self : int , UpperCamelCase__ : List[int])-> Optional[int]:
'''simple docstring'''
super(UpperCAmelCase__ , self).__init__()
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__) or len(UpperCAmelCase__) == 0:
raise ValueError(f"`token_ids` has to be a non-empty list, but is {token_ids}.")
if any((not isinstance(UpperCAmelCase__ , UpperCAmelCase__) or token_id < 0) for token_id in token_ids):
raise ValueError(f"Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.")
__lowerCAmelCase: Union[str, Any] = token_ids
__lowerCAmelCase: Tuple = len(self.token_ids)
__lowerCAmelCase: Tuple = -1 # the index of the currently fulfilled step
__lowerCAmelCase: int = False
def lowercase_ ( self : int)-> List[str]:
'''simple docstring'''
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def lowercase_ ( self : str , UpperCamelCase__ : int)-> Dict:
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__):
raise ValueError(f"`token_id` has to be an `int`, but is {token_id} of type {type(UpperCAmelCase__)}")
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def lowercase_ ( self : str , UpperCamelCase__ : int)-> str:
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__):
raise ValueError(f"`token_id` has to be an `int`, but is {token_id} of type {type(UpperCAmelCase__)}")
__lowerCAmelCase: Any = False
__lowerCAmelCase: Optional[int] = False
__lowerCAmelCase: int = False
if self.does_advance(UpperCAmelCase__):
self.fulfilled_idx += 1
__lowerCAmelCase: Any = True
if self.fulfilled_idx == (self.seqlen - 1):
__lowerCAmelCase: List[str] = True
__lowerCAmelCase: List[Any] = completed
else:
# failed to make progress.
__lowerCAmelCase: Union[str, Any] = True
self.reset()
return stepped, completed, reset
def lowercase_ ( self : Any)-> List[Any]:
'''simple docstring'''
__lowerCAmelCase: int = False
__lowerCAmelCase: Tuple = 0
def lowercase_ ( self : Union[str, Any])-> Any:
'''simple docstring'''
return self.seqlen - (self.fulfilled_idx + 1)
def lowercase_ ( self : int , UpperCamelCase__ : int=False)-> Dict:
'''simple docstring'''
__lowerCAmelCase: Dict = PhrasalConstraint(self.token_ids)
if stateful:
__lowerCAmelCase: Union[str, Any] = self.seqlen
__lowerCAmelCase: Optional[Any] = self.fulfilled_idx
__lowerCAmelCase: List[str] = self.completed
return new_constraint
class snake_case :
def __init__( self : Any , UpperCamelCase__ : List[List[int]] , UpperCamelCase__ : Dict=True)-> int:
'''simple docstring'''
__lowerCAmelCase: str = max([len(UpperCAmelCase__) for one in nested_token_ids])
__lowerCAmelCase: Any = {}
for token_ids in nested_token_ids:
__lowerCAmelCase: Any = root
for tidx, token_id in enumerate(UpperCAmelCase__):
if token_id not in level:
__lowerCAmelCase: int = {}
__lowerCAmelCase: Union[str, Any] = level[token_id]
if no_subsets and self.has_subsets(UpperCAmelCase__ , UpperCAmelCase__):
raise ValueError(
"Each list in `nested_token_ids` can\'t be a complete subset of another list, but is"
f" {nested_token_ids}.")
__lowerCAmelCase: Dict = root
def lowercase_ ( self : int , UpperCamelCase__ : Dict)-> Any:
'''simple docstring'''
__lowerCAmelCase: Any = self.trie
for current_token in current_seq:
__lowerCAmelCase: List[str] = start[current_token]
__lowerCAmelCase: Optional[Any] = list(start.keys())
return next_tokens
def lowercase_ ( self : str , UpperCamelCase__ : List[str])-> Any:
'''simple docstring'''
__lowerCAmelCase: Union[str, Any] = self.next_tokens(UpperCAmelCase__)
return len(UpperCAmelCase__) == 0
def lowercase_ ( self : Optional[Any] , UpperCamelCase__ : Optional[int])-> int:
'''simple docstring'''
__lowerCAmelCase: Any = list(root.values())
if len(UpperCAmelCase__) == 0:
return 1
else:
return sum([self.count_leaves(UpperCAmelCase__) for nn in next_nodes])
def lowercase_ ( self : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : Dict)-> Any:
'''simple docstring'''
__lowerCAmelCase: List[str] = self.count_leaves(UpperCAmelCase__)
return len(UpperCAmelCase__) != leaf_count
class snake_case ( lowercase__ ):
def __init__( self : List[str] , UpperCamelCase__ : List[List[int]])-> Dict:
'''simple docstring'''
super(UpperCAmelCase__ , self).__init__()
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__) or len(UpperCAmelCase__) == 0:
raise ValueError(f"`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.")
if any(not isinstance(UpperCAmelCase__ , UpperCAmelCase__) for token_ids in nested_token_ids):
raise ValueError(f"`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.")
if any(
any((not isinstance(UpperCAmelCase__ , UpperCAmelCase__) or token_id < 0) for token_id in token_ids)
for token_ids in nested_token_ids):
raise ValueError(
f"Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.")
__lowerCAmelCase: Any = DisjunctiveTrie(UpperCAmelCase__)
__lowerCAmelCase: Tuple = nested_token_ids
__lowerCAmelCase: Dict = self.trie.max_height
__lowerCAmelCase: List[str] = []
__lowerCAmelCase: str = False
def lowercase_ ( self : Optional[int])-> Optional[int]:
'''simple docstring'''
__lowerCAmelCase: Any = self.trie.next_tokens(self.current_seq)
if len(UpperCAmelCase__) == 0:
return None
else:
return token_list
def lowercase_ ( self : int , UpperCamelCase__ : int)-> Optional[int]:
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__):
raise ValueError(f"`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCAmelCase__)}")
__lowerCAmelCase: Union[str, Any] = self.trie.next_tokens(self.current_seq)
return token_id in next_tokens
def lowercase_ ( self : Tuple , UpperCamelCase__ : int)-> Optional[Any]:
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__):
raise ValueError(f"`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCAmelCase__)}")
__lowerCAmelCase: Any = False
__lowerCAmelCase: Any = False
__lowerCAmelCase: Optional[Any] = False
if self.does_advance(UpperCAmelCase__):
self.current_seq.append(UpperCAmelCase__)
__lowerCAmelCase: Dict = True
else:
__lowerCAmelCase: Tuple = True
self.reset()
__lowerCAmelCase: Union[str, Any] = self.trie.reached_leaf(self.current_seq)
__lowerCAmelCase: int = completed
return stepped, completed, reset
def lowercase_ ( self : Any)-> List[str]:
'''simple docstring'''
__lowerCAmelCase: int = False
__lowerCAmelCase: Tuple = []
def lowercase_ ( self : Dict)-> Any:
'''simple docstring'''
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq)
def lowercase_ ( self : Dict , UpperCamelCase__ : Any=False)-> int:
'''simple docstring'''
__lowerCAmelCase: Dict = DisjunctiveConstraint(self.token_ids)
if stateful:
__lowerCAmelCase: Union[str, Any] = self.seqlen
__lowerCAmelCase: int = self.current_seq
__lowerCAmelCase: Dict = self.completed
return new_constraint
class snake_case :
def __init__( self : int , UpperCamelCase__ : List[Constraint])-> List[Any]:
'''simple docstring'''
__lowerCAmelCase: List[str] = constraints
# max # of steps required to fulfill a given constraint
__lowerCAmelCase: str = max([c.seqlen for c in constraints])
__lowerCAmelCase: Any = len(UpperCAmelCase__)
__lowerCAmelCase: Union[str, Any] = False
self.init_state()
def lowercase_ ( self : str)-> List[Any]:
'''simple docstring'''
__lowerCAmelCase: List[str] = []
__lowerCAmelCase: Optional[Any] = None
__lowerCAmelCase: List[Any] = [constraint.copy(stateful=UpperCAmelCase__) for constraint in self.constraints]
def lowercase_ ( self : List[str])-> List[str]:
'''simple docstring'''
__lowerCAmelCase: List[Any] = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints) * self.max_seqlen) + add
def lowercase_ ( self : Union[str, Any])-> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase: int = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
__lowerCAmelCase: List[str] = constraint.advance()
if isinstance(UpperCAmelCase__ , UpperCAmelCase__):
token_list.append(UpperCAmelCase__)
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__):
token_list.extend(UpperCAmelCase__)
else:
__lowerCAmelCase: str = self.inprogress_constraint.advance()
if isinstance(UpperCAmelCase__ , UpperCAmelCase__):
token_list.append(UpperCAmelCase__)
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__):
token_list.extend(UpperCAmelCase__)
if len(UpperCAmelCase__) == 0:
return None
else:
return token_list
def lowercase_ ( self : Union[str, Any] , UpperCamelCase__ : Optional[List[int]])-> int:
'''simple docstring'''
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
__lowerCAmelCase: Optional[Any] = self.add(UpperCAmelCase__)
# the entire list of constraints are fulfilled
if self.completed:
break
def lowercase_ ( self : List[str] , UpperCamelCase__ : int)-> int:
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__):
raise ValueError(f"`token_id` should be an `int`, but is `{token_id}`.")
__lowerCAmelCase: List[Any] = False, False
if self.completed:
__lowerCAmelCase: Tuple = True
__lowerCAmelCase: Union[str, Any] = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
__lowerCAmelCase: Union[str, Any] = self.inprogress_constraint.update(UpperCAmelCase__)
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=UpperCAmelCase__))
__lowerCAmelCase: List[str] = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint)
__lowerCAmelCase: Optional[Any] = None
if len(self.pending_constraints) == 0:
# we're done!
__lowerCAmelCase: int = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints):
if pending_constraint.does_advance(UpperCAmelCase__):
__lowerCAmelCase: List[Any] = pending_constraint.update(UpperCAmelCase__)
if not stepped:
raise Exception(
"`constraint.update(token_id)` is not yielding incremental progress, "
"even though `constraint.does_advance(token_id)` is true.")
if complete:
self.complete_constraints.append(UpperCAmelCase__)
__lowerCAmelCase: Union[str, Any] = None
if not complete and stepped:
__lowerCAmelCase: Any = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
__lowerCAmelCase: int = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
__lowerCAmelCase: List[Any] = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def lowercase_ ( self : int , UpperCamelCase__ : Optional[int]=True)-> Union[str, Any]:
'''simple docstring'''
__lowerCAmelCase: int = ConstraintListState(self.constraints) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
__lowerCAmelCase: Tuple = [
constraint.copy(stateful=UpperCAmelCase__) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
__lowerCAmelCase: Dict = self.inprogress_constraint.copy(stateful=UpperCAmelCase__)
__lowerCAmelCase: int = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 346 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCamelCase_ = abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)
def _lowerCAmelCase ( __magic_name__ : int ) -> Tuple:
config.addinivalue_line(
'''markers''' , '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''' , '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''' , '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''' , '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''' , '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''' , '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def _lowerCAmelCase ( __magic_name__ : int ) -> Any:
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : Any ) -> Any:
from transformers.testing_utils import pytest_terminal_summary_main
lowercase : Optional[Any] =terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__magic_name__ , id=__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : Tuple , __magic_name__ : Union[str, Any] ) -> List[str]:
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowercase : Optional[int] =0
# Doctest custom flag to ignore output.
UpperCamelCase_ = doctest.register_optionflag("""IGNORE_RESULT""")
UpperCamelCase_ = doctest.OutputChecker
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
def lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[Any] ):
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
UpperCamelCase_ = CustomOutputChecker
UpperCamelCase_ = HfDoctestModule
UpperCamelCase_ = HfDocTestParser
| 92 | 0 |
import numpy as np
def lowercase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowercase = int(np.ceil((x_end - xa) / h ) )
__lowercase = np.zeros((n + 1,) )
__lowercase = ya
__lowercase = xa
for k in range(_UpperCamelCase ):
__lowercase = f(_UpperCamelCase , y[k] )
__lowercase = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
__lowercase = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
__lowercase = f(x + h , y[k] + h * ka )
__lowercase = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 639 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging
UpperCamelCase_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = ['pixel_values']
def __init__( self : List[Any] , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : Union[int, float] = 1 / 255 , UpperCAmelCase__ : bool = True , UpperCAmelCase__ : int = 8 , **UpperCAmelCase__ : str , ):
'''simple docstring'''
super().__init__(**UpperCAmelCase__ )
lowercase : Union[str, Any] =do_rescale
lowercase : List[Any] =rescale_factor
lowercase : Tuple =do_pad
lowercase : List[str] =pad_size
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : float , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCAmelCase__ : Optional[int] ):
'''simple docstring'''
return rescale(UpperCAmelCase__ , scale=UpperCAmelCase__ , data_format=UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCamelCase_ ( self : Tuple , UpperCAmelCase__ : np.ndarray , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[Union[str, ChannelDimension]] = None ):
'''simple docstring'''
lowercase , lowercase : Union[str, Any] =get_image_size(UpperCAmelCase__ )
lowercase : Tuple =(old_height // size + 1) * size - old_height
lowercase : Tuple =(old_width // size + 1) * size - old_width
return pad(UpperCAmelCase__ , ((0, pad_height), (0, pad_width)) , mode='''symmetric''' , data_format=UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : ImageInput , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[float] = None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[Union[str, TensorType]] = None , UpperCAmelCase__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **UpperCAmelCase__ : Optional[Any] , ):
'''simple docstring'''
lowercase : int =do_rescale if do_rescale is not None else self.do_rescale
lowercase : Tuple =rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase : int =do_pad if do_pad is not None else self.do_pad
lowercase : List[Any] =pad_size if pad_size is not None else self.pad_size
lowercase : Any =make_list_of_images(UpperCAmelCase__ )
if not valid_images(UpperCAmelCase__ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
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 : Dict =[to_numpy_array(UpperCAmelCase__ ) for image in images]
if do_rescale:
lowercase : Tuple =[self.rescale(image=UpperCAmelCase__ , scale=UpperCAmelCase__ ) for image in images]
if do_pad:
lowercase : Union[str, Any] =[self.pad(UpperCAmelCase__ , size=UpperCAmelCase__ ) for image in images]
lowercase : Dict =[to_channel_dimension_format(UpperCAmelCase__ , UpperCAmelCase__ ) for image in images]
lowercase : Any ={'''pixel_values''': images}
return BatchFeature(data=UpperCAmelCase__ , tensor_type=UpperCAmelCase__ )
| 92 | 0 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=2 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=2 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=36 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=6 , __UpperCAmelCase=6 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , __UpperCAmelCase=1_000 , ):
SCREAMING_SNAKE_CASE_ : int =parent
SCREAMING_SNAKE_CASE_ : str =batch_size
SCREAMING_SNAKE_CASE_ : Optional[Any] =num_channels
SCREAMING_SNAKE_CASE_ : Optional[int] =image_size
SCREAMING_SNAKE_CASE_ : Dict =patch_size
SCREAMING_SNAKE_CASE_ : Union[str, Any] =text_seq_length
SCREAMING_SNAKE_CASE_ : int =is_training
SCREAMING_SNAKE_CASE_ : int =use_input_mask
SCREAMING_SNAKE_CASE_ : str =use_token_type_ids
SCREAMING_SNAKE_CASE_ : str =use_labels
SCREAMING_SNAKE_CASE_ : Any =vocab_size
SCREAMING_SNAKE_CASE_ : Optional[Any] =hidden_size
SCREAMING_SNAKE_CASE_ : str =num_hidden_layers
SCREAMING_SNAKE_CASE_ : int =num_attention_heads
SCREAMING_SNAKE_CASE_ : Tuple =intermediate_size
SCREAMING_SNAKE_CASE_ : Dict =hidden_act
SCREAMING_SNAKE_CASE_ : Optional[int] =hidden_dropout_prob
SCREAMING_SNAKE_CASE_ : List[Any] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ : int =max_position_embeddings
SCREAMING_SNAKE_CASE_ : List[Any] =type_vocab_size
SCREAMING_SNAKE_CASE_ : Union[str, Any] =type_sequence_label_size
SCREAMING_SNAKE_CASE_ : List[Any] =initializer_range
SCREAMING_SNAKE_CASE_ : Tuple =coordinate_size
SCREAMING_SNAKE_CASE_ : int =shape_size
SCREAMING_SNAKE_CASE_ : List[str] =num_labels
SCREAMING_SNAKE_CASE_ : Any =num_choices
SCREAMING_SNAKE_CASE_ : str =scope
SCREAMING_SNAKE_CASE_ : Tuple =range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
SCREAMING_SNAKE_CASE_ : Optional[Any] =text_seq_length
SCREAMING_SNAKE_CASE_ : int =(image_size // patch_size) ** 2 + 1
SCREAMING_SNAKE_CASE_ : List[str] =self.text_seq_length + self.image_seq_length
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] =ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE_ : List[str] =ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
SCREAMING_SNAKE_CASE_ : Union[str, Any] =bbox[i, j, 3]
SCREAMING_SNAKE_CASE_ : Any =bbox[i, j, 1]
SCREAMING_SNAKE_CASE_ : List[str] =t
if bbox[i, j, 2] < bbox[i, j, 0]:
SCREAMING_SNAKE_CASE_ : Dict =bbox[i, j, 2]
SCREAMING_SNAKE_CASE_ : Dict =bbox[i, j, 0]
SCREAMING_SNAKE_CASE_ : Any =t
SCREAMING_SNAKE_CASE_ : Optional[Any] =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ : str =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE_ : Optional[Any] =random_attention_mask([self.batch_size, self.text_seq_length] )
SCREAMING_SNAKE_CASE_ : str =None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE_ : Optional[int] =ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE_ : List[Any] =None
SCREAMING_SNAKE_CASE_ : List[str] =None
if self.use_labels:
SCREAMING_SNAKE_CASE_ : List[Any] =ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE_ : Dict =ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =LayoutLMvaConfig(
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 , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def __lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ : Optional[int] =LayoutLMvaModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
# text + image
SCREAMING_SNAKE_CASE_ : Union[str, Any] =model(UpperCAmelCase__ , pixel_values=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Dict =model(
UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : str =model(UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Optional[Any] =model(UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
SCREAMING_SNAKE_CASE_ : Dict =model(UpperCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
SCREAMING_SNAKE_CASE_ : Optional[int] =model(pixel_values=UpperCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def __lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ : Dict =self.num_labels
SCREAMING_SNAKE_CASE_ : Dict =LayoutLMvaForSequenceClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
SCREAMING_SNAKE_CASE_ : Dict =model(
UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ : List[Any] =self.num_labels
SCREAMING_SNAKE_CASE_ : Any =LayoutLMvaForTokenClassification(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
SCREAMING_SNAKE_CASE_ : List[str] =model(
UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def __lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ : Tuple =LayoutLMvaForQuestionAnswering(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
SCREAMING_SNAKE_CASE_ : Optional[int] =model(
UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=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 __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : int =self.prepare_config_and_inputs()
(
SCREAMING_SNAKE_CASE_
) : Dict =config_and_inputs
SCREAMING_SNAKE_CASE_ : List[Any] ={
'''input_ids''': input_ids,
'''bbox''': bbox,
'''pixel_values''': pixel_values,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
_lowercase = False
_lowercase = False
_lowercase = False
_lowercase = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
_lowercase = (
{'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
if is_torch_available()
else {}
)
def __lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
return True
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : int =LayoutLMvaModelTester(self )
SCREAMING_SNAKE_CASE_ : List[Any] =ConfigTester(self , config_class=UpperCAmelCase__ , hidden_size=37 )
def __lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False ):
SCREAMING_SNAKE_CASE_ : Optional[int] =copy.deepcopy(UpperCAmelCase__ )
if model_class in get_values(UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE_ : Dict ={
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(UpperCAmelCase__ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] =torch.ones(self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
elif model_class in get_values(UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE_ : Optional[Any] =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Tuple =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
elif model_class in [
*get_values(UpperCAmelCase__ ),
]:
SCREAMING_SNAKE_CASE_ : Dict =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
elif model_class in [
*get_values(UpperCAmelCase__ ),
]:
SCREAMING_SNAKE_CASE_ : Optional[Any] =torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=UpperCAmelCase__ , )
return inputs_dict
def __lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : Dict =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE_ : Union[str, Any] =type
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__ )
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__ )
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__ )
@slow
def __lowerCamelCase ( self ):
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE_ : Union[str, Any] =LayoutLMvaModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __lowerCamelCase ( self ):
return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase__ ) if is_vision_available() else None
@slow
def __lowerCamelCase ( self ):
SCREAMING_SNAKE_CASE_ : Optional[Any] =LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base' ).to(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =self.default_image_processor
SCREAMING_SNAKE_CASE_ : str =prepare_img()
SCREAMING_SNAKE_CASE_ : int =image_processor(images=UpperCAmelCase__ , return_tensors='pt' ).pixel_values.to(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =torch.tensor([[1, 2]] )
SCREAMING_SNAKE_CASE_ : Any =torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
SCREAMING_SNAKE_CASE_ : Tuple =model(
input_ids=input_ids.to(UpperCAmelCase__ ) , bbox=bbox.to(UpperCAmelCase__ ) , pixel_values=pixel_values.to(UpperCAmelCase__ ) , )
# verify the logits
SCREAMING_SNAKE_CASE_ : str =torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , UpperCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Optional[int] =torch.tensor(
[[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=1E-4 ) )
| 220 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["""MBartTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["""MBartTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MBartForCausalLM""",
"""MBartForConditionalGeneration""",
"""MBartForQuestionAnswering""",
"""MBartForSequenceClassification""",
"""MBartModel""",
"""MBartPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""TFMBartForConditionalGeneration""",
"""TFMBartModel""",
"""TFMBartPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"""FlaxMBartForConditionalGeneration""",
"""FlaxMBartForQuestionAnswering""",
"""FlaxMBartForSequenceClassification""",
"""FlaxMBartModel""",
"""FlaxMBartPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self :Any, snake_case :Tuple, snake_case :int=13, snake_case :Dict=30, snake_case :Any=2, snake_case :Any=3, snake_case :Any=True, snake_case :List[str]=True, snake_case :str=32, snake_case :Optional[int]=2, snake_case :Dict=4, snake_case :Dict=37, snake_case :List[str]="gelu", snake_case :Union[str, Any]=0.1, snake_case :Any=0.1, snake_case :Union[str, Any]=10, snake_case :Any=0.0_2, snake_case :Dict=3, snake_case :int=None, snake_case :Union[str, Any]=2, ):
"""simple docstring"""
_lowercase =parent
_lowercase =batch_size
_lowercase =image_size
_lowercase =patch_size
_lowercase =num_channels
_lowercase =is_training
_lowercase =use_labels
_lowercase =hidden_size
_lowercase =num_hidden_layers
_lowercase =num_attention_heads
_lowercase =intermediate_size
_lowercase =hidden_act
_lowercase =hidden_dropout_prob
_lowercase =attention_probs_dropout_prob
_lowercase =type_sequence_label_size
_lowercase =initializer_range
_lowercase =scope
_lowercase =encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
_lowercase =(image_size // patch_size) ** 2
_lowercase =num_patches + 2
def UpperCamelCase__ ( self :List[str]):
"""simple docstring"""
_lowercase =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_lowercase =None
if self.use_labels:
_lowercase =ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowercase =self.get_config()
return config, pixel_values, labels
def UpperCamelCase__ ( self :Union[str, Any]):
"""simple docstring"""
return DeiTConfig(
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, is_decoder=UpperCAmelCase__, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, )
def UpperCamelCase__ ( self :List[str], snake_case :Dict, snake_case :List[Any], snake_case :Any):
"""simple docstring"""
_lowercase =TFDeiTModel(config=UpperCAmelCase__)
_lowercase =model(UpperCAmelCase__)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def UpperCamelCase__ ( self :Dict, snake_case :Tuple, snake_case :str, snake_case :Union[str, Any]):
"""simple docstring"""
_lowercase =TFDeiTForMaskedImageModeling(config=UpperCAmelCase__)
_lowercase =model(UpperCAmelCase__)
self.parent.assertEqual(
result.reconstruction.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size))
# test greyscale images
_lowercase =1
_lowercase =TFDeiTForMaskedImageModeling(UpperCAmelCase__)
_lowercase =floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
_lowercase =model(UpperCAmelCase__)
self.parent.assertEqual(result.reconstruction.shape, (self.batch_size, 1, self.image_size, self.image_size))
def UpperCamelCase__ ( self :Dict, snake_case :Union[str, Any], snake_case :Any, snake_case :List[str]):
"""simple docstring"""
_lowercase =self.type_sequence_label_size
_lowercase =TFDeiTForImageClassification(UpperCAmelCase__)
_lowercase =model(UpperCAmelCase__, labels=UpperCAmelCase__)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
# test greyscale images
_lowercase =1
_lowercase =TFDeiTForImageClassification(UpperCAmelCase__)
_lowercase =floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
_lowercase =model(UpperCAmelCase__, labels=UpperCAmelCase__)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def UpperCamelCase__ ( self :Optional[int]):
"""simple docstring"""
_lowercase =self.prepare_config_and_inputs()
_lowercase =config_and_inputs
_lowercase ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE_ ( lowercase__ , lowercase__ , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase : Optional[int] =(
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
__lowerCAmelCase : Union[str, Any] =(
{
'''feature-extraction''': TFDeiTModel,
'''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
__lowerCAmelCase : Optional[int] =False
__lowerCAmelCase : Dict =False
__lowerCAmelCase : Union[str, Any] =False
__lowerCAmelCase : int =False
def UpperCamelCase__ ( self :str):
"""simple docstring"""
_lowercase =TFDeiTModelTester(self)
_lowercase =ConfigTester(self, config_class=UpperCAmelCase__, has_text_modality=UpperCAmelCase__, hidden_size=37)
def UpperCamelCase__ ( self :int):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds')
def UpperCamelCase__ ( self :str):
"""simple docstring"""
pass
def UpperCamelCase__ ( self :Tuple):
"""simple docstring"""
_lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase =model_class(UpperCAmelCase__)
self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer))
_lowercase =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase__, tf.keras.layers.Dense))
def UpperCamelCase__ ( self :Union[str, Any]):
"""simple docstring"""
_lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase =model_class(UpperCAmelCase__)
_lowercase =inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowercase =[*signature.parameters.keys()]
_lowercase =['''pixel_values''']
self.assertListEqual(arg_names[:1], UpperCAmelCase__)
def UpperCamelCase__ ( self :str):
"""simple docstring"""
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__)
def UpperCamelCase__ ( self :str):
"""simple docstring"""
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase__)
def UpperCamelCase__ ( self :Dict):
"""simple docstring"""
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__)
def UpperCamelCase__ ( self :Dict, snake_case :List[Any], snake_case :List[str], snake_case :int=False):
"""simple docstring"""
_lowercase =super()._prepare_for_class(UpperCAmelCase__, UpperCAmelCase__, return_labels=UpperCAmelCase__)
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def UpperCamelCase__ ( self :List[str]):
"""simple docstring"""
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase =TFDeiTModel.from_pretrained(UpperCAmelCase__)
self.assertIsNotNone(UpperCAmelCase__)
def _snake_case () -> Optional[Any]:
_lowercase =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
return image
@require_tf
@require_vision
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCamelCase__ ( self :List[Any]):
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224')
if is_vision_available()
else None
)
@slow
def UpperCamelCase__ ( self :Dict):
"""simple docstring"""
_lowercase =TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224')
_lowercase =self.default_image_processor
_lowercase =prepare_img()
_lowercase =image_processor(images=UpperCAmelCase__, return_tensors='tf')
# forward pass
_lowercase =model(**UpperCAmelCase__)
# verify the logits
_lowercase =tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape, UpperCAmelCase__)
_lowercase =tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1])
self.assertTrue(np.allclose(outputs.logits[0, :3], UpperCAmelCase__, atol=1e-4))
| 181 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase_ = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert import AlbertTokenizer
else:
__UpperCamelCase : Union[str, Any] = None
__UpperCamelCase : int = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__UpperCamelCase : str = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
},
}
__UpperCamelCase : List[str] = {
'''albert-base-v1''': 512,
'''albert-large-v1''': 512,
'''albert-xlarge-v1''': 512,
'''albert-xxlarge-v1''': 512,
'''albert-base-v2''': 512,
'''albert-large-v2''': 512,
'''albert-xlarge-v2''': 512,
'''albert-xxlarge-v2''': 512,
}
__UpperCamelCase : Optional[Any] = '''▁'''
class a ( lowercase__ ):
snake_case__ = VOCAB_FILES_NAMES
snake_case__ = PRETRAINED_VOCAB_FILES_MAP
snake_case__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ = AlbertTokenizer
def __init__( self , _snake_case=None , _snake_case=None , _snake_case=True , _snake_case=True , _snake_case=False , _snake_case="[CLS]" , _snake_case="[SEP]" , _snake_case="<unk>" , _snake_case="[SEP]" , _snake_case="<pad>" , _snake_case="[CLS]" , _snake_case="[MASK]" , **_snake_case , ):
"""simple docstring"""
lowerCAmelCase = (
AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ , normalized=UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
else mask_token
)
super().__init__(
UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , do_lower_case=UpperCAmelCase__ , remove_space=UpperCAmelCase__ , keep_accents=UpperCAmelCase__ , bos_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , **UpperCAmelCase__ , )
lowerCAmelCase = do_lower_case
lowerCAmelCase = remove_space
lowerCAmelCase = keep_accents
lowerCAmelCase = vocab_file
lowerCAmelCase = False if not self.vocab_file else True
def UpperCamelCase__ ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
lowerCAmelCase = [self.sep_token_id]
lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCamelCase__ ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
lowerCAmelCase = [self.sep_token_id]
lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase__ ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(UpperCAmelCase__ ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCAmelCase = os.path.join(
UpperCAmelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase__ ):
copyfile(self.vocab_file , UpperCAmelCase__ )
return (out_vocab_file,)
| 4 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
UpperCamelCase_ = logging.getLogger(__name__)
def _lowerCAmelCase ( __magic_name__ : List[Any] , __magic_name__ : str ) -> Union[str, Any]:
return (preds == labels).mean()
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys() )} )
lowerCamelCase_ = field(metadata={'help': 'Should contain the data files for the task.'} )
lowerCamelCase_ = field(
default=1_28 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _lowerCAmelCase ( ) -> 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 : Dict =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase , lowercase , lowercase : List[Any] =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.''' )
# 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''' , __magic_name__ )
# Set seed
set_seed(training_args.seed )
try:
lowercase : Any =processors[data_args.task_name]()
lowercase : Optional[int] =processor.get_labels()
lowercase : str =len(__magic_name__ )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase : List[str] =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__magic_name__ , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
lowercase : int =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 , )
lowercase : Any =AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__magic_name__ , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase : int =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__magic_name__ , task=data_args.task_name , 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 : Union[str, Any] =(
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__magic_name__ , task=data_args.task_name , 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 compute_metrics(__magic_name__ : EvalPrediction ) -> Dict:
lowercase : Dict =np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__magic_name__ , p.label_ids )}
# Data collator
lowercase : List[str] =DataCollatorWithPadding(__magic_name__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase : Dict =Trainer(
model=__magic_name__ , args=__magic_name__ , train_dataset=__magic_name__ , eval_dataset=__magic_name__ , compute_metrics=__magic_name__ , data_collator=__magic_name__ , )
# 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_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase : Optional[Any] ={}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase : List[Any] =trainer.evaluate()
lowercase : Any =os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_master():
with open(__magic_name__ , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __magic_name__ , __magic_name__ )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__magic_name__ )
return results
def _lowerCAmelCase ( __magic_name__ : Any ) -> Optional[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 92 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
_snake_case = random.Random()
def __lowerCamelCase ( _lowercase , _lowercase=1.0 , _lowercase=None , _lowercase=None ) -> Union[str, Any]:
if rng is None:
UpperCamelCase = global_rng
UpperCamelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Union[str, Any]=7 , SCREAMING_SNAKE_CASE__ : Optional[Any]=4_00 , SCREAMING_SNAKE_CASE__ : str=20_00 , SCREAMING_SNAKE_CASE__ : Dict=20_48 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=1_28 , SCREAMING_SNAKE_CASE__ : List[str]=1 , SCREAMING_SNAKE_CASE__ : List[str]=5_12 , SCREAMING_SNAKE_CASE__ : int=30 , SCREAMING_SNAKE_CASE__ : Dict=4_41_00 , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = min_seq_length
UpperCamelCase = max_seq_length
UpperCamelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCamelCase = spectrogram_length
UpperCamelCase = feature_size
UpperCamelCase = num_audio_channels
UpperCamelCase = hop_length
UpperCamelCase = chunk_length
UpperCamelCase = sampling_rate
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def __lowerCAmelCase ( self : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any]=False , SCREAMING_SNAKE_CASE__ : Union[str, Any]=False ):
"""simple docstring"""
def _flatten(SCREAMING_SNAKE_CASE__ : Optional[Any] ):
return list(itertools.chain(*UpperCAmelCase__ ) )
if equal_length:
UpperCamelCase = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
UpperCamelCase = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
UpperCamelCase = [np.asarray(UpperCAmelCase__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class _lowerCAmelCase ( lowercase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict =TvltFeatureExtractor
def __lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = TvltFeatureExtractionTester(self )
def __lowerCAmelCase ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(UpperCAmelCase__ , 'spectrogram_length' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , 'feature_size' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , 'num_audio_channels' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , 'hop_length' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , 'chunk_length' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , 'sampling_rate' ) )
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase = feat_extract_first.save_pretrained(UpperCAmelCase__ )[0]
check_json_file_has_correct_format(UpperCAmelCase__ )
UpperCamelCase = self.feature_extraction_class.from_pretrained(UpperCAmelCase__ )
UpperCamelCase = feat_extract_first.to_dict()
UpperCamelCase = feat_extract_second.to_dict()
UpperCamelCase = dict_first.pop('mel_filters' )
UpperCamelCase = dict_second.pop('mel_filters' )
self.assertTrue(np.allclose(UpperCAmelCase__ , UpperCAmelCase__ ) )
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def __lowerCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase = os.path.join(UpperCAmelCase__ , 'feat_extract.json' )
feat_extract_first.to_json_file(UpperCAmelCase__ )
UpperCamelCase = self.feature_extraction_class.from_json_file(UpperCAmelCase__ )
UpperCamelCase = feat_extract_first.to_dict()
UpperCamelCase = feat_extract_second.to_dict()
UpperCamelCase = dict_first.pop('mel_filters' )
UpperCamelCase = dict_second.pop('mel_filters' )
self.assertTrue(np.allclose(UpperCAmelCase__ , UpperCAmelCase__ ) )
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
UpperCamelCase = [np.asarray(UpperCAmelCase__ ) for speech_input in speech_inputs]
# Test not batched input
UpperCamelCase = feature_extractor(np_speech_inputs[0] , return_tensors='np' , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
UpperCamelCase = feature_extractor(UpperCAmelCase__ , return_tensors='np' , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
UpperCamelCase = feature_extractor(
UpperCAmelCase__ , return_tensors='np' , sampling_rate=4_41_00 , mask_audio=UpperCAmelCase__ ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
UpperCamelCase = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
UpperCamelCase = np.asarray(UpperCAmelCase__ )
UpperCamelCase = feature_extractor(UpperCAmelCase__ , return_tensors='np' , sampling_rate=4_41_00 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def __lowerCAmelCase ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple ):
"""simple docstring"""
UpperCamelCase = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
UpperCamelCase = ds.sort('id' ).select(range(UpperCAmelCase__ ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def __lowerCAmelCase ( self : str ):
"""simple docstring"""
UpperCamelCase = self._load_datasamples(1 )
UpperCamelCase = TvltFeatureExtractor()
UpperCamelCase = feature_extractor(UpperCAmelCase__ , return_tensors='pt' ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 1_92, 1_28) )
UpperCamelCase = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , UpperCAmelCase__ , atol=1e-4 ) )
| 282 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
UpperCamelCase_ = logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_ = """cuda""" if torch.cuda.is_available() else """cpu"""
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : Tuple=100 , __magic_name__ : Optional[int]=" " ) -> List[str]:
lowercase : List[Any] =text.split(__magic_name__ )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(__magic_name__ ) , __magic_name__ )]
def _lowerCAmelCase ( __magic_name__ : dict ) -> dict:
lowercase , lowercase : int =[], []
for title, text in zip(documents['''title'''] , documents['''text'''] ):
if text is not None:
for passage in split_text(__magic_name__ ):
titles.append(title if title is not None else '''''' )
texts.append(__magic_name__ )
return {"title": titles, "text": texts}
def _lowerCAmelCase ( __magic_name__ : dict , __magic_name__ : DPRContextEncoder , __magic_name__ : DPRContextEncoderTokenizerFast ) -> dict:
lowercase : Dict =ctx_tokenizer(
documents['''title'''] , documents['''text'''] , truncation=__magic_name__ , padding='''longest''' , return_tensors='''pt''' )['''input_ids''']
lowercase : Optional[int] =ctx_encoder(input_ids.to(device=__magic_name__ ) , return_dict=__magic_name__ ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def _lowerCAmelCase ( __magic_name__ : "RagExampleArguments" , __magic_name__ : "ProcessingArguments" , __magic_name__ : "IndexHnswArguments" , ) -> str:
######################################
logger.info('''Step 1 - Create the dataset''' )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
lowercase : Tuple =load_dataset(
'''csv''' , data_files=[rag_example_args.csv_path] , split='''train''' , delimiter='''\t''' , column_names=['''title''', '''text'''] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
lowercase : Optional[int] =dataset.map(__magic_name__ , batched=__magic_name__ , num_proc=processing_args.num_proc )
# And compute the embeddings
lowercase : Any =DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=__magic_name__ )
lowercase : Any =DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
lowercase : Optional[int] =Features(
{'''text''': Value('''string''' ), '''title''': Value('''string''' ), '''embeddings''': Sequence(Value('''float32''' ) )} ) # optional, save as float32 instead of float64 to save space
lowercase : Optional[Any] =dataset.map(
partial(__magic_name__ , ctx_encoder=__magic_name__ , ctx_tokenizer=__magic_name__ ) , batched=__magic_name__ , batch_size=processing_args.batch_size , features=__magic_name__ , )
# And finally save your dataset
lowercase : Optional[Any] =os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset''' )
dataset.save_to_disk(__magic_name__ )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info('''Step 2 - Index the dataset''' )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
lowercase : Union[str, Any] =faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index('''embeddings''' , custom_index=__magic_name__ )
# And save the index
lowercase : Dict =os.path.join(rag_example_args.output_dir , '''my_knowledge_dataset_hnsw_index.faiss''' )
dataset.get_index('''embeddings''' ).save(__magic_name__ )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=str(Path(lowercase__ ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv' ) , metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''} , )
lowerCamelCase_ = field(
default=lowercase__ , metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'} , )
lowerCamelCase_ = field(
default='facebook/rag-sequence-nq' , metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''} , )
lowerCamelCase_ = field(
default='facebook/dpr-ctx_encoder-multiset-base' , metadata={
'help': (
'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
' \'facebook/dpr-ctx_encoder-multiset-base\''
)
} , )
lowerCamelCase_ = field(
default=str(Path(lowercase__ ).parent / 'test_run' / 'dummy-kb' ) , metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=lowercase__ , metadata={
'help': 'The number of processes to use to split the documents into passages. Default is single process.'
} , )
lowerCamelCase_ = field(
default=16 , metadata={
'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
lowerCamelCase_ = field(
default=7_68 , metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'} , )
lowerCamelCase_ = field(
default=1_28 , metadata={
'help': (
'The number of bi-directional links created for every new element during the HNSW index construction.'
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
UpperCamelCase_ = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
UpperCamelCase_ = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args)
| 92 | 0 |
"""simple docstring"""
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__magic_name__ = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__magic_name__ = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F'{len(upper_files)} files contain uppercase characters:')
print("""\n""".join(upper_files) + """\n""")
__magic_name__ = [file for file in filepaths if """ """ in file]
if space_files:
print(F'{len(space_files)} files contain space characters:')
print("""\n""".join(space_files) + """\n""")
__magic_name__ = [file for file in filepaths if """-""" in file]
if hyphen_files:
print(F'{len(hyphen_files)} files contain hyphen characters:')
print("""\n""".join(hyphen_files) + """\n""")
__magic_name__ = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F'{len(nodir_files)} files are not in a directory:')
print("""\n""".join(nodir_files) + """\n""")
__magic_name__ = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 129 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
UpperCamelCase_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
UpperCamelCase_ = 128022
UpperCamelCase_ = 128028
@require_sentencepiece
class __SCREAMING_SNAKE_CASE ( lowercase__ , unittest.TestCase ):
lowerCamelCase_ = MaMaaaTokenizer
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = True
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
super().setUp()
lowercase : Dict =['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
lowercase : List[Any] =dict(zip(UpperCAmelCase__ , range(len(UpperCAmelCase__ ) ) ) )
lowercase : List[Any] =Path(self.tmpdirname )
save_json(UpperCAmelCase__ , save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(UpperCAmelCase__ , save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowercase : Tuple =MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase_ ( self : Any , **UpperCAmelCase__ : int ):
'''simple docstring'''
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Dict ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : Tuple ='''</s>'''
lowercase : Union[str, Any] =0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[Any] =self.get_tokenizer()
lowercase : Optional[Any] =list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''</s>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''<s>''' )
self.assertEqual(len(UpperCAmelCase__ ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip('''Skip this test while all models are still to be uploaded.''' )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Union[str, Any] =self.get_tokenizer()
lowercase : str =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(UpperCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [2, 3, 4, 5, 6] , )
lowercase : Optional[int] =tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(UpperCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
lowercase : Tuple =tokenizer.convert_tokens_to_string(UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , '''This is a test''' )
@slow
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
# fmt: off
lowercase : int ={'''input_ids''': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name='''facebook/m2m100_418M''' , revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e''' , )
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
lowerCamelCase_ = 'facebook/m2m100_418M'
lowerCamelCase_ = [
'In my opinion, there are two levels of response from the French government.',
'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
]
lowerCamelCase_ = [
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
]
# fmt: off
lowerCamelCase_ = [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]
@classmethod
def lowerCamelCase_ ( cls : Optional[Any] ):
'''simple docstring'''
lowercase : MaMaaaTokenizer =MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en''' , tgt_lang='''fr''' )
lowercase : Optional[int] =1
return cls
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
self.assertEqual(self.tokenizer.get_lang_id('''ar''' ) , 128006 )
self.assertEqual(self.tokenizer.get_lang_id('''en''' ) , 128022 )
self.assertEqual(self.tokenizer.get_lang_id('''ro''' ) , 128076 )
self.assertEqual(self.tokenizer.get_lang_id('''mr''' ) , 128063 )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : List[str] =self.tokenizer.get_vocab()
self.assertEqual(len(UpperCAmelCase__ ) , self.tokenizer.vocab_size )
self.assertEqual(vocab['''<unk>'''] , 3 )
self.assertIn(self.tokenizer.get_lang_token('''en''' ) , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : List[Any] ='''en'''
lowercase : Optional[Any] =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
self.assertIn(UpperCAmelCase__ , self.tokenizer.all_special_ids )
# fmt: off
lowercase : str =[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
# fmt: on
lowercase : Optional[Any] =self.tokenizer.decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ )
lowercase : Optional[Any] =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCAmelCase__ )
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertNotIn(self.tokenizer.eos_token , UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Any =tempfile.mkdtemp()
lowercase : Tuple =self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(UpperCAmelCase__ )
lowercase : Union[str, Any] =MaMaaaTokenizer.from_pretrained(UpperCAmelCase__ )
self.assertDictEqual(new_tok.lang_token_to_id , UpperCAmelCase__ )
@require_torch
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : List[str] ='''en'''
lowercase : int ='''fr'''
lowercase : Union[str, Any] =self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase__ , return_tensors='''pt''' )
lowercase : str =shift_tokens_right(
batch['''labels'''] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
lowercase : int =batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Optional[int] ='''mr'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
lowercase : Union[str, Any] ='''zh'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : int ='''mr'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
lowercase : Optional[Any] ='''zh'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : Optional[Any] =self.tokenizer._build_translation_inputs('''A test''' , return_tensors='''pt''' , src_lang='''en''' , tgt_lang='''ar''' )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) , {
# en_XX, A, test, EOS
'''input_ids''': [[128022, 58, 4183, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 128006,
} , )
| 92 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetrImageProcessor
class _lowerCAmelCase ( unittest.TestCase ):
def __init__( self , _UpperCamelCase , _UpperCamelCase=7 , _UpperCamelCase=3 , _UpperCamelCase=30 , _UpperCamelCase=400 , _UpperCamelCase=True , _UpperCamelCase=None , _UpperCamelCase=True , _UpperCamelCase=1 / 255 , _UpperCamelCase=True , _UpperCamelCase=[0.5, 0.5, 0.5] , _UpperCamelCase=[0.5, 0.5, 0.5] , _UpperCamelCase=True , ) -> int:
lowerCAmelCase_ = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = num_channels
lowerCAmelCase_ = min_resolution
lowerCAmelCase_ = max_resolution
lowerCAmelCase_ = do_resize
lowerCAmelCase_ = size
lowerCAmelCase_ = do_rescale
lowerCAmelCase_ = rescale_factor
lowerCAmelCase_ = do_normalize
lowerCAmelCase_ = image_mean
lowerCAmelCase_ = image_std
lowerCAmelCase_ = do_pad
def __a ( self ) -> Any:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def __a ( self , _UpperCamelCase , _UpperCamelCase=False ) -> List[Any]:
if not batched:
lowerCAmelCase_ = image_inputs[0]
if isinstance(UpperCAmelCase__ , Image.Image ):
lowerCAmelCase_ = image.size
else:
lowerCAmelCase_ = image.shape[1], image.shape[2]
if w < h:
lowerCAmelCase_ = int(self.size["shortest_edge"] * h / w )
lowerCAmelCase_ = self.size['''shortest_edge''']
elif w > h:
lowerCAmelCase_ = self.size['''shortest_edge''']
lowerCAmelCase_ = int(self.size["shortest_edge"] * w / h )
else:
lowerCAmelCase_ = self.size['''shortest_edge''']
lowerCAmelCase_ = self.size['''shortest_edge''']
else:
lowerCAmelCase_ = []
for image in image_inputs:
lowerCAmelCase_ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowerCAmelCase_ = max(UpperCAmelCase__ , key=lambda _UpperCamelCase : item[0] )[0]
lowerCAmelCase_ = max(UpperCAmelCase__ , key=lambda _UpperCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _lowerCAmelCase ( lowercase__ , unittest.TestCase ):
_lowercase =DetrImageProcessor if is_vision_available() else None
def __a ( self ) -> Tuple:
lowerCAmelCase_ = DetrImageProcessingTester(self )
@property
def __a ( self ) -> Tuple:
return self.image_processor_tester.prepare_image_processor_dict()
def __a ( self ) -> List[str]:
lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase__ , "image_mean" ) )
self.assertTrue(hasattr(UpperCAmelCase__ , "image_std" ) )
self.assertTrue(hasattr(UpperCAmelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(UpperCAmelCase__ , "do_rescale" ) )
self.assertTrue(hasattr(UpperCAmelCase__ , "rescale_factor" ) )
self.assertTrue(hasattr(UpperCAmelCase__ , "do_resize" ) )
self.assertTrue(hasattr(UpperCAmelCase__ , "size" ) )
self.assertTrue(hasattr(UpperCAmelCase__ , "do_pad" ) )
def __a ( self ) -> List[str]:
lowerCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1_333} )
self.assertEqual(image_processor.do_pad , UpperCAmelCase__ )
lowerCAmelCase_ = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCAmelCase__ )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , UpperCAmelCase__ )
def __a ( self ) -> Optional[Any]:
pass
def __a ( self ) -> List[Any]:
lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , Image.Image )
# Test not batched input
lowerCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowerCAmelCase_ = self.image_processor_tester.get_expected_values(UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase_ = self.image_processor_tester.get_expected_values(UpperCAmelCase__ , batched=UpperCAmelCase__ )
lowerCAmelCase_ = image_processing(UpperCAmelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , numpify=UpperCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , np.ndarray )
# Test not batched input
lowerCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowerCAmelCase_ = self.image_processor_tester.get_expected_values(UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase_ = image_processing(UpperCAmelCase__ , return_tensors="pt" ).pixel_values
lowerCAmelCase_ = self.image_processor_tester.get_expected_values(UpperCAmelCase__ , batched=UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __a ( self ) -> Tuple:
lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , torchify=UpperCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase__ , torch.Tensor )
# Test not batched input
lowerCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowerCAmelCase_ = self.image_processor_tester.get_expected_values(UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase_ = image_processing(UpperCAmelCase__ , return_tensors="pt" ).pixel_values
lowerCAmelCase_ = self.image_processor_tester.get_expected_values(UpperCAmelCase__ , batched=UpperCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def __a ( self ) -> Dict:
lowerCAmelCase_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
lowerCAmelCase_ = json.loads(f.read() )
lowerCAmelCase_ = {'''image_id''': 39_769, '''annotations''': target}
# encode them
lowerCAmelCase_ = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50" )
lowerCAmelCase_ = image_processing(images=UpperCAmelCase__ , annotations=UpperCAmelCase__ , return_tensors="pt" )
# verify pixel values
lowerCAmelCase_ = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , UpperCAmelCase__ )
lowerCAmelCase_ = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , UpperCAmelCase__ , atol=1e-4 ) )
# verify area
lowerCAmelCase_ = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , UpperCAmelCase__ ) )
# verify boxes
lowerCAmelCase_ = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , UpperCAmelCase__ )
lowerCAmelCase_ = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , UpperCAmelCase__ , atol=1e-3 ) )
# verify image_id
lowerCAmelCase_ = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , UpperCAmelCase__ ) )
# verify is_crowd
lowerCAmelCase_ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , UpperCAmelCase__ ) )
# verify class_labels
lowerCAmelCase_ = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , UpperCAmelCase__ ) )
# verify orig_size
lowerCAmelCase_ = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , UpperCAmelCase__ ) )
# verify size
lowerCAmelCase_ = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , UpperCAmelCase__ ) )
@slow
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
lowerCAmelCase_ = json.loads(f.read() )
lowerCAmelCase_ = {'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}
lowerCAmelCase_ = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
lowerCAmelCase_ = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic" )
lowerCAmelCase_ = image_processing(images=UpperCAmelCase__ , annotations=UpperCAmelCase__ , masks_path=UpperCAmelCase__ , return_tensors="pt" )
# verify pixel values
lowerCAmelCase_ = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , UpperCAmelCase__ )
lowerCAmelCase_ = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , UpperCAmelCase__ , atol=1e-4 ) )
# verify area
lowerCAmelCase_ = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , UpperCAmelCase__ ) )
# verify boxes
lowerCAmelCase_ = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , UpperCAmelCase__ )
lowerCAmelCase_ = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , UpperCAmelCase__ , atol=1e-3 ) )
# verify image_id
lowerCAmelCase_ = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , UpperCAmelCase__ ) )
# verify is_crowd
lowerCAmelCase_ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , UpperCAmelCase__ ) )
# verify class_labels
lowerCAmelCase_ = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , UpperCAmelCase__ ) )
# verify masks
lowerCAmelCase_ = 822_873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , UpperCAmelCase__ )
# verify orig_size
lowerCAmelCase_ = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , UpperCAmelCase__ ) )
# verify size
lowerCAmelCase_ = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , UpperCAmelCase__ ) )
| 290 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int = 600851475143 ) -> int:
try:
lowercase : Any =int(__magic_name__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
lowercase : Optional[Any] =2
lowercase : Dict =0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
lowercase : Union[str, Any] =i
while n % i == 0:
lowercase : Optional[int] =n // i
i += 1
return int(__magic_name__ )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 92 | 0 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
A__ : Tuple = pd.read_csv('''sample_data.csv''', header=None)
A__ : Dict = df.shape[:1][0]
# If you're using some other dataset input the target column
A__ : List[Any] = df.iloc[:, 1:2]
A__ : str = actual_data.values.reshape(len_data, 1)
A__ : str = MinMaxScaler().fit_transform(actual_data)
A__ : Dict = 1_0
A__ : Any = 5
A__ : int = 2_0
A__ : Any = len_data - periods * look_back
A__ : Optional[int] = actual_data[:division]
A__ : Tuple = actual_data[division - look_back :]
A__ , A__ : Dict = [], []
A__ , A__ : Any = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
A__ : Optional[int] = np.array(train_x)
A__ : int = np.array(test_x)
A__ : List[str] = np.array([list(i.ravel()) for i in train_y])
A__ : Optional[Any] = np.array([list(i.ravel()) for i in test_y])
A__ : str = Sequential()
model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
model.add(Dense(forward_days))
model.compile(loss='''mean_squared_error''', optimizer='''adam''')
A__ : Optional[Any] = model.fit(
x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
)
A__ : Any = model.predict(x_test)
| 286 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = 'speech_to_text_2'
lowerCamelCase_ = ['past_key_values']
lowerCamelCase_ = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : int , UpperCAmelCase__ : Dict=10000 , UpperCAmelCase__ : Optional[int]=6 , UpperCAmelCase__ : str=2048 , UpperCAmelCase__ : Optional[Any]=4 , UpperCAmelCase__ : Union[str, Any]=0.0 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Optional[Any]="relu" , UpperCAmelCase__ : List[str]=256 , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : List[Any]=0.0 , UpperCAmelCase__ : Optional[Any]=0.0 , UpperCAmelCase__ : Tuple=0.02 , UpperCAmelCase__ : int=2 , UpperCAmelCase__ : str=True , UpperCAmelCase__ : str=1 , UpperCAmelCase__ : Optional[Any]=0 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : List[Any]=1024 , **UpperCAmelCase__ : Dict , ):
'''simple docstring'''
lowercase : List[str] =vocab_size
lowercase : Optional[int] =d_model
lowercase : Optional[Any] =decoder_ffn_dim
lowercase : Any =decoder_layers
lowercase : Dict =decoder_attention_heads
lowercase : List[Any] =dropout
lowercase : List[Any] =attention_dropout
lowercase : Any =activation_dropout
lowercase : Optional[Any] =activation_function
lowercase : Optional[int] =init_std
lowercase : Dict =decoder_layerdrop
lowercase : Optional[int] =use_cache
lowercase : Optional[Any] =decoder_layers
lowercase : List[str] =scale_embedding # scale factor will be sqrt(d_model) if True
lowercase : str =max_target_positions
super().__init__(
pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , decoder_start_token_id=UpperCAmelCase__ , **UpperCAmelCase__ , )
| 92 | 0 |
from cva import destroyAllWindows, imread, imshow, waitKey
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ ):
_SCREAMING_SNAKE_CASE = [255, 255, 255] - img[i][j]
return img
if __name__ == "__main__":
# read original image
snake_case : int = imread('image_data/lena.jpg', 1)
# convert to its negative
snake_case : Optional[Any] = convert_to_negative(img)
# show result image
imshow('negative of original image', img)
waitKey(0)
destroyAllWindows()
| 605 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self : str , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[str]=13 , UpperCAmelCase__ : Any=32 , UpperCAmelCase__ : List[str]=3 , UpperCAmelCase__ : Any=4 , UpperCAmelCase__ : List[str]=[10, 20, 30, 40] , UpperCAmelCase__ : Any=[2, 2, 3, 2] , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Optional[Any]=37 , UpperCAmelCase__ : Union[str, Any]="gelu" , UpperCAmelCase__ : Optional[Any]=10 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : Optional[int]=["stage2", "stage3", "stage4"] , UpperCAmelCase__ : Dict=[2, 3, 4] , UpperCAmelCase__ : Optional[int]=None , ):
'''simple docstring'''
lowercase : List[Any] =parent
lowercase : Tuple =batch_size
lowercase : List[str] =image_size
lowercase : List[Any] =num_channels
lowercase : Union[str, Any] =num_stages
lowercase : int =hidden_sizes
lowercase : Any =depths
lowercase : Tuple =is_training
lowercase : str =use_labels
lowercase : List[Any] =intermediate_size
lowercase : int =hidden_act
lowercase : Union[str, Any] =num_labels
lowercase : Optional[int] =initializer_range
lowercase : int =out_features
lowercase : List[str] =out_indices
lowercase : str =scope
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : str =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase : Dict =None
if self.use_labels:
lowercase : List[Any] =ids_tensor([self.batch_size] , self.num_labels )
lowercase : Dict =self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[Any] ):
'''simple docstring'''
lowercase : Dict =ConvNextVaModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[Any] =model(UpperCAmelCase__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCamelCase_ ( self : Any , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Dict , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Dict =ConvNextVaForImageClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : str =model(UpperCAmelCase__ , labels=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : Union[str, Any] =ConvNextVaBackbone(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[int] =model(UpperCAmelCase__ )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
lowercase : Optional[Any] =None
lowercase : str =ConvNextVaBackbone(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowercase : Optional[Any] =model(UpperCAmelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Any =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase : str =config_and_inputs
lowercase : Any ={'''pixel_values''': pixel_values}
return config, inputs_dict
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : str =self.prepare_config_and_inputs()
lowercase , lowercase , lowercase : List[str] =config_and_inputs
lowercase : Optional[Any] ={'''pixel_values''': pixel_values, '''labels''': labels}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ , unittest.TestCase ):
lowerCamelCase_ = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase_ = (
{'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : Dict =ConvNextVaModelTester(self )
lowercase : str =ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=37 )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return
@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds''' )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings''' )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking''' )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase , lowercase : Union[str, Any] =self.model_tester.prepare_config_and_inputs_with_labels()
lowercase : Optional[int] =True
if model_class.__name__ in [
*get_values(UpperCAmelCase__ ),
*get_values(UpperCAmelCase__ ),
]:
continue
lowercase : Dict =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.train()
lowercase : Optional[Any] =self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
lowercase : List[Any] =model(**UpperCAmelCase__ ).loss
loss.backward()
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowercase , lowercase : Any =self.model_tester.prepare_config_and_inputs_with_labels()
lowercase : List[Any] =False
lowercase : Any =True
if (
model_class.__name__
in [*get_values(UpperCAmelCase__ ), *get_values(UpperCAmelCase__ )]
or not model_class.supports_gradient_checkpointing
):
continue
lowercase : Any =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.gradient_checkpointing_enable()
model.train()
lowercase : Optional[Any] =self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ , return_labels=UpperCAmelCase__ )
lowercase : int =model(**UpperCAmelCase__ ).loss
loss.backward()
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase , lowercase : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : Dict =model_class(UpperCAmelCase__ )
lowercase : Union[str, Any] =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase : int =[*signature.parameters.keys()]
lowercase : Optional[Any] =['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
def check_hidden_states_output(UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : Tuple ):
lowercase : int =model_class(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
with torch.no_grad():
lowercase : Any =model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ ) )
lowercase : Dict =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase : List[Any] =self.model_tester.num_stages
self.assertEqual(len(UpperCAmelCase__ ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowercase , lowercase : List[str] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase : List[str] =True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase : Tuple =True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
lowercase : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__ )
@slow
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : List[Any] =ConvNextVaModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def _lowerCAmelCase ( ) -> List[Any]:
lowercase : Union[str, Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : Tuple =ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ).to(UpperCAmelCase__ )
lowercase : int =self.default_image_processor
lowercase : List[str] =prepare_img()
lowercase : List[Any] =preprocessor(images=UpperCAmelCase__ , return_tensors='''pt''' ).to(UpperCAmelCase__ )
# forward pass
with torch.no_grad():
lowercase : Dict =model(**UpperCAmelCase__ )
# verify the logits
lowercase : Optional[Any] =torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase__ )
lowercase : Tuple =torch.tensor([0.99_96, 0.19_66, -0.43_86] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase__ , atol=1E-4 ) )
| 92 | 0 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __a ( A , A , A=1_024 , A=1_024 , A=False , **A ) -> str:
'''simple docstring'''
A__ = AutoTokenizer.from_pretrained(A )
A__ = SeqaSeqDataset(A , A , A , A , type_path="train" , **A )
A__ = tok.pad_token_id
def get_lens(A ):
A__ = tqdm(
DataLoader(A , batch_size=512 , num_workers=8 , shuffle=A , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
A__ = []
for batch in dl:
A__ = batch['''input_ids'''].ne(A ).sum(1 ).tolist()
A__ = batch['''labels'''].ne(A ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(A , A ):
max_lens.append(max(A , A ) )
else:
max_lens.extend(A )
return max_lens
A__ = get_lens(A )
A__ = SeqaSeqDataset(A , A , A , A , type_path="val" , **A )
A__ = get_lens(A )
pickle_save(A , train_ds.len_file )
pickle_save(A , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file) | 337 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
UpperCamelCase_ = object()
# For specifying empty leaf dict `{}`
UpperCamelCase_ = object()
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : int ) -> Optional[int]:
lowercase : Optional[Any] =tuple((re.compile(x + '''$''' ) for x in qs) )
for i in range(len(__magic_name__ ) - len(__magic_name__ ) + 1 ):
lowercase : Union[str, Any] =[x.match(__magic_name__ ) for x, y in zip(__magic_name__ , ks[i:] )]
if matches and all(__magic_name__ ):
return True
return False
def _lowerCAmelCase ( __magic_name__ : Dict ) -> List[str]:
def replace(__magic_name__ : Optional[int] , __magic_name__ : Optional[Any] ):
for rule, replacement in rules:
if _match(__magic_name__ , __magic_name__ ):
return replacement
return val
return replace
def _lowerCAmelCase ( ) -> int:
return [
# embeddings
(("transformer", "wpe", "embedding"), P('''mp''' , __magic_name__ )),
(("transformer", "wte", "embedding"), P('''mp''' , __magic_name__ )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__magic_name__ , '''mp''' )),
(("attention", "out_proj", "kernel"), P('''mp''' , __magic_name__ )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(__magic_name__ , '''mp''' )),
(("mlp", "c_fc", "bias"), P('''mp''' )),
(("mlp", "c_proj", "kernel"), P('''mp''' , __magic_name__ )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def _lowerCAmelCase ( __magic_name__ : str ) -> int:
lowercase : int =_get_partition_rules()
lowercase : Tuple =_replacement_rules(__magic_name__ )
lowercase : Any ={k: _unmatched for k in flatten_dict(__magic_name__ )}
lowercase : Any ={k: replace(__magic_name__ , __magic_name__ ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(__magic_name__ ) )
| 92 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["GPTSw3Tokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_swa import GPTSwaTokenizer
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 346 |
'''simple docstring'''
from collections import defaultdict
def _lowerCAmelCase ( __magic_name__ : int ) -> int:
lowercase : Optional[Any] =1
lowercase : Union[str, Any] =True
for v in tree[start]:
if v not in visited:
ret += dfs(__magic_name__ )
if ret % 2 == 0:
cuts.append(__magic_name__ )
return ret
def _lowerCAmelCase ( ) -> int:
dfs(1 )
if __name__ == "__main__":
UpperCamelCase_ , UpperCamelCase_ = 10, 9
UpperCamelCase_ = defaultdict(list)
UpperCamelCase_ = {}
UpperCamelCase_ = []
UpperCamelCase_ = 0
UpperCamelCase_ = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 92 | 0 |
from math import ceil
def lowercase_ ( _UpperCamelCase = 10_01 ):
'''simple docstring'''
__lowercase = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__lowercase = 2 * i + 1
__lowercase = 2 * i
__lowercase = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
a : Tuple = int(sys.argv[1])
print(solution(n))
except ValueError:
print('''Invalid entry - please enter a number''')
| 639 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
def _lowerCAmelCase ( __magic_name__ : List[str] ) -> Dict:
lowercase : List[str] =R'''\w+[.]\d+'''
lowercase : List[str] =re.findall(__magic_name__ , __magic_name__ )
for pat in pats:
lowercase : Optional[int] =key.replace(__magic_name__ , '''_'''.join(pat.split('''.''' ) ) )
return key
def _lowerCAmelCase ( __magic_name__ : List[str] , __magic_name__ : Union[str, Any] , __magic_name__ : Dict ) -> str:
lowercase : Dict =pt_tuple_key[:-1] + ('''scale''',)
if (
any('''norm''' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
lowercase : str =pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
lowercase : str =pt_tuple_key[:-1] + ('''scale''',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
lowercase : Dict =pt_tuple_key[:-1] + ('''embedding''',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowercase : Tuple =pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
lowercase : Tuple =pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowercase : str =pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight":
lowercase : Optional[Any] =pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowercase : Dict =pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowercase : Union[str, Any] =pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : Optional[Any] , __magic_name__ : Union[str, Any]=42 ) -> List[str]:
# Step 1: Convert pytorch tensor to numpy
lowercase : Optional[Any] ={k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
lowercase : str =flax_model.init_weights(PRNGKey(__magic_name__ ) )
lowercase : Dict =flatten_dict(__magic_name__ )
lowercase : Dict ={}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowercase : Dict =rename_key(__magic_name__ )
lowercase : Optional[int] =tuple(renamed_pt_key.split('''.''' ) )
# Correctly rename weight parameters
lowercase , lowercase : Any =rename_key_and_reshape_tensor(__magic_name__ , __magic_name__ , __magic_name__ )
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}.''' )
# also add unexpected weight so that warning is thrown
lowercase : Tuple =jnp.asarray(__magic_name__ )
return unflatten_dict(__magic_name__ )
| 92 | 0 |
from math import pi, sqrt
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : float ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError('math domain error' )
if num > 171.5:
raise OverflowError('math range error' )
elif num - int(lowerCAmelCase_ ) not in (0, 0.5):
raise NotImplementedError('num must be an integer or a half-integer' )
elif num == 0.5:
return sqrt(lowerCAmelCase_ )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def SCREAMING_SNAKE_CASE__ ( ) -> None:
"""simple docstring"""
assert gamma(0.5 ) == sqrt(lowerCAmelCase_ )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
__SCREAMING_SNAKE_CASE = 1.0
while num:
__SCREAMING_SNAKE_CASE = float(input('Gamma of: '))
print(f"""gamma({num}) = {gamma(num)}""")
print('\nEnter 0 to exit...')
| 220 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase_ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
UpperCamelCase_ = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
UpperCamelCase_ = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, float]:
lowercase : int =len([g for position, g in enumerate(__magic_name__ ) if g == main_target[position]] )
return (item, float(__magic_name__ ))
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, str]:
lowercase : Any =random.randint(0 , len(__magic_name__ ) - 1 )
lowercase : Tuple =parent_a[:random_slice] + parent_a[random_slice:]
lowercase : List[str] =parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : list[str] ) -> str:
lowercase : Union[str, Any] =list(__magic_name__ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
lowercase : Dict =random.choice(__magic_name__ )
return "".join(__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : tuple[str, float] , __magic_name__ : list[tuple[str, float]] , __magic_name__ : list[str] , ) -> list[str]:
lowercase : Any =[]
# Generate more children proportionally to the fitness score.
lowercase : Dict =int(parent_a[1] * 100 ) + 1
lowercase : List[str] =10 if child_n >= 10 else child_n
for _ in range(__magic_name__ ):
lowercase : List[str] =population_score[random.randint(0 , __magic_name__ )][0]
lowercase , lowercase : Dict =crossover(parent_a[0] , __magic_name__ )
# Append new string to the population list.
pop.append(mutate(__magic_name__ , __magic_name__ ) )
pop.append(mutate(__magic_name__ , __magic_name__ ) )
return pop
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : list[str] , __magic_name__ : bool = True ) -> tuple[int, int, str]:
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
lowercase : List[str] =f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
raise ValueError(__magic_name__ )
# Verify that the target contains no genes besides the ones inside genes variable.
lowercase : Optional[int] =sorted({c for c in target if c not in genes} )
if not_in_genes_list:
lowercase : Dict =f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
raise ValueError(__magic_name__ )
# Generate random starting population.
lowercase : int =[]
for _ in range(__magic_name__ ):
population.append(''''''.join([random.choice(__magic_name__ ) for i in range(len(__magic_name__ ) )] ) )
# Just some logs to know what the algorithms is doing.
lowercase , lowercase : Optional[int] =0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(__magic_name__ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
lowercase : List[str] =[evaluate(__magic_name__ , __magic_name__ ) for item in population]
# Check if there is a matching evolution.
lowercase : int =sorted(__magic_name__ , key=lambda __magic_name__ : x[1] , reverse=__magic_name__ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f'''\nGeneration: {generation}'''
f'''\nTotal Population:{total_population}'''
f'''\nBest score: {population_score[0][1]}'''
f'''\nBest string: {population_score[0][0]}''' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
lowercase : Any =population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(__magic_name__ )
# Normalize population score to be between 0 and 1.
lowercase : Dict =[
(item, score / len(__magic_name__ )) for item, score in population_score
]
# This is selection
for i in range(__magic_name__ ):
population.extend(select(population_score[int(__magic_name__ )] , __magic_name__ , __magic_name__ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(__magic_name__ ) > N_POPULATION:
break
if __name__ == "__main__":
UpperCamelCase_ = (
"""This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
)
UpperCamelCase_ = list(
""" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
"""nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
)
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = basic(target_str, genes_list)
print(
f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 92 | 0 |
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