code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case , __snake_case ) -> Any:
if height >= 1:
move_tower(height - 1 , __snake_case , __snake_case , __snake_case )
move_disk(__snake_case , __snake_case )
move_tower(height - 1 , __snake_case , __snake_case , __snake_case )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> List[Any]:
print("""moving disk from""" , __snake_case , """to""" , __snake_case )
def _SCREAMING_SNAKE_CASE ( ) -> Union[str, Any]:
_UpperCAmelCase = int(input("""Height of hanoi: """ ).strip() )
move_tower(__snake_case , """A""" , """B""" , """C""" )
if __name__ == "__main__":
main() | 108 |
"""simple docstring"""
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
SCREAMING_SNAKE_CASE : List[str] = '''bert-base-cased'''
SCREAMING_SNAKE_CASE : Dict = '''google/pegasus-xsum'''
SCREAMING_SNAKE_CASE : int = [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
SCREAMING_SNAKE_CASE : List[Any] = ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
SCREAMING_SNAKE_CASE : str = '''patrickvonplaten/t5-tiny-random'''
SCREAMING_SNAKE_CASE : str = '''sshleifer/bart-tiny-random'''
SCREAMING_SNAKE_CASE : List[str] = '''sshleifer/tiny-mbart'''
SCREAMING_SNAKE_CASE : str = '''sshleifer/tiny-marian-en-de'''
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
A__ = '\n'.join(lowerCAmelCase__ )
Path(lowerCAmelCase__ ).open('w' ).writelines(lowerCAmelCase__ )
def __lowerCamelCase ( lowerCAmelCase__ ):
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(lowerCAmelCase__ ,f'''{split}.source''' ) ,lowerCAmelCase__ )
_dump_articles(os.path.join(lowerCAmelCase__ ,f'''{split}.target''' ) ,lowerCAmelCase__ )
return tmp_dir
class snake_case_ ( _lowerCamelCase ):
"""simple docstring"""
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
A__ = AutoTokenizer.from_pretrained(__a )
A__ = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
A__ = max(len(tokenizer.encode(__a ) ) for a in ARTICLES )
A__ = max(len(tokenizer.encode(__a ) ) for a in SUMMARIES )
A__ = 4
A__ = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
A__ , A__ = 'ro_RO', 'de_DE' # ignored for all but mbart, but never causes error.
A__ = SeqaSeqDataset(
__a , data_dir=__a , type_path='train' , max_source_length=__a , max_target_length=__a , src_lang=__a , tgt_lang=__a , )
A__ = DataLoader(__a , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(__a , __a )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
A__ = shift_tokens_right(batch['labels'] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
A__ = AutoTokenizer.from_pretrained(__a )
A__ = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
A__ = max(len(tokenizer.encode(__a ) ) for a in ARTICLES )
A__ = max(len(tokenizer.encode(__a ) ) for a in SUMMARIES )
A__ = 4
A__ = LegacySeqaSeqDataset(
__a , data_dir=__a , type_path='train' , max_source_length=20 , max_target_length=__a , )
A__ = DataLoader(__a , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ = AutoTokenizer.from_pretrained('facebook/mbart-large-cc25' )
A__ = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
A__ = tmp_dir.joinpath('train.source' ).open().readlines()
A__ = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(__a , __a , 128 , __a )
A__ = {x.name for x in tmp_dir.iterdir()}
A__ = {x.name for x in save_dir.iterdir()}
A__ = save_dir.joinpath('train.source' ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(__a ) < len(__a )
assert len(__a ) == 1
assert len(packed_examples[0] ) == sum(len(__a ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason='This test requires fairseq' )
def _UpperCAmelCase ( self ):
"""simple docstring"""
if not FAIRSEQ_AVAILABLE:
return
A__ , A__ , A__ = self._get_dataset(max_len=64 )
A__ = 64
A__ = ds.make_dynamic_sampler(__a , required_batch_size_multiple=__a )
A__ = [len(__a ) for x in batch_sampler]
assert len(set(__a ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(__a ) == len(__a ) # no dropped or added examples
A__ = DataLoader(__a , batch_sampler=__a , collate_fn=ds.collate_fn , num_workers=2 )
A__ = []
A__ = []
for batch in data_loader:
A__ = batch['input_ids'].shape
A__ = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
A__ = np.product(batch['input_ids'].shape )
num_src_per_batch.append(__a )
if num_src_tokens > (max_tokens * 1.1):
failures.append(__a )
assert num_src_per_batch[0] == max(__a )
if failures:
raise AssertionError(f'''too many tokens in {len(__a )} batches''' )
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ , A__ , A__ = self._get_dataset(max_len=512 )
A__ = 2
A__ = ds.make_sortish_sampler(__a , shuffle=__a )
A__ = DataLoader(__a , batch_size=__a , collate_fn=ds.collate_fn , num_workers=2 )
A__ = DataLoader(__a , batch_size=__a , collate_fn=ds.collate_fn , num_workers=2 , sampler=__a )
A__ = tokenizer.pad_token_id
def count_pad_tokens(__a , __a="input_ids" ):
return [batch[k].eq(__a ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(__a , k='labels' ) ) < sum(count_pad_tokens(__a , k='labels' ) )
assert sum(count_pad_tokens(__a ) ) < sum(count_pad_tokens(__a ) )
assert len(__a ) == len(__a )
def _UpperCAmelCase ( self , __a=1000 , __a=128 ):
"""simple docstring"""
if os.getenv('USE_REAL_DATA' , __a ):
A__ = 'examples/seq2seq/wmt_en_ro'
A__ = max_len * 2 * 64
if not Path(__a ).joinpath('train.len' ).exists():
save_len_file(__a , __a )
else:
A__ = 'examples/seq2seq/test_data/wmt_en_ro'
A__ = max_len * 4
save_len_file(__a , __a )
A__ = AutoTokenizer.from_pretrained(__a )
A__ = SeqaSeqDataset(
__a , data_dir=__a , type_path='train' , max_source_length=__a , max_target_length=__a , n_obs=__a , )
return ds, max_tokens, tokenizer
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ , A__ , A__ = self._get_dataset()
A__ = set(DistributedSortishSampler(__a , 256 , num_replicas=2 , rank=0 , add_extra_examples=__a ) )
A__ = set(DistributedSortishSampler(__a , 256 , num_replicas=2 , rank=1 , add_extra_examples=__a ) )
assert idsa.intersection(__a ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
A__ = AutoTokenizer.from_pretrained(__a , use_fast=__a )
if tok_name == MBART_TINY:
A__ = SeqaSeqDataset(
__a , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='train' , max_source_length=4 , max_target_length=8 , src_lang='EN' , tgt_lang='FR' , )
A__ = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
A__ = SeqaSeqDataset(
__a , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='train' , max_source_length=4 , max_target_length=8 , )
A__ = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(__a ) == 1 if tok_name == BART_TINY else len(__a ) == 0
| 260 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"""microsoft/unispeech-sat-base-100h-libri-ft""": (
"""https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class lowerCamelCase__ ( lowercase__ ):
'''simple docstring'''
A__ = """unispeech-sat"""
def __init__( self : Union[str, Any] , __A : str=32 , __A : Any=768 , __A : Tuple=12 , __A : List[str]=12 , __A : int=3072 , __A : Optional[Any]="gelu" , __A : Tuple=0.1 , __A : List[Any]=0.1 , __A : Optional[int]=0.1 , __A : Optional[Any]=0.0 , __A : Optional[Any]=0.0 , __A : List[str]=0.1 , __A : str=0.1 , __A : Optional[int]=0.0_2 , __A : Optional[int]=1E-5 , __A : Any="group" , __A : Dict="gelu" , __A : List[Any]=(512, 512, 512, 512, 512, 512, 512) , __A : Dict=(5, 2, 2, 2, 2, 2, 2) , __A : Union[str, Any]=(10, 3, 3, 3, 3, 2, 2) , __A : List[Any]=False , __A : Union[str, Any]=128 , __A : Optional[int]=16 , __A : Union[str, Any]=False , __A : Optional[int]=True , __A : List[Any]=0.0_5 , __A : Any=10 , __A : Tuple=2 , __A : Optional[Any]=0.0 , __A : str=10 , __A : List[Any]=0 , __A : Dict=320 , __A : str=2 , __A : Optional[int]=0.1 , __A : int=100 , __A : Any=256 , __A : str=256 , __A : Tuple=0.1 , __A : Dict="mean" , __A : Optional[Any]=False , __A : Optional[Any]=False , __A : Optional[Any]=256 , __A : Optional[int]=(512, 512, 512, 512, 1500) , __A : Optional[Any]=(5, 3, 3, 1, 1) , __A : Optional[int]=(1, 2, 3, 1, 1) , __A : Tuple=512 , __A : Tuple=0 , __A : str=1 , __A : Optional[Any]=2 , __A : List[str]=504 , **__A : Optional[int] , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**__lowercase , pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase )
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = feat_extract_norm
lowerCAmelCase__ = feat_extract_activation
lowerCAmelCase__ = list(__lowercase )
lowerCAmelCase__ = list(__lowercase )
lowerCAmelCase__ = list(__lowercase )
lowerCAmelCase__ = conv_bias
lowerCAmelCase__ = num_conv_pos_embeddings
lowerCAmelCase__ = num_conv_pos_embedding_groups
lowerCAmelCase__ = len(self.conv_dim )
lowerCAmelCase__ = num_hidden_layers
lowerCAmelCase__ = intermediate_size
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = hidden_dropout
lowerCAmelCase__ = attention_dropout
lowerCAmelCase__ = activation_dropout
lowerCAmelCase__ = feat_proj_dropout
lowerCAmelCase__ = final_dropout
lowerCAmelCase__ = layerdrop
lowerCAmelCase__ = layer_norm_eps
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = num_clusters
lowerCAmelCase__ = do_stable_layer_norm
lowerCAmelCase__ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
""" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCAmelCase__ = apply_spec_augment
lowerCAmelCase__ = mask_time_prob
lowerCAmelCase__ = mask_time_length
lowerCAmelCase__ = mask_time_min_masks
lowerCAmelCase__ = mask_feature_prob
lowerCAmelCase__ = mask_feature_length
lowerCAmelCase__ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowerCAmelCase__ = num_codevectors_per_group
lowerCAmelCase__ = num_codevector_groups
lowerCAmelCase__ = contrastive_logits_temperature
lowerCAmelCase__ = feat_quantizer_dropout
lowerCAmelCase__ = num_negatives
lowerCAmelCase__ = codevector_dim
lowerCAmelCase__ = proj_codevector_dim
lowerCAmelCase__ = diversity_loss_weight
# ctc loss
lowerCAmelCase__ = ctc_loss_reduction
lowerCAmelCase__ = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowerCAmelCase__ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowerCAmelCase__ = list(__lowercase )
lowerCAmelCase__ = list(__lowercase )
lowerCAmelCase__ = list(__lowercase )
lowerCAmelCase__ = xvector_output_dim
@property
def lowercase__ ( self : List[str] ) -> Dict:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 715 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_UpperCamelCase = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFormerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
"""SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SwiftFormerForImageClassification""",
"""SwiftFormerModel""",
"""SwiftFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 211 | 0 |
"""simple docstring"""
from __future__ import annotations
a = 'Muhammad Umer Farooq'
a = 'MIT'
a = '1.0.0'
a = 'Muhammad Umer Farooq'
a = 'contact@muhammadumerfarooq.me'
a = 'Alpha'
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ):
def __init__( self : Dict , lowerCAmelCase : str ):
super().__init__()
lowerCAmelCase = []
lowerCAmelCase = domain
def __lowercase ( self : Dict , lowerCAmelCase : str , lowerCAmelCase : list[tuple[str, str | None]] ):
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
lowerCAmelCase = parse.urljoin(self.domain , __snake_case )
self.urls.append(__snake_case )
def lowercase (snake_case__ : Tuple ) -> List[Any]:
'''simple docstring'''
return ".".join(get_sub_domain_name(_A ).split(""".""" )[-2:] )
def lowercase (snake_case__ : int ) -> Optional[int]:
'''simple docstring'''
return parse.urlparse(_A ).netloc
def lowercase (snake_case__ : int = "https://github.com" ) -> str:
'''simple docstring'''
lowerCAmelCase = get_domain_name(_A )
# Initialize the parser
lowerCAmelCase = Parser(_A )
try:
# Open URL
lowerCAmelCase = requests.get(_A )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
lowerCAmelCase = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
lowerCAmelCase = requests.get(_A )
# Get the valid email.
lowerCAmelCase = re.findall("""[a-zA-Z0-9]+@""" + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_A )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_A )
if __name__ == "__main__":
a = emails_from_url('https://github.com')
print(f"""{len(emails)} emails found:""")
print('\n'.join(sorted(emails)))
| 169 |
'''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
lowerCAmelCase: Optional[int] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
lowerCAmelCase: Optional[Any] = [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')
lowerCAmelCase: int = [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')
lowerCAmelCase: Union[str, Any] = [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')
lowerCAmelCase: Union[str, Any] = [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')
lowerCAmelCase: int = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files) | 526 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase = get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_sentencepiece
@require_tokenizers
class lowercase_ ( A , unittest.TestCase ):
__lowerCamelCase = SpeechTaTokenizer
__lowerCamelCase = False
__lowerCamelCase = True
def _snake_case ( self ) -> int:
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE_ : int =SpeechTaTokenizer(__A )
SCREAMING_SNAKE_CASE_ : Optional[Any] =AddedToken('''<mask>''' , lstrip=__A , rstrip=__A )
SCREAMING_SNAKE_CASE_ : Dict =mask_token
tokenizer.add_special_tokens({'''mask_token''': mask_token} )
tokenizer.add_tokens(['''<ctc_blank>'''] )
tokenizer.save_pretrained(self.tmpdirname )
def _snake_case ( self , __A ) -> str:
SCREAMING_SNAKE_CASE_ : Tuple ='''this is a test'''
SCREAMING_SNAKE_CASE_ : Optional[int] ='''this is a test'''
return input_text, output_text
def _snake_case ( self , __A , __A=False , __A=20 , __A=5 ) -> str:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple =self.get_input_output_texts(__A )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =tokenizer.encode(__A , add_special_tokens=__A )
SCREAMING_SNAKE_CASE_ : List[str] =tokenizer.decode(__A , clean_up_tokenization_spaces=__A )
return text, ids
def _snake_case ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE_ : List[str] ='''<pad>'''
SCREAMING_SNAKE_CASE_ : int =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A ) , __A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A ) , __A )
def _snake_case ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE_ : int =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-4] , '''œ''' )
self.assertEqual(vocab_keys[-2] , '''<mask>''' )
self.assertEqual(vocab_keys[-1] , '''<ctc_blank>''' )
self.assertEqual(len(__A ) , 81 )
def _snake_case ( self ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def _snake_case ( self ) -> Dict:
SCREAMING_SNAKE_CASE_ : str =self.get_tokenizers(do_lower_case=__A )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
SCREAMING_SNAKE_CASE_ : List[str] =tokenizer.vocab_size
SCREAMING_SNAKE_CASE_ : str =len(__A )
self.assertNotEqual(__A , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
SCREAMING_SNAKE_CASE_ : Optional[int] =['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
SCREAMING_SNAKE_CASE_ : Optional[Any] =tokenizer.add_tokens(__A )
SCREAMING_SNAKE_CASE_ : str =tokenizer.vocab_size
SCREAMING_SNAKE_CASE_ : int =len(__A )
self.assertNotEqual(__A , 0 )
self.assertEqual(__A , __A )
self.assertEqual(__A , len(__A ) )
self.assertEqual(__A , all_size + len(__A ) )
SCREAMING_SNAKE_CASE_ : Any =tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l''' , add_special_tokens=__A )
self.assertGreaterEqual(len(__A ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
SCREAMING_SNAKE_CASE_ : Dict ={'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
SCREAMING_SNAKE_CASE_ : Tuple =tokenizer.add_special_tokens(__A )
SCREAMING_SNAKE_CASE_ : str =tokenizer.vocab_size
SCREAMING_SNAKE_CASE_ : Dict =len(__A )
self.assertNotEqual(__A , 0 )
self.assertEqual(__A , __A )
self.assertEqual(__A , len(__A ) )
self.assertEqual(__A , all_size_a + len(__A ) )
SCREAMING_SNAKE_CASE_ : str =tokenizer.encode(
'''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l''' , add_special_tokens=__A )
self.assertGreaterEqual(len(__A ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def _snake_case ( self ) -> Any:
pass
def _snake_case ( self ) -> Dict:
pass
def _snake_case ( self ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ : Tuple =self.get_tokenizer()
SCREAMING_SNAKE_CASE_ : int =tokenizer.tokenize('''This is a test''' )
# fmt: off
self.assertListEqual(__A , [SPIECE_UNDERLINE, '''T''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''a''', SPIECE_UNDERLINE, '''t''', '''e''', '''s''', '''t'''] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__A ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
SCREAMING_SNAKE_CASE_ : List[Any] =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__A , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''92000''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =tokenizer.convert_tokens_to_ids(__A )
# fmt: off
self.assertListEqual(__A , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
SCREAMING_SNAKE_CASE_ : Union[str, Any] =tokenizer.convert_ids_to_tokens(__A )
self.assertListEqual(
__A , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''<unk>''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] )
@slow
def _snake_case ( self ) -> Any:
# Use custom sequence because this tokenizer does not handle numbers.
SCREAMING_SNAKE_CASE_ : Dict =[
'''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
'''general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
'''Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '''
'''models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
'''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
'''conditioning on both left and right context in all layers.''',
'''The quick brown fox jumps over the lazy dog.''',
]
# fmt: off
SCREAMING_SNAKE_CASE_ : Union[str, Any] ={
'''input_ids''': [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__A , model_name='''microsoft/speecht5_asr''' , revision='''c5ef64c71905caeccde0e4462ef3f9077224c524''' , sequences=__A , )
| 431 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
_lowercase = logging.get_logger(__name__)
@add_end_docstrings(A )
class lowercase_ ( A ):
def __init__( self , **__A ) -> Dict:
super().__init__(**__A )
if self.framework == "tf":
raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
requires_backends(self , '''vision''' )
self.check_model_type(__A )
def __call__( self , __A , __A = None , **__A , ) -> int:
if "text_queries" in kwargs:
SCREAMING_SNAKE_CASE_ : Dict =kwargs.pop('''text_queries''' )
if isinstance(__A , (str, Image.Image) ):
SCREAMING_SNAKE_CASE_ : Dict ={'''image''': image, '''candidate_labels''': candidate_labels}
else:
SCREAMING_SNAKE_CASE_ : Dict =image
SCREAMING_SNAKE_CASE_ : List[str] =super().__call__(__A , **__A )
return results
def _snake_case ( self , **__A ) -> Any:
SCREAMING_SNAKE_CASE_ : Optional[Any] ={}
if "threshold" in kwargs:
SCREAMING_SNAKE_CASE_ : str =kwargs['''threshold''']
if "top_k" in kwargs:
SCREAMING_SNAKE_CASE_ : Tuple =kwargs['''top_k''']
return {}, {}, postprocess_params
def _snake_case ( self , __A ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ : Optional[Any] =load_image(inputs['''image'''] )
SCREAMING_SNAKE_CASE_ : Optional[Any] =inputs['''candidate_labels''']
if isinstance(__A , __A ):
SCREAMING_SNAKE_CASE_ : List[Any] =candidate_labels.split(''',''' )
SCREAMING_SNAKE_CASE_ : List[str] =torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(__A ):
SCREAMING_SNAKE_CASE_ : str =self.tokenizer(__A , return_tensors=self.framework )
SCREAMING_SNAKE_CASE_ : Any =self.image_processor(__A , return_tensors=self.framework )
yield {
"is_last": i == len(__A ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def _snake_case ( self , __A ) -> List[Any]:
SCREAMING_SNAKE_CASE_ : Optional[int] =model_inputs.pop('''target_size''' )
SCREAMING_SNAKE_CASE_ : List[str] =model_inputs.pop('''candidate_label''' )
SCREAMING_SNAKE_CASE_ : List[Any] =model_inputs.pop('''is_last''' )
SCREAMING_SNAKE_CASE_ : Union[str, Any] =self.model(**__A )
SCREAMING_SNAKE_CASE_ : Optional[Any] ={'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
return model_outputs
def _snake_case ( self , __A , __A=0.1 , __A=None ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ : Union[str, Any] =[]
for model_output in model_outputs:
SCREAMING_SNAKE_CASE_ : List[Any] =model_output['''candidate_label''']
SCREAMING_SNAKE_CASE_ : str =BaseModelOutput(__A )
SCREAMING_SNAKE_CASE_ : Optional[Any] =self.image_processor.post_process_object_detection(
outputs=__A , threshold=__A , target_sizes=model_output['''target_size'''] )[0]
for index in outputs["scores"].nonzero():
SCREAMING_SNAKE_CASE_ : int =outputs['''scores'''][index].item()
SCREAMING_SNAKE_CASE_ : str =self._get_bounding_box(outputs['''boxes'''][index][0] )
SCREAMING_SNAKE_CASE_ : List[Any] ={'''score''': score, '''label''': label, '''box''': box}
results.append(__A )
SCREAMING_SNAKE_CASE_ : int =sorted(__A , key=lambda __A : x["score"] , reverse=__A )
if top_k:
SCREAMING_SNAKE_CASE_ : Optional[Any] =results[:top_k]
return results
def _snake_case ( self , __A ) -> Dict[str, int]:
if self.framework != "pt":
raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.''' )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] =box.int().tolist()
SCREAMING_SNAKE_CASE_ : str ={
'''xmin''': xmin,
'''ymin''': ymin,
'''xmax''': xmax,
'''ymax''': ymax,
}
return bbox
| 431 | 1 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowerCAmelCase :str = {
# 1536-bit
5: {
'''prime''': int(
'''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
+ '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
+ '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
+ '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
+ '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
+ '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
+ '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
+ '''670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF''',
base=1_6,
),
'''generator''': 2,
},
# 2048-bit
1_4: {
'''prime''': int(
'''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
+ '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
+ '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
+ '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
+ '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
+ '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
+ '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
+ '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
+ '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
+ '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
+ '''15728E5A8AACAA68FFFFFFFFFFFFFFFF''',
base=1_6,
),
'''generator''': 2,
},
# 3072-bit
1_5: {
'''prime''': int(
'''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
+ '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
+ '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
+ '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
+ '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
+ '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
+ '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
+ '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
+ '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
+ '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
+ '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''
+ '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''
+ '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''
+ '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
+ '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''
+ '''43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF''',
base=1_6,
),
'''generator''': 2,
},
# 4096-bit
1_6: {
'''prime''': int(
'''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
+ '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
+ '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
+ '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
+ '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
+ '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
+ '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
+ '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
+ '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
+ '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
+ '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''
+ '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''
+ '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''
+ '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
+ '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''
+ '''43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'''
+ '''88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'''
+ '''2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'''
+ '''287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'''
+ '''1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'''
+ '''93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'''
+ '''FFFFFFFFFFFFFFFF''',
base=1_6,
),
'''generator''': 2,
},
# 6144-bit
1_7: {
'''prime''': int(
'''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'''
+ '''8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'''
+ '''302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'''
+ '''A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'''
+ '''49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'''
+ '''FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
+ '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'''
+ '''180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'''
+ '''3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'''
+ '''04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'''
+ '''B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'''
+ '''1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
+ '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'''
+ '''E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'''
+ '''99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'''
+ '''04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'''
+ '''233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'''
+ '''D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'''
+ '''36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'''
+ '''AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'''
+ '''DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'''
+ '''2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'''
+ '''F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'''
+ '''BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'''
+ '''CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'''
+ '''B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'''
+ '''387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'''
+ '''6DCC4024FFFFFFFFFFFFFFFF''',
base=1_6,
),
'''generator''': 2,
},
# 8192-bit
1_8: {
'''prime''': int(
'''FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'''
+ '''29024E088A67CC74020BBEA63B139B22514A08798E3404DD'''
+ '''EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'''
+ '''E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'''
+ '''EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'''
+ '''C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'''
+ '''83655D23DCA3AD961C62F356208552BB9ED529077096966D'''
+ '''670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'''
+ '''E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'''
+ '''DE2BCBF6955817183995497CEA956AE515D2261898FA0510'''
+ '''15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'''
+ '''ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'''
+ '''ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'''
+ '''F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'''
+ '''BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'''
+ '''43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'''
+ '''88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'''
+ '''2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'''
+ '''287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'''
+ '''1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'''
+ '''93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'''
+ '''36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'''
+ '''F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'''
+ '''179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'''
+ '''DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'''
+ '''5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'''
+ '''D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'''
+ '''23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'''
+ '''CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'''
+ '''06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'''
+ '''DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'''
+ '''12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'''
+ '''38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'''
+ '''741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'''
+ '''3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'''
+ '''22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'''
+ '''4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'''
+ '''062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'''
+ '''4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'''
+ '''B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'''
+ '''4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'''
+ '''9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'''
+ '''60C980DD98EDD3DFFFFFFFFFFFFFFFFF''',
base=1_6,
),
'''generator''': 2,
},
}
class _lowerCamelCase :
'''simple docstring'''
def __init__( self : List[Any] , _A : int = 14 ) -> None:
if group not in primes:
raise ValueError('Unsupported Group' )
__magic_name__ : Optional[int] = primes[group]['prime']
__magic_name__ : List[Any] = primes[group]['generator']
__magic_name__ : List[str] = int(hexlify(urandom(32 ) ) , base=16 )
def __lowerCAmelCase ( self : List[str] ) -> str:
return hex(self.__private_key )[2:]
def __lowerCAmelCase ( self : Dict ) -> str:
__magic_name__ : Optional[Any] = pow(self.generator , self.__private_key , self.prime )
return hex(_A )[2:]
def __lowerCAmelCase ( self : Any , _A : int ) -> bool:
# check if the other public key is valid based on NIST SP800-56
return (
2 <= key <= self.prime - 2
and pow(_A , (self.prime - 1) // 2 , self.prime ) == 1
)
def __lowerCAmelCase ( self : str , _A : str ) -> str:
__magic_name__ : Dict = int(_A , base=16 )
if not self.is_valid_public_key(_A ):
raise ValueError('Invalid public key' )
__magic_name__ : Any = pow(_A , self.__private_key , self.prime )
return shaaaa(str(_A ).encode() ).hexdigest()
@staticmethod
def __lowerCAmelCase ( _A : int , _A : int ) -> bool:
# check if the other public key is valid based on NIST SP800-56
return (
2 <= remote_public_key_str <= prime - 2
and pow(_A , (prime - 1) // 2 , _A ) == 1
)
@staticmethod
def __lowerCAmelCase ( _A : str , _A : str , _A : int = 14 ) -> str:
__magic_name__ : Any = int(_A , base=16 )
__magic_name__ : Any = int(_A , base=16 )
__magic_name__ : Optional[int] = primes[group]['prime']
if not DiffieHellman.is_valid_public_key_static(_A , _A ):
raise ValueError('Invalid public key' )
__magic_name__ : str = pow(_A , _A , _A )
return shaaaa(str(_A ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod() | 561 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _lowerCamelCase ( lowercase__ , unittest.TestCase ):
'''simple docstring'''
A_ : Any = KandinskyVaaInpaintPipeline
A_ : str = ["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
A_ : Optional[int] = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
A_ : Optional[Any] = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
A_ : List[str] = False
@property
def __lowerCAmelCase ( self : Dict ) -> Union[str, Any]:
return 32
@property
def __lowerCAmelCase ( self : int ) -> Union[str, Any]:
return 32
@property
def __lowerCAmelCase ( self : Optional[int] ) -> Any:
return self.time_input_dim
@property
def __lowerCAmelCase ( self : List[str] ) -> List[Any]:
return self.time_input_dim * 4
@property
def __lowerCAmelCase ( self : List[Any] ) -> List[Any]:
return 100
@property
def __lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
torch.manual_seed(0 )
__magic_name__ : int = {
'in_channels': 9,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
__magic_name__ : List[str] = UNetaDConditionModel(**_A )
return model
@property
def __lowerCAmelCase ( self : Optional[Any] ) -> List[Any]:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowerCAmelCase ( self : str ) -> str:
torch.manual_seed(0 )
__magic_name__ : List[str] = VQModel(**self.dummy_movq_kwargs )
return model
def __lowerCAmelCase ( self : Any ) -> str:
__magic_name__ : str = self.dummy_unet
__magic_name__ : Tuple = self.dummy_movq
__magic_name__ : Dict = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='linear' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=_A , set_alpha_to_one=_A , steps_offset=1 , prediction_type='epsilon' , thresholding=_A , )
__magic_name__ : Dict = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def __lowerCAmelCase ( self : int , _A : Union[str, Any] , _A : Union[str, Any]=0 ) -> Optional[int]:
__magic_name__ : List[str] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_A ) ).to(_A )
__magic_name__ : List[Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_A )
# create init_image
__magic_name__ : List[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(_A ) ).to(_A )
__magic_name__ : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__magic_name__ : List[str] = Image.fromarray(np.uinta(_A ) ).convert('RGB' ).resize((256, 256) )
# create mask
__magic_name__ : List[Any] = np.ones((64, 64) , dtype=np.floataa )
__magic_name__ : Optional[int] = 0
if str(_A ).startswith('mps' ):
__magic_name__ : Union[str, Any] = torch.manual_seed(_A )
else:
__magic_name__ : Any = torch.Generator(device=_A ).manual_seed(_A )
__magic_name__ : Optional[Any] = {
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def __lowerCAmelCase ( self : str ) -> Tuple:
__magic_name__ : Dict = 'cpu'
__magic_name__ : Union[str, Any] = self.get_dummy_components()
__magic_name__ : str = self.pipeline_class(**_A )
__magic_name__ : List[Any] = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__magic_name__ : Tuple = pipe(**self.get_dummy_inputs(_A ) )
__magic_name__ : Tuple = output.images
__magic_name__ : str = pipe(
**self.get_dummy_inputs(_A ) , return_dict=_A , )[0]
__magic_name__ : List[str] = image[0, -3:, -3:, -1]
__magic_name__ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
print(F'image.shape {image.shape}' )
assert image.shape == (1, 64, 64, 3)
__magic_name__ : Dict = np.array(
[0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
def __lowerCAmelCase ( self : Any ) -> List[Any]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : int ) -> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self : Optional[Any] ) -> Dict:
__magic_name__ : Optional[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy' )
__magic_name__ : List[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
__magic_name__ : List[Any] = np.ones((768, 768) , dtype=np.floataa )
__magic_name__ : Optional[int] = 0
__magic_name__ : List[Any] = 'a hat'
__magic_name__ : Optional[Any] = KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(_A )
__magic_name__ : Dict = KandinskyVaaInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-decoder-inpaint' , torch_dtype=torch.floataa )
__magic_name__ : List[Any] = pipeline.to(_A )
pipeline.set_progress_bar_config(disable=_A )
__magic_name__ : Union[str, Any] = torch.Generator(device='cpu' ).manual_seed(0 )
__magic_name__ , __magic_name__ : Any = pipe_prior(
_A , generator=_A , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
__magic_name__ : Optional[Any] = pipeline(
image=_A , mask_image=_A , image_embeds=_A , negative_image_embeds=_A , generator=_A , num_inference_steps=100 , height=768 , width=768 , output_type='np' , )
__magic_name__ : Tuple = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_A , _A ) | 561 | 1 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
lowercase__ =False
try:
lowercase__ =_is_package_available('google.colab')
except ModuleNotFoundError:
pass
@input.register
class a_ :
def __init__( self , UpperCAmelCase = None , UpperCAmelCase = [] ):
a_ = 0
a_ = choices
a_ = prompt
if sys.platform == "win32":
a_ = """*"""
else:
a_ = """➔ """
def lowerCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase = "" ):
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , UpperCAmelCase )
else:
forceWrite(self.choices[index] , UpperCAmelCase )
def lowerCAmelCase__ ( self , UpperCAmelCase ):
if index == self.position:
forceWrite(f''' {self.arrow_char} ''' )
self.write_choice(UpperCAmelCase )
else:
forceWrite(f''' {self.choices[index]}''' )
reset_cursor()
def lowerCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase = 1 ):
a_ = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(UpperCAmelCase )
move_cursor(UpperCAmelCase , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP["""up"""] )
def lowerCAmelCase__ ( self ):
self.move_direction(Direction.UP )
@input.mark(KEYMAP["""down"""] )
def lowerCAmelCase__ ( self ):
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP["""newline"""] )
def lowerCAmelCase__ ( self ):
move_cursor(len(self.choices ) - self.position , """DOWN""" )
return self.position
@input.mark(KEYMAP["""interrupt"""] )
def lowerCAmelCase__ ( self ):
move_cursor(len(self.choices ) - self.position , """DOWN""" )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(UpperCAmelCase )] for number in range(10 )] )
def lowerCAmelCase__ ( self ):
a_ = int(chr(self.current_selection ) )
a_ = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , UpperCAmelCase )
else:
return
else:
return
def lowerCAmelCase__ ( self , UpperCAmelCase = 0 ):
if self.prompt:
linebreak()
forceWrite(self.prompt , """\n""" )
if in_colab:
forceWrite("""Please input a choice index (starting from 0), and press enter""" , """\n""" )
else:
forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter""" , """\n""" )
a_ = default_choice
for i in range(len(self.choices ) ):
self.print_choice(UpperCAmelCase )
forceWrite("""\n""" )
move_cursor(len(self.choices ) - self.position , """UP""" )
with cursor.hide():
while True:
if in_colab:
try:
a_ = int(builtins.input() )
except ValueError:
a_ = default_choice
else:
a_ = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , """UP""" )
clear_line()
self.write_choice(UpperCAmelCase , """\n""" )
return choice
| 511 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
lowercase__ ='pt'
elif is_tf_available():
lowercase__ ='tf'
else:
lowercase__ ='jax'
class a_ ( UpperCamelCase__ , unittest.TestCase ):
lowerCamelCase__ : int = PerceiverTokenizer
lowerCamelCase__ : Optional[int] = False
def lowerCAmelCase__ ( self ):
super().setUp()
a_ = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowerCAmelCase__ ( self ):
return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver""" )
def lowerCAmelCase__ ( self , **UpperCAmelCase ):
return self.tokenizer_class.from_pretrained(self.tmpdirname , **UpperCAmelCase )
def lowerCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase=False , UpperCAmelCase=20 , UpperCAmelCase=5 ):
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for Perceiver because single bytes might not be
# valid utf-8 (byte 128 for instance).
# Here we're overriding the smallest possible method to provide
# a clean sequence without making the same assumption.
a_ = []
for i in range(len(UpperCAmelCase ) ):
try:
a_ = tokenizer.decode([i] , clean_up_tokenization_spaces=UpperCAmelCase )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
a_ = list(filter(lambda UpperCAmelCase : re.match(R"""^[ a-zA-Z]+$""" , t[1] ) , UpperCAmelCase ) )
a_ = list(filter(lambda UpperCAmelCase : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=UpperCAmelCase ) , UpperCAmelCase ) )
if max_length is not None and len(UpperCAmelCase ) > max_length:
a_ = toks[:max_length]
if min_length is not None and len(UpperCAmelCase ) < min_length and len(UpperCAmelCase ) > 0:
while len(UpperCAmelCase ) < min_length:
a_ = toks + toks
# toks_str = [t[1] for t in toks]
a_ = [t[0] for t in toks]
# Ensure consistency
a_ = tokenizer.decode(UpperCAmelCase , clean_up_tokenization_spaces=UpperCAmelCase )
if " " not in output_txt and len(UpperCAmelCase ) > 1:
a_ = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=UpperCAmelCase )
+ """ """
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=UpperCAmelCase )
)
if with_prefix_space:
a_ = """ """ + output_txt
a_ = tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase )
return output_txt, output_ids
def lowerCAmelCase__ ( self ):
a_ = self.perceiver_tokenizer
a_ = """Unicode €."""
a_ = tokenizer(UpperCAmelCase )
a_ = [4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
self.assertEqual(encoded["""input_ids"""] , UpperCAmelCase )
# decoding
a_ = tokenizer.decode(UpperCAmelCase )
self.assertEqual(UpperCAmelCase , """[CLS]Unicode €.[SEP]""" )
a_ = tokenizer("""e è é ê ë""" )
a_ = [4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
self.assertEqual(encoded["""input_ids"""] , UpperCAmelCase )
# decoding
a_ = tokenizer.decode(UpperCAmelCase )
self.assertEqual(UpperCAmelCase , """[CLS]e è é ê ë[SEP]""" )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë""" ) ) , """[CLS]e è é ê ë[SEP]""" )
def lowerCAmelCase__ ( self ):
a_ = self.perceiver_tokenizer
a_ = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
# fmt: off
a_ = [4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
# fmt: on
a_ = tokenizer(UpperCAmelCase , padding=UpperCAmelCase , return_tensors=UpperCAmelCase )
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
if FRAMEWORK != "jax":
a_ = list(batch.input_ids.numpy()[0] )
else:
a_ = list(batch.input_ids.tolist()[0] )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
self.assertEqual((2, 38) , batch.input_ids.shape )
self.assertEqual((2, 38) , batch.attention_mask.shape )
def lowerCAmelCase__ ( self ):
a_ = self.perceiver_tokenizer
a_ = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
a_ = tokenizer(UpperCAmelCase , padding=UpperCAmelCase , return_tensors=UpperCAmelCase )
# check if input_ids are returned and no decoder_input_ids
self.assertIn("""input_ids""" , UpperCAmelCase )
self.assertIn("""attention_mask""" , UpperCAmelCase )
self.assertNotIn("""decoder_input_ids""" , UpperCAmelCase )
self.assertNotIn("""decoder_attention_mask""" , UpperCAmelCase )
def lowerCAmelCase__ ( self ):
a_ = self.perceiver_tokenizer
a_ = [
"""Summary of the text.""",
"""Another summary.""",
]
a_ = tokenizer(
text_target=UpperCAmelCase , max_length=32 , padding="""max_length""" , truncation=UpperCAmelCase , return_tensors=UpperCAmelCase )
self.assertEqual(32 , targets["""input_ids"""].shape[1] )
def lowerCAmelCase__ ( self ):
# safety check on max_len default value so we are sure the test works
a_ = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
a_ = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
a_ = tempfile.mkdtemp()
a_ = """ He is very happy, UNwant\u00E9d,running"""
a_ = tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase )
tokenizer.save_pretrained(UpperCAmelCase )
a_ = tokenizer.__class__.from_pretrained(UpperCAmelCase )
a_ = after_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
shutil.rmtree(UpperCAmelCase )
a_ = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
# Isolate this from the other tests because we save additional tokens/etc
a_ = tempfile.mkdtemp()
a_ = """ He is very happy, UNwant\u00E9d,running"""
tokenizer.add_tokens(["""bim""", """bambam"""] )
a_ = tokenizer.additional_special_tokens
additional_special_tokens.append("""new_additional_special_token""" )
tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
a_ = tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase )
tokenizer.save_pretrained(UpperCAmelCase )
a_ = tokenizer.__class__.from_pretrained(UpperCAmelCase )
a_ = after_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
self.assertIn("""new_additional_special_token""" , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
a_ = tokenizer.__class__.from_pretrained(UpperCAmelCase , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(UpperCAmelCase )
def lowerCAmelCase__ ( self ):
a_ = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(UpperCAmelCase )
with open(os.path.join(UpperCAmelCase , """special_tokens_map.json""" ) , encoding="""utf-8""" ) as json_file:
a_ = json.load(UpperCAmelCase )
with open(os.path.join(UpperCAmelCase , """tokenizer_config.json""" ) , encoding="""utf-8""" ) as json_file:
a_ = json.load(UpperCAmelCase )
a_ = [f'''<extra_id_{i}>''' for i in range(1_25 )]
a_ = added_tokens_extra_ids + [
"""an_additional_special_token"""
]
a_ = added_tokens_extra_ids + [
"""an_additional_special_token"""
]
with open(os.path.join(UpperCAmelCase , """special_tokens_map.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(UpperCAmelCase , UpperCAmelCase )
with open(os.path.join(UpperCAmelCase , """tokenizer_config.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(UpperCAmelCase , UpperCAmelCase )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
a_ = tokenizer_class.from_pretrained(
UpperCAmelCase , )
self.assertIn(
"""an_additional_special_token""" , tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
["""an_additional_special_token"""] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
a_ = added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token""" , lstrip=UpperCAmelCase )]
a_ = tokenizer_class.from_pretrained(
UpperCAmelCase , additional_special_tokens=UpperCAmelCase , )
self.assertIn("""a_new_additional_special_token""" , tokenizer.additional_special_tokens )
self.assertEqual(
["""a_new_additional_special_token"""] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""] ) ) , )
def lowerCAmelCase__ ( self ):
a_ = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_78] ) , """�""" )
def lowerCAmelCase__ ( self ):
pass
def lowerCAmelCase__ ( self ):
pass
def lowerCAmelCase__ ( self ):
pass
def lowerCAmelCase__ ( self ):
pass
def lowerCAmelCase__ ( self ):
# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
# strings and special added tokens as tokens
a_ = self.get_tokenizers(fast=UpperCAmelCase , do_lower_case=UpperCAmelCase )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
a_ = ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
a_ = tokenizer.convert_tokens_to_string(UpperCAmelCase )
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
| 511 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class _UpperCamelCase (a_ , a_ , a_ , unittest.TestCase ):
snake_case_ = StableUnCLIPImgaImgPipeline
snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
snake_case_ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
snake_case_ = frozenset([] )
def __UpperCAmelCase ( self )-> List[Any]:
__lowerCAmelCase = 3_2
__lowerCAmelCase = embedder_hidden_size
# image encoding components
__lowerCAmelCase = CLIPImageProcessor(crop_size=3_2 , size=3_2 )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=__UpperCamelCase , projection_dim=__UpperCamelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=3_2 , intermediate_size=3_7 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
__lowerCAmelCase = StableUnCLIPImageNormalizer(embedding_dim=__UpperCamelCase )
__lowerCAmelCase = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=__UpperCamelCase , projection_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) )
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDConditionModel(
sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(3_2, 6_4) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=__UpperCamelCase , layers_per_block=1 , upcast_attention=__UpperCamelCase , use_linear_projection=__UpperCamelCase , )
torch.manual_seed(0 )
__lowerCAmelCase = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , prediction_type="v_prediction" , set_alpha_to_one=__UpperCamelCase , steps_offset=1 , )
torch.manual_seed(0 )
__lowerCAmelCase = AutoencoderKL()
__lowerCAmelCase = {
# image encoding components
"feature_extractor": feature_extractor,
"image_encoder": image_encoder.eval(),
# image noising components
"image_normalizer": image_normalizer.eval(),
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder.eval(),
"unet": unet.eval(),
"scheduler": scheduler,
"vae": vae.eval(),
}
return components
def __UpperCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=0 , __UpperCamelCase=True )-> Tuple:
if str(__UpperCamelCase ).startswith("mps" ):
__lowerCAmelCase = torch.manual_seed(__UpperCamelCase )
else:
__lowerCAmelCase = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
__lowerCAmelCase = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
if pil_image:
__lowerCAmelCase = input_image * 0.5 + 0.5
__lowerCAmelCase = input_image.clamp(0 , 1 )
__lowerCAmelCase = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__lowerCAmelCase = DiffusionPipeline.numpy_to_pil(__UpperCamelCase )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def __UpperCAmelCase ( self )-> Optional[Any]:
__lowerCAmelCase = "cpu" # ensure determinism for the device-dependent torch.Generator
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = StableUnCLIPImgaImgPipeline(**__UpperCamelCase )
__lowerCAmelCase = sd_pipe.to(__UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )
__lowerCAmelCase = self.get_dummy_inputs(__UpperCamelCase )
inputs.update({"image_embeds": None} )
__lowerCAmelCase = sd_pipe(**__UpperCamelCase ).images
__lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
__lowerCAmelCase = np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __UpperCAmelCase ( self )-> Any:
__lowerCAmelCase = torch_device in ["cpu", "mps"]
self._test_attention_slicing_forward_pass(test_max_difference=__UpperCamelCase )
def __UpperCAmelCase ( self )-> List[Any]:
__lowerCAmelCase = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=__UpperCamelCase )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def __UpperCAmelCase ( self )-> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__UpperCamelCase )
@slow
@require_torch_gpu
class _UpperCamelCase (unittest.TestCase ):
def __UpperCAmelCase ( self )-> str:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self )-> Optional[int]:
__lowerCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy" )
__lowerCAmelCase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-l-img2img" , torch_dtype=torch.floataa )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = pipe(__UpperCamelCase , "anime turle" , generator=__UpperCamelCase , output_type="np" )
__lowerCAmelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(__UpperCamelCase , __UpperCamelCase )
def __UpperCAmelCase ( self )-> Optional[int]:
__lowerCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
__lowerCAmelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy" )
__lowerCAmelCase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = torch.Generator(device="cpu" ).manual_seed(0 )
__lowerCAmelCase = pipe(__UpperCamelCase , "anime turle" , generator=__UpperCamelCase , output_type="np" )
__lowerCAmelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(__UpperCamelCase , __UpperCamelCase )
def __UpperCAmelCase ( self )-> List[str]:
__lowerCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowerCAmelCase = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
__lowerCAmelCase = pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
__lowerCAmelCase = pipe(
__UpperCamelCase , "anime turtle" , num_inference_steps=2 , output_type="np" , )
__lowerCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 1_0**9
| 367 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a__ : Optional[Any] = logging.get_logger(__name__)
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , *lowerCAmelCase : Optional[Any] , **lowerCAmelCase : List[Any]) -> None:
"""simple docstring"""
warnings.warn(
'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use GLPNImageProcessor instead.' , lowerCAmelCase , )
super().__init__(*lowerCAmelCase , **lowerCAmelCase)
| 622 | 0 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def A (__lowerCamelCase :int ):
_lowerCAmelCase = tmp_path / """file.csv"""
_lowerCAmelCase = textwrap.dedent(
"""\
header1,header2
1,2
10,20
""" )
with open(__lowerCamelCase , """w""" ) as f:
f.write(__lowerCamelCase )
return str(__lowerCamelCase )
@pytest.fixture
def A (__lowerCamelCase :Dict ):
_lowerCAmelCase = tmp_path / """malformed_file.csv"""
_lowerCAmelCase = textwrap.dedent(
"""\
header1,header2
1,2
10,20,
""" )
with open(__lowerCamelCase , """w""" ) as f:
f.write(__lowerCamelCase )
return str(__lowerCamelCase )
@pytest.fixture
def A (__lowerCamelCase :Tuple , __lowerCamelCase :List[str] ):
_lowerCAmelCase = tmp_path / """csv_with_image.csv"""
_lowerCAmelCase = textwrap.dedent(
f'\\n image\n {image_file}\n ' )
with open(__lowerCamelCase , """w""" ) as f:
f.write(__lowerCamelCase )
return str(__lowerCamelCase )
@pytest.fixture
def A (__lowerCamelCase :Optional[Any] ):
_lowerCAmelCase = tmp_path / """csv_with_label.csv"""
_lowerCAmelCase = textwrap.dedent(
"""\
label
good
bad
good
""" )
with open(__lowerCamelCase , """w""" ) as f:
f.write(__lowerCamelCase )
return str(__lowerCamelCase )
@pytest.fixture
def A (__lowerCamelCase :str ):
_lowerCAmelCase = tmp_path / """csv_with_int_list.csv"""
_lowerCAmelCase = textwrap.dedent(
"""\
int_list
1 2 3
4 5 6
7 8 9
""" )
with open(__lowerCamelCase , """w""" ) as f:
f.write(__lowerCamelCase )
return str(__lowerCamelCase )
def A (__lowerCamelCase :Tuple , __lowerCamelCase :Tuple , __lowerCamelCase :List[str] ):
_lowerCAmelCase = Csv()
_lowerCAmelCase = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(__lowerCamelCase , match="""Error tokenizing data""" ):
for _ in generator:
pass
assert any(
record.levelname == """ERROR"""
and """Failed to read file""" in record.message
and os.path.basename(__lowerCamelCase ) in record.message
for record in caplog.records )
@require_pil
def A (__lowerCamelCase :Union[str, Any] ):
with open(__lowerCamelCase , encoding="""utf-8""" ) as f:
_lowerCAmelCase = f.read().splitlines()[1]
_lowerCAmelCase = Csv(encoding="""utf-8""" , features=Features({"""image""": Image()} ) )
_lowerCAmelCase = csv._generate_tables([[csv_file_with_image]] )
_lowerCAmelCase = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""image""" ).type == Image()()
_lowerCAmelCase = pa_table.to_pydict()["""image"""]
assert generated_content == [{"path": image_file, "bytes": None}]
def A (__lowerCamelCase :str ):
with open(__lowerCamelCase , encoding="""utf-8""" ) as f:
_lowerCAmelCase = f.read().splitlines()[1:]
_lowerCAmelCase = Csv(encoding="""utf-8""" , features=Features({"""label""": ClassLabel(names=["""good""", """bad"""] )} ) )
_lowerCAmelCase = csv._generate_tables([[csv_file_with_label]] )
_lowerCAmelCase = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""label""" ).type == ClassLabel(names=["""good""", """bad"""] )()
_lowerCAmelCase = pa_table.to_pydict()["""label"""]
assert generated_content == [ClassLabel(names=["""good""", """bad"""] ).straint(__lowerCamelCase ) for label in labels]
def A (__lowerCamelCase :List[str] ):
_lowerCAmelCase = Csv(encoding="""utf-8""" , sep=""",""" , converters={"""int_list""": lambda __lowerCamelCase : [int(__lowerCamelCase ) for i in x.split()]} )
_lowerCAmelCase = csv._generate_tables([[csv_file_with_int_list]] )
_lowerCAmelCase = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("""int_list""" ).type )
_lowerCAmelCase = pa_table.to_pydict()["""int_list"""]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 162 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[Any] = '''table-transformer'''
_lowercase : List[str] = ['''past_key_values''']
_lowercase : Any = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self , _lowercase=True , _lowercase=None , _lowercase=3 , _lowercase=100 , _lowercase=6 , _lowercase=2_048 , _lowercase=8 , _lowercase=6 , _lowercase=2_048 , _lowercase=8 , _lowercase=0.0 , _lowercase=0.0 , _lowercase=True , _lowercase="relu" , _lowercase=256 , _lowercase=0.1 , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.02 , _lowercase=1.0 , _lowercase=False , _lowercase="sine" , _lowercase="resnet50" , _lowercase=True , _lowercase=False , _lowercase=1 , _lowercase=5 , _lowercase=2 , _lowercase=1 , _lowercase=1 , _lowercase=5 , _lowercase=2 , _lowercase=0.1 , **_lowercase , ):
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
_lowerCAmelCase = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = backbone_config.get("""model_type""" )
_lowerCAmelCase = CONFIG_MAPPING[backbone_model_type]
_lowerCAmelCase = config_class.from_dict(_lowercase )
# set timm attributes to None
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None, None, None
_lowerCAmelCase = use_timm_backbone
_lowerCAmelCase = backbone_config
_lowerCAmelCase = num_channels
_lowerCAmelCase = num_queries
_lowerCAmelCase = d_model
_lowerCAmelCase = encoder_ffn_dim
_lowerCAmelCase = encoder_layers
_lowerCAmelCase = encoder_attention_heads
_lowerCAmelCase = decoder_ffn_dim
_lowerCAmelCase = decoder_layers
_lowerCAmelCase = decoder_attention_heads
_lowerCAmelCase = dropout
_lowerCAmelCase = attention_dropout
_lowerCAmelCase = activation_dropout
_lowerCAmelCase = activation_function
_lowerCAmelCase = init_std
_lowerCAmelCase = init_xavier_std
_lowerCAmelCase = encoder_layerdrop
_lowerCAmelCase = decoder_layerdrop
_lowerCAmelCase = encoder_layers
_lowerCAmelCase = auxiliary_loss
_lowerCAmelCase = position_embedding_type
_lowerCAmelCase = backbone
_lowerCAmelCase = use_pretrained_backbone
_lowerCAmelCase = dilation
# Hungarian matcher
_lowerCAmelCase = class_cost
_lowerCAmelCase = bbox_cost
_lowerCAmelCase = giou_cost
# Loss coefficients
_lowerCAmelCase = mask_loss_coefficient
_lowerCAmelCase = dice_loss_coefficient
_lowerCAmelCase = bbox_loss_coefficient
_lowerCAmelCase = giou_loss_coefficient
_lowerCAmelCase = eos_coefficient
super().__init__(is_encoder_decoder=_lowercase , **_lowercase )
@property
def _lowercase ( self ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def _lowercase ( self ):
"""simple docstring"""
return self.d_model
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Optional[Any] = version.parse('''1.11''' )
@property
def _lowercase ( self ):
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowercase ( self ):
"""simple docstring"""
return 1e-5
@property
def _lowercase ( self ):
"""simple docstring"""
return 12
| 162 | 1 |
'''simple docstring'''
def UpperCamelCase_ ( A__ ):
a_ = current_set.copy()
for row_index, row in enumerate(A__ ):
a_ = row[0]
for column_index, column in enumerate(A__ ):
if magnitude == 0:
a_ = column
continue
a_ = column / magnitude
# Subtract to cancel term
a_ = current_set[0]
a_ = [first_row]
a_ = current_set[1::]
for row in current_set:
a_ = []
# If first term is 0, it is already in form we want, so we preserve it
if row[0] == 0:
final_set.append(A__ )
continue
for column_index in range(len(A__ ) ):
temp_row.append(first_row[column_index] - row[column_index] )
final_set.append(A__ )
# Create next recursion iteration set
if len(final_set[0] ) != 3:
a_ = final_set[0]
a_ = []
a_ = []
for row in final_set[1::]:
current_first_column.append(row[0] )
next_iteration.append(row[1::] )
a_ = simplify(A__ )
for i in range(len(A__ ) ):
resultant[i].insert(0 , current_first_column[i] )
resultant.insert(0 , A__ )
a_ = resultant
return final_set
def UpperCamelCase_ ( A__ ):
if len(A__ ) == 0:
raise IndexError("""solve_simultaneous() requires n lists of length n+1""" )
a_ = len(A__ ) + 1
if any(len(A__ ) != _length for item in equations ):
raise IndexError("""solve_simultaneous() requires n lists of length n+1""" )
for row in equations:
if any(not isinstance(A__ , (int, float) ) for column in row ):
raise ValueError("""solve_simultaneous() requires lists of integers""" )
if len(A__ ) == 1:
return [equations[0][-1] / equations[0][0]]
a_ = equations.copy()
if any(0 in row for row in data_set ):
a_ = data_set.copy()
a_ = []
for row_index, row in enumerate(A__ ):
if 0 not in row:
a_ = data_set.pop(A__ )
break
if not full_row:
raise ValueError("""solve_simultaneous() requires at least 1 full equation""" )
data_set.insert(0 , A__ )
a_ = data_set.copy()
a_ = simplify(A__ )
a_ = simplified[::-1]
a_ = []
for row in simplified:
a_ = row[-1]
if not solutions:
if row[-2] == 0:
solutions.append(0 )
continue
solutions.append(current_solution / row[-2] )
continue
a_ = row.copy()[: len(A__ ) - 1 :]
while temp_row[0] == 0:
temp_row.pop(0 )
if len(A__ ) == 0:
solutions.append(0 )
continue
a_ = temp_row[1::]
a_ = temp_row[::-1]
for column_index, column in enumerate(A__ ):
current_solution -= column * solutions[column_index]
solutions.append(A__ )
a_ = []
for item in solutions:
final.append(float(round(A__ , 5 ) ) )
return final[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase__ =[
[2, 1, 1, 1, 1, 4],
[1, 2, 1, 1, 1, 5],
[1, 1, 2, 1, 1, 6],
[1, 1, 1, 2, 1, 7],
[1, 1, 1, 1, 2, 8],
]
print(solve_simultaneous(eq))
print(solve_simultaneous([[4, 2]]))
| 263 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def UpperCamelCase_ ( A__ , A__ ):
a_ = []
for part_id in partition_order:
a_ = df.where(F'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
for row_idx, row in enumerate(A__ ):
expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase_ ( ):
a_ = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
a_ = spark.range(1_00 ).repartition(1 )
a_ = Spark(A__ )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase_ ( ):
a_ = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
a_ = spark.range(10 ).repartition(2 )
a_ = [1, 0]
a_ = _generate_iterable_examples(A__ , A__ ) # Reverse the partitions.
a_ = _get_expected_row_ids_and_row_dicts_for_partition_order(A__ , A__ )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
a_ , a_ = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase_ ( ):
a_ = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
a_ = spark.range(10 ).repartition(1 )
a_ = SparkExamplesIterable(A__ )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(A__ ):
assert row_id == F'''0_{i}'''
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase_ ( ):
a_ = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
a_ = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("""numpy.random.Generator""" ) as generator_mock:
a_ = lambda A__ : x.reverse()
a_ = _get_expected_row_ids_and_row_dicts_for_partition_order(A__ , [2, 1, 0] )
a_ = SparkExamplesIterable(A__ ).shuffle_data_sources(A__ )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(A__ ):
a_ , a_ = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase_ ( ):
a_ = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
a_ = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
a_ = SparkExamplesIterable(A__ ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
a_ = _get_expected_row_ids_and_row_dicts_for_partition_order(A__ , [0, 2] )
for i, (row_id, row_dict) in enumerate(A__ ):
a_ , a_ = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
a_ = SparkExamplesIterable(A__ ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
a_ = _get_expected_row_ids_and_row_dicts_for_partition_order(A__ , [1, 3] )
for i, (row_id, row_dict) in enumerate(A__ ):
a_ , a_ = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase_ ( ):
a_ = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
a_ = spark.range(1_00 ).repartition(1 )
a_ = Spark(A__ )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 1_00
| 263 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A_ : int =logging.get_logger(__name__)
A_ : int =torch.device('''cpu''')
def snake_case_ ( ) -> List[str]:
lowerCAmelCase_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowerCAmelCase_ = Image.open(requests.get(__snake_case , stream=__snake_case).raw)
return im
def snake_case_ ( __snake_case : List[str]) -> Tuple:
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1])
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1])
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2])
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2])
def snake_case_ ( __snake_case : List[str] , __snake_case : str , __snake_case : List[str]) -> Union[str, Any]:
lowerCAmelCase_ = dct.pop(__snake_case)
lowerCAmelCase_ = val
def snake_case_ ( __snake_case : Union[str, Any]) -> Any:
lowerCAmelCase_ = []
for k in state_dict.keys():
lowerCAmelCase_ = k
if ".pwconv" in k:
lowerCAmelCase_ = k_new.replace('''.pwconv''' , '''.point_wise_conv''')
if ".dwconv" in k:
lowerCAmelCase_ = k_new.replace('''.dwconv''' , '''.depth_wise_conv''')
if ".Proj." in k:
lowerCAmelCase_ = k_new.replace('''.Proj.''' , '''.proj.''')
if "patch_embed" in k_new:
lowerCAmelCase_ = k_new.replace('''patch_embed''' , '''swiftformer.patch_embed.patch_embedding''')
if "network" in k_new:
lowerCAmelCase_ = k_new.split('''.''')
if ls[2].isdigit():
lowerCAmelCase_ = '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:])
else:
lowerCAmelCase_ = k_new.replace('''network''' , '''swiftformer.encoder.network''')
rename_keys.append((k, k_new))
return rename_keys
@torch.no_grad()
def snake_case_ ( __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str]) -> Tuple:
lowerCAmelCase_ = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
lowerCAmelCase_ = 1000
lowerCAmelCase_ = '''huggingface/label-files'''
lowerCAmelCase_ = '''imagenet-1k-id2label.json'''
lowerCAmelCase_ = json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type='''dataset''') , '''r'''))
lowerCAmelCase_ = {int(__snake_case): v for k, v in idalabel.items()}
lowerCAmelCase_ = idalabel
lowerCAmelCase_ = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
lowerCAmelCase_ = [3, 3, 6, 4]
lowerCAmelCase_ = [48, 56, 112, 220]
elif swiftformer_name == "swiftformer_s":
lowerCAmelCase_ = [3, 3, 9, 6]
lowerCAmelCase_ = [48, 64, 168, 224]
elif swiftformer_name == "swiftformer_l1":
lowerCAmelCase_ = [4, 3, 10, 5]
lowerCAmelCase_ = [48, 96, 192, 384]
elif swiftformer_name == "swiftformer_l3":
lowerCAmelCase_ = [4, 4, 12, 6]
lowerCAmelCase_ = [64, 128, 320, 512]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith('''https'''):
lowerCAmelCase_ = torch.hub.load_state_dict_from_url(__snake_case , map_location='''cpu''' , check_hash=__snake_case)
else:
lowerCAmelCase_ = torch.load(__snake_case , map_location='''cpu''')
lowerCAmelCase_ = checkpoint
lowerCAmelCase_ = create_rename_keys(__snake_case)
for rename_key_src, rename_key_dest in rename_keys:
rename_key(__snake_case , __snake_case , __snake_case)
# load HuggingFace model
lowerCAmelCase_ = SwiftFormerForImageClassification(__snake_case).eval()
hf_model.load_state_dict(__snake_case)
# prepare test inputs
lowerCAmelCase_ = prepare_img()
lowerCAmelCase_ = ViTImageProcessor.from_pretrained('''preprocessor_config''')
lowerCAmelCase_ = processor(images=__snake_case , return_tensors='''pt''')
# compare outputs from both models
lowerCAmelCase_ = get_expected_output(__snake_case)
lowerCAmelCase_ = hf_model(inputs['''pixel_values''']).logits
assert hf_logits.shape == torch.Size([1, 1000])
assert torch.allclose(hf_logits[0, 0:5] , __snake_case , atol=1E-3)
Path(__snake_case).mkdir(exist_ok=__snake_case)
print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}''')
hf_model.save_pretrained(__snake_case)
if __name__ == "__main__":
A_ : Optional[Any] =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swiftformer_name''',
default='''swiftformer_xs''',
choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
type=str,
help='''Name of the SwiftFormer model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''./converted_outputs/''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')
A_ : Dict =parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 712 | '''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def snake_case_ ( __snake_case : Callable) -> Callable:
@wraps(__snake_case)
def _inner_fn(*__snake_case : str , **__snake_case : Optional[int]):
warnings.warn(
(F'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''') , __snake_case , )
return fn(*__snake_case , **__snake_case)
return _inner_fn
| 606 | 0 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_lowercase = datasets.utils.logging.get_logger(__name__)
@dataclass
class _UpperCAmelCase ( datasets.BuilderConfig ):
UpperCamelCase__ = None
UpperCamelCase__ = "utf-8"
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = True # deprecated
UpperCamelCase__ = None # deprecated
UpperCamelCase__ = 10 << 20 # 10MB
UpperCamelCase__ = None
class _UpperCAmelCase ( datasets.ArrowBasedBuilder ):
UpperCamelCase__ = JsonConfig
def snake_case_ ( self):
if self.config.block_size is not None:
logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''')
A__ = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
'''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''')
if self.config.newlines_in_values is not None:
raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''')
return datasets.DatasetInfo(features=self.config.features)
def snake_case_ ( self , a__):
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}")
A__ = dl_manager.download_and_extract(self.config.data_files)
if isinstance(UpperCamelCase__ , (str, list, tuple)):
A__ = data_files
if isinstance(UpperCamelCase__ , UpperCamelCase__):
A__ = [files]
A__ = [dl_manager.iter_files(UpperCamelCase__) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files})]
A__ = []
for split_name, files in data_files.items():
if isinstance(UpperCamelCase__ , UpperCamelCase__):
A__ = [files]
A__ = [dl_manager.iter_files(UpperCamelCase__) for file in files]
splits.append(datasets.SplitGenerator(name=UpperCamelCase__ , gen_kwargs={'''files''': files}))
return splits
def snake_case_ ( self , a__):
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features) - set(pa_table.column_names):
A__ = self.config.features.arrow_schema.field(UpperCamelCase__).type
A__ = pa_table.append_column(UpperCamelCase__ , pa.array([None] * len(UpperCamelCase__) , type=UpperCamelCase__))
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
A__ = table_cast(UpperCamelCase__ , self.config.features.arrow_schema)
return pa_table
def snake_case_ ( self , a__):
for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__)):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(UpperCamelCase__ , encoding=self.config.encoding , errors=self.config.encoding_errors) as f:
A__ = json.load(UpperCamelCase__)
# We keep only the field we are interested in
A__ = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(UpperCamelCase__ , (list, tuple)):
A__ = set().union(*[row.keys() for row in dataset])
A__ = {col: [row.get(UpperCamelCase__) for row in dataset] for col in keys}
else:
A__ = dataset
A__ = pa.Table.from_pydict(UpperCamelCase__)
yield file_idx, self._cast_table(UpperCamelCase__)
# If the file has one json object per line
else:
with open(UpperCamelCase__ , '''rb''') as f:
A__ = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
A__ = max(self.config.chunksize // 3_2 , 1_6 << 1_0)
A__ = (
self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
)
while True:
A__ = f.read(self.config.chunksize)
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(UpperCamelCase__)
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
A__ = batch.decode(self.config.encoding , errors=UpperCamelCase__).encode('''utf-8''')
try:
while True:
try:
A__ = paj.read_json(
io.BytesIO(UpperCamelCase__) , read_options=paj.ReadOptions(block_size=UpperCamelCase__))
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(UpperCamelCase__ , pa.ArrowInvalid)
and "straddling" not in str(UpperCamelCase__)
or block_size > len(UpperCamelCase__)
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
F"Batch of {len(UpperCamelCase__)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.")
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
UpperCamelCase__ , encoding=self.config.encoding , errors=self.config.encoding_errors) as f:
A__ = json.load(UpperCamelCase__)
except json.JSONDecodeError:
logger.error(F"Failed to read file \'{file}\' with error {type(UpperCamelCase__)}: {e}")
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(UpperCamelCase__ , UpperCamelCase__): # list is the only sequence type supported in JSON
try:
A__ = set().union(*[row.keys() for row in dataset])
A__ = {col: [row.get(UpperCamelCase__) for row in dataset] for col in keys}
A__ = pa.Table.from_pydict(UpperCamelCase__)
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(F"Failed to read file \'{file}\' with error {type(UpperCamelCase__)}: {e}")
raise ValueError(F"Not able to read records in the JSON file at {file}.") from None
yield file_idx, self._cast_table(UpperCamelCase__)
break
else:
logger.error(F"Failed to read file \'{file}\' with error {type(UpperCamelCase__)}: {e}")
raise ValueError(
F"Not able to read records in the JSON file at {file}. "
F"You should probably indicate the field of the JSON file containing your records. "
F"This JSON file contain the following fields: {str(list(dataset.keys()))}. "
F"Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ") from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(UpperCamelCase__)
batch_idx += 1
| 632 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : List[str] , *UpperCamelCase__ : List[Any] , **UpperCamelCase__ : Tuple ):
warnings.warn(
'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use ImageGPTImageProcessor instead.' , UpperCamelCase__ , )
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
| 699 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
a__ : Optional[int] =version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def lowercase__ ( __lowercase : int , __lowercase : tuple , __lowercase : Path , __lowercase : Any , __lowercase : Optional[int] , __lowercase : List[Any] , __lowercase : int , __lowercase : Optional[int]=False , ) -> List[str]:
"""simple docstring"""
output_path.parent.mkdir(parents=__lowercase , exist_ok=__lowercase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
__lowercase , __lowercase , f=output_path.as_posix() , input_names=__lowercase , output_names=__lowercase , dynamic_axes=__lowercase , do_constant_folding=__lowercase , use_external_data_format=__lowercase , enable_onnx_checker=__lowercase , opset_version=__lowercase , )
else:
export(
__lowercase , __lowercase , f=output_path.as_posix() , input_names=__lowercase , output_names=__lowercase , dynamic_axes=__lowercase , do_constant_folding=__lowercase , opset_version=__lowercase , )
@torch.no_grad()
def lowercase__ ( __lowercase : str , __lowercase : str , __lowercase : int , __lowercase : bool = False ) -> Optional[int]:
"""simple docstring"""
__UpperCamelCase = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__UpperCamelCase = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
__UpperCamelCase = 'cpu'
__UpperCamelCase = Path(__lowercase )
# VAE DECODER
__UpperCamelCase = AutoencoderKL.from_pretrained(model_path + '/vae' )
__UpperCamelCase = vae_decoder.config.latent_channels
# forward only through the decoder part
__UpperCamelCase = vae_decoder.decode
onnx_export(
__lowercase , model_args=(
torch.randn(1 , __lowercase , 25 , 25 ).to(device=__lowercase , dtype=__lowercase ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=__lowercase , )
del vae_decoder
if __name__ == "__main__":
a__ : Optional[Any] =argparse.ArgumentParser()
parser.add_argument(
'''--model_path''',
type=str,
required=True,
help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
)
parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--opset''',
default=14,
type=int,
help='''The version of the ONNX operator set to use.''',
)
parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')
a__ : List[Any] =parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print('''SD: Done: ONNX''')
| 434 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def lowercase__ ( __lowercase : List[str] ) -> Tuple:
"""simple docstring"""
return 1 / (1 + np.exp(-z ))
def lowercase__ ( __lowercase : Optional[Any] , __lowercase : Dict ) -> Any:
"""simple docstring"""
return (-y * np.log(__lowercase ) - (1 - y) * np.log(1 - h )).mean()
def lowercase__ ( __lowercase : str , __lowercase : str , __lowercase : str ) -> Optional[Any]:
"""simple docstring"""
__UpperCamelCase = np.dot(__lowercase , __lowercase )
return np.sum(y * scores - np.log(1 + np.exp(__lowercase ) ) )
def lowercase__ ( __lowercase : List[Any] , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : int=70000 ) -> List[str]:
"""simple docstring"""
__UpperCamelCase = np.zeros(x.shape[1] )
for iterations in range(__lowercase ):
__UpperCamelCase = np.dot(__lowercase , __lowercase )
__UpperCamelCase = sigmoid_function(__lowercase )
__UpperCamelCase = np.dot(x.T , h - y ) / y.size
__UpperCamelCase = theta - alpha * gradient # updating the weights
__UpperCamelCase = np.dot(__lowercase , __lowercase )
__UpperCamelCase = sigmoid_function(__lowercase )
__UpperCamelCase = cost_function(__lowercase , __lowercase )
if iterations % 100 == 0:
print(F'''loss: {j} \t''' ) # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
a__ : Optional[Any] =datasets.load_iris()
a__ : List[str] =iris.data[:, :2]
a__ : Union[str, Any] =(iris.target != 0) * 1
a__ : List[str] =0.1
a__ : List[str] =logistic_reg(alpha, x, y, max_iterations=70_000)
print('''theta: ''', theta) # printing the theta i.e our weights vector
def lowercase__ ( __lowercase : Dict ) -> str:
"""simple docstring"""
return sigmoid_function(
np.dot(__lowercase , __lowercase ) ) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
((a__) , (a__)) : Optional[int] =(x[:, 0].min(), x[:, 0].max())
((a__) , (a__)) : Optional[int] =(x[:, 1].min(), x[:, 1].max())
((a__) , (a__)) : Optional[Any] =np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
a__ : str =np.c_[xxa.ravel(), xxa.ravel()]
a__ : Dict =predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')
plt.legend()
plt.show()
| 434 | 1 |
from __future__ import annotations
def a ( a , a , a , ) ->tuple[str, float]:
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif stress < 0:
raise ValueError('''Stress cannot be negative''' )
elif tangential_force < 0:
raise ValueError('''Tangential Force cannot be negative''' )
elif area < 0:
raise ValueError('''Area cannot be negative''' )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 201 |
from collections.abc import Iterable
from typing import Any
class lowerCamelCase :
def __init__( self :Optional[int] , lowercase :int | None = None ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = value
SCREAMING_SNAKE_CASE = None # Added in order to delete a node easier
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
def __repr__( self :Tuple ) -> str:
"""simple docstring"""
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({f"""{self.value}""": (self.left, self.right)} , indent=1 )
class lowerCamelCase :
def __init__( self :Union[str, Any] , lowercase :Node | None = None ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE = root
def __str__( self :int ) -> str:
"""simple docstring"""
return str(self.root )
def snake_case__ ( self :Optional[Any] , lowercase :Node , lowercase :Node | None ) -> None:
"""simple docstring"""
if new_children is not None: # reset its kids
SCREAMING_SNAKE_CASE = node.parent
if node.parent is not None: # reset its parent
if self.is_right(lowercase ): # If it is the right children
SCREAMING_SNAKE_CASE = new_children
else:
SCREAMING_SNAKE_CASE = new_children
else:
SCREAMING_SNAKE_CASE = new_children
def snake_case__ ( self :List[str] , lowercase :Node ) -> bool:
"""simple docstring"""
if node.parent and node.parent.right:
return node == node.parent.right
return False
def snake_case__ ( self :Tuple ) -> bool:
"""simple docstring"""
return self.root is None
def snake_case__ ( self :Union[str, Any] , lowercase :List[Any] ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE = Node(lowercase ) # create a new Node
if self.empty(): # if Tree is empty
SCREAMING_SNAKE_CASE = new_node # set its root
else: # Tree is not empty
SCREAMING_SNAKE_CASE = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
SCREAMING_SNAKE_CASE = new_node # We insert the new node in a leaf
break
else:
SCREAMING_SNAKE_CASE = parent_node.left
else:
if parent_node.right is None:
SCREAMING_SNAKE_CASE = new_node
break
else:
SCREAMING_SNAKE_CASE = parent_node.right
SCREAMING_SNAKE_CASE = parent_node
def snake_case__ ( self :Union[str, Any] , *lowercase :Optional[int] ) -> None:
"""simple docstring"""
for value in values:
self.__insert(lowercase )
def snake_case__ ( self :Union[str, Any] , lowercase :Any ) -> Node | None:
"""simple docstring"""
if self.empty():
raise IndexError('''Warning: Tree is empty! please use another.''' )
else:
SCREAMING_SNAKE_CASE = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
SCREAMING_SNAKE_CASE = node.left if value < node.value else node.right
return node
def snake_case__ ( self :str , lowercase :Node | None = None ) -> Node | None:
"""simple docstring"""
if node is None:
if self.root is None:
return None
SCREAMING_SNAKE_CASE = self.root
if not self.empty():
while node.right is not None:
SCREAMING_SNAKE_CASE = node.right
return node
def snake_case__ ( self :int , lowercase :Node | None = None ) -> Node | None:
"""simple docstring"""
if node is None:
SCREAMING_SNAKE_CASE = self.root
if self.root is None:
return None
if not self.empty():
SCREAMING_SNAKE_CASE = self.root
while node.left is not None:
SCREAMING_SNAKE_CASE = node.left
return node
def snake_case__ ( self :Optional[int] , lowercase :int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE = self.search(lowercase ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(lowercase , lowercase )
elif node.left is None: # Has only right children
self.__reassign_nodes(lowercase , node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(lowercase , node.left )
else:
SCREAMING_SNAKE_CASE = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
SCREAMING_SNAKE_CASE = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def snake_case__ ( self :Dict , lowercase :Node | None ) -> Iterable:
"""simple docstring"""
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def snake_case__ ( self :Tuple , lowercase :List[str]=None ) -> Any:
"""simple docstring"""
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def snake_case__ ( self :Optional[Any] , lowercase :list , lowercase :Node | None ) -> None:
"""simple docstring"""
if node:
self.inorder(lowercase , node.left )
arr.append(node.value )
self.inorder(lowercase , node.right )
def snake_case__ ( self :Tuple , lowercase :int , lowercase :Node ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE = []
self.inorder(lowercase , lowercase ) # append all values to list using inorder traversal
return arr[k - 1]
def a ( a ) ->list[Node]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
if curr_node is not None:
SCREAMING_SNAKE_CASE = postorder(curr_node.left ) + postorder(curr_node.right ) + [curr_node]
return node_list
def a ( ) ->None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = (8, 3, 6, 1, 10, 14, 13, 4, 7)
SCREAMING_SNAKE_CASE = BinarySearchTree()
for i in testlist:
t.insert(a )
# Prints all the elements of the list in order traversal
print(a )
if t.search(6 ) is not None:
print('''The value 6 exists''' )
else:
print('''The value 6 doesn\'t exist''' )
if t.search(-1 ) is not None:
print('''The value -1 exists''' )
else:
print('''The value -1 doesn\'t exist''' )
if not t.empty():
print('''Max Value: ''' , t.get_max().value ) # type: ignore
print('''Min Value: ''' , t.get_min().value ) # type: ignore
for i in testlist:
t.remove(a )
print(a )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) | 201 | 1 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
SCREAMING_SNAKE_CASE__ = ["""\nclass""", """\ndef""", """\n#""", """\n@""", """\nprint""", """\nif"""]
class __lowerCamelCase ( snake_case_ ):
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=1 ) -> Dict:
'''simple docstring'''
lowercase_ = tokenizer
lowercase_ = dataset
lowercase_ = len(UpperCAmelCase ) if n_tasks is None else n_tasks
lowercase_ = n_copies
def __iter__( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]["prompt"].strip() )
lowercase_ = self.tokenizer(UpperCAmelCase , padding=UpperCAmelCase , return_tensors="pt" )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class __lowerCamelCase ( snake_case_ ):
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase_ = start_length
lowercase_ = eof_strings
lowercase_ = tokenizer
def __call__( self , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
lowercase_ = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(UpperCAmelCase )
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: Union[str, Any] ):
'''simple docstring'''
lowercase_ = re.split("(%s)" % "|".join(__lowerCamelCase ) , __lowerCamelCase )
# last string should be ""
return "".join(string_list[:-2] )
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: List[Any] , __lowerCamelCase: List[str] , __lowerCamelCase: List[str] , __lowerCamelCase: List[str] , __lowerCamelCase: Union[str, Any] , __lowerCamelCase: int=20 , **__lowerCamelCase: Optional[int] ):
'''simple docstring'''
lowercase_ = defaultdict(__lowerCamelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(__lowerCamelCase ) ):
with torch.no_grad():
lowercase_ = batch["ids"].shape[-1]
lowercase_ = accelerator.unwrap_model(__lowerCamelCase ).generate(
input_ids=batch["ids"][:, : batch["input_len"]] , num_return_sequences=__lowerCamelCase , **__lowerCamelCase )
# each task is generated batch_size times
lowercase_ = batch["task_id"].repeat(__lowerCamelCase )
lowercase_ = accelerator.pad_across_processes(
__lowerCamelCase , dim=1 , pad_index=tokenizer.pad_token_id )
lowercase_ , lowercase_ = accelerator.gather((generated_tokens, generated_tasks) )
lowercase_ = generated_tokens.cpu().numpy()
lowercase_ = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(__lowerCamelCase , __lowerCamelCase ):
gen_token_dict[task].append(__lowerCamelCase )
lowercase_ = [[] for _ in range(__lowerCamelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
lowercase_ = tokenizer.decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase , clean_up_tokenization_spaces=__lowerCamelCase )
code_gens[task].append(remove_last_block(__lowerCamelCase ) )
return code_gens
def SCREAMING_SNAKE_CASE_ ( ):
'''simple docstring'''
lowercase_ = HfArgumentParser(__lowerCamelCase )
lowercase_ = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
lowercase_ = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
lowercase_ = "false"
if args.num_workers is None:
lowercase_ = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
lowercase_ = Accelerator()
set_seed(args.seed , device_specific=__lowerCamelCase )
# Load model and tokenizer
lowercase_ = AutoTokenizer.from_pretrained(args.model_ckpt )
lowercase_ = tokenizer.eos_token
lowercase_ = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
lowercase_ = {
"do_sample": args.do_sample,
"temperature": args.temperature,
"max_new_tokens": args.max_new_tokens,
"top_p": args.top_p,
"top_k": args.top_k,
"stopping_criteria": StoppingCriteriaList([EndOfFunctionCriteria(0 , __lowerCamelCase , __lowerCamelCase )] ),
}
# Load evaluation dataset and metric
lowercase_ = load_dataset("openai_humaneval" )
lowercase_ = load_metric("code_eval" )
lowercase_ = args.num_tasks if args.num_tasks is not None else len(human_eval["test"] )
lowercase_ = args.n_samples // args.batch_size
lowercase_ = TokenizedDataset(__lowerCamelCase , human_eval["test"] , n_copies=__lowerCamelCase , n_tasks=__lowerCamelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
lowercase_ = DataLoader(__lowerCamelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
lowercase_ = code_eval_metric.compute(references=[""] , predictions=[[""]] )
except ValueError as exception:
print(
"Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL=\"1\"`"
" flag to enable code evaluation." )
raise exception
lowercase_ , lowercase_ = accelerator.prepare(__lowerCamelCase , __lowerCamelCase )
lowercase_ = complete_code(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , n_tasks=__lowerCamelCase , batch_size=args.batch_size , **__lowerCamelCase , )
if accelerator.is_main_process:
lowercase_ = []
for task in tqdm(range(__lowerCamelCase ) ):
lowercase_ = human_eval["test"][task]["test"]
lowercase_ = F'check({human_eval["test"][task]["entry_point"]})'
references.append("\n" + test_func + "\n" + entry_point )
# Evaluate completions with "code_eval" metric
lowercase_ , lowercase_ = code_eval_metric.compute(
references=__lowerCamelCase , predictions=__lowerCamelCase , num_workers=args.num_workers )
print(F'Results: {pass_at_k}' )
# Save results to json file
with open(args.output_file , "w" ) as fp:
json.dump(__lowerCamelCase , __lowerCamelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 601 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
SCREAMING_SNAKE_CASE__ = sys.version_info >= (3, 1_0)
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: List[Any]=None , __lowerCamelCase: List[str]=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=__lowerCamelCase )
@dataclass
class __lowerCamelCase :
"""simple docstring"""
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
@dataclass
class __lowerCamelCase :
"""simple docstring"""
lowerCAmelCase__ = 42
lowerCAmelCase__ = field(default="toto" , metadata={"help": "help message"} )
@dataclass
class __lowerCamelCase :
"""simple docstring"""
lowerCAmelCase__ = False
lowerCAmelCase__ = True
lowerCAmelCase__ = None
class __lowerCamelCase ( snake_case_ ):
"""simple docstring"""
lowerCAmelCase__ = "titi"
lowerCAmelCase__ = "toto"
class __lowerCamelCase ( snake_case_ ):
"""simple docstring"""
lowerCAmelCase__ = "titi"
lowerCAmelCase__ = "toto"
lowerCAmelCase__ = 42
@dataclass
class __lowerCamelCase :
"""simple docstring"""
lowerCAmelCase__ = "toto"
def A__ ( self ) -> int:
'''simple docstring'''
lowercase_ = BasicEnum(self.foo )
@dataclass
class __lowerCamelCase :
"""simple docstring"""
lowerCAmelCase__ = "toto"
def A__ ( self ) -> Dict:
'''simple docstring'''
lowercase_ = MixedTypeEnum(self.foo )
@dataclass
class __lowerCamelCase :
"""simple docstring"""
lowerCAmelCase__ = None
lowerCAmelCase__ = field(default=snake_case_ , metadata={"help": "help message"} )
lowerCAmelCase__ = None
lowerCAmelCase__ = list_field(default=[] )
lowerCAmelCase__ = list_field(default=[] )
@dataclass
class __lowerCamelCase :
"""simple docstring"""
lowerCAmelCase__ = list_field(default=[] )
lowerCAmelCase__ = list_field(default=[1, 2, 3] )
lowerCAmelCase__ = list_field(default=["Hallo", "Bonjour", "Hello"] )
lowerCAmelCase__ = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class __lowerCamelCase :
"""simple docstring"""
lowerCAmelCase__ = field()
lowerCAmelCase__ = field()
lowerCAmelCase__ = field()
def A__ ( self ) -> Dict:
'''simple docstring'''
lowercase_ = BasicEnum(self.required_enum )
@dataclass
class __lowerCamelCase :
"""simple docstring"""
lowerCAmelCase__ = 42
lowerCAmelCase__ = field()
lowerCAmelCase__ = None
lowerCAmelCase__ = field(default="toto" , metadata={"help": "help message"} )
lowerCAmelCase__ = list_field(default=["Hallo", "Bonjour", "Hello"] )
if is_python_no_less_than_3_10:
@dataclass
class __lowerCamelCase :
"""simple docstring"""
lowerCAmelCase__ = False
lowerCAmelCase__ = True
lowerCAmelCase__ = None
@dataclass
class __lowerCamelCase :
"""simple docstring"""
lowerCAmelCase__ = None
lowerCAmelCase__ = field(default=snake_case_ , metadata={"help": "help message"} )
lowerCAmelCase__ = None
lowerCAmelCase__ = list_field(default=[] )
lowerCAmelCase__ = list_field(default=[] )
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
'''simple docstring'''
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
lowercase_ = {k: v for k, v in vars(UpperCAmelCase ).items() if k != "container"}
lowercase_ = {k: v for k, v in vars(UpperCAmelCase ).items() if k != "container"}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get("choices" , UpperCAmelCase ) and yy.get("choices" , UpperCAmelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["type"](UpperCAmelCase ) , yy["type"](UpperCAmelCase ) )
del xx["type"], yy["type"]
self.assertEqual(UpperCAmelCase , UpperCAmelCase )
def A__ ( self ) -> Any:
'''simple docstring'''
lowercase_ = HfArgumentParser(UpperCAmelCase )
lowercase_ = argparse.ArgumentParser()
expected.add_argument("--foo" , type=UpperCAmelCase , required=UpperCAmelCase )
expected.add_argument("--bar" , type=UpperCAmelCase , required=UpperCAmelCase )
expected.add_argument("--baz" , type=UpperCAmelCase , required=UpperCAmelCase )
expected.add_argument("--flag" , type=UpperCAmelCase , default=UpperCAmelCase , const=UpperCAmelCase , nargs="?" )
self.argparsersEqual(UpperCAmelCase , UpperCAmelCase )
lowercase_ = ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
((lowercase_) , ) = parser.parse_args_into_dataclasses(UpperCAmelCase , look_for_args_file=UpperCAmelCase )
self.assertFalse(example.flag )
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = HfArgumentParser(UpperCAmelCase )
lowercase_ = argparse.ArgumentParser()
expected.add_argument("--foo" , default=42 , type=UpperCAmelCase )
expected.add_argument("--baz" , default="toto" , type=UpperCAmelCase , help="help message" )
self.argparsersEqual(UpperCAmelCase , UpperCAmelCase )
def A__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase_ = argparse.ArgumentParser()
expected.add_argument("--foo" , type=UpperCAmelCase , default=UpperCAmelCase , const=UpperCAmelCase , nargs="?" )
expected.add_argument("--baz" , type=UpperCAmelCase , default=UpperCAmelCase , const=UpperCAmelCase , nargs="?" )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument("--no_baz" , action="store_false" , default=UpperCAmelCase , dest="baz" )
expected.add_argument("--opt" , type=UpperCAmelCase , default=UpperCAmelCase )
lowercase_ = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(UpperCAmelCase )
for dataclass_type in dataclass_types:
lowercase_ = HfArgumentParser(UpperCAmelCase )
self.argparsersEqual(UpperCAmelCase , UpperCAmelCase )
lowercase_ = parser.parse_args([] )
self.assertEqual(UpperCAmelCase , Namespace(foo=UpperCAmelCase , baz=UpperCAmelCase , opt=UpperCAmelCase ) )
lowercase_ = parser.parse_args(["--foo", "--no_baz"] )
self.assertEqual(UpperCAmelCase , Namespace(foo=UpperCAmelCase , baz=UpperCAmelCase , opt=UpperCAmelCase ) )
lowercase_ = parser.parse_args(["--foo", "--baz"] )
self.assertEqual(UpperCAmelCase , Namespace(foo=UpperCAmelCase , baz=UpperCAmelCase , opt=UpperCAmelCase ) )
lowercase_ = parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"] )
self.assertEqual(UpperCAmelCase , Namespace(foo=UpperCAmelCase , baz=UpperCAmelCase , opt=UpperCAmelCase ) )
lowercase_ = parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"] )
self.assertEqual(UpperCAmelCase , Namespace(foo=UpperCAmelCase , baz=UpperCAmelCase , opt=UpperCAmelCase ) )
def A__ ( self ) -> str:
'''simple docstring'''
lowercase_ = HfArgumentParser(UpperCAmelCase )
lowercase_ = argparse.ArgumentParser()
expected.add_argument(
"--foo" , default="toto" , choices=["titi", "toto", 42] , type=make_choice_type_function(["titi", "toto", 42] ) , )
self.argparsersEqual(UpperCAmelCase , UpperCAmelCase )
lowercase_ = parser.parse_args([] )
self.assertEqual(args.foo , "toto" )
lowercase_ = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
lowercase_ = parser.parse_args(["--foo", "titi"] )
self.assertEqual(args.foo , "titi" )
lowercase_ = parser.parse_args_into_dataclasses(["--foo", "titi"] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
lowercase_ = parser.parse_args(["--foo", "42"] )
self.assertEqual(args.foo , 42 )
lowercase_ = parser.parse_args_into_dataclasses(["--foo", "42"] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
@dataclass
class __lowerCamelCase :
"""simple docstring"""
lowerCAmelCase__ = "toto"
lowercase_ = HfArgumentParser(UpperCAmelCase )
lowercase_ = argparse.ArgumentParser()
expected.add_argument(
"--foo" , default="toto" , choices=("titi", "toto", 42) , type=make_choice_type_function(["titi", "toto", 42] ) , )
self.argparsersEqual(UpperCAmelCase , UpperCAmelCase )
lowercase_ = parser.parse_args([] )
self.assertEqual(args.foo , "toto" )
lowercase_ = parser.parse_args(["--foo", "titi"] )
self.assertEqual(args.foo , "titi" )
lowercase_ = parser.parse_args(["--foo", "42"] )
self.assertEqual(args.foo , 42 )
def A__ ( self ) -> Any:
'''simple docstring'''
lowercase_ = HfArgumentParser(UpperCAmelCase )
lowercase_ = argparse.ArgumentParser()
expected.add_argument("--foo_int" , nargs="+" , default=[] , type=UpperCAmelCase )
expected.add_argument("--bar_int" , nargs="+" , default=[1, 2, 3] , type=UpperCAmelCase )
expected.add_argument("--foo_str" , nargs="+" , default=["Hallo", "Bonjour", "Hello"] , type=UpperCAmelCase )
expected.add_argument("--foo_float" , nargs="+" , default=[0.1, 0.2, 0.3] , type=UpperCAmelCase )
self.argparsersEqual(UpperCAmelCase , UpperCAmelCase )
lowercase_ = parser.parse_args([] )
self.assertEqual(
UpperCAmelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["Hallo", "Bonjour", "Hello"] , foo_float=[0.1, 0.2, 0.3] ) , )
lowercase_ = parser.parse_args("--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7".split() )
self.assertEqual(UpperCAmelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["a", "b", "c"] , foo_float=[0.1, 0.7] ) )
def A__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase_ = argparse.ArgumentParser()
expected.add_argument("--foo" , default=UpperCAmelCase , type=UpperCAmelCase )
expected.add_argument("--bar" , default=UpperCAmelCase , type=UpperCAmelCase , help="help message" )
expected.add_argument("--baz" , default=UpperCAmelCase , type=UpperCAmelCase )
expected.add_argument("--ces" , nargs="+" , default=[] , type=UpperCAmelCase )
expected.add_argument("--des" , nargs="+" , default=[] , type=UpperCAmelCase )
lowercase_ = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(UpperCAmelCase )
for dataclass_type in dataclass_types:
lowercase_ = HfArgumentParser(UpperCAmelCase )
self.argparsersEqual(UpperCAmelCase , UpperCAmelCase )
lowercase_ = parser.parse_args([] )
self.assertEqual(UpperCAmelCase , Namespace(foo=UpperCAmelCase , bar=UpperCAmelCase , baz=UpperCAmelCase , ces=[] , des=[] ) )
lowercase_ = parser.parse_args("--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3".split() )
self.assertEqual(UpperCAmelCase , Namespace(foo=12 , bar=3.14 , baz="42" , ces=["a", "b", "c"] , des=[1, 2, 3] ) )
def A__ ( self ) -> List[str]:
'''simple docstring'''
lowercase_ = HfArgumentParser(UpperCAmelCase )
lowercase_ = argparse.ArgumentParser()
expected.add_argument("--required_list" , nargs="+" , type=UpperCAmelCase , required=UpperCAmelCase )
expected.add_argument("--required_str" , type=UpperCAmelCase , required=UpperCAmelCase )
expected.add_argument(
"--required_enum" , type=make_choice_type_function(["titi", "toto"] ) , choices=["titi", "toto"] , required=UpperCAmelCase , )
self.argparsersEqual(UpperCAmelCase , UpperCAmelCase )
def A__ ( self ) -> int:
'''simple docstring'''
lowercase_ = HfArgumentParser(UpperCAmelCase )
lowercase_ = argparse.ArgumentParser()
expected.add_argument("--foo" , type=UpperCAmelCase , required=UpperCAmelCase )
expected.add_argument(
"--required_enum" , type=make_choice_type_function(["titi", "toto"] ) , choices=["titi", "toto"] , required=UpperCAmelCase , )
expected.add_argument("--opt" , type=UpperCAmelCase , default=UpperCAmelCase )
expected.add_argument("--baz" , default="toto" , type=UpperCAmelCase , help="help message" )
expected.add_argument("--foo_str" , nargs="+" , default=["Hallo", "Bonjour", "Hello"] , type=UpperCAmelCase )
self.argparsersEqual(UpperCAmelCase , UpperCAmelCase )
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
lowercase_ = HfArgumentParser(UpperCAmelCase )
lowercase_ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
lowercase_ = parser.parse_dict(UpperCAmelCase )[0]
lowercase_ = BasicExample(**UpperCAmelCase )
self.assertEqual(UpperCAmelCase , UpperCAmelCase )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = HfArgumentParser(UpperCAmelCase )
lowercase_ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
"extra": 42,
}
self.assertRaises(UpperCAmelCase , parser.parse_dict , UpperCAmelCase , allow_extra_keys=UpperCAmelCase )
def A__ ( self ) -> Any:
'''simple docstring'''
lowercase_ = HfArgumentParser(UpperCAmelCase )
lowercase_ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ = os.path.join(UpperCAmelCase , "temp_json" )
os.mkdir(UpperCAmelCase )
with open(temp_local_path + ".json" , "w+" ) as f:
json.dump(UpperCAmelCase , UpperCAmelCase )
lowercase_ = parser.parse_yaml_file(Path(temp_local_path + ".json" ) )[0]
lowercase_ = BasicExample(**UpperCAmelCase )
self.assertEqual(UpperCAmelCase , UpperCAmelCase )
def A__ ( self ) -> List[Any]:
'''simple docstring'''
lowercase_ = HfArgumentParser(UpperCAmelCase )
lowercase_ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ = os.path.join(UpperCAmelCase , "temp_yaml" )
os.mkdir(UpperCAmelCase )
with open(temp_local_path + ".yaml" , "w+" ) as f:
yaml.dump(UpperCAmelCase , UpperCAmelCase )
lowercase_ = parser.parse_yaml_file(Path(temp_local_path + ".yaml" ) )[0]
lowercase_ = BasicExample(**UpperCAmelCase )
self.assertEqual(UpperCAmelCase , UpperCAmelCase )
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
lowercase_ = HfArgumentParser(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
| 601 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : int = {
"""configuration_xmod""": [
"""XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XmodConfig""",
"""XmodOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : List[Any] = [
"""XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XmodForCausalLM""",
"""XmodForMaskedLM""",
"""XmodForMultipleChoice""",
"""XmodForQuestionAnswering""",
"""XmodForSequenceClassification""",
"""XmodForTokenClassification""",
"""XmodModel""",
"""XmodPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 244 | '''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = (DDPMScheduler,)
def _A ( self : Any , **A : List[str] ):
_UpperCAmelCase : int = {
"num_train_timesteps": 1000,
"beta_start": 0.0_001,
"beta_end": 0.02,
"beta_schedule": "linear",
"variance_type": "fixed_small",
"clip_sample": True,
}
config.update(**A )
return config
def _A ( self : List[Any] ):
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=A )
def _A ( self : Union[str, Any] ):
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=A , beta_end=A )
def _A ( self : Optional[Any] ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=A )
def _A ( self : int ):
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=A )
def _A ( self : Any ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=A )
def _A ( self : Union[str, Any] ):
self.check_over_configs(thresholding=A )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=A , prediction_type=A , sample_max_value=A , )
def _A ( self : List[str] ):
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=A )
def _A ( self : Union[str, Any] ):
for t in [0, 500, 999]:
self.check_over_forward(time_step=A )
def _A ( self : Tuple ):
_UpperCAmelCase : Union[str, Any] = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**A )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _A ( self : List[Any] ):
_UpperCAmelCase : Optional[Any] = self.scheduler_classes[0]
_UpperCAmelCase : Optional[Any] = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**A )
_UpperCAmelCase : Optional[Any] = len(A )
_UpperCAmelCase : List[Any] = self.dummy_model()
_UpperCAmelCase : List[str] = self.dummy_sample_deter
_UpperCAmelCase : List[str] = torch.manual_seed(0 )
for t in reversed(range(A ) ):
# 1. predict noise residual
_UpperCAmelCase : List[Any] = model(A , A )
# 2. predict previous mean of sample x_t-1
_UpperCAmelCase : List[Any] = scheduler.step(A , A , A , generator=A ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCAmelCase : Any = pred_prev_sample
_UpperCAmelCase : str = torch.sum(torch.abs(A ) )
_UpperCAmelCase : Tuple = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : List[Any] = self.scheduler_classes[0]
_UpperCAmelCase : Dict = self.get_scheduler_config(prediction_type="v_prediction" )
_UpperCAmelCase : Optional[int] = scheduler_class(**A )
_UpperCAmelCase : Union[str, Any] = len(A )
_UpperCAmelCase : Optional[int] = self.dummy_model()
_UpperCAmelCase : Optional[Any] = self.dummy_sample_deter
_UpperCAmelCase : List[Any] = torch.manual_seed(0 )
for t in reversed(range(A ) ):
# 1. predict noise residual
_UpperCAmelCase : Tuple = model(A , A )
# 2. predict previous mean of sample x_t-1
_UpperCAmelCase : List[Any] = scheduler.step(A , A , A , generator=A ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCAmelCase : Tuple = pred_prev_sample
_UpperCAmelCase : List[str] = torch.sum(torch.abs(A ) )
_UpperCAmelCase : int = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Optional[Any] = self.scheduler_classes[0]
_UpperCAmelCase : Optional[int] = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**A )
_UpperCAmelCase : Any = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=A )
_UpperCAmelCase : Optional[Any] = scheduler.timesteps
for i, timestep in enumerate(A ):
if i == len(A ) - 1:
_UpperCAmelCase : int = -1
else:
_UpperCAmelCase : str = timesteps[i + 1]
_UpperCAmelCase : Any = scheduler.previous_timestep(A )
_UpperCAmelCase : Optional[Any] = prev_t.item()
self.assertEqual(A , A )
def _A ( self : Optional[int] ):
_UpperCAmelCase : List[Any] = self.scheduler_classes[0]
_UpperCAmelCase : Union[str, Any] = self.get_scheduler_config()
_UpperCAmelCase : Optional[Any] = scheduler_class(**A )
_UpperCAmelCase : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(A , msg="`custom_timesteps` must be in descending order." ):
scheduler.set_timesteps(timesteps=A )
def _A ( self : Dict ):
_UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
_UpperCAmelCase : Tuple = self.get_scheduler_config()
_UpperCAmelCase : str = scheduler_class(**A )
_UpperCAmelCase : str = [100, 87, 50, 1, 0]
_UpperCAmelCase : Tuple = len(A )
with self.assertRaises(A , msg="Can only pass one of `num_inference_steps` or `custom_timesteps`." ):
scheduler.set_timesteps(num_inference_steps=A , timesteps=A )
def _A ( self : List[str] ):
_UpperCAmelCase : List[str] = self.scheduler_classes[0]
_UpperCAmelCase : str = self.get_scheduler_config()
_UpperCAmelCase : int = scheduler_class(**A )
_UpperCAmelCase : List[Any] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
A , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" , ):
scheduler.set_timesteps(timesteps=A )
| 244 | 1 |
def A__ ( lowerCamelCase ) -> List[str]:
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
UpperCamelCase_: Optional[int] = [True] * (num + 1)
UpperCamelCase_: int = 2
while p * p <= num:
if primes[p]:
for i in range(p * p , num + 1 , snake_case__ ):
UpperCamelCase_: List[str] = False
p += 1
return [prime for prime in range(2 , num + 1 ) if primes[prime]]
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase_ : List[str] = int(input("""Enter a positive integer: """).strip())
print(prime_sieve_eratosthenes(user_num))
| 700 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Tuple=None , **snake_case_ : List[str] ):
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" , snake_case_ , )
super().__init__(args=snake_case_ , **snake_case_ )
| 670 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class a_ ( _lowerCAmelCase ):
__A = (UniPCMultistepScheduler,)
__A = (("num_inference_steps", 25),)
def lowercase__ ( self : int , **lowercase : Union[str, Any] ):
"""simple docstring"""
lowercase_ :List[Any] = {
"num_train_timesteps": 1_000,
"beta_start": 0.00_01,
"beta_end": 0.02,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**lowercase )
return config
def lowercase__ ( self : List[Any] , lowercase : List[Any]=0 , **lowercase : Dict ):
"""simple docstring"""
lowercase_ :Union[str, Any] = dict(self.forward_default_kwargs )
lowercase_ :Any = kwargs.pop("num_inference_steps" , lowercase )
lowercase_ :Optional[Any] = self.dummy_sample
lowercase_ :List[str] = 0.1 * sample
lowercase_ :Dict = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowercase_ :Dict = self.get_scheduler_config(**lowercase )
lowercase_ :Dict = scheduler_class(**lowercase )
scheduler.set_timesteps(lowercase )
# copy over dummy past residuals
lowercase_ :Tuple = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase )
lowercase_ :List[Any] = scheduler_class.from_pretrained(lowercase )
new_scheduler.set_timesteps(lowercase )
# copy over dummy past residuals
lowercase_ :List[str] = dummy_past_residuals[: new_scheduler.config.solver_order]
lowercase_ , lowercase_ :int = sample, sample
for t in range(lowercase , time_step + scheduler.config.solver_order + 1 ):
lowercase_ :Dict = scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
lowercase_ :Optional[Any] = new_scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : Optional[int] , lowercase : Optional[int]=0 , **lowercase : Dict ):
"""simple docstring"""
lowercase_ :Tuple = dict(self.forward_default_kwargs )
lowercase_ :List[str] = kwargs.pop("num_inference_steps" , lowercase )
lowercase_ :str = self.dummy_sample
lowercase_ :int = 0.1 * sample
lowercase_ :str = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowercase_ :Optional[Any] = self.get_scheduler_config()
lowercase_ :List[str] = scheduler_class(**lowercase )
scheduler.set_timesteps(lowercase )
# copy over dummy past residuals (must be after setting timesteps)
lowercase_ :Tuple = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase )
lowercase_ :Any = scheduler_class.from_pretrained(lowercase )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowercase )
# copy over dummy past residual (must be after setting timesteps)
lowercase_ :List[str] = dummy_past_residuals[: new_scheduler.config.solver_order]
lowercase_ :List[str] = scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
lowercase_ :Any = new_scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : List[Any] , lowercase : str=None , **lowercase : List[Any] ):
"""simple docstring"""
if scheduler is None:
lowercase_ :Any = self.scheduler_classes[0]
lowercase_ :List[str] = self.get_scheduler_config(**lowercase )
lowercase_ :Optional[Any] = scheduler_class(**lowercase )
lowercase_ :List[Any] = self.scheduler_classes[0]
lowercase_ :List[str] = self.get_scheduler_config(**lowercase )
lowercase_ :Tuple = scheduler_class(**lowercase )
lowercase_ :Any = 10
lowercase_ :Optional[int] = self.dummy_model()
lowercase_ :Dict = self.dummy_sample_deter
scheduler.set_timesteps(lowercase )
for i, t in enumerate(scheduler.timesteps ):
lowercase_ :List[Any] = model(lowercase , lowercase )
lowercase_ :List[Any] = scheduler.step(lowercase , lowercase , lowercase ).prev_sample
return sample
def lowercase__ ( self : Optional[int] ):
"""simple docstring"""
lowercase_ :Tuple = dict(self.forward_default_kwargs )
lowercase_ :Union[str, Any] = kwargs.pop("num_inference_steps" , lowercase )
for scheduler_class in self.scheduler_classes:
lowercase_ :List[Any] = self.get_scheduler_config()
lowercase_ :List[str] = scheduler_class(**lowercase )
lowercase_ :int = self.dummy_sample
lowercase_ :int = 0.1 * sample
if num_inference_steps is not None and hasattr(lowercase , "set_timesteps" ):
scheduler.set_timesteps(lowercase )
elif num_inference_steps is not None and not hasattr(lowercase , "set_timesteps" ):
lowercase_ :List[Any] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
lowercase_ :List[Any] = [residual + 0.2, residual + 0.15, residual + 0.10]
lowercase_ :int = dummy_past_residuals[: scheduler.config.solver_order]
lowercase_ :str = scheduler.timesteps[5]
lowercase_ :Optional[int] = scheduler.timesteps[6]
lowercase_ :List[Any] = scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
lowercase_ :Union[str, Any] = scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowercase__ ( self : Optional[int] ):
"""simple docstring"""
lowercase_ :Union[str, Any] = UniPCMultistepScheduler(**self.get_scheduler_config() )
lowercase_ :str = self.full_loop(scheduler=lowercase )
lowercase_ :Optional[Any] = torch.mean(torch.abs(lowercase ) )
assert abs(result_mean.item() - 0.24_64 ) < 1e-3
lowercase_ :Union[str, Any] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
lowercase_ :List[Any] = DEISMultistepScheduler.from_config(scheduler.config )
lowercase_ :Optional[Any] = DPMSolverMultistepScheduler.from_config(scheduler.config )
lowercase_ :str = UniPCMultistepScheduler.from_config(scheduler.config )
lowercase_ :List[str] = self.full_loop(scheduler=lowercase )
lowercase_ :str = torch.mean(torch.abs(lowercase ) )
assert abs(result_mean.item() - 0.24_64 ) < 1e-3
def lowercase__ ( self : Union[str, Any] ):
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1_000]:
self.check_over_configs(num_train_timesteps=lowercase )
def lowercase__ ( self : List[str] ):
"""simple docstring"""
self.check_over_configs(thresholding=lowercase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=lowercase , prediction_type=lowercase , sample_max_value=lowercase , solver_order=lowercase , solver_type=lowercase , )
def lowercase__ ( self : Optional[int] ):
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase )
def lowercase__ ( self : Dict ):
"""simple docstring"""
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=lowercase , solver_type=lowercase , prediction_type=lowercase , )
lowercase_ :int = self.full_loop(
solver_order=lowercase , solver_type=lowercase , prediction_type=lowercase , )
assert not torch.isnan(lowercase ).any(), "Samples have nan numbers"
def lowercase__ ( self : Dict ):
"""simple docstring"""
self.check_over_configs(lower_order_final=lowercase )
self.check_over_configs(lower_order_final=lowercase )
def lowercase__ ( self : int ):
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
self.check_over_forward(num_inference_steps=lowercase , time_step=0 )
def lowercase__ ( self : Tuple ):
"""simple docstring"""
lowercase_ :str = self.full_loop()
lowercase_ :Optional[Any] = torch.mean(torch.abs(lowercase ) )
assert abs(result_mean.item() - 0.24_64 ) < 1e-3
def lowercase__ ( self : Tuple ):
"""simple docstring"""
lowercase_ :Any = self.full_loop(prediction_type="v_prediction" )
lowercase_ :Optional[int] = torch.mean(torch.abs(lowercase ) )
assert abs(result_mean.item() - 0.10_14 ) < 1e-3
def lowercase__ ( self : Any ):
"""simple docstring"""
lowercase_ :Optional[int] = self.scheduler_classes[0]
lowercase_ :str = self.get_scheduler_config(thresholding=lowercase , dynamic_thresholding_ratio=0 )
lowercase_ :str = scheduler_class(**lowercase )
lowercase_ :str = 10
lowercase_ :int = self.dummy_model()
lowercase_ :Tuple = self.dummy_sample_deter.half()
scheduler.set_timesteps(lowercase )
for i, t in enumerate(scheduler.timesteps ):
lowercase_ :int = model(lowercase , lowercase )
lowercase_ :Optional[int] = scheduler.step(lowercase , lowercase , lowercase ).prev_sample
assert sample.dtype == torch.floataa
def lowercase__ ( self : Union[str, Any] , **lowercase : Any ):
"""simple docstring"""
for scheduler_class in self.scheduler_classes:
lowercase_ :Optional[int] = self.get_scheduler_config(**lowercase )
lowercase_ :Union[str, Any] = scheduler_class(**lowercase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 172 |
'''simple docstring'''
from __future__ import annotations
class a_ :
def __init__( self : List[str] , lowercase : Optional[Any]=None ):
"""simple docstring"""
lowercase_ :Optional[int] = data
lowercase_ :int = None
def __repr__( self : Dict ):
"""simple docstring"""
lowercase_ :Any = []
lowercase_ :Tuple = self
while temp:
string_rep.append(F'{temp.data}' )
lowercase_ :Optional[Any] = temp.next
return "->".join(lowercase )
def UpperCAmelCase_ ( __lowerCamelCase : list ):
if not elements_list:
raise Exception("The Elements List is empty" )
lowercase_ :int = Node(elements_list[0] )
for i in range(1 ,len(__lowerCamelCase ) ):
lowercase_ :Optional[Any] = Node(elements_list[i] )
lowercase_ :Optional[Any] = current.next
return head
def UpperCAmelCase_ ( __lowerCamelCase : Node ):
if head_node is not None and isinstance(__lowerCamelCase ,__lowerCamelCase ):
print_reverse(head_node.next )
print(head_node.data )
def UpperCAmelCase_ ( ):
from doctest import testmod
testmod()
lowercase_ :Dict = make_linked_list([14, 52, 14, 12, 43] )
print("Linked List:" )
print(__lowerCamelCase )
print("Elements in Reverse:" )
print_reverse(__lowerCamelCase )
if __name__ == "__main__":
main()
| 172 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class snake_case ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase) ->None:
warnings.warn(
"The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use LayoutLMv2ImageProcessor instead." , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase) | 210 |
"""simple docstring"""
from math import sqrt
def UpperCamelCase ( UpperCAmelCase = 1_000_000 ) ->int:
"""simple docstring"""
a_ = 0
a_ = 0
a_ = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(UpperCAmelCase , sum_shortest_sides // 2 )
- max(1 , sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(F"""{solution() = }""") | 210 | 1 |
import os
import sys
import unittest
a : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
a : str = os.path.join(git_repo_path, '''src''', '''transformers''')
a : Optional[Any] = '''\n{0} = None\n'''
a : Optional[int] = '''\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n'''
a : Union[str, Any] = '''\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n'''
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def A ( self ) -> List[str]:
'''simple docstring'''
__lowercase = find_backend(''' _import_structure["models.albert"].append("AlbertTokenizerFast")''' )
self.assertIsNone(snake_case_ )
__lowercase = find_backend(''' if not is_tokenizers_available():''' )
self.assertEqual(snake_case_ , '''tokenizers''' )
__lowercase = find_backend(''' if not is_tensorflow_text_available():''' )
self.assertEqual(snake_case_ , '''tensorflow_text''' )
__lowercase = find_backend(''' if not (is_sentencepiece_available() and is_tokenizers_available()):''' )
self.assertEqual(snake_case_ , '''sentencepiece_and_tokenizers''' )
__lowercase = find_backend(
''' if not (is_sentencepiece_available() and is_tensorflow_text_available()):''' )
self.assertEqual(snake_case_ , '''sentencepiece_and_tensorflow_text''' )
__lowercase = find_backend(
''' if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):''' )
self.assertEqual(snake_case_ , '''sentencepiece_and_tokenizers_and_vision''' )
def A ( self ) -> int:
'''simple docstring'''
__lowercase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('''torch''' , snake_case_ )
self.assertIn('''tensorflow_text''' , snake_case_ )
self.assertIn('''sentencepiece_and_tokenizers''' , snake_case_ )
# Likewise, we can't assert on the exact content of a key
self.assertIn('''BertModel''' , objects['''torch'''] )
self.assertIn('''TFBertModel''' , objects['''tf'''] )
self.assertIn('''FlaxBertModel''' , objects['''flax'''] )
self.assertIn('''BertModel''' , objects['''torch'''] )
self.assertIn('''TFBertTokenizer''' , objects['''tensorflow_text'''] )
self.assertIn('''convert_slow_tokenizer''' , objects['''sentencepiece_and_tokenizers'''] )
def A ( self ) -> str:
'''simple docstring'''
__lowercase = create_dummy_object('''CONSTANT''' , '''\'torch\'''' )
self.assertEqual(snake_case_ , '''\nCONSTANT = None\n''' )
__lowercase = create_dummy_object('''function''' , '''\'torch\'''' )
self.assertEqual(
snake_case_ , '''\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n''' )
__lowercase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n'
__lowercase = create_dummy_object('''FakeClass''' , '''\'torch\'''' )
self.assertEqual(snake_case_ , snake_case_ )
def A ( self ) -> Dict:
'''simple docstring'''
__lowercase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n'
__lowercase = create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']} )
self.assertEqual(dummy_files['''torch'''] , snake_case_ )
| 639 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger("transformers.models.speecht5")
def lowercase__( __UpperCamelCase: List[Any] ,__UpperCamelCase: List[Any] ,__UpperCamelCase: Any ):
"""simple docstring"""
hf_model.apply_weight_norm()
SCREAMING_SNAKE_CASE : Any = checkpoint['input_conv.weight_g']
SCREAMING_SNAKE_CASE : List[Any] = checkpoint['input_conv.weight_v']
SCREAMING_SNAKE_CASE : str = checkpoint['input_conv.bias']
for i in range(len(config.upsample_rates ) ):
SCREAMING_SNAKE_CASE : Optional[int] = checkpoint[f"upsamples.{i}.1.weight_g"]
SCREAMING_SNAKE_CASE : Dict = checkpoint[f"upsamples.{i}.1.weight_v"]
SCREAMING_SNAKE_CASE : Union[str, Any] = checkpoint[f"upsamples.{i}.1.bias"]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
SCREAMING_SNAKE_CASE : int = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"]
SCREAMING_SNAKE_CASE : str = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"]
SCREAMING_SNAKE_CASE : Union[str, Any] = checkpoint[f"blocks.{i}.convs1.{j}.1.bias"]
SCREAMING_SNAKE_CASE : Dict = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"]
SCREAMING_SNAKE_CASE : Union[str, Any] = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"]
SCREAMING_SNAKE_CASE : Tuple = checkpoint[f"blocks.{i}.convs2.{j}.1.bias"]
SCREAMING_SNAKE_CASE : Optional[Any] = checkpoint['output_conv.1.weight_g']
SCREAMING_SNAKE_CASE : List[Any] = checkpoint['output_conv.1.weight_v']
SCREAMING_SNAKE_CASE : Union[str, Any] = checkpoint['output_conv.1.bias']
hf_model.remove_weight_norm()
@torch.no_grad()
def lowercase__( __UpperCamelCase: str ,__UpperCamelCase: int ,__UpperCamelCase: Any ,__UpperCamelCase: str=None ,__UpperCamelCase: Tuple=None ,):
"""simple docstring"""
if config_path is not None:
SCREAMING_SNAKE_CASE : List[Any] = SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE : Optional[int] = SpeechTaHifiGanConfig()
SCREAMING_SNAKE_CASE : Optional[Any] = SpeechTaHifiGan(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.load(__UpperCamelCase )
load_weights(orig_checkpoint['model']['generator'] ,__UpperCamelCase ,__UpperCamelCase )
SCREAMING_SNAKE_CASE : int = np.load(__UpperCamelCase )
SCREAMING_SNAKE_CASE : List[Any] = stats[0].reshape(-1 )
SCREAMING_SNAKE_CASE : Tuple = stats[1].reshape(-1 )
SCREAMING_SNAKE_CASE : Tuple = torch.from_numpy(__UpperCamelCase ).float()
SCREAMING_SNAKE_CASE : Optional[Any] = torch.from_numpy(__UpperCamelCase ).float()
model.save_pretrained(__UpperCamelCase )
if repo_id:
print('Pushing to the hub...' )
model.push_to_hub(__UpperCamelCase )
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
UpperCamelCase_ = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 28 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( lowerCamelCase__ : list[int] , lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : int ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
lowercase__ , lowercase__ : Tuple = array[indexa], array[indexa]
def _lowerCamelCase ( lowerCamelCase__ : list[int] , lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : int ):
if length > 1:
lowercase__ : Dict = int(length / 2 )
for i in range(lowerCamelCase__ , low + middle ):
comp_and_swap(lowerCamelCase__ , lowerCamelCase__ , i + middle , lowerCamelCase__ )
bitonic_merge(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
bitonic_merge(lowerCamelCase__ , low + middle , lowerCamelCase__ , lowerCamelCase__ )
def _lowerCamelCase ( lowerCamelCase__ : list[int] , lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : int ):
if length > 1:
lowercase__ : Optional[Any] = int(length / 2 )
bitonic_sort(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , 1 )
bitonic_sort(lowerCamelCase__ , low + middle , lowerCamelCase__ , 0 )
bitonic_merge(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
if __name__ == "__main__":
__snake_case = input('Enter numbers separated by a comma:\n').strip()
__snake_case = [int(item.strip()) for item in user_input.split(',')]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print('\nSorted array in ascending order is: ', end='')
print(*unsorted, sep=', ')
bitonic_merge(unsorted, 0, len(unsorted), 0)
print('Sorted array in descending order is: ', end='')
print(*unsorted, sep=', ') | 128 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCAmelCase__( lowerCamelCase__ ) -> Optional[Any]:
raise NotImplementedError()
@abstractmethod
def UpperCAmelCase__( self ) -> Dict:
raise NotImplementedError() | 128 | 1 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : Dict = nn.ModuleList(A )
def UpperCamelCase_ ( self, A, A, A, A, A, A = None, A = None, A = None, A = None, A = False, A = True, ):
'''simple docstring'''
for i, (image, scale, controlnet) in enumerate(zip(A, A, self.nets ) ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = controlnet(
A, A, A, A, A, A, A, A, A, A, A, )
# merge samples
if i == 0:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = down_samples, mid_sample
else:
SCREAMING_SNAKE_CASE : str = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(A, A )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def UpperCamelCase_ ( self, A, A = True, A = None, A = False, A = None, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : Optional[int] = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
A, is_main_process=A, save_function=A, safe_serialization=A, variant=A, )
idx += 1
SCREAMING_SNAKE_CASE : List[Any] = model_path_to_save + F"_{idx}"
@classmethod
def UpperCamelCase_ ( cls, A, **A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : List[Any] = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
SCREAMING_SNAKE_CASE : Optional[Any] = pretrained_model_path
while os.path.isdir(A ):
SCREAMING_SNAKE_CASE : Optional[int] = ControlNetModel.from_pretrained(A, **A )
controlnets.append(A )
idx += 1
SCREAMING_SNAKE_CASE : Union[str, Any] = pretrained_model_path + F"_{idx}"
logger.info(F"{len(A )} controlnets loaded from {pretrained_model_path}." )
if len(A ) == 0:
raise ValueError(
F"No ControlNets found under {os.path.dirname(A )}. Expected at least {pretrained_model_path + '_0'}." )
return cls(A )
| 28 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
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 PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
A : Union[str, Any] = StableDiffusionXLImgaImgPipeline
A : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
A : str = PipelineTesterMixin.required_optional_params - {'''latents'''}
A : List[str] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
A : Dict = IMAGE_TO_IMAGE_IMAGE_PARAMS
A : int = IMAGE_TO_IMAGE_IMAGE_PARAMS
def UpperCamelCase_ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = 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'), attention_head_dim=(2, 4), use_linear_projection=A, addition_embed_type='text_time', addition_time_embed_dim=8, transformer_layers_per_block=(1, 2), projection_class_embeddings_input_dim=80, cross_attention_dim=64, )
SCREAMING_SNAKE_CASE : str = EulerDiscreteScheduler(
beta_start=0.0_00_85, beta_end=0.0_12, steps_offset=1, beta_schedule='scaled_linear', timestep_spacing='leading', )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = 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, sample_size=128, )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[str] = 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=1_000, hidden_act='gelu', projection_dim=32, )
SCREAMING_SNAKE_CASE : int = CLIPTextModel(A )
SCREAMING_SNAKE_CASE : List[Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip', local_files_only=A )
SCREAMING_SNAKE_CASE : Optional[int] = CLIPTextModelWithProjection(A )
SCREAMING_SNAKE_CASE : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip', local_files_only=A )
SCREAMING_SNAKE_CASE : List[str] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'text_encoder_2': text_encoder_a,
'tokenizer_2': tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def UpperCamelCase_ ( self, A, A=0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = floats_tensor((1, 3, 32, 32), rng=random.Random(A ) ).to(A )
SCREAMING_SNAKE_CASE : str = image / 2 + 0.5
if str(A ).startswith('mps' ):
SCREAMING_SNAKE_CASE : Tuple = torch.manual_seed(A )
else:
SCREAMING_SNAKE_CASE : Tuple = torch.Generator(device=A ).manual_seed(A )
SCREAMING_SNAKE_CASE : List[Any] = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 5.0,
'output_type': 'numpy',
'strength': 0.75,
}
return inputs
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = 'cpu' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE : str = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Optional[int] = StableDiffusionXLImgaImgPipeline(**A )
SCREAMING_SNAKE_CASE : Optional[int] = sd_pipe.to(A )
sd_pipe.set_progress_bar_config(disable=A )
SCREAMING_SNAKE_CASE : List[str] = self.get_dummy_inputs(A )
SCREAMING_SNAKE_CASE : Any = sd_pipe(**A ).images
SCREAMING_SNAKE_CASE : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : List[Any] = np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase_ ( self ):
'''simple docstring'''
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def UpperCamelCase_ ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def UpperCamelCase_ ( self ):
'''simple docstring'''
pass
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE : List[str] = StableDiffusionXLImgaImgPipeline(**A )
SCREAMING_SNAKE_CASE : str = sd_pipe.to(A )
SCREAMING_SNAKE_CASE : Optional[Any] = sd_pipe.to(A )
sd_pipe.set_progress_bar_config(disable=A )
# forward without prompt embeds
SCREAMING_SNAKE_CASE : List[str] = self.get_dummy_inputs(A )
SCREAMING_SNAKE_CASE : Optional[Any] = 3 * ['this is a negative prompt']
SCREAMING_SNAKE_CASE : Optional[int] = negative_prompt
SCREAMING_SNAKE_CASE : Optional[int] = 3 * [inputs['prompt']]
SCREAMING_SNAKE_CASE : int = sd_pipe(**A )
SCREAMING_SNAKE_CASE : List[Any] = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_inputs(A )
SCREAMING_SNAKE_CASE : str = 3 * ['this is a negative prompt']
SCREAMING_SNAKE_CASE : int = 3 * [inputs.pop('prompt' )]
(
(
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) ,
) : Optional[Any] = sd_pipe.encode_prompt(A, negative_prompt=A )
SCREAMING_SNAKE_CASE : Optional[Any] = sd_pipe(
**A, prompt_embeds=A, negative_prompt_embeds=A, pooled_prompt_embeds=A, negative_pooled_prompt_embeds=A, )
SCREAMING_SNAKE_CASE : Optional[int] = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self, A, A="cpu", A=torch.floataa, A=0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = torch.Generator(device=A ).manual_seed(A )
SCREAMING_SNAKE_CASE : Optional[Any] = np.random.RandomState(A ).standard_normal((1, 4, 64, 64) )
SCREAMING_SNAKE_CASE : str = torch.from_numpy(A ).to(device=A, dtype=A )
SCREAMING_SNAKE_CASE : Union[str, Any] = {
'prompt': 'a photograph of an astronaut riding a horse',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base' )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_inputs(A )
SCREAMING_SNAKE_CASE : str = pipe(**A ).images
SCREAMING_SNAKE_CASE : Union[str, Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Dict = np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 28 | 1 |
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> list[tuple[int, int]]:
'''simple docstring'''
__snake_case , __snake_case = position
__snake_case = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
__snake_case = []
for position in positions:
__snake_case , __snake_case = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(_lowerCAmelCase )
return permissible_positions
def _lowerCAmelCase ( _lowerCAmelCase ) -> bool:
'''simple docstring'''
return not any(elem == 0 for row in board for elem in row )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> bool:
'''simple docstring'''
if is_complete(_lowerCAmelCase ):
return True
for position in get_valid_pos(_lowerCAmelCase , len(_lowerCAmelCase ) ):
__snake_case , __snake_case = position
if board[y][x] == 0:
__snake_case = curr + 1
if open_knight_tour_helper(_lowerCAmelCase , _lowerCAmelCase , curr + 1 ):
return True
__snake_case = 0
return False
def _lowerCAmelCase ( _lowerCAmelCase ) -> list[list[int]]:
'''simple docstring'''
__snake_case = [[0 for i in range(_lowerCAmelCase )] for j in range(_lowerCAmelCase )]
for i in range(_lowerCAmelCase ):
for j in range(_lowerCAmelCase ):
__snake_case = 1
if open_knight_tour_helper(_lowerCAmelCase , (i, j) , 1 ):
return board
__snake_case = 0
__snake_case = F'''Open Kight Tour cannot be performed on a board of size {n}'''
raise ValueError(_lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 473 |
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase ) -> list:
'''simple docstring'''
if len(_lowerCAmelCase ) == 0:
return []
__snake_case , __snake_case = min(_lowerCAmelCase ), max(_lowerCAmelCase )
__snake_case = int(max_value - min_value ) + 1
__snake_case = [[] for _ in range(_lowerCAmelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_lowerCAmelCase )
return [v for bucket in buckets for v in sorted(_lowerCAmelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| 473 | 1 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
)
_lowercase : Optional[int] = None
_lowercase : str = {
"7B": 1_1008,
"13B": 1_3824,
"30B": 1_7920,
"65B": 2_2016,
"70B": 2_8672,
}
_lowercase : Any = {
"7B": 1,
"7Bf": 1,
"13B": 2,
"13Bf": 2,
"30B": 4,
"65B": 8,
"70B": 8,
"70Bf": 8,
}
def _lowerCAmelCase ( UpperCamelCase__: Any , UpperCamelCase__: Tuple=1 , UpperCamelCase__: List[str]=2_56 ) -> Union[str, Any]:
"""simple docstring"""
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def _lowerCAmelCase ( UpperCamelCase__: Optional[Any] ) -> Optional[int]:
"""simple docstring"""
with open(UpperCamelCase__ , """r""" ) as f:
return json.load(UpperCamelCase__ )
def _lowerCAmelCase ( UpperCamelCase__: List[Any] , UpperCamelCase__: List[Any] ) -> str:
"""simple docstring"""
with open(UpperCamelCase__ , """w""" ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
def _lowerCAmelCase ( UpperCamelCase__: Dict , UpperCamelCase__: str , UpperCamelCase__: Any , UpperCamelCase__: Union[str, Any]=True ) -> str:
"""simple docstring"""
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
A = os.path.join(UpperCamelCase__ , """tmp""" )
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
A = read_json(os.path.join(UpperCamelCase__ , """params.json""" ) )
A = NUM_SHARDS[model_size]
A = params["n_layers"]
A = params["n_heads"]
A = n_heads // num_shards
A = params["dim"]
A = dim // n_heads
A = 1_00_00.0
A = 1.0 / (base ** (torch.arange(0 , UpperCamelCase__ , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
A = params["n_kv_heads"] # for GQA / MQA
A = n_heads_per_shard // num_key_value_heads
A = dim // num_key_value_heads
else: # compatibility with other checkpoints
A = n_heads
A = n_heads_per_shard
A = dim
# permute for sliced rotary
def permute(UpperCamelCase__: Any , UpperCamelCase__: List[str]=n_heads , UpperCamelCase__: Tuple=dim , UpperCamelCase__: str=dim ):
return w.view(UpperCamelCase__ , dima // n_heads // 2 , 2 , UpperCamelCase__ ).transpose(1 , 2 ).reshape(UpperCamelCase__ , UpperCamelCase__ )
print(f'Fetching all parameters from the checkpoint at {input_base_path}.' )
# Load weights
if model_size == "7B":
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
A = torch.load(os.path.join(UpperCamelCase__ , """consolidated.00.pth""" ) , map_location="""cpu""" )
else:
# Sharded
A = [
torch.load(os.path.join(UpperCamelCase__ , f'consolidated.{i:02d}.pth' ) , map_location="""cpu""" )
for i in range(UpperCamelCase__ )
]
A = 0
A = {"weight_map": {}}
for layer_i in range(UpperCamelCase__ ):
A = f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
A = {
f'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wq.weight'] ),
f'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wk.weight'] ),
f'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[f'layers.{layer_i}.attention.wv.weight'],
f'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[f'layers.{layer_i}.attention.wo.weight'],
f'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w1.weight'],
f'model.layers.{layer_i}.mlp.down_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w2.weight'],
f'model.layers.{layer_i}.mlp.up_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w3.weight'],
f'model.layers.{layer_i}.input_layernorm.weight': loaded[f'layers.{layer_i}.attention_norm.weight'],
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[f'layers.{layer_i}.ffn_norm.weight'],
}
else:
# Sharded
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
A = {
f'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
f'layers.{layer_i}.attention_norm.weight'
].clone(),
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
f'layers.{layer_i}.ffn_norm.weight'
].clone(),
}
A = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for i in range(UpperCamelCase__ )
] , dim=0 , ).reshape(UpperCamelCase__ , UpperCamelCase__ ) )
A = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for i in range(UpperCamelCase__ )
] , dim=0 , ).reshape(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , )
A = torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
for i in range(UpperCamelCase__ )
] , dim=0 , ).reshape(UpperCamelCase__ , UpperCamelCase__ )
A = torch.cat(
[loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(UpperCamelCase__ )] , dim=1 )
A = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(UpperCamelCase__ )] , dim=0 )
A = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(UpperCamelCase__ )] , dim=1 )
A = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(UpperCamelCase__ )] , dim=0 )
A = inv_freq
for k, v in state_dict.items():
A = filename
param_count += v.numel()
torch.save(UpperCamelCase__ , os.path.join(UpperCamelCase__ , UpperCamelCase__ ) )
A = f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
A = {
"model.embed_tokens.weight": loaded["tok_embeddings.weight"],
"model.norm.weight": loaded["norm.weight"],
"lm_head.weight": loaded["output.weight"],
}
else:
A = {
"model.norm.weight": loaded[0]["norm.weight"],
"model.embed_tokens.weight": torch.cat(
[loaded[i]["""tok_embeddings.weight"""] for i in range(UpperCamelCase__ )] , dim=1 ),
"lm_head.weight": torch.cat([loaded[i]["""output.weight"""] for i in range(UpperCamelCase__ )] , dim=0 ),
}
for k, v in state_dict.items():
A = filename
param_count += v.numel()
torch.save(UpperCamelCase__ , os.path.join(UpperCamelCase__ , UpperCamelCase__ ) )
# Write configs
A = {"total_size": param_count * 2}
write_json(UpperCamelCase__ , os.path.join(UpperCamelCase__ , """pytorch_model.bin.index.json""" ) )
A = params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
A = params["multiple_of"] if "multiple_of" in params else 2_56
A = LlamaConfig(
hidden_size=UpperCamelCase__ , intermediate_size=compute_intermediate_size(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) , num_attention_heads=params["""n_heads"""] , num_hidden_layers=params["""n_layers"""] , rms_norm_eps=params["""norm_eps"""] , num_key_value_heads=UpperCamelCase__ , )
config.save_pretrained(UpperCamelCase__ )
# Make space so we can load the model properly now.
del state_dict
del loaded
gc.collect()
print("""Loading the checkpoint in a Llama model.""" )
A = LlamaForCausalLM.from_pretrained(UpperCamelCase__ , torch_dtype=torch.floataa , low_cpu_mem_usage=UpperCamelCase__ )
# Avoid saving this as part of the config.
del model.config._name_or_path
print("""Saving in the Transformers format.""" )
model.save_pretrained(UpperCamelCase__ , safe_serialization=UpperCamelCase__ )
shutil.rmtree(UpperCamelCase__ )
def _lowerCAmelCase ( UpperCamelCase__: Any , UpperCamelCase__: int ) -> Dict:
"""simple docstring"""
A = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.' )
A = tokenizer_class(UpperCamelCase__ )
tokenizer.save_pretrained(UpperCamelCase__ )
def _lowerCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
A = argparse.ArgumentParser()
parser.add_argument(
"""--input_dir""" , help="""Location of LLaMA weights, which contains tokenizer.model and model folders""" , )
parser.add_argument(
"""--model_size""" , choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""] , )
parser.add_argument(
"""--output_dir""" , help="""Location to write HF model and tokenizer""" , )
parser.add_argument("""--safe_serialization""" , type=UpperCamelCase__ , help="""Whether or not to save using `safetensors`.""" )
A = parser.parse_args()
if args.model_size != "tokenizer_only":
write_model(
model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , )
A = os.path.join(args.input_dir , """tokenizer.model""" )
write_tokenizer(args.output_dir , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 641 |
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_ :
def __init__( self : Any , __A : Optional[int] , __A : Optional[int]=2 , __A : int=3 , __A : Union[str, Any]=4 , __A : Tuple=2 , __A : Union[str, Any]=7 , __A : Any=True , __A : List[str]=True , __A : Tuple=True , __A : Tuple=True , __A : List[str]=99 , __A : Tuple=36 , __A : Union[str, Any]=3 , __A : str=4 , __A : str=37 , __A : int="gelu" , __A : Union[str, Any]=0.1 , __A : str=0.1 , __A : List[Any]=512 , __A : Optional[int]=16 , __A : int=2 , __A : List[Any]=0.02 , __A : Optional[Any]=6 , __A : int=6 , __A : str=3 , __A : Optional[int]=4 , __A : Union[str, Any]=None , __A : Tuple=1000 , ) ->Any:
"""simple docstring"""
a__ :Any = parent
a__ :Optional[int] = batch_size
a__ :Union[str, Any] = num_channels
a__ :Any = image_size
a__ :Optional[Any] = patch_size
a__ :Optional[Any] = text_seq_length
a__ :int = is_training
a__ :Tuple = use_input_mask
a__ :Any = use_token_type_ids
a__ :int = use_labels
a__ :str = vocab_size
a__ :List[str] = hidden_size
a__ :Optional[int] = num_hidden_layers
a__ :List[str] = num_attention_heads
a__ :List[str] = intermediate_size
a__ :int = hidden_act
a__ :Optional[Any] = hidden_dropout_prob
a__ :Union[str, Any] = attention_probs_dropout_prob
a__ :int = max_position_embeddings
a__ :Tuple = type_vocab_size
a__ :Union[str, Any] = type_sequence_label_size
a__ :List[Any] = initializer_range
a__ :str = coordinate_size
a__ :Union[str, Any] = shape_size
a__ :int = num_labels
a__ :Optional[int] = num_choices
a__ :str = scope
a__ :int = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
a__ :str = text_seq_length
a__ :Tuple = (image_size // patch_size) ** 2 + 1
a__ :Optional[int] = self.text_seq_length + self.image_seq_length
def _snake_case ( self : Optional[Any] ) ->Dict:
"""simple docstring"""
a__ :str = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
a__ :Optional[Any] = 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]:
a__ :Optional[Any] = bbox[i, j, 3]
a__ :List[str] = bbox[i, j, 1]
a__ :str = t
if bbox[i, j, 2] < bbox[i, j, 0]:
a__ :Any = bbox[i, j, 2]
a__ :int = bbox[i, j, 0]
a__ :Optional[Any] = t
a__ :int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a__ :List[Any] = None
if self.use_input_mask:
a__ :str = random_attention_mask([self.batch_size, self.text_seq_length] )
a__ :Optional[Any] = None
if self.use_token_type_ids:
a__ :str = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
a__ :List[str] = None
a__ :List[str] = None
if self.use_labels:
a__ :str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ :List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
a__ :Tuple = 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 _snake_case ( self : Tuple , __A : Any , __A : Union[str, Any] , __A : List[str] , __A : Dict , __A : int , __A : Union[str, Any] , __A : Union[str, Any] , __A : Any ) ->Dict:
"""simple docstring"""
a__ :Optional[int] = LayoutLMvaModel(config=__A )
model.to(__A )
model.eval()
# text + image
a__ :List[Any] = model(__A , pixel_values=__A )
a__ :int = model(
__A , bbox=__A , pixel_values=__A , attention_mask=__A , token_type_ids=__A )
a__ :Union[str, Any] = model(__A , bbox=__A , pixel_values=__A , token_type_ids=__A )
a__ :Optional[Any] = model(__A , bbox=__A , pixel_values=__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
a__ :Dict = model(__A )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
a__ :Dict = model(pixel_values=__A )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _snake_case ( self : Tuple , __A : List[str] , __A : str , __A : Union[str, Any] , __A : str , __A : Any , __A : List[Any] , __A : str , __A : Tuple ) ->Tuple:
"""simple docstring"""
a__ :Optional[Any] = self.num_labels
a__ :Tuple = LayoutLMvaForSequenceClassification(__A )
model.to(__A )
model.eval()
a__ :str = model(
__A , bbox=__A , pixel_values=__A , attention_mask=__A , token_type_ids=__A , labels=__A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self : Optional[int] , __A : str , __A : Tuple , __A : Union[str, Any] , __A : Union[str, Any] , __A : Dict , __A : int , __A : Optional[int] , __A : int ) ->List[str]:
"""simple docstring"""
a__ :Dict = self.num_labels
a__ :Dict = LayoutLMvaForTokenClassification(config=__A )
model.to(__A )
model.eval()
a__ :Tuple = model(
__A , bbox=__A , pixel_values=__A , attention_mask=__A , token_type_ids=__A , labels=__A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _snake_case ( self : str , __A : Optional[Any] , __A : Optional[Any] , __A : List[str] , __A : Union[str, Any] , __A : int , __A : Optional[int] , __A : Union[str, Any] , __A : str ) ->Dict:
"""simple docstring"""
a__ :List[str] = LayoutLMvaForQuestionAnswering(config=__A )
model.to(__A )
model.eval()
a__ :List[str] = model(
__A , bbox=__A , pixel_values=__A , attention_mask=__A , token_type_ids=__A , start_positions=__A , end_positions=__A , )
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 _snake_case ( self : List[Any] ) ->Dict:
"""simple docstring"""
a__ :str = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
) :str = config_and_inputs
a__ :Tuple = {
"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_ ( _a ,_a ,unittest.TestCase):
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase_ = (
{'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
if is_torch_available()
else {}
)
def _snake_case ( self : List[str] , __A : Union[str, Any] , __A : Optional[Any] , __A : Optional[int] , __A : List[str] , __A : Dict ) ->Dict:
"""simple docstring"""
return True
def _snake_case ( self : Optional[int] ) ->Optional[Any]:
"""simple docstring"""
a__ :int = LayoutLMvaModelTester(self )
a__ :Union[str, Any] = ConfigTester(self , config_class=__A , hidden_size=37 )
def _snake_case ( self : int , __A : int , __A : List[Any] , __A : Optional[int]=False ) ->Optional[Any]:
"""simple docstring"""
a__ :Union[str, Any] = copy.deepcopy(__A )
if model_class in get_values(__A ):
a__ :Dict = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(__A , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(__A ):
a__ :List[str] = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=__A )
elif model_class in get_values(__A ):
a__ :int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__A )
a__ :Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__A )
elif model_class in [
*get_values(__A ),
]:
a__ :List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__A )
elif model_class in [
*get_values(__A ),
]:
a__ :List[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=__A , )
return inputs_dict
def _snake_case ( self : Optional[Any] ) ->List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _snake_case ( self : List[Any] ) ->List[Any]:
"""simple docstring"""
a__ :Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def _snake_case ( self : int ) ->Optional[Any]:
"""simple docstring"""
a__ :str = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
a__ :List[Any] = type
self.model_tester.create_and_check_model(*__A )
def _snake_case ( self : Tuple ) ->str:
"""simple docstring"""
a__ :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__A )
def _snake_case ( self : List[Any] ) ->List[str]:
"""simple docstring"""
a__ :Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__A )
def _snake_case ( self : Optional[int] ) ->Dict:
"""simple docstring"""
a__ :Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__A )
@slow
def _snake_case ( self : Union[str, Any] ) ->str:
"""simple docstring"""
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ :int = LayoutLMvaModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def lowerCamelCase__ ( ) -> Optional[Any]:
"""simple docstring"""
a__ :List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class lowerCAmelCase_ ( unittest.TestCase):
@cached_property
def _snake_case ( self : Union[str, Any] ) ->Dict:
"""simple docstring"""
return LayoutLMvaImageProcessor(apply_ocr=__A ) if is_vision_available() else None
@slow
def _snake_case ( self : List[Any] ) ->Union[str, Any]:
"""simple docstring"""
a__ :Optional[Any] = LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ).to(__A )
a__ :str = self.default_image_processor
a__ :List[str] = prepare_img()
a__ :Tuple = image_processor(images=__A , return_tensors="pt" ).pixel_values.to(__A )
a__ :Dict = torch.tensor([[1, 2]] )
a__ :Optional[Any] = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
a__ :int = model(
input_ids=input_ids.to(__A ) , bbox=bbox.to(__A ) , pixel_values=pixel_values.to(__A ) , )
# verify the logits
a__ :int = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , __A )
a__ :Any = 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(__A )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , __A , atol=1E-4 ) )
| 395 | 0 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class _UpperCAmelCase( unittest.TestCase ):
@slow
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''')
_UpperCamelCase = AutoTokenizer.from_pretrained('''google/mt5-small''')
_UpperCamelCase = tokenizer('''Hello there''' , return_tensors='''np''').input_ids
_UpperCamelCase = tokenizer('''Hi I am''' , return_tensors='''np''').input_ids
_UpperCamelCase = shift_tokens_right(__a , model.config.pad_token_id , model.config.decoder_start_token_id)
_UpperCamelCase = model(__a , decoder_input_ids=__a).logits
_UpperCamelCase = optax.softmax_cross_entropy(__a , onehot(__a , logits.shape[-1])).mean()
_UpperCamelCase = -(labels.shape[-1] * loss.item())
_UpperCamelCase = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)
| 78 |
"""simple docstring"""
def lowerCamelCase__ ( ) -> list[list[int]]:
"""simple docstring"""
return [list(range(10_00 - i, -10_00 - i, -1 ) ) for i in range(10_00 )]
_a = generate_large_matrix()
_a = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def lowerCamelCase__ ( __snake_case ) -> None:
"""simple docstring"""
assert all(row == sorted(__snake_case, reverse=__snake_case ) for row in grid )
assert all(list(__snake_case ) == sorted(__snake_case, reverse=__snake_case ) for col in zip(*__snake_case ) )
def lowerCamelCase__ ( __snake_case ) -> int:
"""simple docstring"""
_UpperCamelCase = 0
_UpperCamelCase = len(__snake_case ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
_UpperCamelCase = (left + right) // 2
_UpperCamelCase = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
_UpperCamelCase = mid + 1
else:
_UpperCamelCase = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__snake_case )
def lowerCamelCase__ ( __snake_case ) -> int:
"""simple docstring"""
_UpperCamelCase = 0
_UpperCamelCase = len(grid[0] )
for i in range(len(__snake_case ) ):
_UpperCamelCase = find_negative_index(grid[i][:bound] )
total += bound
return (len(__snake_case ) * len(grid[0] )) - total
def lowerCamelCase__ ( __snake_case ) -> int:
"""simple docstring"""
return len([number for row in grid for number in row if number < 0] )
def lowerCamelCase__ ( __snake_case ) -> int:
"""simple docstring"""
_UpperCamelCase = 0
for row in grid:
for i, number in enumerate(__snake_case ):
if number < 0:
total += len(__snake_case ) - i
break
return total
def lowerCamelCase__ ( ) -> None:
"""simple docstring"""
from timeit import timeit
print('''Running benchmarks''' )
_UpperCamelCase = (
'''from __main__ import count_negatives_binary_search, '''
'''count_negatives_brute_force, count_negatives_brute_force_with_break, grid'''
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
_UpperCamelCase = timeit(F'''{func}(grid=grid)''', setup=__snake_case, number=5_00 )
print(F'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 78 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__snake_case : Optional[Any] = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
' Distillation'
)
)
parser.add_argument('--model_type', default='bert', choices=['bert'])
parser.add_argument('--model_name', default='bert-base-uncased', type=str)
parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
parser.add_argument('--vocab_transform', action='store_true')
__snake_case : List[Any] = parser.parse_args()
if args.model_type == "bert":
__snake_case : int = BertForMaskedLM.from_pretrained(args.model_name)
__snake_case : Tuple = 'bert'
else:
raise ValueError('args.model_type should be "bert".')
__snake_case : List[Any] = model.state_dict()
__snake_case : Any = {}
for w in ["word_embeddings", "position_embeddings"]:
__snake_case : List[str] = state_dict[F"""{prefix}.embeddings.{w}.weight"""]
for w in ["weight", "bias"]:
__snake_case : Optional[int] = state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]
__snake_case : List[str] = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
__snake_case : str = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
]
__snake_case : Tuple = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
]
__snake_case : str = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
]
__snake_case : Any = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
]
__snake_case : Union[str, Any] = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
]
__snake_case : Union[str, Any] = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
]
__snake_case : Any = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
]
__snake_case : Optional[int] = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
]
std_idx += 1
__snake_case : Union[str, Any] = state_dict['cls.predictions.decoder.weight']
__snake_case : Union[str, Any] = state_dict['cls.predictions.bias']
if args.vocab_transform:
for w in ["weight", "bias"]:
__snake_case : Union[str, Any] = state_dict[F"""cls.predictions.transform.dense.{w}"""]
__snake_case : List[str] = state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]
print(F"""N layers selected for distillation: {std_idx}""")
print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint) | 293 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : Tuple = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AutoformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[Any] = [
'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'AutoformerForPrediction',
'AutoformerModel',
'AutoformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
__snake_case : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 293 | 1 |
'''simple docstring'''
import requests
def UpperCAmelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : str):
lowerCamelCase : Dict = {'Content-Type': 'application/json'}
lowerCamelCase : Optional[int] = requests.post(UpperCAmelCase__ , json={'text': message_body} , headers=UpperCAmelCase__)
if response.status_code != 2_00:
lowerCamelCase : List[str] = (
'Request to slack returned an error '
F'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(UpperCAmelCase__)
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')
| 706 |
'''simple docstring'''
# Imports
import numpy as np
class __snake_case :
def __init__( self, A=None, A=None, A=None, A=None, A=None ):
"""simple docstring"""
self.set_matricies(red=A, green=A, blue=A, red_edge=A, nir=A )
def UpperCAmelCase_ ( self, A=None, A=None, A=None, A=None, A=None ):
"""simple docstring"""
if red is not None:
lowerCamelCase : Optional[int] = red
if green is not None:
lowerCamelCase : Optional[int] = green
if blue is not None:
lowerCamelCase : List[str] = blue
if red_edge is not None:
lowerCamelCase : Tuple = red_edge
if nir is not None:
lowerCamelCase : Any = nir
return True
def UpperCAmelCase_ ( self, A="", A=None, A=None, A=None, A=None, A=None ):
"""simple docstring"""
self.set_matricies(red=A, green=A, blue=A, red_edge=A, nir=A )
lowerCamelCase : Optional[int] = {
'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 UpperCAmelCase_ ( self ):
"""simple docstring"""
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return self.nir * (self.red / (self.green**2))
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.nir - self.red) / (self.nir + self.red)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.nir - self.blue) / (self.nir + self.blue)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.redEdge - self.red) / (self.redEdge + self.red)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.nir - self.green) / (self.nir + self.green)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def UpperCAmelCase_ ( self, A=0.08, A=1.22, A=0.03 ):
"""simple docstring"""
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.nir / self.green) - 1
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.nir / self.redEdge) - 1
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.red - self.blue) / self.red
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return self.nir - self.green
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : str = (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 UpperCAmelCase_ ( self, A=0.16 ):
"""simple docstring"""
return (self.nir - self.green) / (self.nir + self.green + y)
def UpperCAmelCase_ ( self, A=0.5 ):
"""simple docstring"""
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def UpperCAmelCase_ ( self, A=None, A=None ):
"""simple docstring"""
return (self.nir - b) / (a * self.red)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.red + self.green + self.blue) / 30.5
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return self.nir / self.red
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.rvi() - 1) / (self.rvi() + 1)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return self.green / (self.nir + self.red + self.green)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return self.nir / (self.nir + self.red + self.green)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return self.red / (self.nir + self.red + self.green)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.green - self.red) / (self.green + self.red)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.red - self.green) / (self.red + self.green)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
lowerCamelCase : Tuple = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return self.nir / self.red
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.ndvi() + 0.5) ** (1 / 2)
def UpperCAmelCase_ ( self ):
"""simple docstring"""
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 449 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Union[str, Any] = 'lxmert'
__snake_case :Union[str, Any] = {}
def __init__( self : List[str] , _lowerCAmelCase : Dict=3_0522 , _lowerCAmelCase : List[str]=768 , _lowerCAmelCase : Union[str, Any]=12 , _lowerCAmelCase : Union[str, Any]=9500 , _lowerCAmelCase : Union[str, Any]=1600 , _lowerCAmelCase : Optional[Any]=400 , _lowerCAmelCase : Tuple=3072 , _lowerCAmelCase : List[Any]="gelu" , _lowerCAmelCase : int=0.1 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Tuple=512 , _lowerCAmelCase : Tuple=2 , _lowerCAmelCase : Optional[Any]=0.02 , _lowerCAmelCase : List[str]=1e-12 , _lowerCAmelCase : Any=9 , _lowerCAmelCase : Optional[Any]=5 , _lowerCAmelCase : Any=5 , _lowerCAmelCase : Dict=2048 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Optional[Any]=6.67 , _lowerCAmelCase : Optional[Any]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , _lowerCAmelCase : str=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , **_lowerCAmelCase : Tuple , ) -> Dict:
"""simple docstring"""
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = num_qa_labels
__lowercase = num_object_labels
__lowercase = num_attr_labels
__lowercase = l_layers
__lowercase = x_layers
__lowercase = r_layers
__lowercase = visual_feat_dim
__lowercase = visual_pos_dim
__lowercase = visual_loss_normalizer
__lowercase = task_matched
__lowercase = task_mask_lm
__lowercase = task_obj_predict
__lowercase = task_qa
__lowercase = visual_obj_loss
__lowercase = visual_attr_loss
__lowercase = visual_feat_loss
__lowercase = {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
super().__init__(**_lowerCAmelCase )
| 80 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ ( a_ , unittest.TestCase ):
__UpperCAmelCase = DebertaTokenizer
__UpperCAmelCase = True
__UpperCAmelCase = DebertaTokenizerFast
def __snake_case ( self : Dict ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case : List[Any] =[
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''[UNK]''',
]
snake_case : Dict =dict(zip(_snake_case, range(len(_snake_case ) ) ) )
snake_case : Tuple =['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
snake_case : List[Any] ={'''unk_token''': '''[UNK]'''}
snake_case : List[Any] =os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case : Tuple =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(_snake_case ) + '''\n''' )
with open(self.merges_file, '''w''', encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_snake_case ) )
def __snake_case ( self : str, **_snake_case : List[str] ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname, **_snake_case )
def __snake_case ( self : List[str], _snake_case : List[str] ):
'''simple docstring'''
snake_case : List[str] ='''lower newer'''
snake_case : Optional[int] ='''lower newer'''
return input_text, output_text
def __snake_case ( self : Any ):
'''simple docstring'''
snake_case : List[Any] =self.get_tokenizer()
snake_case : List[Any] ='''lower newer'''
snake_case : Union[str, Any] =['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
snake_case : str =tokenizer.tokenize(_snake_case )
self.assertListEqual(_snake_case, _snake_case )
snake_case : Any =tokens + [tokenizer.unk_token]
snake_case : List[Any] =[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case ), _snake_case )
def __snake_case ( self : Tuple ):
'''simple docstring'''
snake_case : Optional[Any] =self.get_tokenizer()
snake_case : Any =tokenizer('''Hello''', '''World''' )
snake_case : List[Any] =[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd['''token_type_ids'''], _snake_case )
@slow
def __snake_case ( self : Optional[Any] ):
'''simple docstring'''
snake_case : int =self.tokenizer_class.from_pretrained('''microsoft/deberta-base''' )
snake_case : List[Any] =tokenizer.encode('''sequence builders''', add_special_tokens=_snake_case )
snake_case : str =tokenizer.encode('''multi-sequence build''', add_special_tokens=_snake_case )
snake_case : Union[str, Any] =tokenizer.encode(
'''sequence builders''', add_special_tokens=_snake_case, add_prefix_space=_snake_case )
snake_case : Optional[int] =tokenizer.encode(
'''sequence builders''', '''multi-sequence build''', add_special_tokens=_snake_case, add_prefix_space=_snake_case )
snake_case : str =tokenizer.build_inputs_with_special_tokens(_snake_case )
snake_case : Tuple =tokenizer.build_inputs_with_special_tokens(_snake_case, _snake_case )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def __snake_case ( self : Dict ):
'''simple docstring'''
snake_case : int =[self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class )
for tokenizer_class in tokenizer_classes:
snake_case : Optional[int] =tokenizer_class.from_pretrained('''microsoft/deberta-base''' )
snake_case : Optional[Any] =[
'''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
'''ALBERT incorporates two parameter reduction techniques''',
'''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
''' vocabulary embedding.''',
]
snake_case : int =tokenizer(_snake_case, padding=_snake_case )
snake_case : str =[tokenizer.decode(_snake_case, skip_special_tokens=_snake_case ) for seq in encoding['''input_ids''']]
# fmt: off
snake_case : Optional[Any] ={
'''input_ids''': [
[1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 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, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
],
'''token_type_ids''': [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 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],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
]
}
# fmt: on
snake_case : Tuple =[
'''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
'''ALBERT incorporates two parameter reduction techniques''',
'''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
''' vocabulary embedding.''',
]
self.assertDictEqual(encoding.data, _snake_case )
for expected, decoded in zip(_snake_case, _snake_case ):
self.assertEqual(_snake_case, _snake_case )
| 349 | 0 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class snake_case ( datasets.BuilderConfig ):
'''simple docstring'''
snake_case_ : Optional[datasets.Features] = None
class snake_case ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
snake_case_ : Optional[Any] = PandasConfig
def UpperCamelCase_ ( self : List[str]) -> int:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features)
def UpperCamelCase_ ( self : Optional[Any] , lowerCAmelCase : Optional[int]) -> Optional[Any]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}''')
_snake_case : Optional[int] = dl_manager.download_and_extract(self.config.data_files)
if isinstance(lowerCAmelCase , (str, list, tuple)):
_snake_case : Optional[Any] = data_files
if isinstance(lowerCAmelCase , lowerCAmelCase):
_snake_case : str = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
_snake_case : str = [dl_manager.iter_files(lowerCAmelCase) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files})]
_snake_case : str = []
for split_name, files in data_files.items():
if isinstance(lowerCAmelCase , lowerCAmelCase):
_snake_case : Tuple = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
_snake_case : int = [dl_manager.iter_files(lowerCAmelCase) for file in files]
splits.append(datasets.SplitGenerator(name=lowerCAmelCase , gen_kwargs={"""files""": files}))
return splits
def UpperCamelCase_ ( self : Any , lowerCAmelCase : pa.Table) -> pa.Table:
"""simple docstring"""
if self.config.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
_snake_case : Dict = table_cast(lowerCAmelCase , self.config.features.arrow_schema)
return pa_table
def UpperCamelCase_ ( self : str , lowerCAmelCase : Any) -> int:
"""simple docstring"""
for i, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase)):
with open(lowerCAmelCase , """rb""") as f:
_snake_case : str = pa.Table.from_pandas(pd.read_pickle(lowerCAmelCase))
yield i, self._cast_table(lowerCAmelCase)
| 198 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
a__ = logging.get_logger(__name__)
def lowercase ( SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Optional[Any]:
# initialize config
if "resnet-50" in model_name:
_snake_case : List[Any] = ResNetConfig.from_pretrained("""microsoft/resnet-50""" )
elif "resnet-101" in model_name:
_snake_case : Any = ResNetConfig.from_pretrained("""microsoft/resnet-101""" )
else:
raise ValueError("""Model name should include either resnet50 or resnet101""" )
_snake_case : Union[str, Any] = DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE__ , backbone_config=SCREAMING_SNAKE_CASE__ )
# set label attributes
_snake_case : List[str] = """panoptic""" in model_name
if is_panoptic:
_snake_case : Optional[int] = 250
else:
_snake_case : Optional[int] = 91
_snake_case : Optional[Any] = """huggingface/label-files"""
_snake_case : Optional[int] = """coco-detection-id2label.json"""
_snake_case : str = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type="""dataset""" ) , """r""" ) )
_snake_case : Dict = {int(SCREAMING_SNAKE_CASE__ ): v for k, v in idalabel.items()}
_snake_case : List[str] = idalabel
_snake_case : str = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def lowercase ( SCREAMING_SNAKE_CASE__ : Tuple ) -> Optional[int]:
# here we list all keys to be renamed (original name on the left, our name on the right)
_snake_case : Optional[Any] = []
# stem
# fmt: off
rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""") )
rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""") )
rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""") )
rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""") )
rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""") )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
) )
# fmt: on
for i in range(config.encoder_layers ):
# 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 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.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"""),
] )
return rename_keys
def lowercase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ) -> Optional[Any]:
_snake_case : str = state_dict.pop(SCREAMING_SNAKE_CASE__ )
_snake_case : str = val
def lowercase ( SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : str=False ) -> Union[str, Any]:
_snake_case : Optional[Any] = """"""
if is_panoptic:
_snake_case : Optional[Any] = """detr."""
# 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)
_snake_case : Any = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
_snake_case : List[str] = 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
_snake_case : Optional[Any] = in_proj_weight[:256, :]
_snake_case : Any = in_proj_bias[:256]
_snake_case : List[Any] = in_proj_weight[256:512, :]
_snake_case : Optional[int] = in_proj_bias[256:512]
_snake_case : int = in_proj_weight[-256:, :]
_snake_case : Union[str, Any] = 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
_snake_case : Tuple = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
_snake_case : Dict = 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
_snake_case : Optional[int] = in_proj_weight[:256, :]
_snake_case : Optional[int] = in_proj_bias[:256]
_snake_case : Any = in_proj_weight[256:512, :]
_snake_case : int = in_proj_bias[256:512]
_snake_case : int = in_proj_weight[-256:, :]
_snake_case : Tuple = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
_snake_case : List[str] = state_dict.pop(
F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
_snake_case : Optional[Any] = 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
_snake_case : int = in_proj_weight_cross_attn[:256, :]
_snake_case : Tuple = in_proj_bias_cross_attn[:256]
_snake_case : int = in_proj_weight_cross_attn[256:512, :]
_snake_case : Tuple = in_proj_bias_cross_attn[256:512]
_snake_case : Dict = in_proj_weight_cross_attn[-256:, :]
_snake_case : Union[str, Any] = in_proj_bias_cross_attn[-256:]
def lowercase ( ) -> Optional[Any]:
_snake_case : Dict = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_snake_case : Dict = Image.open(requests.get(SCREAMING_SNAKE_CASE__ , stream=SCREAMING_SNAKE_CASE__ ).raw )
return im
@torch.no_grad()
def lowercase ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Dict=None , SCREAMING_SNAKE_CASE__ : Any=False ) -> List[Any]:
_snake_case , _snake_case : Union[str, Any] = get_detr_config(SCREAMING_SNAKE_CASE__ )
# load original model from torch hub
_snake_case : Dict = {
"""detr-resnet-50""": """detr_resnet50""",
"""detr-resnet-101""": """detr_resnet101""",
}
logger.info(F'''Converting model {model_name}...''' )
_snake_case : List[Any] = torch.hub.load("""facebookresearch/detr""" , model_name_to_original_name[model_name] , pretrained=SCREAMING_SNAKE_CASE__ ).eval()
_snake_case : Optional[int] = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE__ ):
if is_panoptic:
_snake_case : int = """detr.""" + src
rename_key(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# query, key and value matrices need special treatment
read_in_q_k_v(SCREAMING_SNAKE_CASE__ , is_panoptic=SCREAMING_SNAKE_CASE__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_snake_case : List[Any] = """detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
_snake_case : List[str] = state_dict.pop(SCREAMING_SNAKE_CASE__ )
_snake_case : Optional[int] = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
_snake_case : Tuple = state_dict.pop(SCREAMING_SNAKE_CASE__ )
_snake_case : List[str] = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
_snake_case : Optional[int] = state_dict.pop(SCREAMING_SNAKE_CASE__ )
_snake_case : Dict = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
_snake_case : Union[str, Any] = state_dict.pop(SCREAMING_SNAKE_CASE__ )
_snake_case : Tuple = val
# finally, create HuggingFace model and load state dict
_snake_case : int = DetrForSegmentation(SCREAMING_SNAKE_CASE__ ) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__ )
model.load_state_dict(SCREAMING_SNAKE_CASE__ )
model.eval()
# verify our conversion on an image
_snake_case : List[Any] = """coco_panoptic""" if is_panoptic else """coco_detection"""
_snake_case : int = DetrImageProcessor(format=SCREAMING_SNAKE_CASE__ )
_snake_case : int = processor(images=prepare_img() , return_tensors="""pt""" )
_snake_case : str = encoding["""pixel_values"""]
_snake_case : Tuple = detr(SCREAMING_SNAKE_CASE__ )
_snake_case : Optional[int] = model(SCREAMING_SNAKE_CASE__ )
assert torch.allclose(outputs.logits , original_outputs["""pred_logits"""] , atol=1e-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs["""pred_boxes"""] , atol=1e-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["""pred_masks"""] , 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(SCREAMING_SNAKE_CASE__ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
processor.save_pretrained(SCREAMING_SNAKE_CASE__ )
if push_to_hub:
# Upload model and image processor to the hub
logger.info("""Uploading PyTorch model and image processor to the hub...""" )
model.push_to_hub(F'''nielsr/{model_name}''' )
processor.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""detr-resnet-50""",
type=str,
choices=["""detr-resnet-50""", """detr-resnet-101"""],
help="""Name of the DETR model 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 to push the model to the hub or not.""")
a__ = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 198 | 1 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def lowerCamelCase_ ( _UpperCamelCase=None ) -> Optional[int]:
"""simple docstring"""
if subparsers is not None:
snake_case_ : Union[str, Any] = subparsers.add_parser('''env''' )
else:
snake_case_ : Optional[int] = argparse.ArgumentParser('''Accelerate env command''' )
parser.add_argument(
'''--config_file''' , default=_UpperCamelCase , help='''The config file to use for the default values in the launching script.''' )
if subparsers is not None:
parser.set_defaults(func=_UpperCamelCase )
return parser
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Tuple = torch.__version__
snake_case_ : List[Any] = torch.cuda.is_available()
snake_case_ : str = is_xpu_available()
snake_case_ : List[Any] = is_npu_available()
snake_case_ : List[Any] = '''Not found'''
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(_UpperCamelCase ):
snake_case_ : Any = load_config_from_file(args.config_file ).to_dict()
snake_case_ : Dict = {
'''`Accelerate` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''Numpy version''': np.__version__,
'''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
'''PyTorch XPU available''': str(_UpperCamelCase ),
'''PyTorch NPU available''': str(_UpperCamelCase ),
'''System RAM''': f'''{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB''',
}
if pt_cuda_available:
snake_case_ : List[str] = torch.cuda.get_device_name()
print('''\nCopy-and-paste the text below in your GitHub issue\n''' )
print('''\n'''.join([f'''- {prop}: {val}''' for prop, val in info.items()] ) )
print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:''' )
snake_case_ : Union[str, Any] = (
'''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
if isinstance(_UpperCamelCase , _UpperCamelCase )
else f'''\t{accelerate_config}'''
)
print(_UpperCamelCase )
snake_case_ : Dict = accelerate_config
return info
def lowerCamelCase_ ( ) -> int:
"""simple docstring"""
snake_case_ : List[Any] = env_command_parser()
snake_case_ : int = parser.parse_args()
env_command(_UpperCamelCase )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 60 |
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
if index == number_of_items:
return 0
UpperCamelCase__ : str = 0
UpperCamelCase__ : Optional[Any] = 0
UpperCamelCase__ : int = knapsack(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , index + 1 )
if weights[index] <= max_weight:
UpperCamelCase__ : List[Any] = values[index] + knapsack(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , max_weight - weights[index] , index + 1 )
return max(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 285 | 0 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
AccelerateTestCase,
TempDirTestCase,
execute_subprocess_async,
require_cuda,
require_fsdp,
require_multi_gpu,
slow,
)
from accelerate.utils.constants import (
FSDP_AUTO_WRAP_POLICY,
FSDP_BACKWARD_PREFETCH,
FSDP_SHARDING_STRATEGY,
FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment
set_seed(42)
__lowerCamelCase : List[Any] = """bert-base-cased"""
__lowerCamelCase : List[str] = """fp16"""
__lowerCamelCase : Any = """bf16"""
__lowerCamelCase : Union[str, Any] = [FPaa, BFaa]
@require_fsdp
@require_cuda
class lowerCAmelCase__ ( _lowerCAmelCase ):
def __UpperCamelCase ( self : Dict ) -> str:
"""simple docstring"""
super().setUp()
lowerCamelCase_ : str = dict(
ACCELERATE_USE_FSDP='''true''' , MASTER_ADDR='''localhost''' , MASTER_PORT='''10999''' , RANK='''0''' , LOCAL_RANK='''0''' , WORLD_SIZE='''1''' , )
def __UpperCamelCase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
for i, strategy in enumerate(UpperCamelCase_ ):
lowerCamelCase_ : Union[str, Any] = self.dist_env.copy()
lowerCamelCase_ : int = F"""{i + 1}"""
lowerCamelCase_ : List[str] = strategy
with mockenv_context(**UpperCamelCase_ ):
lowerCamelCase_ : Tuple = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.sharding_strategy , ShardingStrategy(i + 1 ) )
def __UpperCamelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch
for i, prefetch_policy in enumerate(UpperCamelCase_ ):
lowerCamelCase_ : str = self.dist_env.copy()
lowerCamelCase_ : Tuple = prefetch_policy
with mockenv_context(**UpperCamelCase_ ):
lowerCamelCase_ : str = FullyShardedDataParallelPlugin()
if prefetch_policy == "NO_PREFETCH":
self.assertIsNone(fsdp_plugin.backward_prefetch )
else:
self.assertEqual(fsdp_plugin.backward_prefetch , BackwardPrefetch(i + 1 ) )
def __UpperCamelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
for i, state_dict_type in enumerate(UpperCamelCase_ ):
lowerCamelCase_ : str = self.dist_env.copy()
lowerCamelCase_ : int = state_dict_type
with mockenv_context(**UpperCamelCase_ ):
lowerCamelCase_ : Optional[int] = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.state_dict_type , StateDictType(i + 1 ) )
if state_dict_type == "FULL_STATE_DICT":
self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu )
self.assertTrue(fsdp_plugin.state_dict_config.ranka_only )
def __UpperCamelCase ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ : int = AutoModel.from_pretrained(UpperCamelCase_ )
for policy in FSDP_AUTO_WRAP_POLICY:
lowerCamelCase_ : int = self.dist_env.copy()
lowerCamelCase_ : List[Any] = policy
if policy == "TRANSFORMER_BASED_WRAP":
lowerCamelCase_ : Dict = '''BertLayer'''
elif policy == "SIZE_BASED_WRAP":
lowerCamelCase_ : List[Any] = '''2000'''
with mockenv_context(**UpperCamelCase_ ):
lowerCamelCase_ : Any = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(UpperCamelCase_ )
if policy == "NO_WRAP":
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
else:
self.assertIsNotNone(fsdp_plugin.auto_wrap_policy )
lowerCamelCase_ : Optional[Any] = self.dist_env.copy()
lowerCamelCase_ : Optional[int] = '''TRANSFORMER_BASED_WRAP'''
lowerCamelCase_ : List[Any] = '''T5Layer'''
with mockenv_context(**UpperCamelCase_ ):
lowerCamelCase_ : Union[str, Any] = FullyShardedDataParallelPlugin()
with self.assertRaises(UpperCamelCase_ ) as cm:
fsdp_plugin.set_auto_wrap_policy(UpperCamelCase_ )
self.assertTrue('''Could not find the transformer layer class to wrap in the model.''' in str(cm.exception ) )
lowerCamelCase_ : Any = self.dist_env.copy()
lowerCamelCase_ : Optional[Any] = '''SIZE_BASED_WRAP'''
lowerCamelCase_ : Optional[Any] = '''0'''
with mockenv_context(**UpperCamelCase_ ):
lowerCamelCase_ : str = FullyShardedDataParallelPlugin()
fsdp_plugin.set_auto_wrap_policy(UpperCamelCase_ )
self.assertIsNone(fsdp_plugin.auto_wrap_policy )
def __UpperCamelCase ( self : Any ) -> Tuple:
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
for mp_dtype in dtypes:
lowerCamelCase_ : List[str] = self.dist_env.copy()
lowerCamelCase_ : List[str] = mp_dtype
with mockenv_context(**UpperCamelCase_ ):
lowerCamelCase_ : Tuple = Accelerator()
if mp_dtype == "fp16":
lowerCamelCase_ : Optional[int] = torch.floataa
elif mp_dtype == "bf16":
lowerCamelCase_ : int = torch.bfloataa
lowerCamelCase_ : List[str] = MixedPrecision(param_dtype=UpperCamelCase_ , reduce_dtype=UpperCamelCase_ , buffer_dtype=UpperCamelCase_ )
self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy , UpperCamelCase_ )
if mp_dtype == FPaa:
self.assertTrue(isinstance(accelerator.scaler , UpperCamelCase_ ) )
elif mp_dtype == BFaa:
self.assertIsNone(accelerator.scaler )
AcceleratorState._reset_state(UpperCamelCase_ )
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload
for flag in [True, False]:
lowerCamelCase_ : List[Any] = self.dist_env.copy()
lowerCamelCase_ : Union[str, Any] = str(UpperCamelCase_ ).lower()
with mockenv_context(**UpperCamelCase_ ):
lowerCamelCase_ : Optional[int] = FullyShardedDataParallelPlugin()
self.assertEqual(fsdp_plugin.cpu_offload , CPUOffload(offload_params=UpperCamelCase_ ) )
@require_fsdp
@require_multi_gpu
@slow
class lowerCAmelCase__ ( _lowerCAmelCase ):
def __UpperCamelCase ( self : int ) -> int:
"""simple docstring"""
super().setUp()
lowerCamelCase_ : Union[str, Any] = 0.82
lowerCamelCase_ : List[Any] = [
'''fsdp_shard_grad_op_transformer_based_wrap''',
'''fsdp_full_shard_transformer_based_wrap''',
]
lowerCamelCase_ : Optional[int] = {
'''multi_gpu_fp16''': 3_200,
'''fsdp_shard_grad_op_transformer_based_wrap_fp16''': 2_000,
'''fsdp_full_shard_transformer_based_wrap_fp16''': 1_900,
# Disabling below test as it overwhelms the RAM memory usage
# on CI self-hosted runner leading to tests getting killed.
# "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500, # fp16 was leading to indefinite hang
}
lowerCamelCase_ : Tuple = 160
lowerCamelCase_ : List[Any] = 160
lowerCamelCase_ : Optional[int] = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ : str = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''external_deps'''] )
def __UpperCamelCase ( self : int ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ : List[str] = os.path.join(self.test_scripts_folder , '''test_performance.py''' )
lowerCamelCase_ : int = ['''accelerate''', '''launch''', '''--num_processes=2''', '''--num_machines=1''', '''--machine_rank=0''', '''--use_fsdp''']
for config in self.performance_configs:
lowerCamelCase_ : List[Any] = cmd.copy()
for i, strategy in enumerate(UpperCamelCase_ ):
if strategy.lower() in config:
cmd_config.append(F"""--fsdp_sharding_strategy={i+1}""" )
break
if "fp32" in config:
cmd_config.append('''--mixed_precision=no''' )
else:
cmd_config.append('''--mixed_precision=fp16''' )
if "cpu_offload" in config:
cmd_config.append('''--fsdp_offload_params=True''' )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in config:
cmd_config.append(F"""--fsdp_auto_wrap_policy={policy}""" )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer''' )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append('''--fsdp_min_num_params=2000''' )
cmd_config.extend(
[
self.test_file_path,
F"""--output_dir={self.tmpdir}""",
F"""--performance_lower_bound={self.performance_lower_bound}""",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(UpperCamelCase_ , env=os.environ.copy() )
def __UpperCamelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ : Optional[int] = os.path.join(self.test_scripts_folder , '''test_checkpointing.py''' )
lowerCamelCase_ : List[str] = [
'''accelerate''',
'''launch''',
'''--num_processes=2''',
'''--num_machines=1''',
'''--machine_rank=0''',
'''--use_fsdp''',
'''--mixed_precision=fp16''',
'''--fsdp_transformer_layer_cls_to_wrap=BertLayer''',
]
for i, strategy in enumerate(UpperCamelCase_ ):
lowerCamelCase_ : Optional[Any] = cmd.copy()
cmd_config.append(F"""--fsdp_sharding_strategy={i+1}""" )
if strategy != "FULL_SHARD":
continue
lowerCamelCase_ : Union[str, Any] = len(UpperCamelCase_ )
for state_dict_type in FSDP_STATE_DICT_TYPE:
lowerCamelCase_ : Union[str, Any] = cmd_config[:state_dict_config_index]
cmd_config.append(F"""--fsdp_state_dict_type={state_dict_type}""" )
cmd_config.extend(
[
self.test_file_path,
F"""--output_dir={self.tmpdir}""",
'''--partial_train_epoch=1''',
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(UpperCamelCase_ , env=os.environ.copy() )
lowerCamelCase_ : Dict = cmd_config[:-1]
lowerCamelCase_ : Dict = os.path.join(self.tmpdir , '''epoch_0''' )
cmd_config.extend(
[
F"""--resume_from_checkpoint={resume_from_checkpoint}""",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(UpperCamelCase_ , env=os.environ.copy() )
def __UpperCamelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
lowerCamelCase_ : Tuple = os.path.join(self.test_scripts_folder , '''test_peak_memory_usage.py''' )
lowerCamelCase_ : Tuple = [
'''accelerate''',
'''launch''',
'''--num_processes=2''',
'''--num_machines=1''',
'''--machine_rank=0''',
]
for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
lowerCamelCase_ : Optional[Any] = cmd.copy()
if "fp16" in spec:
cmd_config.extend(['''--mixed_precision=fp16'''] )
else:
cmd_config.extend(['''--mixed_precision=no'''] )
if "multi_gpu" in spec:
continue
else:
cmd_config.extend(['''--use_fsdp'''] )
for i, strategy in enumerate(UpperCamelCase_ ):
if strategy.lower() in spec:
cmd_config.append(F"""--fsdp_sharding_strategy={i+1}""" )
break
if "cpu_offload" in spec:
cmd_config.append('''--fsdp_offload_params=True''' )
for policy in FSDP_AUTO_WRAP_POLICY:
if policy.lower() in spec:
cmd_config.append(F"""--fsdp_auto_wrap_policy={policy}""" )
break
if policy == "TRANSFORMER_BASED_WRAP":
cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer''' )
elif policy == "SIZE_BASED_WRAP":
cmd_config.append('''--fsdp_min_num_params=2000''' )
cmd_config.extend(
[
self.test_file_path,
F"""--output_dir={self.tmpdir}""",
F"""--peak_memory_upper_bound={peak_mem_upper_bound}""",
F"""--n_train={self.n_train}""",
F"""--n_val={self.n_val}""",
] )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(UpperCamelCase_ , env=os.environ.copy() )
| 716 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Tuple = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : int = ["""FNetTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = ["""FNetTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Optional[int] = [
"""FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FNetForMaskedLM""",
"""FNetForMultipleChoice""",
"""FNetForNextSentencePrediction""",
"""FNetForPreTraining""",
"""FNetForQuestionAnswering""",
"""FNetForSequenceClassification""",
"""FNetForTokenClassification""",
"""FNetLayer""",
"""FNetModel""",
"""FNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
__lowerCamelCase : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 418 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def snake_case ( self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ :List[str] = UNetaDModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = self.dummy_uncond_unet
lowerCAmelCase__ :int = PNDMScheduler()
lowerCAmelCase__ :Any = PNDMPipeline(unet=__UpperCAmelCase , scheduler=__UpperCAmelCase )
pndm.to(__UpperCAmelCase )
pndm.set_progress_bar_config(disable=__UpperCAmelCase )
lowerCAmelCase__ :Optional[int] = torch.manual_seed(0 )
lowerCAmelCase__ :List[str] = pndm(generator=__UpperCAmelCase , num_inference_steps=2_0 , output_type='numpy' ).images
lowerCAmelCase__ :str = torch.manual_seed(0 )
lowerCAmelCase__ :Union[str, Any] = pndm(generator=__UpperCAmelCase , num_inference_steps=2_0 , output_type='numpy' , return_dict=__UpperCAmelCase )[0]
lowerCAmelCase__ :List[Any] = image[0, -3:, -3:, -1]
lowerCAmelCase__ :List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
lowerCAmelCase__ :Optional[int] = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = 'google/ddpm-cifar10-32'
lowerCAmelCase__ :Optional[Any] = UNetaDModel.from_pretrained(__UpperCAmelCase )
lowerCAmelCase__ :Any = PNDMScheduler()
lowerCAmelCase__ :Dict = PNDMPipeline(unet=__UpperCAmelCase , scheduler=__UpperCAmelCase )
pndm.to(__UpperCAmelCase )
pndm.set_progress_bar_config(disable=__UpperCAmelCase )
lowerCAmelCase__ :Union[str, Any] = torch.manual_seed(0 )
lowerCAmelCase__ :str = pndm(generator=__UpperCAmelCase , output_type='numpy' ).images
lowerCAmelCase__ :List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
lowerCAmelCase__ :int = np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 93 |
'''simple docstring'''
import argparse
import os
import re
lowercase__ : Optional[int] = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
lowercase__ : Dict = re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase__ : List[str] = re.compile(r'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowercase__ : Tuple = re.compile(r'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
lowercase__ : str = re.compile(r'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowercase__ : str = re.compile(r'''\[([^\]]+)\]''')
def _lowerCAmelCase ( __snake_case : str ) -> Tuple:
__A : List[Any] = _re_indent.search(__snake_case )
return "" if search is None else search.groups()[0]
def _lowerCAmelCase ( __snake_case : Optional[Any] , __snake_case : str="" , __snake_case : Any=None , __snake_case : List[Any]=None ) -> Optional[int]:
__A : Tuple = 0
__A : Optional[int] = code.split('\n' )
if start_prompt is not None:
while not lines[index].startswith(__snake_case ):
index += 1
__A : Optional[int] = ['\n'.join(lines[:index] )]
else:
__A : Any = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
__A : Tuple = [lines[index]]
index += 1
while index < len(__snake_case ) and (end_prompt is None or not lines[index].startswith(__snake_case )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(__snake_case ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + ' ' ):
current_block.append(lines[index] )
blocks.append('\n'.join(__snake_case ) )
if index < len(__snake_case ) - 1:
__A : Union[str, Any] = [lines[index + 1]]
index += 1
else:
__A : Union[str, Any] = []
else:
blocks.append('\n'.join(__snake_case ) )
__A : Optional[Any] = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(__snake_case ) > 0:
blocks.append('\n'.join(__snake_case ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(__snake_case ):
blocks.append('\n'.join(lines[index:] ) )
return blocks
def _lowerCAmelCase ( __snake_case : List[Any] ) -> int:
def _inner(__snake_case : List[Any] ):
return key(__snake_case ).lower().replace('_' , '' )
return _inner
def _lowerCAmelCase ( __snake_case : Dict , __snake_case : Any=None ) -> List[Any]:
# If no key is provided, we use a noop.
def noop(__snake_case : List[Any] ):
return x
if key is None:
__A : Optional[Any] = noop
# Constants are all uppercase, they go first.
__A : str = [obj for obj in objects if key(__snake_case ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
__A : List[str] = [obj for obj in objects if key(__snake_case )[0].isupper() and not key(__snake_case ).isupper()]
# Functions begin with a lowercase, they go last.
__A : str = [obj for obj in objects if not key(__snake_case )[0].isupper()]
__A : Tuple = ignore_underscore(__snake_case )
return sorted(__snake_case , key=__snake_case ) + sorted(__snake_case , key=__snake_case ) + sorted(__snake_case , key=__snake_case )
def _lowerCAmelCase ( __snake_case : Optional[int] ) -> Tuple:
# This inner function sort imports between [ ].
def _replace(__snake_case : Tuple ):
__A : List[str] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
__A : int = [part.strip().replace('"' , '' ) for part in imports.split(',' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
__A : Dict = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(__snake_case )] ) + "]"
__A : List[Any] = import_statement.split('\n' )
if len(__snake_case ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
__A : Optional[int] = 2 if lines[1].strip() == '[' else 1
__A : Any = [(i, _re_strip_line.search(__snake_case ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
__A : Optional[int] = sort_objects(__snake_case , key=lambda __snake_case : x[1] )
__A : Any = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(__snake_case ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
__A : Union[str, Any] = _re_bracket_content.sub(_replace , lines[1] )
else:
__A : Dict = [part.strip().replace('"' , '' ) for part in lines[1].split(',' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
__A : Tuple = keys[:-1]
__A : List[Any] = get_indent(lines[1] ) + ', '.join([f'"{k}"' for k in sort_objects(__snake_case )] )
return "\n".join(__snake_case )
else:
# Finally we have to deal with imports fitting on one line
__A : Optional[Any] = _re_bracket_content.sub(_replace , __snake_case )
return import_statement
def _lowerCAmelCase ( __snake_case : List[Any] , __snake_case : List[Any]=True ) -> Optional[Any]:
with open(__snake_case , 'r' ) as f:
__A : Dict = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
__A : str = split_code_in_indented_blocks(
__snake_case , start_prompt='_import_structure = {' , end_prompt='if TYPE_CHECKING:' )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(__snake_case ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
__A : Tuple = main_blocks[block_idx]
__A : int = block.split('\n' )
# Get to the start of the imports.
__A : Tuple = 0
while line_idx < len(__snake_case ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
__A : Optional[int] = len(__snake_case )
else:
line_idx += 1
if line_idx >= len(__snake_case ):
continue
# Ignore beginning and last line: they don't contain anything.
__A : Dict = '\n'.join(block_lines[line_idx:-1] )
__A : int = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
__A : Optional[int] = split_code_in_indented_blocks(__snake_case , indent_level=__snake_case )
# We have two categories of import key: list or _import_structure[key].append/extend
__A : Any = _re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
__A : Dict = [(pattern.search(__snake_case ).groups()[0] if pattern.search(__snake_case ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
__A : Optional[Any] = [(i, key) for i, key in enumerate(__snake_case ) if key is not None]
__A : Tuple = [x[0] for x in sorted(__snake_case , key=lambda __snake_case : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
__A : str = 0
__A : Any = []
for i in range(len(__snake_case ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
__A : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(__snake_case )
count += 1
# And we put our main block back together with its first and last line.
__A : int = '\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(__snake_case ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(__snake_case , 'w' ) as f:
f.write('\n'.join(__snake_case ) )
def _lowerCAmelCase ( __snake_case : int=True ) -> Optional[Any]:
__A : Tuple = []
for root, _, files in os.walk(__snake_case ):
if "__init__.py" in files:
__A : List[Any] = sort_imports(os.path.join(__snake_case , '__init__.py' ) , check_only=__snake_case )
if result:
__A : Dict = [os.path.join(__snake_case , '__init__.py' )]
if len(__snake_case ) > 0:
raise ValueError(f'Would overwrite {len(__snake_case )} files, run `make style`.' )
if __name__ == "__main__":
lowercase__ : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
lowercase__ : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only) | 8 | 0 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
SCREAMING_SNAKE_CASE_ = """src/transformers"""
SCREAMING_SNAKE_CASE_ = """docs/source/en/tasks"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) -> List[str]:
with open(SCREAMING_SNAKE_CASE__, "r", encoding="utf-8", newline="\n" ) as f:
a_ : Dict = f.readlines()
# Find the start prompt.
a_ : str = 0
while not lines[start_index].startswith(SCREAMING_SNAKE_CASE__ ):
start_index += 1
start_index += 1
a_ : Optional[Any] = start_index
while not lines[end_index].startswith(SCREAMING_SNAKE_CASE__ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE_ = direct_transformers_import(TRANSFORMERS_PATH)
SCREAMING_SNAKE_CASE_ = {
"""asr.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
"""audio_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
"""language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
"""image_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
"""masked_language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
"""multiple_choice.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
"""object_detection.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
"""question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
"""semantic_segmentation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
"""sequence_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
"""summarization.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"""token_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
"""translation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"""video_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
"""document_question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
"""monocular_depth_estimation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
SCREAMING_SNAKE_CASE_ = {
"""summarization.md""": ("""nllb""",),
"""translation.md""": ("""nllb""",),
}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
a_ : List[Any] = TASK_GUIDE_TO_MODELS[task_guide]
a_ : str = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(SCREAMING_SNAKE_CASE__, set() )
a_ : int = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([F"""[{name}](../model_doc/{code})""" for code, name in model_names.items()] ) + "\n"
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=False ) -> int:
a_ : List[Any] = _find_text_in_file(
filename=os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ), start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->", end_prompt="<!--End of the generated tip-->", )
a_ : Dict = get_model_list_for_task(SCREAMING_SNAKE_CASE__ )
if current_list != new_list:
if overwrite:
with open(os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ), "w", encoding="utf-8", newline="\n" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
F"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
" to fix this." )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
SCREAMING_SNAKE_CASE_ = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite) | 705 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
"""BigBirdPegasusOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BigBirdPegasusForCausalLM""",
"""BigBirdPegasusForConditionalGeneration""",
"""BigBirdPegasusForQuestionAnswering""",
"""BigBirdPegasusForSequenceClassification""",
"""BigBirdPegasusModel""",
"""BigBirdPegasusPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 370 | 0 |
from __future__ import annotations
lowerCAmelCase_ = []
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_UpperCamelCase ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(_UpperCamelCase , -1 , -1 ) , range(_UpperCamelCase , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(_UpperCamelCase , -1 , -1 ) , range(_UpperCamelCase , len(_UpperCamelCase ) ) ):
if board[i][j] == 1:
return False
return True
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
if row >= len(_UpperCamelCase ):
solution.append(_UpperCamelCase )
printboard(_UpperCamelCase )
print()
return True
for i in range(len(_UpperCamelCase ) ):
if is_safe(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = 1
solve(_UpperCamelCase , row + 1 )
snake_case_ : Dict = 0
return False
def lowerCamelCase_ ( _UpperCamelCase ) -> None:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
for j in range(len(_UpperCamelCase ) ):
if board[i][j] == 1:
print('''Q''' , end=''' ''' )
else:
print('''.''' , end=''' ''' )
print()
# n=int(input("The no. of queens"))
lowerCAmelCase_ = 8
lowerCAmelCase_ = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))
| 60 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
__lowercase = hex_num[0] == '''-'''
if is_negative:
__lowercase = hex_num[1:]
try:
__lowercase = int(_UpperCamelCase , 16 )
except ValueError:
raise ValueError('''Invalid value was passed to the function''' )
__lowercase = ''''''
while int_num > 0:
__lowercase = str(int_num % 2 ) + bin_str
int_num >>= 1
return int(('''-''' + bin_str) if is_negative else bin_str )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 639 | 0 |
# Algorithm for the pigeonhole sorting
def _snake_case ( __snake_case ) -> int:
'''simple docstring'''
UpperCAmelCase_ : List[str] = min(__snake_case ) # min() finds the minimum value
UpperCAmelCase_ : Optional[int] = max(__snake_case ) # max() finds the maximum value
UpperCAmelCase_ : Any = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
UpperCAmelCase_ : int = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(__snake_case , __snake_case ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
UpperCAmelCase_ : Dict = 0
for count in range(__snake_case ):
while holes[count] > 0:
holes[count] -= 1
UpperCAmelCase_ : Union[str, Any] = count + min_val
i += 1
def _snake_case ( ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_ : str = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(__snake_case )
print("Sorted order is:" , " ".join(__snake_case ) )
if __name__ == "__main__":
main()
| 713 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCamelCase = get_tests_dir('''fixtures/test_sentencepiece.model''')
__lowerCamelCase = {'''target_lang''': '''fi''', '''source_lang''': '''en'''}
__lowerCamelCase = '''>>zh<<'''
__lowerCamelCase = '''Helsinki-NLP/'''
if is_torch_available():
__lowerCamelCase = '''pt'''
elif is_tf_available():
__lowerCamelCase = '''tf'''
else:
__lowerCamelCase = '''jax'''
@require_sentencepiece
class snake_case_ (lowercase__ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = MarianTokenizer
_lowerCamelCase = False
_lowerCamelCase = True
def A_ ( self):
"""simple docstring"""
super().setUp()
UpperCAmelCase_ : Union[str, Any] = ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
UpperCAmelCase_ : Union[str, Any] = dict(zip(lowercase ,range(len(lowercase))))
UpperCAmelCase_ : int = Path(self.tmpdirname)
save_json(lowercase ,save_dir / VOCAB_FILES_NAMES["vocab"])
save_json(lowercase ,save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"])
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(lowercase ,save_dir / VOCAB_FILES_NAMES["source_spm"])
copyfile(lowercase ,save_dir / VOCAB_FILES_NAMES["target_spm"])
UpperCAmelCase_ : str = MarianTokenizer.from_pretrained(self.tmpdirname)
tokenizer.save_pretrained(self.tmpdirname)
def A_ ( self ,**lowercase):
"""simple docstring"""
return MarianTokenizer.from_pretrained(self.tmpdirname ,**lowercase)
def A_ ( self ,lowercase):
"""simple docstring"""
return (
"This is a test",
"This is a test",
)
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = "</s>"
UpperCAmelCase_ : List[str] = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase) ,lowercase)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase) ,lowercase)
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : List[str] = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] ,"</s>")
self.assertEqual(vocab_keys[1] ,"<unk>")
self.assertEqual(vocab_keys[-1] ,"<pad>")
self.assertEqual(len(lowercase) ,9)
def A_ ( self):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size ,9)
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : List[str] = MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de""")
UpperCAmelCase_ : Optional[int] = en_de_tokenizer(["I am a small frog"] ,return_tensors=lowercase)
self.assertIsInstance(lowercase ,lowercase)
UpperCAmelCase_ : Union[str, Any] = [38, 121, 14, 697, 38848, 0]
self.assertListEqual(lowercase ,batch.input_ids[0])
UpperCAmelCase_ : Optional[int] = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(lowercase)
UpperCAmelCase_ : Any = [x.name for x in Path(lowercase).glob("*")]
self.assertIn("source.spm" ,lowercase)
MarianTokenizer.from_pretrained(lowercase)
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : Any = self.get_tokenizer()
UpperCAmelCase_ : List[Any] = tok(
["I am a small frog" * 1000, "I am a small frog"] ,padding=lowercase ,truncation=lowercase ,return_tensors=lowercase)
self.assertIsInstance(lowercase ,lowercase)
self.assertEqual(batch.input_ids.shape ,(2, 512))
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = self.get_tokenizer()
UpperCAmelCase_ : Optional[int] = tok(["I am a tiny frog", "I am a small frog"] ,padding=lowercase ,return_tensors=lowercase)
self.assertIsInstance(lowercase ,lowercase)
self.assertEqual(batch_smaller.input_ids.shape ,(2, 10))
@slow
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : Dict = {"input_ids": [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=lowercase ,model_name="Helsinki-NLP/opus-mt-en-de" ,revision="1a8c2263da11e68e50938f97e10cd57820bd504c" ,decode_kwargs={"use_source_tokenizer": True} ,)
def A_ ( self):
"""simple docstring"""
UpperCAmelCase_ : Dict = MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs")
UpperCAmelCase_ : Any = "Tämä on testi"
UpperCAmelCase_ : List[str] = "This is a test"
UpperCAmelCase_ : int = [76, 7, 2047, 2]
UpperCAmelCase_ : Any = [69, 12, 11, 940, 2]
UpperCAmelCase_ : Any = tokenizer(lowercase).input_ids
self.assertListEqual(lowercase ,lowercase)
UpperCAmelCase_ : Any = tokenizer(text_target=lowercase).input_ids
self.assertListEqual(lowercase ,lowercase)
UpperCAmelCase_ : List[Any] = tokenizer.decode(lowercase ,skip_special_tokens=lowercase)
self.assertEqual(lowercase ,lowercase)
| 455 | 0 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase = logging.getLogger(__name__)
_lowerCAmelCase = '''Hello world! cécé herlolip'''
_lowerCAmelCase = namedtuple(
'''BertAbsConfig''',
[
'''temp_dir''',
'''large''',
'''use_bert_emb''',
'''finetune_bert''',
'''encoder''',
'''share_emb''',
'''max_pos''',
'''enc_layers''',
'''enc_hidden_size''',
'''enc_heads''',
'''enc_ff_size''',
'''enc_dropout''',
'''dec_layers''',
'''dec_hidden_size''',
'''dec_heads''',
'''dec_ff_size''',
'''dec_dropout''',
],
)
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : int = BertAbsConfig(
temp_dir=""".""" , finetune_bert=lowerCAmelCase_ , large=lowerCAmelCase_ , share_emb=lowerCAmelCase_ , use_bert_emb=lowerCAmelCase_ , encoder="""bert""" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
lowerCAmelCase__ : int = torch.load(lowerCAmelCase_ , lambda UpperCamelCase , UpperCamelCase : storage )
lowerCAmelCase__ : Optional[Any] = AbsSummarizer(lowerCAmelCase_ , torch.device("""cpu""" ) , lowerCAmelCase_ )
original.eval()
lowerCAmelCase__ : Optional[int] = BertAbsSummarizer(lowerCAmelCase_ , torch.device("""cpu""" ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("""convert the model""" )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("""Make sure that the models' outputs are identical""" )
lowerCAmelCase__ : Union[str, Any] = BertTokenizer.from_pretrained("""bert-base-uncased""" )
# prepare the model inputs
lowerCAmelCase__ : Union[str, Any] = tokenizer.encode("""This is sample éàalj'-.""" )
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(lowerCAmelCase_ )) )
lowerCAmelCase__ : int = torch.tensor(lowerCAmelCase_ ).unsqueeze(0 )
lowerCAmelCase__ : str = tokenizer.encode("""This is sample 3 éàalj'-.""" )
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(lowerCAmelCase_ )) )
lowerCAmelCase__ : str = torch.tensor(lowerCAmelCase_ ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
lowerCAmelCase__ : str = encoder_input_ids
lowerCAmelCase__ : List[Any] = decoder_input_ids
lowerCAmelCase__ : Union[str, Any] = None
lowerCAmelCase__ : Union[str, Any] = None
lowerCAmelCase__ : Optional[Any] = None
lowerCAmelCase__ : List[str] = None
lowerCAmelCase__ : str = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
lowerCAmelCase__ : int = original(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )[0]
lowerCAmelCase__ : List[str] = original.generator(lowerCAmelCase_ )
lowerCAmelCase__ : int = new_model(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )[0]
lowerCAmelCase__ : str = new_model.generator(lowerCAmelCase_ )
lowerCAmelCase__ : Optional[int] = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCAmelCase_ ) )
lowerCAmelCase__ : int = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCAmelCase_ ) )
lowerCAmelCase__ : List[Any] = torch.allclose(lowerCAmelCase_ , lowerCAmelCase_ , atol=1e-3 )
if are_identical:
logging.info("""all weights are equal up to 1e-3""" )
else:
raise ValueError("""the weights are different. The new model is likely different from the original one.""" )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("""saving the model's state dictionary""" )
torch.save(
new_model.state_dict() , """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin""" )
if __name__ == "__main__":
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'''--bertabs_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch dump.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the output PyTorch model.''',
)
_lowerCAmelCase = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 565 |
from __future__ import annotations
def _lowerCAmelCase ( lowerCAmelCase_ :int , lowerCAmelCase_ :int )->list[str]:
'''simple docstring'''
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_bytes:
raise ValueError("partitions can not > number_of_bytes!" )
snake_case_ = number_of_bytes // partitions
snake_case_ = []
for i in range(lowerCAmelCase_ ):
snake_case_ = i * bytes_per_partition + 1
snake_case_ = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(F'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 283 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
a_ :List[Any] = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
a_ :Dict = {
'vocab_file': {
'google/electra-small-generator': (
'https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'
),
'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt',
'google/electra-large-generator': (
'https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'
),
'google/electra-small-discriminator': (
'https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'
),
'google/electra-base-discriminator': (
'https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'
),
'google/electra-large-discriminator': (
'https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'google/electra-small-generator': (
'https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'
),
'google/electra-base-generator': (
'https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'
),
'google/electra-large-generator': (
'https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'
),
'google/electra-small-discriminator': (
'https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'
),
'google/electra-base-discriminator': (
'https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'
),
'google/electra-large-discriminator': (
'https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'
),
},
}
a_ :Union[str, Any] = {
'google/electra-small-generator': 5_12,
'google/electra-base-generator': 5_12,
'google/electra-large-generator': 5_12,
'google/electra-small-discriminator': 5_12,
'google/electra-base-discriminator': 5_12,
'google/electra-large-discriminator': 5_12,
}
a_ :List[Any] = {
'google/electra-small-generator': {'do_lower_case': True},
'google/electra-base-generator': {'do_lower_case': True},
'google/electra-large-generator': {'do_lower_case': True},
'google/electra-small-discriminator': {'do_lower_case': True},
'google/electra-base-discriminator': {'do_lower_case': True},
'google/electra-large-discriminator': {'do_lower_case': True},
}
class lowercase ( _UpperCAmelCase ):
lowerCamelCase : Any = VOCAB_FILES_NAMES
lowerCamelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : Optional[Any] = PRETRAINED_INIT_CONFIGURATION
lowerCamelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : Dict = ElectraTokenizer
def __init__( self : int , _lowercase : Union[str, Any]=None , _lowercase : str=None , _lowercase : Optional[Any]=True , _lowercase : List[str]="[UNK]" , _lowercase : Union[str, Any]="[SEP]" , _lowercase : str="[PAD]" , _lowercase : str="[CLS]" , _lowercase : str="[MASK]" , _lowercase : Union[str, Any]=True , _lowercase : int=None , **_lowercase : Optional[Any] , ):
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , tokenize_chinese_chars=_lowercase , strip_accents=_lowercase , **_lowercase , )
SCREAMING_SNAKE_CASE__ : Any = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , _lowercase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , _lowercase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , _lowercase ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE__ : str = getattr(_lowercase , normalizer_state.pop('''type''' ) )
SCREAMING_SNAKE_CASE__ : Dict = do_lower_case
SCREAMING_SNAKE_CASE__ : List[Any] = strip_accents
SCREAMING_SNAKE_CASE__ : Any = tokenize_chinese_chars
SCREAMING_SNAKE_CASE__ : List[Any] = normalizer_class(**_lowercase )
SCREAMING_SNAKE_CASE__ : int = do_lower_case
def lowercase__ ( self : Optional[int] , _lowercase : Tuple , _lowercase : Optional[Any]=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 lowercase__ ( self : List[str] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : int = [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 lowercase__ ( self : Union[str, Any] , _lowercase : str , _lowercase : Optional[str] = None ):
SCREAMING_SNAKE_CASE__ : Optional[int] = self._tokenizer.model.save(_lowercase , name=_lowercase )
return tuple(_lowercase )
| 250 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
a_ :List[Any] = logging.get_logger(__name__)
def a ( A__ , A__ , A__ ) -> str:
'''simple docstring'''
return [
int(1_0_0_0 * (box[0] / width) ),
int(1_0_0_0 * (box[1] / height) ),
int(1_0_0_0 * (box[2] / width) ),
int(1_0_0_0 * (box[3] / height) ),
]
def a ( A__ , A__ , A__ = None ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = tesseract_config if tesseract_config is not None else ''''''
# apply OCR
SCREAMING_SNAKE_CASE__ : str = to_pil_image(A__ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = pil_image.size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pytesseract.image_to_data(A__ , lang=A__ , output_type='''dict''' , config=A__ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']
# filter empty words and corresponding coordinates
SCREAMING_SNAKE_CASE__ : Any = [idx for idx, word in enumerate(A__ ) if not word.strip()]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [word for idx, word in enumerate(A__ ) if idx not in irrelevant_indices]
SCREAMING_SNAKE_CASE__ : Optional[int] = [coord for idx, coord in enumerate(A__ ) if idx not in irrelevant_indices]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [coord for idx, coord in enumerate(A__ ) if idx not in irrelevant_indices]
SCREAMING_SNAKE_CASE__ : Any = [coord for idx, coord in enumerate(A__ ) if idx not in irrelevant_indices]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [coord for idx, coord in enumerate(A__ ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
SCREAMING_SNAKE_CASE__ : str = []
for x, y, w, h in zip(A__ , A__ , A__ , A__ ):
SCREAMING_SNAKE_CASE__ : List[Any] = [x, y, x + w, y + h]
actual_boxes.append(A__ )
# finally, normalize the bounding boxes
SCREAMING_SNAKE_CASE__ : int = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(A__ , A__ , A__ ) )
assert len(A__ ) == len(A__ ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class lowercase ( _UpperCAmelCase ):
lowerCamelCase : List[Any] = ['''pixel_values''']
def __init__( self : List[str] , _lowercase : bool = True , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = PILImageResampling.BILINEAR , _lowercase : bool = True , _lowercase : Optional[str] = None , _lowercase : Optional[str] = "" , **_lowercase : List[str] , ):
super().__init__(**_lowercase )
SCREAMING_SNAKE_CASE__ : Any = size if size is not None else {'''height''': 2_24, '''width''': 2_24}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_size_dict(_lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = do_resize
SCREAMING_SNAKE_CASE__ : List[str] = size
SCREAMING_SNAKE_CASE__ : Tuple = resample
SCREAMING_SNAKE_CASE__ : Union[str, Any] = apply_ocr
SCREAMING_SNAKE_CASE__ : List[str] = ocr_lang
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tesseract_config
def lowercase__ ( self : Optional[Any] , _lowercase : np.ndarray , _lowercase : Dict[str, int] , _lowercase : PILImageResampling = PILImageResampling.BILINEAR , _lowercase : Optional[Union[str, ChannelDimension]] = None , **_lowercase : Optional[int] , ):
SCREAMING_SNAKE_CASE__ : List[Any] = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
SCREAMING_SNAKE_CASE__ : str = (size['''height'''], size['''width'''])
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def lowercase__ ( self : List[str] , _lowercase : ImageInput , _lowercase : bool = None , _lowercase : Dict[str, int] = None , _lowercase : PILImageResampling = None , _lowercase : bool = None , _lowercase : Optional[str] = None , _lowercase : Optional[str] = None , _lowercase : Optional[Union[str, TensorType]] = None , _lowercase : ChannelDimension = ChannelDimension.FIRST , **_lowercase : List[Any] , ):
SCREAMING_SNAKE_CASE__ : Tuple = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE__ : Any = size if size is not None else self.size
SCREAMING_SNAKE_CASE__ : str = get_size_dict(_lowercase )
SCREAMING_SNAKE_CASE__ : int = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE__ : List[Any] = apply_ocr if apply_ocr is not None else self.apply_ocr
SCREAMING_SNAKE_CASE__ : Any = ocr_lang if ocr_lang is not None else self.ocr_lang
SCREAMING_SNAKE_CASE__ : List[Any] = tesseract_config if tesseract_config is not None else self.tesseract_config
SCREAMING_SNAKE_CASE__ : str = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [to_numpy_array(_lowercase ) for image in images]
if apply_ocr:
requires_backends(self , '''pytesseract''' )
SCREAMING_SNAKE_CASE__ : int = []
SCREAMING_SNAKE_CASE__ : Any = []
for image in images:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = apply_tesseract(_lowercase , _lowercase , _lowercase )
words_batch.append(_lowercase )
boxes_batch.append(_lowercase )
if do_resize:
SCREAMING_SNAKE_CASE__ : List[Any] = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
# flip color channels from RGB to BGR (as Detectron2 requires this)
SCREAMING_SNAKE_CASE__ : List[str] = [flip_channel_order(_lowercase ) for image in images]
SCREAMING_SNAKE_CASE__ : List[Any] = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
SCREAMING_SNAKE_CASE__ : str = BatchFeature(data={'''pixel_values''': images} , tensor_type=_lowercase )
if apply_ocr:
SCREAMING_SNAKE_CASE__ : List[str] = words_batch
SCREAMING_SNAKE_CASE__ : List[str] = boxes_batch
return data
| 250 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_snake_case = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'mask2former'
__lowerCamelCase = ['swin']
__lowerCamelCase = {'hidden_size': 'hidden_dim'}
def __init__( self :str , _lowercase :Optional[Dict] = None , _lowercase :int = 2_56 , _lowercase :int = 2_56 , _lowercase :int = 2_56 , _lowercase :int = 10_24 , _lowercase :str = "relu" , _lowercase :int = 6 , _lowercase :int = 10 , _lowercase :int = 8 , _lowercase :float = 0.0 , _lowercase :int = 20_48 , _lowercase :bool = False , _lowercase :bool = False , _lowercase :int = 4 , _lowercase :int = 2_55 , _lowercase :int = 1_00 , _lowercase :float = 0.1 , _lowercase :float = 2.0 , _lowercase :float = 5.0 , _lowercase :float = 5.0 , _lowercase :int = 1_25_44 , _lowercase :float = 3.0 , _lowercase :float = 0.75 , _lowercase :float = 0.02 , _lowercase :float = 1.0 , _lowercase :bool = True , _lowercase :List[int] = [4, 8, 16, 32] , _lowercase :bool = None , **_lowercase :List[Any] , ):
'''simple docstring'''
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone." )
lowercase__ = CONFIG_MAPPING["swin"](
image_size=2_24 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=_lowercase , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(_lowercase , _lowercase ):
lowercase__ = backbone_config.pop("model_type" )
lowercase__ = CONFIG_MAPPING[backbone_model_type]
lowercase__ = config_class.from_dict(_lowercase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
f'''Supported model types: {','.join(self.backbones_supported )}''' )
lowercase__ = backbone_config
lowercase__ = feature_size
lowercase__ = mask_feature_size
lowercase__ = hidden_dim
lowercase__ = encoder_feedforward_dim
lowercase__ = activation_function
lowercase__ = encoder_layers
lowercase__ = decoder_layers
lowercase__ = num_attention_heads
lowercase__ = dropout
lowercase__ = dim_feedforward
lowercase__ = pre_norm
lowercase__ = enforce_input_projection
lowercase__ = common_stride
lowercase__ = ignore_value
lowercase__ = num_queries
lowercase__ = no_object_weight
lowercase__ = class_weight
lowercase__ = mask_weight
lowercase__ = dice_weight
lowercase__ = train_num_points
lowercase__ = oversample_ratio
lowercase__ = importance_sample_ratio
lowercase__ = init_std
lowercase__ = init_xavier_std
lowercase__ = use_auxiliary_loss
lowercase__ = feature_strides
lowercase__ = output_auxiliary_logits
lowercase__ = decoder_layers
super().__init__(**_lowercase )
@classmethod
def UpperCAmelCase ( cls :int , _lowercase :PretrainedConfig , **_lowercase :Optional[int] ):
'''simple docstring'''
return cls(
backbone_config=_lowercase , **_lowercase , )
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = copy.deepcopy(self.__dict__ )
lowercase__ = self.backbone_config.to_dict()
lowercase__ = self.__class__.model_type
return output
| 655 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModel.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModel.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForPreTraining.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForPreTraining.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForCausalLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForCausalLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForCausalLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForCausalLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForMaskedLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForMaskedLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForMaskedLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForMaskedLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForSequenceClassification.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForSequenceClassification.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForQuestionAnswering.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForQuestionAnswering.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
| 655 | 1 |
'''simple docstring'''
from __future__ import annotations
__A : Dict = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def UpperCamelCase_ ( A__ : Optional[int] , A__ : List[Any] , A__ : str , A__ : str , A__ : Optional[int] , ):
'''simple docstring'''
lowerCAmelCase_ : int = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_SCREAMING_SNAKE_CASE ) )
] # the reference grid
lowerCAmelCase_ : str = 1
lowerCAmelCase_ : List[str] = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_SCREAMING_SNAKE_CASE ) )
] # the action grid
lowerCAmelCase_ : str = init[0]
lowerCAmelCase_ : Dict = init[1]
lowerCAmelCase_ : str = 0
lowerCAmelCase_ : Union[str, Any] = g + heuristic[x][y] # cost from starting cell to destination cell
lowerCAmelCase_ : str = [[f, g, x, y]]
lowerCAmelCase_ : List[str] = False # flag that is set when search is complete
lowerCAmelCase_ : Union[str, Any] = False # flag set if we can't find expand
while not found and not resign:
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("""Algorithm is unable to find solution""" )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
lowerCAmelCase_ : int = cell.pop()
lowerCAmelCase_ : str = next_cell[2]
lowerCAmelCase_ : int = next_cell[3]
lowerCAmelCase_ : Any = next_cell[1]
if x == goal[0] and y == goal[1]:
lowerCAmelCase_ : List[str] = True
else:
for i in range(len(_SCREAMING_SNAKE_CASE ) ): # to try out different valid actions
lowerCAmelCase_ : Union[str, Any] = x + DIRECTIONS[i][0]
lowerCAmelCase_ : Any = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(_SCREAMING_SNAKE_CASE ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
lowerCAmelCase_ : Union[str, Any] = g + cost
lowerCAmelCase_ : Any = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
lowerCAmelCase_ : List[str] = 1
lowerCAmelCase_ : int = i
lowerCAmelCase_ : List[str] = []
lowerCAmelCase_ : Optional[int] = goal[0]
lowerCAmelCase_ : Any = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
lowerCAmelCase_ : List[str] = x - DIRECTIONS[action[x][y]][0]
lowerCAmelCase_ : Optional[int] = y - DIRECTIONS[action[x][y]][1]
lowerCAmelCase_ : Tuple = xa
lowerCAmelCase_ : List[str] = ya
invpath.append([x, y] )
lowerCAmelCase_ : int = []
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
path.append(invpath[len(_SCREAMING_SNAKE_CASE ) - 1 - i] )
return path, action
if __name__ == "__main__":
__A : List[str] = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
__A : List[str] = [0, 0]
# all coordinates are given in format [y,x]
__A : Any = [len(grid) - 1, len(grid[0]) - 1]
__A : Optional[Any] = 1
# the cost map which pushes the path closer to the goal
__A : Dict = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
__A : Tuple = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
__A : Optional[int] = 99
__A : Optional[Any] = search(grid, init, goal, cost, heuristic)
print("ACTION MAP")
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 702 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = "▁"
__A : Tuple = {"vocab_file": "sentencepiece.bpe.model"}
__A : Tuple = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
__A : int = {
"facebook/xglm-564M": 2048,
}
class __snake_case ( _SCREAMING_SNAKE_CASE):
"""simple docstring"""
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ['input_ids', 'attention_mask']
def __init__( self : Any , lowerCamelCase : Any , lowerCamelCase : str="<s>" , lowerCamelCase : Optional[int]="</s>" , lowerCamelCase : Optional[Any]="</s>" , lowerCamelCase : List[Any]="<s>" , lowerCamelCase : Optional[Any]="<unk>" , lowerCamelCase : int="<pad>" , lowerCamelCase : Optional[Dict[str, Any]] = None , **lowerCamelCase : Optional[Any] , ) -> None:
lowerCAmelCase_ : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
lowerCAmelCase_ : str = 7
lowerCAmelCase_ : Any = [F'<madeupword{i}>' for i in range(self.num_madeup_words )]
lowerCAmelCase_ : Optional[Any] = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=lowerCamelCase , eos_token=lowerCamelCase , unk_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , pad_token=lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase , )
lowerCAmelCase_ : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCamelCase ) )
lowerCAmelCase_ : int = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
lowerCAmelCase_ : List[str] = 1
# Mimic fairseq token-to-id alignment for the first 4 token
lowerCAmelCase_ : Any = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
lowerCAmelCase_ : Union[str, Any] = len(self.sp_model )
lowerCAmelCase_ : Any = {F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(lowerCamelCase )
lowerCAmelCase_ : int = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : int ) -> Union[str, Any]:
lowerCAmelCase_ : Union[str, Any] = self.__dict__.copy()
lowerCAmelCase_ : str = None
lowerCAmelCase_ : List[str] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Dict , lowerCamelCase : List[Any] ) -> List[Any]:
lowerCAmelCase_ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowerCAmelCase_ : int = {}
lowerCAmelCase_ : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def __lowercase ( self : List[Any] , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
lowerCAmelCase_ : List[Any] = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def __lowercase ( self : Tuple , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None , lowerCamelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase , token_ids_a=lowerCamelCase , already_has_special_tokens=lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase ))
return [1] + ([0] * len(lowerCamelCase )) + [1, 1] + ([0] * len(lowerCamelCase ))
def __lowercase ( self : str , lowerCamelCase : List[int] , lowerCamelCase : Optional[List[int]] = None ) -> List[int]:
lowerCAmelCase_ : Dict = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def __lowercase ( self : str ) -> Union[str, Any]:
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def __lowercase ( self : Optional[Any] ) -> Dict:
lowerCAmelCase_ : Optional[Any] = {self.convert_ids_to_tokens(lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __lowercase ( self : int , lowerCamelCase : str ) -> List[str]:
return self.sp_model.encode(lowerCamelCase , out_type=lowerCamelCase )
def __lowercase ( self : int , lowerCamelCase : Dict ) -> Any:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowerCAmelCase_ : int = self.sp_model.PieceToId(lowerCamelCase )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def __lowercase ( self : Dict , lowerCamelCase : Optional[int] ) -> Any:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def __lowercase ( self : List[str] , lowerCamelCase : Optional[Any] ) -> Optional[Any]:
lowerCAmelCase_ : str = """""".join(lowerCamelCase ).replace(lowerCamelCase , """ """ ).strip()
return out_string
def __lowercase ( self : Optional[Any] , lowerCamelCase : str , lowerCamelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(lowerCamelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCAmelCase_ : List[str] = os.path.join(
lowerCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCamelCase , """wb""" ) as fi:
lowerCAmelCase_ : Union[str, Any] = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase )
return (out_vocab_file,)
| 398 | 0 |
from typing import Any
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase__ : Any ) -> str:
snake_case__ = data
snake_case__ = None
def __repr__( self : Optional[Any] ) -> str:
return f'''Node({self.data})'''
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : str ) -> List[Any]:
snake_case__ = None
def __iter__( self : str ) -> Any:
snake_case__ = self.head
while node:
yield node.data
snake_case__ = node.next
def __len__( self : int ) -> int:
return sum(1 for _ in self )
def __repr__( self : Dict ) -> str:
return "->".join([str(lowerCAmelCase__ ) for item in self] )
def __getitem__( self : int , lowerCAmelCase__ : int ) -> Any:
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self : Optional[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : Any ) -> None:
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
snake_case__ = self.head
for _ in range(lowerCAmelCase__ ):
snake_case__ = current.next
snake_case__ = data
def UpperCAmelCase_ ( self : int , lowerCAmelCase__ : Any ) -> None:
self.insert_nth(len(self ) , lowerCAmelCase__ )
def UpperCAmelCase_ ( self : int , lowerCAmelCase__ : Any ) -> None:
self.insert_nth(0 , lowerCAmelCase__ )
def UpperCAmelCase_ ( self : str , lowerCAmelCase__ : int , lowerCAmelCase__ : Any ) -> None:
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
snake_case__ = Node(lowerCAmelCase__ )
if self.head is None:
snake_case__ = new_node
elif index == 0:
snake_case__ = self.head # link new_node to head
snake_case__ = new_node
else:
snake_case__ = self.head
for _ in range(index - 1 ):
snake_case__ = temp.next
snake_case__ = temp.next
snake_case__ = new_node
def UpperCAmelCase_ ( self : Optional[int] ) -> None: # print every node data
print(self )
def UpperCAmelCase_ ( self : Dict ) -> Any:
return self.delete_nth(0 )
def UpperCAmelCase_ ( self : List[Any] ) -> Any: # delete from tail
return self.delete_nth(len(self ) - 1 )
def UpperCAmelCase_ ( self : List[str] , lowerCAmelCase__ : int = 0 ) -> Any:
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
snake_case__ = self.head # default first node
if index == 0:
snake_case__ = self.head.next
else:
snake_case__ = self.head
for _ in range(index - 1 ):
snake_case__ = temp.next
snake_case__ = temp.next
snake_case__ = temp.next.next
return delete_node.data
def UpperCAmelCase_ ( self : Tuple ) -> bool:
return self.head is None
def UpperCAmelCase_ ( self : Any ) -> None:
snake_case__ = None
snake_case__ = self.head
while current:
# Store the current node's next node.
snake_case__ = current.next
# Make the current node's next point backwards
snake_case__ = prev
# Make the previous node be the current node
snake_case__ = current
# Make the current node the next node (to progress iteration)
snake_case__ = next_node
# Return prev in order to put the head at the end
snake_case__ = prev
def _lowercase ( ):
snake_case__ = LinkedList()
assert linked_list.is_empty() is True
assert str(__UpperCamelCase ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10 ):
assert len(__UpperCamelCase ) == i
linked_list.insert_nth(__UpperCamelCase , i + 1 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 11 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(11 )
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(0 , 12 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 10
assert linked_list.delete_tail() == 11
assert len(__UpperCamelCase ) == 9
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1 , 10 ) )
assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True
for i in range(0 , 9 ):
snake_case__ = -i
assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True
linked_list.reverse()
assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(-8 , 1 ) )
def _lowercase ( ):
snake_case__ = [
-9,
100,
Node(7734_5112 ),
"""dlrow olleH""",
7,
5555,
0,
-1_9_2.5_5_5_5_5,
"""Hello, world!""",
7_7.9,
Node(10 ),
None,
None,
1_2.2_0,
]
snake_case__ = LinkedList()
for i in test_input:
linked_list.insert_tail(__UpperCamelCase )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(__UpperCamelCase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
snake_case__ = linked_list.delete_head()
assert result == -9
assert (
str(__UpperCamelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
snake_case__ = linked_list.delete_tail()
assert result == 1_2.2
assert (
str(__UpperCamelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
snake_case__ = linked_list.delete_nth(10 )
assert result is None
assert (
str(__UpperCamelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(__UpperCamelCase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(__UpperCamelCase )
assert (
str(__UpperCamelCase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(__UpperCamelCase )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def _lowercase ( ):
from doctest import testmod
testmod()
snake_case__ = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(__UpperCamelCase )
print("""\nReading/changing Node data using indexing:""" )
print(F'''Element at Position 1: {linked_list[1]}''' )
snake_case__ = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(__UpperCamelCase )
print(F'''length of linked_list is : {len(__UpperCamelCase )}''' )
if __name__ == "__main__":
main()
| 214 |
from math import factorial
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Union[str, Any] ) -> Tuple:
snake_case__ = real
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case__ = [1] * rank
else:
snake_case__ = rank
def __repr__( self : int ) -> Union[str, Any]:
return (
f'''{self.real}+'''
f'''{'+'.join(str(lowerCAmelCase__ )+'E'+str(n+1 )for n,dual in enumerate(self.duals ) )}'''
)
def UpperCAmelCase_ ( self : str ) -> Dict:
snake_case__ = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1 )
return Dual(self.real , lowerCAmelCase__ )
def __add__( self : List[Any] , lowerCAmelCase__ : Optional[Any] ) -> Union[str, Any]:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
return Dual(self.real + other , self.duals )
snake_case__ = self.duals.copy()
snake_case__ = other.duals.copy()
if len(lowerCAmelCase__ ) > len(lowerCAmelCase__ ):
o_dual.extend([1] * (len(lowerCAmelCase__ ) - len(lowerCAmelCase__ )) )
elif len(lowerCAmelCase__ ) < len(lowerCAmelCase__ ):
s_dual.extend([1] * (len(lowerCAmelCase__ ) - len(lowerCAmelCase__ )) )
snake_case__ = []
for i in range(len(lowerCAmelCase__ ) ):
new_duals.append(s_dual[i] + o_dual[i] )
return Dual(self.real + other.real , lowerCAmelCase__ )
UpperCamelCase__ : int = __add__
def __sub__( self : Optional[Any] , lowerCAmelCase__ : Union[str, Any] ) -> Optional[Any]:
return self + other * -1
def __mul__( self : Tuple , lowerCAmelCase__ : List[str] ) -> str:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case__ = []
for i in self.duals:
new_duals.append(i * other )
return Dual(self.real * other , lowerCAmelCase__ )
snake_case__ = [0] * (len(self.duals ) + len(other.duals ) + 1)
for i, item in enumerate(self.duals ):
for j, jtem in enumerate(other.duals ):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals ) ):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals ) ):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real , lowerCAmelCase__ )
UpperCamelCase__ : int = __mul__
def __truediv__( self : Dict , lowerCAmelCase__ : Tuple ) -> List[str]:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case__ = []
for i in self.duals:
new_duals.append(i / other )
return Dual(self.real / other , lowerCAmelCase__ )
raise ValueError
def __floordiv__( self : int , lowerCAmelCase__ : Union[str, Any] ) -> Optional[Any]:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case__ = []
for i in self.duals:
new_duals.append(i // other )
return Dual(self.real // other , lowerCAmelCase__ )
raise ValueError
def __pow__( self : int , lowerCAmelCase__ : Optional[int] ) -> int:
if n < 0 or isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError("""power must be a positive integer""" )
if n == 0:
return 1
if n == 1:
return self
snake_case__ = self
for _ in range(n - 1 ):
x *= self
return x
def _lowercase ( __UpperCamelCase : List[str] , __UpperCamelCase : List[str] , __UpperCamelCase : int ):
if not callable(__UpperCamelCase ):
raise ValueError("""differentiate() requires a function as input for func""" )
if not isinstance(__UpperCamelCase , (float, int) ):
raise ValueError("""differentiate() requires a float as input for position""" )
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError("""differentiate() requires an int as input for order""" )
snake_case__ = Dual(__UpperCamelCase , 1 )
snake_case__ = func(__UpperCamelCase )
if order == 0:
return result.real
return result.duals[order - 1] * factorial(__UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
def _lowercase ( __UpperCamelCase : Optional[Any] ):
return y**2 * y**4
print(differentiate(f, 9, 2))
| 214 | 1 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def A__ ( lowerCamelCase , lowerCamelCase=10 ) -> List[str]:
UpperCamelCase_: Dict = []
for _ in range(lowerCamelCase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def A__ ( lowerCamelCase , lowerCamelCase=10 ) -> Optional[int]:
UpperCamelCase_: Tuple = []
for step in range(lowerCamelCase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase_: List[str] = os.path.join(lowerCamelCase , """schedule.bin""" )
torch.save(scheduler.state_dict() , lowerCamelCase )
UpperCamelCase_: int = torch.load(lowerCamelCase )
scheduler.load_state_dict(lowerCamelCase )
return lrs
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Optional[Any] , snake_case_ : Tuple , snake_case_ : Any , snake_case_ : Optional[int] ):
self.assertEqual(len(snake_case_ ) , len(snake_case_ ) )
for a, b in zip(snake_case_ , snake_case_ ):
self.assertAlmostEqual(snake_case_ , snake_case_ , delta=snake_case_ )
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Tuple = torch.tensor([0.1, -0.2, -0.1] , requires_grad=snake_case_ )
UpperCamelCase_: int = torch.tensor([0.4, 0.2, -0.5] )
UpperCamelCase_: int = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
UpperCamelCase_: Union[str, Any] = AdamW(params=[w] , lr=2e-1 , weight_decay=0.0 )
for _ in range(100 ):
UpperCamelCase_: List[Any] = criterion(snake_case_ , snake_case_ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1e-2 )
def lowerCAmelCase__ ( self : List[Any] ):
UpperCamelCase_: Optional[int] = torch.tensor([0.1, -0.2, -0.1] , requires_grad=snake_case_ )
UpperCamelCase_: Tuple = torch.tensor([0.4, 0.2, -0.5] )
UpperCamelCase_: Union[str, Any] = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
UpperCamelCase_: List[Any] = Adafactor(
params=[w] , lr=1e-2 , eps=(1e-30, 1e-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=snake_case_ , weight_decay=0.0 , relative_step=snake_case_ , scale_parameter=snake_case_ , warmup_init=snake_case_ , )
for _ in range(1000 ):
UpperCamelCase_: Tuple = criterion(snake_case_ , snake_case_ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1e-2 )
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : int = nn.Linear(50 , 50 ) if is_torch_available() else None
__UpperCamelCase : Union[str, Any] = AdamW(m.parameters() , lr=10.0 ) if is_torch_available() else None
__UpperCamelCase : Any = 10
def lowerCAmelCase__ ( self : str , snake_case_ : int , snake_case_ : Optional[Any] , snake_case_ : Tuple , snake_case_ : Dict=None ):
self.assertEqual(len(snake_case_ ) , len(snake_case_ ) )
for a, b in zip(snake_case_ , snake_case_ ):
self.assertAlmostEqual(snake_case_ , snake_case_ , delta=snake_case_ , msg=snake_case_ )
def lowerCAmelCase__ ( self : Optional[int] ):
UpperCamelCase_: str = {"""num_warmup_steps""": 2, """num_training_steps""": 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
UpperCamelCase_: List[str] = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{"""num_warmup_steps""": 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, """num_cycles""": 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, """power""": 2.0, """lr_end""": 1e-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{"""num_warmup_steps""": 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
UpperCamelCase_, UpperCamelCase_: Optional[int] = data
UpperCamelCase_: int = scheduler_func(self.optimizer , **snake_case_ )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
UpperCamelCase_: Any = unwrap_schedule(snake_case_ , self.num_steps )
self.assertListAlmostEqual(
snake_case_ , snake_case_ , tol=1e-2 , msg=f'''failed for {scheduler_func} in normal scheduler''' , )
UpperCamelCase_: Optional[int] = scheduler_func(self.optimizer , **snake_case_ )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(snake_case_ ) # wrap to test picklability of the schedule
UpperCamelCase_: Union[str, Any] = unwrap_and_save_reload_schedule(snake_case_ , self.num_steps )
self.assertListEqual(snake_case_ , snake_case_ , msg=f'''failed for {scheduler_func} in save and reload''' )
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : Optional[int] ):
UpperCamelCase_: List[str] = fn
def __call__( self : Union[str, Any] , *snake_case_ : str , **snake_case_ : Any ):
return self.fn(*snake_case_ , **snake_case_ )
@classmethod
def lowerCAmelCase__ ( self : List[Any] , snake_case_ : Optional[int] ):
UpperCamelCase_: Any = list(map(self , scheduler.lr_lambdas ) )
| 670 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : List[str] ):
UpperCamelCase_: Union[str, Any] = """laion/clap-htsat-unfused"""
UpperCamelCase_: List[str] = tempfile.mkdtemp()
def lowerCAmelCase__ ( self : Tuple , **snake_case_ : Optional[Any] ):
return RobertaTokenizer.from_pretrained(self.checkpoint , **snake_case_ )
def lowerCAmelCase__ ( self : str , **snake_case_ : Any ):
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **snake_case_ )
def lowerCAmelCase__ ( self : Tuple ):
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase__ ( self : str ):
UpperCamelCase_: Union[str, Any] = self.get_tokenizer()
UpperCamelCase_: int = self.get_feature_extractor()
UpperCamelCase_: List[Any] = ClapProcessor(tokenizer=snake_case_ , feature_extractor=snake_case_ )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase_: Any = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , snake_case_ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , snake_case_ )
def lowerCAmelCase__ ( self : Optional[Any] ):
UpperCamelCase_: Any = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase_: Optional[int] = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
UpperCamelCase_: Dict = self.get_feature_extractor(do_normalize=snake_case_ , padding_value=1.0 )
UpperCamelCase_: List[str] = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=snake_case_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , snake_case_ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , snake_case_ )
def lowerCAmelCase__ ( self : Tuple ):
UpperCamelCase_: int = self.get_feature_extractor()
UpperCamelCase_: Optional[Any] = self.get_tokenizer()
UpperCamelCase_: Dict = ClapProcessor(tokenizer=snake_case_ , feature_extractor=snake_case_ )
UpperCamelCase_: Optional[Any] = floats_list((3, 1000) )
UpperCamelCase_: List[str] = feature_extractor(snake_case_ , return_tensors="""np""" )
UpperCamelCase_: int = processor(audios=snake_case_ , 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 : Union[str, Any] ):
UpperCamelCase_: List[Any] = self.get_feature_extractor()
UpperCamelCase_: List[str] = self.get_tokenizer()
UpperCamelCase_: List[Any] = ClapProcessor(tokenizer=snake_case_ , feature_extractor=snake_case_ )
UpperCamelCase_: Dict = """This is a test string"""
UpperCamelCase_: Tuple = processor(text=snake_case_ )
UpperCamelCase_: Optional[int] = tokenizer(snake_case_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCAmelCase__ ( self : Any ):
UpperCamelCase_: List[str] = self.get_feature_extractor()
UpperCamelCase_: Any = self.get_tokenizer()
UpperCamelCase_: Optional[Any] = ClapProcessor(tokenizer=snake_case_ , feature_extractor=snake_case_ )
UpperCamelCase_: str = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase_: Tuple = processor.batch_decode(snake_case_ )
UpperCamelCase_: str = tokenizer.batch_decode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
def lowerCAmelCase__ ( self : List[str] ):
UpperCamelCase_: Any = self.get_feature_extractor()
UpperCamelCase_: str = self.get_tokenizer()
UpperCamelCase_: Optional[Any] = ClapProcessor(tokenizer=snake_case_ , feature_extractor=snake_case_ )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
| 670 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCAmelCase_ ( a):
def snake_case__ ( self, __a):
'''simple docstring'''
with open(__a, encoding="utf-8") as input_file:
_lowerCAmelCase : int = re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)")
_lowerCAmelCase : List[Any] = input_file.read()
_lowerCAmelCase : Optional[int] = regexp.search(__a)
return match
def snake_case__ ( self, __a):
'''simple docstring'''
with open(__a, encoding="utf-8") as input_file:
_lowerCAmelCase : Optional[int] = re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()", re.DOTALL)
_lowerCAmelCase : Union[str, Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
_lowerCAmelCase : Union[str, Any] = regexp.finditer(__a)
_lowerCAmelCase : Any = [match for match in matches if match is not None and match.group(1) is not None]
return matches[0] if matches else None
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = Path("./datasets")
_lowerCAmelCase : Optional[int] = list(dataset_paths.absolute().glob("**/*.py"))
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__a)):
raise AssertionError(f"open(...) must use utf-8 encoding in {dataset}")
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[str] = Path("./datasets")
_lowerCAmelCase : Optional[int] = list(dataset_paths.absolute().glob("**/*.py"))
for dataset in dataset_files:
if self._no_print_statements(str(__a)):
raise AssertionError(f"print statement found in {dataset}. Use datasets.logger/logging instead.")
| 500 |
import math
class UpperCAmelCase_ :
def snake_case__ ( self, __a, __a):
'''simple docstring'''
_lowerCAmelCase : int = 0.0
_lowerCAmelCase : Union[str, Any] = 0.0
for i in range(len(__a)):
da += math.pow((sample[i] - weights[0][i]), 2)
da += math.pow((sample[i] - weights[1][i]), 2)
return 0 if da > da else 1
return 0
def snake_case__ ( self, __a, __a, __a, __a):
'''simple docstring'''
for i in range(len(__a)):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def A ( ):
'''simple docstring'''
_lowerCAmelCase : int = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
_lowerCAmelCase : Optional[int] = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
_lowerCAmelCase : Dict = SelfOrganizingMap()
_lowerCAmelCase : List[str] = 3
_lowerCAmelCase : str = 0.5
for _ in range(_lowerCamelCase ):
for j in range(len(_lowerCamelCase ) ):
# training sample
_lowerCAmelCase : int = training_samples[j]
# Compute the winning vector
_lowerCAmelCase : Any = self_organizing_map.get_winner(_lowerCamelCase , _lowerCamelCase )
# Update the winning vector
_lowerCAmelCase : int = self_organizing_map.update(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# classify test sample
_lowerCAmelCase : Optional[Any] = [0, 0, 0, 1]
_lowerCAmelCase : Dict = self_organizing_map.get_winner(_lowerCamelCase , _lowerCamelCase )
# results
print(F"Clusters that the test sample belongs to : {winner}" )
print(F"Weights that have been trained : {weights}" )
# running the main() function
if __name__ == "__main__":
main()
| 500 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : List[Any] = f"{file}_{class_name}_{test_name}"
done_test[_id] += 1
with open(_lowerCAmelCase , """r""" ) as f:
snake_case__ : List[Any] = f.readlines()
snake_case__ : int = f"class {class_name}("
snake_case__ : int = f"{4 * ' '}def {test_name}("
snake_case__ : List[Any] = f"{8 * ' '}{correct_line.split()[0]}"
snake_case__ : List[str] = f"{16 * ' '}{correct_line.split()[0]}"
snake_case__ : Tuple = False
snake_case__ : str = False
snake_case__ : Optional[int] = False
snake_case__ : List[Any] = False
snake_case__ : Union[str, Any] = 0
snake_case__ : Optional[int] = 0
snake_case__ : Optional[Any] = []
for line in lines:
if line.startswith(_lowerCAmelCase ):
snake_case__ : List[Any] = True
elif in_class and line.startswith(_lowerCAmelCase ):
snake_case__ : Tuple = True
elif in_class and in_func and (line.startswith(_lowerCAmelCase ) or line.startswith(_lowerCAmelCase )):
snake_case__ : str = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
snake_case__ : Any = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
snake_case__ : Optional[int] = True
if in_class and in_func and in_line and insert_line:
new_lines.append(f"{spaces * ' '}{correct_line}" )
snake_case__ : Optional[int] = False
else:
new_lines.append(_lowerCAmelCase )
with open(_lowerCAmelCase , """w""" ) as f:
for line in new_lines:
f.write(_lowerCAmelCase )
def __snake_case( _lowerCAmelCase , _lowerCAmelCase=None ) -> Any:
if fail is not None:
with open(_lowerCAmelCase , """r""" ) as f:
snake_case__ : Union[str, Any] = {l.strip() for l in f.readlines()}
else:
snake_case__ : Tuple = None
with open(_lowerCAmelCase , """r""" ) as f:
snake_case__ : List[Any] = f.readlines()
snake_case__ : Tuple = defaultdict(_lowerCAmelCase )
for line in correct_lines:
snake_case__ , snake_case__ , snake_case__ , snake_case__ : Tuple = line.split(""";""" )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument("--correct_filename", help="filename of tests with expected result")
parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
__a = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 301 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
__a = list[list[float | int]]
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Matrix:
snake_case__ : int = len(_lowerCAmelCase )
snake_case__ : Matrix = [[0 for _ in range(size + 1 )] for _ in range(_lowerCAmelCase )]
snake_case__ : int
snake_case__ : int
snake_case__ : int
snake_case__ : int
snake_case__ : int
snake_case__ : float
for row in range(_lowerCAmelCase ):
for col in range(_lowerCAmelCase ):
snake_case__ : Optional[int] = matrix[row][col]
snake_case__ : Optional[Any] = vector[row][0]
snake_case__ : List[str] = 0
snake_case__ : Optional[int] = 0
while row < size and col < size:
# pivoting
snake_case__ : List[Any] = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_lowerCAmelCase , _lowerCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
snake_case__ , snake_case__ : Dict = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _lowerCAmelCase ):
snake_case__ : int = augmented[rowa][col] / augmented[row][col]
snake_case__ : Optional[Any] = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , _lowerCAmelCase ):
for row in range(_lowerCAmelCase ):
snake_case__ : str = augmented[row][col] / augmented[col][col]
for cola in range(_lowerCAmelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(_lowerCAmelCase )
]
def __snake_case( _lowerCAmelCase ) -> Callable[[int], int]:
snake_case__ : int = len(_lowerCAmelCase )
snake_case__ : Matrix = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lowerCAmelCase )]
snake_case__ : Matrix = [[0] for _ in range(_lowerCAmelCase )]
snake_case__ : Matrix
snake_case__ : int
snake_case__ : int
snake_case__ : int
for x_val, y_val in enumerate(_lowerCAmelCase ):
for col in range(_lowerCAmelCase ):
snake_case__ : str = (x_val + 1) ** (size - col - 1)
snake_case__ : List[str] = y_val
snake_case__ : List[Any] = solve(_lowerCAmelCase , _lowerCAmelCase )
def interpolated_func(_lowerCAmelCase ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(_lowerCAmelCase ) )
return interpolated_func
def __snake_case( _lowerCAmelCase ) -> int:
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def __snake_case( _lowerCAmelCase = question_function , _lowerCAmelCase = 10 ) -> int:
snake_case__ : list[int] = [func(_lowerCAmelCase ) for x_val in range(1 , order + 1 )]
snake_case__ : list[Callable[[int], int]] = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
snake_case__ : int = 0
snake_case__ : Callable[[int], int]
snake_case__ : int
for poly in polynomials:
snake_case__ : Optional[Any] = 1
while func(_lowerCAmelCase ) == poly(_lowerCAmelCase ):
x_val += 1
ret += poly(_lowerCAmelCase )
return ret
if __name__ == "__main__":
print(F"{solution() = }")
| 301 | 1 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
__snake_case = logging.getLogger(__name__)
__snake_case = 5_0 # max width of layer names
__snake_case = 7_0 # max width of quantizer names
def _A ( _lowercase ) -> Union[str, Any]:
"""simple docstring"""
__UpperCamelCase = parser.add_argument_group('quant_trainer arguments' )
group.add_argument('--wprec' , type=_lowercase , default=8 , help='weight precision' )
group.add_argument('--aprec' , type=_lowercase , default=8 , help='activation precision' )
group.add_argument('--quant-per-tensor' , action='store_true' , help='per tensor weight scaling' )
group.add_argument('--quant-disable' , action='store_true' , help='disable all quantizers' )
group.add_argument('--quant-disable-embeddings' , action='store_true' , help='disable all embeddings quantizers' )
group.add_argument('--quant-disable-keyword' , type=_lowercase , nargs='+' , help='disable quantizers by keyword' )
group.add_argument('--quant-disable-layer-module' , type=_lowercase , help='disable quantizers by keyword under layer.' )
group.add_argument('--quant-enable-layer-module' , type=_lowercase , help='enable quantizers by keyword under layer' )
group.add_argument('--calibrator' , default='max' , help='which quantization range calibrator to use' )
group.add_argument('--percentile' , default=_lowercase , type=_lowercase , help='percentile for PercentileCalibrator' )
group.add_argument('--fuse-qkv' , action='store_true' , help='use the same scale factor for qkv' )
group.add_argument('--clip-gelu' , metavar='N' , type=_lowercase , help='clip gelu output maximum value to N' )
group.add_argument(
'--recalibrate-weights' , action='store_true' , help=(
'recalibrate weight amaxes by taking the max of the weights.'
' amaxes will be computed with the current quantization granularity (axis).'
) , )
def _A ( _lowercase ) -> str:
"""simple docstring"""
if args.calibrator == "max":
__UpperCamelCase = 'max'
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError('Specify --percentile when using percentile calibrator' )
__UpperCamelCase = 'histogram'
elif args.calibrator == "mse":
__UpperCamelCase = 'histogram'
else:
raise ValueError(f'''Invalid calibrator {args.calibrator}''' )
__UpperCamelCase = QuantDescriptor(num_bits=args.aprec , calib_method=_lowercase )
__UpperCamelCase = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(_lowercase )
quant_nn.QuantLinear.set_default_quant_desc_weight(_lowercase )
def _A ( _lowercase , _lowercase , _lowercase=False , _lowercase=False ) -> Tuple:
"""simple docstring"""
logger.info('Configuring Model for Quantization' )
logger.info(f'''using quantization package {pytorch_quantization.__file__}''' )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(_lowercase , ['embeddings'] , which='weight' , _disabled=_lowercase )
if args.quant_disable:
set_quantizer_by_name(_lowercase , [''] , _disabled=_lowercase )
if args.quant_disable_keyword:
set_quantizer_by_name(_lowercase , args.quant_disable_keyword , _disabled=_lowercase )
if args.quant_disable_layer_module:
set_quantizer_by_name(_lowercase , [r'layer.\d+.' + args.quant_disable_layer_module] , _disabled=_lowercase )
if args.quant_enable_layer_module:
set_quantizer_by_name(_lowercase , [r'layer.\d+.' + args.quant_enable_layer_module] , _disabled=_lowercase )
if args.recalibrate_weights:
recalibrate_weights(_lowercase )
if args.fuse_qkv:
fuse_qkv(_lowercase , _lowercase )
if args.clip_gelu:
clip_gelu(_lowercase , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(_lowercase )
def _A ( _lowercase ) -> Dict:
"""simple docstring"""
logger.info('Enabling Calibration' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(f'''{name:80}: {module}''' )
def _A ( _lowercase , _lowercase ) -> Any:
"""simple docstring"""
logger.info('Loading calibrated amax' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax('percentile' , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(_lowercase )
def _A ( _lowercase , _lowercase ) -> Union[str, Any]:
"""simple docstring"""
def fusea(_lowercase , _lowercase , _lowercase ):
for mod in [qq, qk, qv]:
if not hasattr(_lowercase , '_amax' ):
print(' WARNING: NO AMAX BUFFER' )
return
__UpperCamelCase = qq._amax.detach().item()
__UpperCamelCase = qk._amax.detach().item()
__UpperCamelCase = qv._amax.detach().item()
__UpperCamelCase = max(_lowercase , _lowercase , _lowercase )
qq._amax.fill_(_lowercase )
qk._amax.fill_(_lowercase )
qv._amax.fill_(_lowercase )
logger.info(f''' q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''' )
for name, mod in model.named_modules():
if name.endswith('.attention.self' ):
logger.info(f'''FUSE_QKV: {name:{name_width}}''' )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def _A ( _lowercase , _lowercase ) -> Any:
"""simple docstring"""
for name, mod in model.named_modules():
if name.endswith('.output.dense' ) and not name.endswith('attention.output.dense' ):
__UpperCamelCase = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=_lowercase )
__UpperCamelCase = mod._input_quantizer._amax.data.detach().item()
logger.info(f'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''' )
def _A ( _lowercase ) -> Any:
"""simple docstring"""
for name, mod in model.named_modules():
if hasattr(_lowercase , '_weight_quantizer' ) and mod._weight_quantizer.axis is not None:
__UpperCamelCase = mod.weight.shape[0]
__UpperCamelCase = mod._weight_quantizer._amax.detach()
__UpperCamelCase = torch.ones(_lowercase , dtype=amax.dtype , device=amax.device ) * amax
print(f'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''' )
def _A ( _lowercase ) -> str:
"""simple docstring"""
for name, mod in model.named_modules():
if hasattr(_lowercase , '_weight_quantizer' ):
if not hasattr(mod.weight_quantizer , '_amax' ):
print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER' )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
__UpperCamelCase = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
__UpperCamelCase = set(range(len(mod.weight.size() ) ) ) - axis_set
__UpperCamelCase = pytorch_quantization.utils.reduce_amax(mod.weight , axis=_lowercase , keepdims=_lowercase ).detach()
logger.info(f'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
__UpperCamelCase = amax
def _A ( _lowercase , _lowercase=25 , _lowercase=1_80 , _lowercase=None ) -> Dict:
"""simple docstring"""
if ignore is None:
__UpperCamelCase = []
elif not isinstance(_lowercase , _lowercase ):
__UpperCamelCase = [ignore]
__UpperCamelCase = 0
for name, mod in model.named_modules():
if not hasattr(_lowercase , 'weight' ):
continue
__UpperCamelCase = max(_lowercase , len(_lowercase ) )
for name, mod in model.named_modules():
__UpperCamelCase = getattr(_lowercase , '_input_quantizer' , _lowercase )
__UpperCamelCase = getattr(_lowercase , '_weight_quantizer' , _lowercase )
if not hasattr(_lowercase , 'weight' ):
continue
if type(_lowercase ) in ignore:
continue
if [True for s in ignore if type(_lowercase ) is str and s in name]:
continue
__UpperCamelCase = f'''Act:{input_q.extra_repr()}'''
__UpperCamelCase = f'''Wgt:{weight_q.extra_repr()}'''
__UpperCamelCase = f'''{name:{name_width}} {act_str} {wgt_str}'''
if len(_lowercase ) <= line_width:
logger.info(_lowercase )
else:
logger.info(f'''{name:{name_width}} {act_str}''' )
logger.info(f'''{' ':{name_width}} {wgt_str}''' )
def _A ( _lowercase ) -> int:
"""simple docstring"""
__UpperCamelCase = 0
for name, mod in model.named_modules():
if isinstance(_lowercase , pytorch_quantization.nn.TensorQuantizer ):
print(f'''{name:80} {mod}''' )
count += 1
print(f'''{count} TensorQuantizers found in model''' )
def _A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> Tuple:
"""simple docstring"""
__UpperCamelCase = getattr(_lowercase , _lowercase , _lowercase )
if quantizer_mod is not None:
assert hasattr(_lowercase , _lowercase )
setattr(_lowercase , _lowercase , _lowercase )
else:
logger.warning(f'''{name} has no {quantizer}''' )
def _A ( _lowercase , _lowercase , _lowercase="both" , **_lowercase ) -> List[Any]:
"""simple docstring"""
__UpperCamelCase = f'''Warning: changing {which} quantizers of {name:{qname_width}}'''
for k, v in kwargs.items():
s += f''' {k}={v}'''
if which in ["input", "both"]:
set_quantizer(_lowercase , _lowercase , '_input_quantizer' , _lowercase , _lowercase )
if which in ["weight", "both"]:
set_quantizer(_lowercase , _lowercase , '_weight_quantizer' , _lowercase , _lowercase )
logger.info(_lowercase )
def _A ( _lowercase , _lowercase , **_lowercase ) -> str:
"""simple docstring"""
for name, mod in model.named_modules():
if hasattr(_lowercase , '_input_quantizer' ) or hasattr(_lowercase , '_weight_quantizer' ):
for n in names:
if re.search(_lowercase , _lowercase ):
set_quantizers(_lowercase , _lowercase , **_lowercase )
elif name.endswith('_quantizer' ):
for n in names:
if re.search(_lowercase , _lowercase ):
__UpperCamelCase = f'''Warning: changing {name:{name_width}}'''
for k, v in kwargs.items():
s += f''' {k}={v}'''
setattr(_lowercase , _lowercase , _lowercase )
logger.info(_lowercase )
| 1 |
__snake_case = {
'''a''': '''AAAAA''',
'''b''': '''AAAAB''',
'''c''': '''AAABA''',
'''d''': '''AAABB''',
'''e''': '''AABAA''',
'''f''': '''AABAB''',
'''g''': '''AABBA''',
'''h''': '''AABBB''',
'''i''': '''ABAAA''',
'''j''': '''BBBAA''',
'''k''': '''ABAAB''',
'''l''': '''ABABA''',
'''m''': '''ABABB''',
'''n''': '''ABBAA''',
'''o''': '''ABBAB''',
'''p''': '''ABBBA''',
'''q''': '''ABBBB''',
'''r''': '''BAAAA''',
'''s''': '''BAAAB''',
'''t''': '''BAABA''',
'''u''': '''BAABB''',
'''v''': '''BBBAB''',
'''w''': '''BABAA''',
'''x''': '''BABAB''',
'''y''': '''BABBA''',
'''z''': '''BABBB''',
''' ''': ''' ''',
}
__snake_case = {value: key for key, value in encode_dict.items()}
def _A ( _lowercase ) -> str:
"""simple docstring"""
__UpperCamelCase = ''
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception('encode() accepts only letters of the alphabet and spaces' )
return encoded
def _A ( _lowercase ) -> str:
"""simple docstring"""
if set(_lowercase ) - {"A", "B", " "} != set():
raise Exception('decode() accepts only \'A\', \'B\' and spaces' )
__UpperCamelCase = ''
for word in coded.split():
while len(_lowercase ) != 0:
decoded += decode_dict[word[:5]]
__UpperCamelCase = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 1 | 1 |
'''simple docstring'''
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
lowercase : Optional[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class _lowerCAmelCase ( datasets.BuilderConfig ):
"""simple docstring"""
lowerCAmelCase = 10000
lowerCAmelCase = None
lowerCAmelCase = None
class _lowerCAmelCase ( datasets.ArrowBasedBuilder ):
"""simple docstring"""
lowerCAmelCase = ParquetConfig
def __A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def __A ( self : Any , SCREAMING_SNAKE_CASE : Optional[Any] ) -> int:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}" )
lowerCAmelCase = dl_manager.download_and_extract(self.config.data_files )
if isinstance(SCREAMING_SNAKE_CASE , (str, list, tuple) ):
lowerCAmelCase = data_files
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
lowerCAmelCase = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCAmelCase = [dl_manager.iter_files(SCREAMING_SNAKE_CASE ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
lowerCAmelCase = []
for split_name, files in data_files.items():
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
lowerCAmelCase = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCAmelCase = [dl_manager.iter_files(SCREAMING_SNAKE_CASE ) 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(SCREAMING_SNAKE_CASE ):
with open(SCREAMING_SNAKE_CASE , "rb" ) as f:
lowerCAmelCase = datasets.Features.from_arrow_schema(pq.read_schema(SCREAMING_SNAKE_CASE ) )
break
splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE , gen_kwargs={"files": files} ) )
return splits
def __A ( self : Optional[int] , SCREAMING_SNAKE_CASE : pa.Table ) -> pa.Table:
"""simple docstring"""
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
lowerCAmelCase = table_cast(SCREAMING_SNAKE_CASE , self.info.features.arrow_schema )
return pa_table
def __A ( self : Optional[Any] , SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase = 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(SCREAMING_SNAKE_CASE ) ):
with open(SCREAMING_SNAKE_CASE , "rb" ) as f:
lowerCAmelCase = pq.ParquetFile(SCREAMING_SNAKE_CASE )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
lowerCAmelCase = 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(SCREAMING_SNAKE_CASE )
except ValueError as e:
logger.error(f"Failed to read file '{file}' with error {type(SCREAMING_SNAKE_CASE )}: {e}" )
raise
| 159 |
'''simple docstring'''
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowercase : Tuple = {
'gwf-440k': {
'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
'sample_rate': 4_8_0_0_0,
'sample_size': 6_5_5_3_6,
},
'jmann-small-190k': {
'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
'sample_rate': 4_8_0_0_0,
'sample_size': 6_5_5_3_6,
},
'jmann-large-580k': {
'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
'sample_rate': 4_8_0_0_0,
'sample_size': 1_3_1_0_7_2,
},
'maestro-uncond-150k': {
'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
'sample_rate': 1_6_0_0_0,
'sample_size': 6_5_5_3_6,
},
'unlocked-uncond-250k': {
'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
'sample_rate': 1_6_0_0_0,
'sample_size': 6_5_5_3_6,
},
'honk-140k': {
'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
'sample_rate': 1_6_0_0_0,
'sample_size': 6_5_5_3_6,
},
}
def __a ( A__ , A__ ) -> Optional[Any]:
return torch.atana(A__ , A__ ) / math.pi * 2
def __a ( A__ ) -> List[str]:
lowerCAmelCase = torch.sin(t * math.pi / 2 ) ** 2
lowerCAmelCase = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(A__ , A__ )
class _lowerCAmelCase ( UpperCamelCase_ ):
"""simple docstring"""
pass
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE : List[Any] ) -> Dict:
"""simple docstring"""
super().__init__()
lowerCAmelCase = DiffusionAttnUnetaD(SCREAMING_SNAKE_CASE , n_attn_layers=4 )
lowerCAmelCase = deepcopy(self.diffusion )
lowerCAmelCase = torch.quasirandom.SobolEngine(1 , scramble=SCREAMING_SNAKE_CASE )
def __a ( A__ ) -> Dict:
lowerCAmelCase = MODELS_MAP[model_name]["url"]
os.system(f"wget {url} ./" )
return f"./{model_name}.ckpt"
lowercase : List[Any] = {
'1': 'resnets.0',
'2': 'attentions.0',
'3': 'resnets.1',
'4': 'attentions.1',
'5': 'resnets.2',
'6': 'attentions.2',
}
lowercase : int = {
'8': 'resnets.0',
'9': 'attentions.0',
'10': 'resnets.1',
'11': 'attentions.1',
'12': 'resnets.2',
'13': 'attentions.2',
}
lowercase : Optional[Any] = {
'1': 'resnets.0',
'2': 'attentions.0',
'3': 'resnets.1',
'4': 'attentions.1',
'5': 'resnets.2',
'6': 'attentions.2',
'8': 'resnets.3',
'9': 'attentions.3',
'10': 'resnets.4',
'11': 'attentions.4',
'12': 'resnets.5',
'13': 'attentions.5',
}
lowercase : List[Any] = {
'0': 'resnets.0',
'1': 'resnets.1',
'2': 'resnets.2',
'4': 'resnets.0',
'5': 'resnets.1',
'6': 'resnets.2',
}
lowercase : Optional[Any] = {
'skip': 'conv_skip',
'main.0': 'conv_1',
'main.1': 'group_norm_1',
'main.3': 'conv_2',
'main.4': 'group_norm_2',
}
lowercase : Union[str, Any] = {
'norm': 'group_norm',
'qkv_proj': ['query', 'key', 'value'],
'out_proj': ['proj_attn'],
}
def __a ( A__ ) -> str:
if name.startswith("skip" ):
return name.replace("skip" , RES_CONV_MAP["skip"] )
# name has to be of format main.{digit}
if not name.startswith("main." ):
raise ValueError(f"ResConvBlock error with {name}" )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def __a ( A__ ) -> List[Any]:
for key, value in ATTN_MAP.items():
if name.startswith(A__ ) and not isinstance(A__ , A__ ):
return name.replace(A__ , A__ )
elif name.startswith(A__ ):
return [name.replace(A__ , A__ ) for v in value]
raise ValueError(f"Attn error with {name}" )
def __a ( A__ , A__=13 ) -> str:
lowerCAmelCase = input_string
if string.split("." )[0] == "timestep_embed":
return string.replace("timestep_embed" , "time_proj" )
lowerCAmelCase = 0
if string.startswith("net.3." ):
depth += 1
lowerCAmelCase = string[6:]
elif string.startswith("net." ):
lowerCAmelCase = string[4:]
while string.startswith("main.7." ):
depth += 1
lowerCAmelCase = string[7:]
if string.startswith("main." ):
lowerCAmelCase = string[5:]
# mid block
if string[:2].isdigit():
lowerCAmelCase = string[:2]
lowerCAmelCase = string[2:]
else:
lowerCAmelCase = string[0]
lowerCAmelCase = string[1:]
if depth == max_depth:
lowerCAmelCase = MID_NUM_TO_LAYER[layer_num]
lowerCAmelCase = "mid_block"
elif depth > 0 and int(A__ ) < 7:
lowerCAmelCase = DOWN_NUM_TO_LAYER[layer_num]
lowerCAmelCase = f"down_blocks.{depth}"
elif depth > 0 and int(A__ ) > 7:
lowerCAmelCase = UP_NUM_TO_LAYER[layer_num]
lowerCAmelCase = f"up_blocks.{max_depth - depth - 1}"
elif depth == 0:
lowerCAmelCase = DEPTH_0_TO_LAYER[layer_num]
lowerCAmelCase = f"up_blocks.{max_depth - 1}" if int(A__ ) > 3 else "down_blocks.0"
if not string_left.startswith("." ):
raise ValueError(f"Naming error with {input_string} and string_left: {string_left}." )
lowerCAmelCase = string_left[1:]
if "resnets" in new_layer:
lowerCAmelCase = convert_resconv_naming(A__ )
elif "attentions" in new_layer:
lowerCAmelCase = convert_attn_naming(A__ )
lowerCAmelCase = new_string_left
if not isinstance(A__ , A__ ):
lowerCAmelCase = prefix + "." + new_layer + "." + string_left
else:
lowerCAmelCase = [prefix + "." + new_layer + "." + s for s in string_left]
return new_string
def __a ( A__ ) -> str:
lowerCAmelCase = {}
for k, v in state_dict.items():
if k.endswith("kernel" ):
# up- and downsample layers, don't have trainable weights
continue
lowerCAmelCase = rename(A__ )
# check if we need to transform from Conv => Linear for attention
if isinstance(A__ , A__ ):
lowerCAmelCase = transform_conv_attns(A__ , A__ , A__ )
else:
lowerCAmelCase = v
return new_state_dict
def __a ( A__ , A__ , A__ ) -> Any:
if len(A__ ) == 1:
if len(v.shape ) == 3:
# weight
lowerCAmelCase = v[:, :, 0]
else:
# bias
lowerCAmelCase = v
else:
# qkv matrices
lowerCAmelCase = v.shape[0]
lowerCAmelCase = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
lowerCAmelCase = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
lowerCAmelCase = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def __a ( A__ ) -> Dict:
lowerCAmelCase = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
lowerCAmelCase = args.model_path.split("/" )[-1].split("." )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), f"Make sure to provide one of the official model names {MODELS_MAP.keys()}"
lowerCAmelCase = download(A__ )
lowerCAmelCase = MODELS_MAP[model_name]["sample_rate"]
lowerCAmelCase = MODELS_MAP[model_name]["sample_size"]
lowerCAmelCase = Object()
lowerCAmelCase = sample_size
lowerCAmelCase = sample_rate
lowerCAmelCase = 0
lowerCAmelCase = UNetaDModel(sample_size=A__ , sample_rate=A__ )
lowerCAmelCase = diffusers_model.state_dict()
lowerCAmelCase = DiffusionUncond(A__ )
orig_model.load_state_dict(torch.load(args.model_path , map_location=A__ )["state_dict"] )
lowerCAmelCase = orig_model.diffusion_ema.eval()
lowerCAmelCase = orig_model.state_dict()
lowerCAmelCase = rename_orig_weights(A__ )
lowerCAmelCase = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
lowerCAmelCase = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(A__ ) == 0, f"Problem with {renamed_minus_diffusers}"
assert all(k.endswith("kernel" ) for k in list(A__ ) ), f"Problem with {diffusers_minus_renamed}"
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), f"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"
if key == "time_proj.weight":
lowerCAmelCase = value.squeeze()
lowerCAmelCase = value
diffusers_model.load_state_dict(A__ )
lowerCAmelCase = 100
lowerCAmelCase = 33
lowerCAmelCase = IPNDMScheduler(num_train_timesteps=A__ )
lowerCAmelCase = torch.manual_seed(A__ )
lowerCAmelCase = torch.randn([1, 2, config.sample_size] , generator=A__ ).to(A__ )
lowerCAmelCase = torch.linspace(1 , 0 , steps + 1 , device=A__ )[:-1]
lowerCAmelCase = get_crash_schedule(A__ )
lowerCAmelCase = DanceDiffusionPipeline(unet=A__ , scheduler=A__ )
lowerCAmelCase = torch.manual_seed(33 )
lowerCAmelCase = pipe(num_inference_steps=A__ , generator=A__ ).audios
lowerCAmelCase = sampling.iplms_sample(A__ , A__ , A__ , {} )
lowerCAmelCase = generated.clamp(-1 , 1 )
lowerCAmelCase = (generated - audio).abs().sum()
lowerCAmelCase = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print("Diff sum" , A__ )
print("Diff max" , A__ )
assert diff_max < 1e-3, f"Diff max: {diff_max} is too much :-/"
print(f"Conversion for {model_name} successful!" )
if __name__ == "__main__":
lowercase : List[Any] = argparse.ArgumentParser()
parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
parser.add_argument(
'--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
)
parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
lowercase : Tuple = parser.parse_args()
main(args)
| 159 | 1 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
snake_case_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ :
__lowerCamelCase : List[str] = None
@experimental
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any] ):
'''simple docstring'''
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return _map_with_joblib(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
'''simple docstring'''
lowercase__ : Tuple = num_proc if num_proc <= len(SCREAMING_SNAKE_CASE_ ) else len(SCREAMING_SNAKE_CASE_ )
lowercase__ : int = [] # We organize the splits ourselve (contiguous splits)
for index in range(SCREAMING_SNAKE_CASE_ ):
lowercase__ : Any = len(SCREAMING_SNAKE_CASE_ ) // num_proc
lowercase__ : List[str] = len(SCREAMING_SNAKE_CASE_ ) % num_proc
lowercase__ : Dict = div * index + min(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase__ : Optional[Any] = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(SCREAMING_SNAKE_CASE_ ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
f"""Error dividing inputs iterable among processes. """
f"""Total number of objects {len(SCREAMING_SNAKE_CASE_ )}, """
f"""length: {sum(len(i[1] ) for i in split_kwds )}""" )
logger.info(
f"""Spawning {num_proc} processes for {len(SCREAMING_SNAKE_CASE_ )} objects in slices of {[len(i[1] ) for i in split_kwds]}""" )
lowercase__ , lowercase__ : Optional[int] = None, None
if not disable_tqdm:
lowercase__ , lowercase__ : Any = (RLock(),), tqdm.set_lock
with Pool(SCREAMING_SNAKE_CASE_ , initargs=SCREAMING_SNAKE_CASE_ , initializer=SCREAMING_SNAKE_CASE_ ) as pool:
lowercase__ : Dict = pool.map(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(f"""Finished {num_proc} processes""" )
lowercase__ : int = [obj for proc_res in mapped for obj in proc_res]
logger.info(f"""Unpacked {len(SCREAMING_SNAKE_CASE_ )} objects""" )
return mapped
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
'''simple docstring'''
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name , n_jobs=SCREAMING_SNAKE_CASE_ ):
return joblib.Parallel()(
joblib.delayed(SCREAMING_SNAKE_CASE_ )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def snake_case__ ( SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
lowercase__ : Union[str, Any] = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
lowercase__ : List[str] = None
| 164 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case_ = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructConfig''',
'''Pix2StructTextConfig''',
'''Pix2StructVisionConfig''',
],
'''processing_pix2struct''': ['''Pix2StructProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ['''Pix2StructImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
'''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Pix2StructPreTrainedModel''',
'''Pix2StructForConditionalGeneration''',
'''Pix2StructVisionModel''',
'''Pix2StructTextModel''',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 164 | 1 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
snake_case_ : Optional[int] =sys.version_info >= (3, 10)
def UpperCAmelCase ( lowerCAmelCase__=None , lowerCAmelCase__=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=lowerCAmelCase__ )
@dataclass
class a__ :
UpperCAmelCase_ : int
UpperCAmelCase_ : float
UpperCAmelCase_ : str
UpperCAmelCase_ : bool
@dataclass
class a__ :
UpperCAmelCase_ : int = 42
UpperCAmelCase_ : str = field(default='toto' , metadata={'help': 'help message'} )
@dataclass
class a__ :
UpperCAmelCase_ : bool = False
UpperCAmelCase_ : bool = True
UpperCAmelCase_ : Optional[bool] = None
class a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ : List[Any] = 'titi'
UpperCAmelCase_ : Optional[Any] = 'toto'
class a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ : Union[str, Any] = 'titi'
UpperCAmelCase_ : Optional[int] = 'toto'
UpperCAmelCase_ : Optional[int] = 42
@dataclass
class a__ :
UpperCAmelCase_ : BasicEnum = "toto"
def _lowerCamelCase ( self ) -> Optional[int]:
__A = BasicEnum(self.foo )
@dataclass
class a__ :
UpperCAmelCase_ : MixedTypeEnum = "toto"
def _lowerCamelCase ( self ) -> str:
__A = MixedTypeEnum(self.foo )
@dataclass
class a__ :
UpperCAmelCase_ : Optional[int] = None
UpperCAmelCase_ : Optional[float] = field(default=lowerCAmelCase__ , metadata={'help': 'help message'} )
UpperCAmelCase_ : Optional[str] = None
UpperCAmelCase_ : Optional[List[str]] = list_field(default=[] )
UpperCAmelCase_ : Optional[List[int]] = list_field(default=[] )
@dataclass
class a__ :
UpperCAmelCase_ : List[int] = list_field(default=[] )
UpperCAmelCase_ : List[int] = list_field(default=[1, 2, 3] )
UpperCAmelCase_ : List[str] = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
UpperCAmelCase_ : List[float] = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class a__ :
UpperCAmelCase_ : List[int] = field()
UpperCAmelCase_ : str = field()
UpperCAmelCase_ : BasicEnum = field()
def _lowerCamelCase ( self ) -> List[str]:
__A = BasicEnum(self.required_enum )
@dataclass
class a__ :
UpperCAmelCase_ : int
UpperCAmelCase_ : "BasicEnum" = field()
UpperCAmelCase_ : "Optional[bool]" = None
UpperCAmelCase_ : "str" = field(default='toto' , metadata={'help': 'help message'} )
UpperCAmelCase_ : "List[str]" = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
if is_python_no_less_than_3_10:
@dataclass
class a__ :
UpperCAmelCase_ : bool = False
UpperCAmelCase_ : bool = True
UpperCAmelCase_ : bool | None = None
@dataclass
class a__ :
UpperCAmelCase_ : int | None = None
UpperCAmelCase_ : float | None = field(default=lowerCAmelCase__ , metadata={'help': 'help message'} )
UpperCAmelCase_ : str | None = None
UpperCAmelCase_ : list[str] | None = list_field(default=[] )
UpperCAmelCase_ : list[int] | None = list_field(default=[] )
class a__ ( unittest.TestCase ):
def _lowerCamelCase ( self , lowercase__ , lowercase__ ) -> str:
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
__A = {k: v for k, v in vars(lowercase__ ).items() if k != "container"}
__A = {k: v for k, v in vars(lowercase__ ).items() if k != "container"}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get("choices" , lowercase__ ) and yy.get("choices" , lowercase__ ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["type"](lowercase__ ) , yy["type"](lowercase__ ) )
del xx["type"], yy["type"]
self.assertEqual(lowercase__ , lowercase__ )
def _lowerCamelCase ( self ) -> Dict:
__A = HfArgumentParser(lowercase__ )
__A = argparse.ArgumentParser()
expected.add_argument("--foo" , type=lowercase__ , required=lowercase__ )
expected.add_argument("--bar" , type=lowercase__ , required=lowercase__ )
expected.add_argument("--baz" , type=lowercase__ , required=lowercase__ )
expected.add_argument("--flag" , type=lowercase__ , default=lowercase__ , const=lowercase__ , nargs="?" )
self.argparsersEqual(lowercase__ , lowercase__ )
__A = ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
((__A) , ) = parser.parse_args_into_dataclasses(lowercase__ , look_for_args_file=lowercase__ )
self.assertFalse(example.flag )
def _lowerCamelCase ( self ) -> str:
__A = HfArgumentParser(lowercase__ )
__A = argparse.ArgumentParser()
expected.add_argument("--foo" , default=42 , type=lowercase__ )
expected.add_argument("--baz" , default="toto" , type=lowercase__ , help="help message" )
self.argparsersEqual(lowercase__ , lowercase__ )
def _lowerCamelCase ( self ) -> Union[str, Any]:
__A = argparse.ArgumentParser()
expected.add_argument("--foo" , type=lowercase__ , default=lowercase__ , const=lowercase__ , nargs="?" )
expected.add_argument("--baz" , type=lowercase__ , default=lowercase__ , const=lowercase__ , nargs="?" )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument("--no_baz" , action="store_false" , default=lowercase__ , dest="baz" )
expected.add_argument("--opt" , type=lowercase__ , default=lowercase__ )
__A = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(lowercase__ )
for dataclass_type in dataclass_types:
__A = HfArgumentParser(lowercase__ )
self.argparsersEqual(lowercase__ , lowercase__ )
__A = parser.parse_args([] )
self.assertEqual(lowercase__ , Namespace(foo=lowercase__ , baz=lowercase__ , opt=lowercase__ ) )
__A = parser.parse_args(["--foo", "--no_baz"] )
self.assertEqual(lowercase__ , Namespace(foo=lowercase__ , baz=lowercase__ , opt=lowercase__ ) )
__A = parser.parse_args(["--foo", "--baz"] )
self.assertEqual(lowercase__ , Namespace(foo=lowercase__ , baz=lowercase__ , opt=lowercase__ ) )
__A = parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"] )
self.assertEqual(lowercase__ , Namespace(foo=lowercase__ , baz=lowercase__ , opt=lowercase__ ) )
__A = parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"] )
self.assertEqual(lowercase__ , Namespace(foo=lowercase__ , baz=lowercase__ , opt=lowercase__ ) )
def _lowerCamelCase ( self ) -> Any:
__A = HfArgumentParser(lowercase__ )
__A = argparse.ArgumentParser()
expected.add_argument(
"--foo" , default="toto" , choices=["titi", "toto", 42] , type=make_choice_type_function(["titi", "toto", 42] ) , )
self.argparsersEqual(lowercase__ , lowercase__ )
__A = parser.parse_args([] )
self.assertEqual(args.foo , "toto" )
__A = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
__A = parser.parse_args(["--foo", "titi"] )
self.assertEqual(args.foo , "titi" )
__A = parser.parse_args_into_dataclasses(["--foo", "titi"] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
__A = parser.parse_args(["--foo", "42"] )
self.assertEqual(args.foo , 42 )
__A = parser.parse_args_into_dataclasses(["--foo", "42"] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def _lowerCamelCase ( self ) -> int:
@dataclass
class a__ :
UpperCAmelCase_ : Literal["titi", "toto", 42] = "toto"
__A = HfArgumentParser(lowercase__ )
__A = argparse.ArgumentParser()
expected.add_argument(
"--foo" , default="toto" , choices=("titi", "toto", 42) , type=make_choice_type_function(["titi", "toto", 42] ) , )
self.argparsersEqual(lowercase__ , lowercase__ )
__A = parser.parse_args([] )
self.assertEqual(args.foo , "toto" )
__A = parser.parse_args(["--foo", "titi"] )
self.assertEqual(args.foo , "titi" )
__A = parser.parse_args(["--foo", "42"] )
self.assertEqual(args.foo , 42 )
def _lowerCamelCase ( self ) -> Any:
__A = HfArgumentParser(lowercase__ )
__A = argparse.ArgumentParser()
expected.add_argument("--foo_int" , nargs="+" , default=[] , type=lowercase__ )
expected.add_argument("--bar_int" , nargs="+" , default=[1, 2, 3] , type=lowercase__ )
expected.add_argument("--foo_str" , nargs="+" , default=["Hallo", "Bonjour", "Hello"] , type=lowercase__ )
expected.add_argument("--foo_float" , nargs="+" , default=[0.1, 0.2, 0.3] , type=lowercase__ )
self.argparsersEqual(lowercase__ , lowercase__ )
__A = parser.parse_args([] )
self.assertEqual(
lowercase__ , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["Hallo", "Bonjour", "Hello"] , foo_float=[0.1, 0.2, 0.3] ) , )
__A = parser.parse_args("--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7".split() )
self.assertEqual(lowercase__ , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["a", "b", "c"] , foo_float=[0.1, 0.7] ) )
def _lowerCamelCase ( self ) -> Union[str, Any]:
__A = argparse.ArgumentParser()
expected.add_argument("--foo" , default=lowercase__ , type=lowercase__ )
expected.add_argument("--bar" , default=lowercase__ , type=lowercase__ , help="help message" )
expected.add_argument("--baz" , default=lowercase__ , type=lowercase__ )
expected.add_argument("--ces" , nargs="+" , default=[] , type=lowercase__ )
expected.add_argument("--des" , nargs="+" , default=[] , type=lowercase__ )
__A = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(lowercase__ )
for dataclass_type in dataclass_types:
__A = HfArgumentParser(lowercase__ )
self.argparsersEqual(lowercase__ , lowercase__ )
__A = parser.parse_args([] )
self.assertEqual(lowercase__ , Namespace(foo=lowercase__ , bar=lowercase__ , baz=lowercase__ , ces=[] , des=[] ) )
__A = parser.parse_args("--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3".split() )
self.assertEqual(lowercase__ , Namespace(foo=12 , bar=3.14 , baz="42" , ces=["a", "b", "c"] , des=[1, 2, 3] ) )
def _lowerCamelCase ( self ) -> Any:
__A = HfArgumentParser(lowercase__ )
__A = argparse.ArgumentParser()
expected.add_argument("--required_list" , nargs="+" , type=lowercase__ , required=lowercase__ )
expected.add_argument("--required_str" , type=lowercase__ , required=lowercase__ )
expected.add_argument(
"--required_enum" , type=make_choice_type_function(["titi", "toto"] ) , choices=["titi", "toto"] , required=lowercase__ , )
self.argparsersEqual(lowercase__ , lowercase__ )
def _lowerCamelCase ( self ) -> Optional[Any]:
__A = HfArgumentParser(lowercase__ )
__A = argparse.ArgumentParser()
expected.add_argument("--foo" , type=lowercase__ , required=lowercase__ )
expected.add_argument(
"--required_enum" , type=make_choice_type_function(["titi", "toto"] ) , choices=["titi", "toto"] , required=lowercase__ , )
expected.add_argument("--opt" , type=lowercase__ , default=lowercase__ )
expected.add_argument("--baz" , default="toto" , type=lowercase__ , help="help message" )
expected.add_argument("--foo_str" , nargs="+" , default=["Hallo", "Bonjour", "Hello"] , type=lowercase__ )
self.argparsersEqual(lowercase__ , lowercase__ )
def _lowerCamelCase ( self ) -> int:
__A = HfArgumentParser(lowercase__ )
__A = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
__A = parser.parse_dict(lowercase__ )[0]
__A = BasicExample(**lowercase__ )
self.assertEqual(lowercase__ , lowercase__ )
def _lowerCamelCase ( self ) -> List[Any]:
__A = HfArgumentParser(lowercase__ )
__A = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
"extra": 42,
}
self.assertRaises(lowercase__ , parser.parse_dict , lowercase__ , allow_extra_keys=lowercase__ )
def _lowerCamelCase ( self ) -> List[str]:
__A = HfArgumentParser(lowercase__ )
__A = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
__A = os.path.join(lowercase__ , "temp_json" )
os.mkdir(lowercase__ )
with open(temp_local_path + ".json" , "w+" ) as f:
json.dump(lowercase__ , lowercase__ )
__A = parser.parse_yaml_file(Path(temp_local_path + ".json" ) )[0]
__A = BasicExample(**lowercase__ )
self.assertEqual(lowercase__ , lowercase__ )
def _lowerCamelCase ( self ) -> str:
__A = HfArgumentParser(lowercase__ )
__A = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
__A = os.path.join(lowercase__ , "temp_yaml" )
os.mkdir(lowercase__ )
with open(temp_local_path + ".yaml" , "w+" ) as f:
yaml.dump(lowercase__ , lowercase__ )
__A = parser.parse_yaml_file(Path(temp_local_path + ".yaml" ) )[0]
__A = BasicExample(**lowercase__ )
self.assertEqual(lowercase__ , lowercase__ )
def _lowerCamelCase ( self ) -> Optional[int]:
__A = HfArgumentParser(lowercase__ )
self.assertIsNotNone(lowercase__ )
| 205 |
def UpperCAmelCase ( lowerCAmelCase__ ):
'''simple docstring'''
assert column_title.isupper()
__A = 0
__A = len(lowerCAmelCase__ ) - 1
__A = 0
while index >= 0:
__A = (ord(column_title[index] ) - 64) * pow(26 , lowerCAmelCase__ )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 205 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
snake_case_ = {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ["""BeitFeatureExtractor"""]
snake_case_ = ["""BeitImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BeitForImageClassification""",
"""BeitForMaskedImageModeling""",
"""BeitForSemanticSegmentation""",
"""BeitModel""",
"""BeitPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""FlaxBeitForImageClassification""",
"""FlaxBeitForMaskedImageModeling""",
"""FlaxBeitModel""",
"""FlaxBeitPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 507 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
snake_case_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
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"""
),
},
}
snake_case_ = {
"""unc-nlp/lxmert-base-uncased""": 512,
}
snake_case_ = {
"""unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}
class a__ ( _lowercase ):
__magic_name__ : Any = VOCAB_FILES_NAMES
__magic_name__ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
__magic_name__ : Dict = PRETRAINED_INIT_CONFIGURATION
__magic_name__ : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__magic_name__ : Any = LxmertTokenizer
def __init__(self : Tuple, __UpperCAmelCase : int=None, __UpperCAmelCase : Optional[Any]=None, __UpperCAmelCase : Union[str, Any]=True, __UpperCAmelCase : int="[UNK]", __UpperCAmelCase : Optional[int]="[SEP]", __UpperCAmelCase : Optional[int]="[PAD]", __UpperCAmelCase : List[str]="[CLS]", __UpperCAmelCase : List[str]="[MASK]", __UpperCAmelCase : List[str]=True, __UpperCAmelCase : Optional[int]=None, **__UpperCAmelCase : int, ) -> int:
"""simple docstring"""
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 : List[str] = 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 : Dict = getattr(__UpperCAmelCase, normalizer_state.pop('''type''' ) )
SCREAMING_SNAKE_CASE : Dict = do_lower_case
SCREAMING_SNAKE_CASE : List[Any] = strip_accents
SCREAMING_SNAKE_CASE : List[str] = tokenize_chinese_chars
SCREAMING_SNAKE_CASE : Dict = normalizer_class(**__UpperCAmelCase )
SCREAMING_SNAKE_CASE : List[str] = do_lower_case
def lowercase__ (self : int, __UpperCAmelCase : List[str], __UpperCAmelCase : List[Any]=None ) -> str:
"""simple docstring"""
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 lowercase__ (self : Optional[Any], __UpperCAmelCase : List[int], __UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Tuple = [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 lowercase__ (self : Union[str, Any], __UpperCAmelCase : str, __UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self._tokenizer.model.save(__UpperCAmelCase, name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
| 507 | 1 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> Optional[Any]:
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
UpperCAmelCase = flax_key_tuple[:-1] + ("weight",)
UpperCAmelCase = torch.permute(lowerCamelCase_ , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_ ):
# linear layer
UpperCAmelCase = flax_key_tuple[:-1] + ("weight",)
UpperCAmelCase = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
UpperCAmelCase = flax_key_tuple[:-1] + ("weight",)
return flax_key_tuple, flax_tensor
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> int:
if "metadata" in layer:
UpperCAmelCase = layer.split("metadata" )
UpperCAmelCase = "".join(split_layer[0] )[:-1]
UpperCAmelCase = [tuple(("metadata" + split_layer[1]).split("/" ) )]
elif "kvstore" in layer:
UpperCAmelCase = layer.split("kvstore" )
UpperCAmelCase = "".join(split_layer[0] )[:-1]
UpperCAmelCase = [tuple(("kvstore" + split_layer[1]).split("/" ) )]
else:
UpperCAmelCase = layer.split("/" )
UpperCAmelCase = "/".join(split_layer[:-1] )
UpperCAmelCase = (split_layer[-1],)
if "kvstore/path" in layer:
UpperCAmelCase = F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
elif "kvstore/driver" in layer:
UpperCAmelCase = "file"
else:
UpperCAmelCase = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> List[str]:
UpperCAmelCase = rename_keys(lowerCamelCase_ )
UpperCAmelCase = {}
for k, v in current_block.items():
UpperCAmelCase = v
UpperCAmelCase = new_current_block
torch.save(lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = WEIGHTS_NAME ) -> List[str]:
UpperCAmelCase = convert_file_size_to_int(lowerCamelCase_ )
UpperCAmelCase = []
UpperCAmelCase = {}
UpperCAmelCase = 0
UpperCAmelCase = 0
os.makedirs(lowerCamelCase_ , exist_ok=lowerCamelCase_ )
with gfile.GFile(switch_checkpoint_path + "/checkpoint" , "rb" ) as fp:
UpperCAmelCase = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
UpperCAmelCase = flatten_dict(lowerCamelCase_ , sep="/" )
UpperCAmelCase = {}
for layer in checkpoint_info.keys():
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = get_key_and_tensorstore_dict(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
if curr_real_layer_name in all_layers:
UpperCAmelCase = content
else:
UpperCAmelCase = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
UpperCAmelCase = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
UpperCAmelCase = torch.tensor(lowerCamelCase_ )
UpperCAmelCase = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
UpperCAmelCase , UpperCAmelCase = rename_base_flax_keys(tuple(key.split("/" ) ) , lowerCamelCase_ )
UpperCAmelCase = "/".join(lowerCamelCase_ )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
UpperCAmelCase = os.path.join(
lowerCamelCase_ , weights_name.replace(".bin" , F'-{len(lowerCamelCase_ )+1:05d}-of-???.bin' ) )
rename_and_save_block(lowerCamelCase_ , lowerCamelCase_ )
sharded_state_dicts.append(current_block.keys() )
del current_block
UpperCAmelCase = {}
UpperCAmelCase = 0
UpperCAmelCase = raw_weights.to(getattr(lowerCamelCase_ , lowerCamelCase_ ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
UpperCAmelCase = os.path.join(lowerCamelCase_ , weights_name.replace(".bin" , F'-{len(lowerCamelCase_ )+1:05d}-of-???.bin' ) )
rename_and_save_block(lowerCamelCase_ , lowerCamelCase_ )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(lowerCamelCase_ ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
UpperCAmelCase = {}
UpperCAmelCase = {}
for idx, shard in enumerate(lowerCamelCase_ ):
UpperCAmelCase = weights_name.replace(
".bin" , F'-{idx+1:05d}-of-{len(lowerCamelCase_ ):05d}.bin' ) # len(sharded_state_dicts):05d}
UpperCAmelCase = os.path.join(lowerCamelCase_ , weights_name.replace(".bin" , F'-{idx+1:05d}-of-???.bin' ) )
os.rename(lowerCamelCase_ , os.path.join(lowerCamelCase_ , lowerCamelCase_ ) )
UpperCAmelCase = shard
for key in shard:
UpperCAmelCase = shard_file
# Add the metadata
UpperCAmelCase = {"total_size": total_size}
UpperCAmelCase = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(lowerCamelCase_ , lowerCamelCase_ ) , "w" , encoding="utf-8" ) as f:
UpperCAmelCase = json.dumps(lowerCamelCase_ , indent=2 , sort_keys=lowerCamelCase_ ) + "\n"
f.write(lowerCamelCase_ )
return metadata, index
if __name__ == "__main__":
__lowerCamelCase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
type=str,
required=False,
help="Path to a directory containing a folder per layer. Follows the original Google format.",
)
parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
parser.add_argument(
"--pytorch_dump_folder_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
type=str,
required=False,
help="Path to the output pytorch model.",
)
__lowerCamelCase : Union[str, Any] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def lowerCamelCase_() -> Dict:
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
UpperCAmelCase = SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
UpperCAmelCase = SwitchTransformersForConditionalGeneration.from_pretrained(
"/home/arthur_huggingface_co/transformers/switch_converted" , device_map="auto" )
UpperCAmelCase = TaTokenizer.from_pretrained("t5-small" )
UpperCAmelCase = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
UpperCAmelCase = tokenizer(lowerCamelCase_ , return_tensors="pt" ).input_ids
UpperCAmelCase = model.generate(lowerCamelCase_ , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 713 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class __magic_name__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self : int ) -> str:
'''simple docstring'''
UpperCAmelCase = get_activation("swish" )
self.assertIsInstance(UpperCamelCase__ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = get_activation("silu" )
self.assertIsInstance(UpperCamelCase__ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> int:
'''simple docstring'''
UpperCAmelCase = get_activation("mish" )
self.assertIsInstance(UpperCamelCase__ , nn.Mish )
self.assertEqual(act(torch.tensor(-2_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def SCREAMING_SNAKE_CASE_ ( self : str ) -> List[str]:
'''simple docstring'''
UpperCAmelCase = get_activation("gelu" )
self.assertIsInstance(UpperCamelCase__ , nn.GELU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
| 457 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowercase__ : Optional[int] = {
'''xlm-mlm-en-2048''': '''https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json''',
'''xlm-mlm-ende-1024''': '''https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json''',
'''xlm-mlm-enfr-1024''': '''https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json''',
'''xlm-mlm-enro-1024''': '''https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json''',
'''xlm-mlm-tlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json''',
'''xlm-mlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json''',
'''xlm-clm-enfr-1024''': '''https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json''',
'''xlm-clm-ende-1024''': '''https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json''',
'''xlm-mlm-17-1280''': '''https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json''',
'''xlm-mlm-100-1280''': '''https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = '''xlm'''
lowerCAmelCase = {
'''hidden_size''': '''emb_dim''',
'''num_attention_heads''': '''n_heads''',
'''num_hidden_layers''': '''n_layers''',
'''n_words''': '''vocab_size''', # For backward compatibility
}
def __init__( self , _UpperCAmelCase=3_0145 , _UpperCAmelCase=2048 , _UpperCAmelCase=12 , _UpperCAmelCase=16 , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=True , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=1 , _UpperCAmelCase=True , _UpperCAmelCase=512 , _UpperCAmelCase=2048**-0.5 , _UpperCAmelCase=1e-1_2 , _UpperCAmelCase=0.02 , _UpperCAmelCase=0 , _UpperCAmelCase=1 , _UpperCAmelCase=2 , _UpperCAmelCase=3 , _UpperCAmelCase=5 , _UpperCAmelCase=True , _UpperCAmelCase="first" , _UpperCAmelCase=True , _UpperCAmelCase=None , _UpperCAmelCase=True , _UpperCAmelCase=0.1 , _UpperCAmelCase=5 , _UpperCAmelCase=5 , _UpperCAmelCase=0 , _UpperCAmelCase=0 , _UpperCAmelCase=2 , _UpperCAmelCase=0 , **_UpperCAmelCase , ):
'''simple docstring'''
__A : int = vocab_size
__A : Optional[int] = emb_dim
__A : Any = n_layers
__A : Optional[Any] = n_heads
__A : Optional[Any] = dropout
__A : Optional[int] = attention_dropout
__A : List[str] = gelu_activation
__A : Any = sinusoidal_embeddings
__A : List[Any] = causal
__A : Any = asm
__A : int = n_langs
__A : List[Any] = use_lang_emb
__A : Tuple = layer_norm_eps
__A : Any = bos_index
__A : Any = eos_index
__A : Optional[Any] = pad_index
__A : int = unk_index
__A : List[Any] = mask_index
__A : List[str] = is_encoder
__A : Dict = max_position_embeddings
__A : Any = embed_init_std
__A : Tuple = init_std
__A : Any = summary_type
__A : Dict = summary_use_proj
__A : Dict = summary_activation
__A : Dict = summary_proj_to_labels
__A : List[Any] = summary_first_dropout
__A : str = start_n_top
__A : Any = end_n_top
__A : Tuple = mask_token_id
__A : Tuple = lang_id
if "n_words" in kwargs:
__A : Dict = kwargs['n_words']
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , **_UpperCAmelCase)
class SCREAMING_SNAKE_CASE (a__ ):
@property
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
if self.task == "multiple-choice":
__A : List[Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__A : List[str] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
]) | 8 |
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase ):
if any(not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(__lowerCAmelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 276 | 0 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __snake_case ( _UpperCAmelCase ):
"""simple docstring"""
lowercase = int(number**0.5 )
return number == sq * sq
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
lowercase = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
lowercase = x_den * y_den * z_den
lowercase = gcd(_UpperCAmelCase , _UpperCAmelCase )
top //= hcf
bottom //= hcf
return top, bottom
def __snake_case ( _UpperCAmelCase = 35 ):
"""simple docstring"""
lowercase = set()
lowercase = 42
lowercase = Fraction(0 )
lowercase = 42
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
lowercase = x_num * y_den + x_den * y_num
lowercase = x_den * y_den
lowercase = gcd(_UpperCAmelCase , _UpperCAmelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase = add_three(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
unique_s.add(_UpperCAmelCase )
# n=2
lowercase = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
lowercase = x_den * x_den * y_den * y_den
if is_sq(_UpperCAmelCase ) and is_sq(_UpperCAmelCase ):
lowercase = int(sqrt(_UpperCAmelCase ) )
lowercase = int(sqrt(_UpperCAmelCase ) )
lowercase = gcd(_UpperCAmelCase , _UpperCAmelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase = add_three(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
unique_s.add(_UpperCAmelCase )
# n=-1
lowercase = x_num * y_num
lowercase = x_den * y_num + x_num * y_den
lowercase = gcd(_UpperCAmelCase , _UpperCAmelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase = add_three(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
unique_s.add(_UpperCAmelCase )
# n=2
lowercase = x_num * x_num * y_num * y_num
lowercase = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(_UpperCAmelCase ) and is_sq(_UpperCAmelCase ):
lowercase = int(sqrt(_UpperCAmelCase ) )
lowercase = int(sqrt(_UpperCAmelCase ) )
lowercase = gcd(_UpperCAmelCase , _UpperCAmelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowercase = add_three(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
unique_s.add(_UpperCAmelCase )
for num, den in unique_s:
total += Fraction(_UpperCAmelCase , _UpperCAmelCase )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F"""{solution() = }""")
| 314 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
return number | (1 << position)
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
return number & ~(1 << position)
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
return number ^ (1 << position)
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
return ((number >> position) & 1) == 1
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 314 | 1 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
lowercase__ = remove_duplicates(key.upper() )
lowercase__ = len(SCREAMING_SNAKE_CASE_ )
# First fill cipher with key characters
lowercase__ = {alphabet[i]: char for i, char in enumerate(SCREAMING_SNAKE_CASE_ )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(SCREAMING_SNAKE_CASE_ ) , 26 ):
lowercase__ = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
lowercase__ = alphabet[i - offset]
lowercase__ = char
return cipher_alphabet
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return "".join(cipher_map.get(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for ch in message.upper() )
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for ch in message.upper() )
def __lowerCAmelCase ( ):
lowercase__ = input("Enter message to encode or decode: " ).strip()
lowercase__ = input("Enter keyword: " ).strip()
lowercase__ = input("Encipher or decipher? E/D:" ).strip()[0].lower()
try:
lowercase__ = {"e": encipher, "d": decipher}[option]
except KeyError:
raise KeyError("invalid input option" )
lowercase__ = create_cipher_map(SCREAMING_SNAKE_CASE_ )
print(func(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 413 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
lowercase_ = [
{"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
{"""dataset""": """snli""", """config_name""": """plain_text"""},
{"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
{"""dataset""": """wiki40b""", """config_name""": """en"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
{"""dataset""": """natural_questions""", """config_name""": """default"""},
]
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_=True ):
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=lowercase__))
class _snake_case ( lowercase__):
UpperCamelCase__ : Optional[int] =None
UpperCamelCase__ : List[Any] =None
def A__ ( self : Tuple, __lowercase : Optional[Any], __lowercase : int ):
with TemporaryDirectory() as tmp_dir:
lowercase__ = dataset_module_factory(__lowercase, cache_dir=__lowercase )
lowercase__ = import_main_class(dataset_module.module_path, dataset=__lowercase )
lowercase__ = builder_cls(
cache_dir=__lowercase, config_name=__lowercase, hash=dataset_module.hash, )
lowercase__ = "/".join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=__lowercase ).replace(os.sep, "/" ),
config.DATASET_INFO_FILENAME,
] )
lowercase__ = cached_path(__lowercase, cache_dir=__lowercase )
self.assertTrue(os.path.exists(__lowercase ) )
@pytest.mark.integration
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = tmp_path_factory.mktemp("test_hf_gcp" ) / "test_wikipedia_simple"
lowercase__ = dataset_module_factory("wikipedia" , cache_dir=SCREAMING_SNAKE_CASE_ )
lowercase__ = import_main_class(dataset_module.module_path )
lowercase__ = builder_cls(
cache_dir=SCREAMING_SNAKE_CASE_ , config_name="20220301.frr" , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
lowercase__ = None
builder_instance.download_and_prepare()
lowercase__ = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = dataset_module_factory("wikipedia" , cache_dir=SCREAMING_SNAKE_CASE_ )
lowercase__ = import_main_class(dataset_module.module_path , dataset=SCREAMING_SNAKE_CASE_ )
lowercase__ = builder_cls(
cache_dir=SCREAMING_SNAKE_CASE_ , config_name="20220301.frr" , hash=dataset_module.hash , )
lowercase__ = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert "train" in ds
assert isinstance(ds["train"] , SCREAMING_SNAKE_CASE_ )
assert next(iter(ds["train"] ) )
| 413 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = tempfile.mkdtemp()
# fmt: off
__SCREAMING_SNAKE_CASE : List[Any] = ['''''', '''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
# fmt: on
__SCREAMING_SNAKE_CASE : Any = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) )
__SCREAMING_SNAKE_CASE : List[Any] = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
__SCREAMING_SNAKE_CASE : str = {'''unk_token''': '''<unk>'''}
__SCREAMING_SNAKE_CASE : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
__SCREAMING_SNAKE_CASE : int = 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(_lowerCamelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Dict = {
'''do_resize''': True,
'''size''': 2_0,
'''do_center_crop''': True,
'''crop_size''': 1_8,
'''do_normalize''': True,
'''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
'''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , **_lowerCamelCase :Tuple ):
return CLIPTokenizer.from_pretrained(self.tmpdirname , pad_token='''!''' , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , **_lowerCamelCase :Tuple ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , pad_token='''!''' , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , **_lowerCamelCase :Optional[int] ):
return OwlViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :str ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Tuple = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE : List[Any] = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = OwlViTProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor_slow.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Any = OwlViTProcessor.from_pretrained(self.tmpdirname , use_fast=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = OwlViTProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor_fast.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Optional[Any] = OwlViTProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _lowerCamelCase )
self.assertIsInstance(processor_fast.tokenizer , _lowerCamelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _lowerCamelCase )
self.assertIsInstance(processor_fast.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = OwlViTProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor(do_normalize=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = OwlViTProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Tuple = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : int = OwlViTProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : int = image_processor(_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Any = OwlViTProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : int = processor(text=_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(_lowerCamelCase , return_tensors='''np''' )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key][0].tolist() , encoded_processor[key][0].tolist() )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Dict = OwlViTProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Any = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Tuple = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = '''google/owlvit-base-patch32'''
__SCREAMING_SNAKE_CASE : Tuple = OwlViTProcessor.from_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''cat''', '''nasa badge''']
__SCREAMING_SNAKE_CASE : Dict = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = 1_6
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''attention_mask'''] )
self.assertEqual(inputs['''input_ids'''].shape , (2, seq_length) )
# test if it raises when no input is passed
with pytest.raises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : List[Any] = '''google/owlvit-base-patch32'''
__SCREAMING_SNAKE_CASE : Optional[int] = OwlViTProcessor.from_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = [['''cat''', '''nasa badge'''], ['''person''']]
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = 1_6
__SCREAMING_SNAKE_CASE : List[str] = len(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = max([len(_lowerCamelCase ) for texts in input_texts] )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''attention_mask'''] )
self.assertEqual(inputs['''input_ids'''].shape , (batch_size * num_max_text_queries, seq_length) )
# test if it raises when no input is passed
with pytest.raises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : str = '''google/owlvit-base-patch32'''
__SCREAMING_SNAKE_CASE : Tuple = OwlViTProcessor.from_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = ['''cat''', '''nasa badge''']
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1_6
__SCREAMING_SNAKE_CASE : int = inputs['''input_ids''']
__SCREAMING_SNAKE_CASE : Optional[int] = [
[4_9_4_0_6, 2_3_6_8, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[4_9_4_0_6, 6_8_4_1, 1_1_3_0_1, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''attention_mask'''] )
self.assertEqual(inputs['''input_ids'''].shape , (2, seq_length) )
self.assertListEqual(list(input_ids[0] ) , predicted_ids[0] )
self.assertListEqual(list(input_ids[1] ) , predicted_ids[1] )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : List[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : List[str] = OwlViTProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Union[str, Any] = processor(images=_lowerCamelCase , query_images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''query_pixel_values''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : List[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Any = OwlViTProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE : Optional[int] = processor.batch_decode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
| 401 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[Any] = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def lowerCAmelCase_ ( ):
'''simple docstring'''
print(sum_of_series(1 , 1 , 10 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 401 | 1 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase : Optional[Any] = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")
@require_sentencepiece
@require_tokenizers
class _UpperCamelCase ( __snake_case , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase = GPTSwaTokenizer
lowerCAmelCase = False
lowerCAmelCase = True
lowerCAmelCase = False
def _UpperCAmelCase ( self ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
A = GPTSwaTokenizer(a__ , eos_token="""<unk>""" , bos_token="""<unk>""" , pad_token="""<unk>""" )
tokenizer.save_pretrained(self.tmpdirname )
def _UpperCAmelCase ( self , a__ ) -> Tuple:
A = """This is a test"""
A = """This is a test"""
return input_text, output_text
def _UpperCAmelCase ( self ) -> List[str]:
A = """<s>"""
A = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__ ) , a__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__ ) , a__ )
def _UpperCAmelCase ( self ) -> Any:
A = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(a__ ) , 2000 )
def _UpperCAmelCase ( self ) -> Optional[int]:
self.assertEqual(self.get_tokenizer().vocab_size , 2000 )
def _UpperCAmelCase ( self ) -> Dict:
A = GPTSwaTokenizer(a__ )
A = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(a__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(a__ ) , [465, 287, 265, 631, 842] )
A = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
# fmt: off
self.assertListEqual(
a__ , ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] , )
# fmt: on
A = tokenizer.convert_tokens_to_ids(a__ )
self.assertListEqual(
a__ , [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] , )
A = tokenizer.convert_ids_to_tokens(a__ )
# fmt: off
self.assertListEqual(
a__ , ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] )
# fmt: on
def _UpperCAmelCase ( self ) -> Dict:
A = GPTSwaTokenizer(a__ )
A = ["""This is a test""", """I was born in 92000, and this is falsé."""]
A = [
[465, 287, 265, 631, 842],
[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(a__ , a__ ):
self.assertListEqual(tokenizer.encode_fast(a__ ) , a__ )
# Test that decode_fast returns the input text
for text, token_ids in zip(a__ , a__ ):
self.assertEqual(tokenizer.decode_fast(a__ ) , a__ )
@slow
def _UpperCAmelCase ( self ) -> Any:
A = [
"""<|python|>def fibonacci(n)\n if n < 0:\n print('Incorrect input')""",
"""Hey there, how are you doing this fine day?""",
"""This is a text with a trailing spaces followed by a dot .""",
"""Häj sväjs lillebrör! =)""",
"""Det är inget fel på Mr. Cool""",
]
# fmt: off
A = {"""input_ids""": [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 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, 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, 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], [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]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=a__ , model_name="""AI-Sweden/gpt-sw3-126m""" , sequences=a__ , )
| 641 |
_lowercase : Dict = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 641 | 1 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class a__ :
@staticmethod
def __SCREAMING_SNAKE_CASE ( *UpperCAmelCase , **UpperCAmelCase ) -> Union[str, Any]:
pass
@is_pipeline_test
@require_torch
@require_vision
class a__ ( unittest.TestCase ):
A__ : List[Any] = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Any:
__a = pipeline('visual-question-answering' , model='hf-internal-testing/tiny-vilt-random-vqa' )
__a = [
{
'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
'question': 'How many cats are there?',
},
{
'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
'question': 'How many cats are there?',
},
]
return vqa_pipeline, examples
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
__a = vqa_pipeline(UpperCAmelCase_ , top_k=1 )
self.assertEqual(
UpperCAmelCase_ , [
[{'score': ANY(UpperCAmelCase_ ), 'answer': ANY(UpperCAmelCase_ )}],
[{'score': ANY(UpperCAmelCase_ ), 'answer': ANY(UpperCAmelCase_ )}],
] , )
@require_torch
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
__a = pipeline('visual-question-answering' , model='hf-internal-testing/tiny-vilt-random-vqa' )
__a = './tests/fixtures/tests_samples/COCO/000000039769.png'
__a = 'How many cats are there?'
__a = vqa_pipeline(image=UpperCAmelCase_ , question='How many cats are there?' , top_k=2 )
self.assertEqual(
UpperCAmelCase_ , [{'score': ANY(UpperCAmelCase_ ), 'answer': ANY(UpperCAmelCase_ )}, {'score': ANY(UpperCAmelCase_ ), 'answer': ANY(UpperCAmelCase_ )}] )
__a = vqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
UpperCAmelCase_ , [{'score': ANY(UpperCAmelCase_ ), 'answer': ANY(UpperCAmelCase_ )}, {'score': ANY(UpperCAmelCase_ ), 'answer': ANY(UpperCAmelCase_ )}] )
@slow
@require_torch
def __SCREAMING_SNAKE_CASE ( self ) -> int:
__a = pipeline('visual-question-answering' , model='dandelin/vilt-b32-finetuned-vqa' )
__a = './tests/fixtures/tests_samples/COCO/000000039769.png'
__a = 'How many cats are there?'
__a = vqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [{'score': 0.8_799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}] )
__a = vqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [{'score': 0.8_799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}] )
__a = vqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [[{'score': 0.8_799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2 , )
@require_tf
@unittest.skip('Visual question answering not implemented in TF' )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
pass
| 702 | 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_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class a__ ( unittest.TestCase ):
def __init__( self , UpperCAmelCase , UpperCAmelCase=7 , UpperCAmelCase=3 , UpperCAmelCase=1_0 , UpperCAmelCase=1_8 , UpperCAmelCase=3_0 , UpperCAmelCase=4_0_0 , UpperCAmelCase=True , UpperCAmelCase=None , UpperCAmelCase=True , UpperCAmelCase=[0.5, 0.5, 0.5] , UpperCAmelCase=[0.5, 0.5, 0.5] , UpperCAmelCase=None , ) -> Tuple:
__a = size if size is not None else {'shortest_edge': 1_8}
__a = crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
__a = parent
__a = batch_size
__a = num_channels
__a = num_frames
__a = image_size
__a = min_resolution
__a = max_resolution
__a = do_resize
__a = size
__a = do_normalize
__a = image_mean
__a = image_std
__a = crop_size
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class a__ ( __snake_case , unittest.TestCase ):
A__ : Tuple = VivitImageProcessor if is_vision_available() else None
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
__a = VivitImageProcessingTester(self )
@property
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def __SCREAMING_SNAKE_CASE ( self ) -> Tuple:
__a = 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 __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
__a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 1_8} )
self.assertEqual(image_processor.crop_size , {'height': 1_8, 'width': 1_8} )
__a = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {'shortest_edge': 4_2} )
self.assertEqual(image_processor.crop_size , {'height': 8_4, 'width': 8_4} )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
# Initialize image_processing
__a = self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
__a = prepare_video_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase )
for video in video_inputs:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
__a = image_processing(video_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__a = image_processing(UpperCAmelCase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def __SCREAMING_SNAKE_CASE ( self ) -> Tuple:
# Initialize image_processing
__a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__a = prepare_video_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase , numpify=UpperCAmelCase )
for video in video_inputs:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
__a = image_processing(video_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__a = image_processing(UpperCAmelCase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
# Initialize image_processing
__a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__a = prepare_video_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase , torchify=UpperCAmelCase )
for video in video_inputs:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
__a = image_processing(video_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__a = image_processing(UpperCAmelCase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 246 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ ( UpperCAmelCase_ ) ->list[int]:
"""simple docstring"""
if len(UpperCAmelCase_ ) == 0:
return array
__UpperCAmelCase , __UpperCAmelCase : Any = min(UpperCAmelCase_ ), max(UpperCAmelCase_ )
# Compute the variables
__UpperCAmelCase : Optional[Any] = _max - _min + 1
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = [0] * holes_range, [0] * holes_range
# Make the sorting.
for i in array:
__UpperCAmelCase : List[Any] = i - _min
__UpperCAmelCase : int = i
holes_repeat[index] += 1
# Makes the array back by replacing the numbers.
__UpperCAmelCase : int = 0
for i in range(UpperCAmelCase_ ):
while holes_repeat[i] > 0:
__UpperCAmelCase : int = holes[i]
index += 1
holes_repeat[i] -= 1
# Returns the sorted array.
return array
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase__ :Union[str, Any] = input('Enter numbers separated by comma:\n')
lowercase__ :Dict = [int(x) for x in user_input.split(',')]
print(pigeon_sort(unsorted)) | 522 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase__ :Optional[int] = logging.get_logger(__name__)
lowercase__ :Union[str, Any] = {'vocab_file': 'vocab.txt'}
lowercase__ :int = {
'vocab_file': {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
}
}
lowercase__ :Dict = {
'YituTech/conv-bert-base': 5_1_2,
'YituTech/conv-bert-medium-small': 5_1_2,
'YituTech/conv-bert-small': 5_1_2,
}
lowercase__ :List[str] = {
'YituTech/conv-bert-base': {'do_lower_case': True},
'YituTech/conv-bert-medium-small': {'do_lower_case': True},
'YituTech/conv-bert-small': {'do_lower_case': True},
}
class snake_case ( __UpperCAmelCase ):
'''simple docstring'''
_A : Union[str, Any] = VOCAB_FILES_NAMES
_A : int = PRETRAINED_VOCAB_FILES_MAP
_A : str = PRETRAINED_INIT_CONFIGURATION
_A : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A : List[Any] = ConvBertTokenizer
def __init__( self : int , __lowercase : List[Any]=None , __lowercase : int=None , __lowercase : Any=True , __lowercase : Dict="[UNK]" , __lowercase : Dict="[SEP]" , __lowercase : Dict="[PAD]" , __lowercase : int="[CLS]" , __lowercase : int="[MASK]" , __lowercase : List[str]=True , __lowercase : Optional[int]=None , **__lowercase : Any , ):
'''simple docstring'''
super().__init__(
__lowercase , tokenizer_file=__lowercase , do_lower_case=__lowercase , unk_token=__lowercase , sep_token=__lowercase , pad_token=__lowercase , cls_token=__lowercase , mask_token=__lowercase , tokenize_chinese_chars=__lowercase , strip_accents=__lowercase , **__lowercase , )
__UpperCAmelCase : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __lowercase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __lowercase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __lowercase ) != tokenize_chinese_chars
):
__UpperCAmelCase : Optional[Any] = getattr(__lowercase , normalizer_state.pop('''type''' ) )
__UpperCAmelCase : Any = do_lower_case
__UpperCAmelCase : int = strip_accents
__UpperCAmelCase : List[str] = tokenize_chinese_chars
__UpperCAmelCase : Optional[Any] = normalizer_class(**__lowercase )
__UpperCAmelCase : Any = do_lower_case
def A_ ( self : Optional[int] , __lowercase : Optional[int] , __lowercase : Dict=None ):
'''simple docstring'''
__UpperCAmelCase : Dict = [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 A_ ( self : Union[str, Any] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = [self.sep_token_id]
__UpperCAmelCase : Optional[int] = [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 A_ ( self : Optional[int] , __lowercase : str , __lowercase : Optional[str] = None ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = self._tokenizer.model.save(__lowercase , name=__lowercase )
return tuple(__lowercase ) | 522 | 1 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__A : Any = logging.get_logger("transformers.models.speecht5")
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
hf_model.apply_weight_norm()
__lowerCAmelCase = checkpoint["""input_conv.weight_g"""]
__lowerCAmelCase = checkpoint["""input_conv.weight_v"""]
__lowerCAmelCase = checkpoint["""input_conv.bias"""]
for i in range(len(config.upsample_rates ) ):
__lowerCAmelCase = checkpoint[F'''upsamples.{i}.1.weight_g''']
__lowerCAmelCase = checkpoint[F'''upsamples.{i}.1.weight_v''']
__lowerCAmelCase = checkpoint[F'''upsamples.{i}.1.bias''']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
__lowerCAmelCase = checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
__lowerCAmelCase = checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
__lowerCAmelCase = checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']
__lowerCAmelCase = checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
__lowerCAmelCase = checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
__lowerCAmelCase = checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']
__lowerCAmelCase = checkpoint["""output_conv.1.weight_g"""]
__lowerCAmelCase = checkpoint["""output_conv.1.weight_v"""]
__lowerCAmelCase = checkpoint["""output_conv.1.bias"""]
hf_model.remove_weight_norm()
@torch.no_grad()
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , ) -> Optional[int]:
'''simple docstring'''
if config_path is not None:
__lowerCAmelCase = SpeechTaHifiGanConfig.from_pretrained(UpperCamelCase__ )
else:
__lowerCAmelCase = SpeechTaHifiGanConfig()
__lowerCAmelCase = SpeechTaHifiGan(UpperCamelCase__ )
__lowerCAmelCase = torch.load(UpperCamelCase__ )
load_weights(orig_checkpoint["""model"""]["""generator"""] , UpperCamelCase__ , UpperCamelCase__ )
__lowerCAmelCase = np.load(UpperCamelCase__ )
__lowerCAmelCase = stats[0].reshape(-1 )
__lowerCAmelCase = stats[1].reshape(-1 )
__lowerCAmelCase = torch.from_numpy(UpperCamelCase__ ).float()
__lowerCAmelCase = torch.from_numpy(UpperCamelCase__ ).float()
model.save_pretrained(UpperCamelCase__ )
if repo_id:
print("""Pushing to the hub...""" )
model.push_to_hub(UpperCamelCase__ )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
__A : str = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 334 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowercase_ ( lowerCAmelCase__ , lowerCAmelCase__ ):
@register_to_config
def __init__( self: Optional[int], _lowercase: int = 128, _lowercase: int = 256, _lowercase: float = 2_000.0, _lowercase: int = 768, _lowercase: int = 12, _lowercase: int = 12, _lowercase: int = 64, _lowercase: int = 2048, _lowercase: float = 0.1, ):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = nn.Sequential(
nn.Linear(_lowercase, d_model * 4, bias=_lowercase), nn.SiLU(), nn.Linear(d_model * 4, d_model * 4, bias=_lowercase), nn.SiLU(), )
__lowerCAmelCase = nn.Embedding(_lowercase, _lowercase)
__lowerCAmelCase = False
__lowerCAmelCase = nn.Linear(_lowercase, _lowercase, bias=_lowercase)
__lowerCAmelCase = nn.Dropout(p=_lowercase)
__lowerCAmelCase = nn.ModuleList()
for lyr_num in range(_lowercase):
# FiLM conditional T5 decoder
__lowerCAmelCase = DecoderLayer(d_model=_lowercase, d_kv=_lowercase, num_heads=_lowercase, d_ff=_lowercase, dropout_rate=_lowercase)
self.decoders.append(_lowercase)
__lowerCAmelCase = TaLayerNorm(_lowercase)
__lowerCAmelCase = nn.Dropout(p=_lowercase)
__lowerCAmelCase = nn.Linear(_lowercase, _lowercase, bias=_lowercase)
def _lowercase ( self: Optional[int], _lowercase: Any, _lowercase: Dict):
'''simple docstring'''
__lowerCAmelCase = torch.mul(query_input.unsqueeze(-1), key_input.unsqueeze(-2))
return mask.unsqueeze(-3)
def _lowercase ( self: Union[str, Any], _lowercase: Optional[int], _lowercase: Optional[Any], _lowercase: Dict):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
__lowerCAmelCase = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time, embedding_dim=self.config.d_model, max_period=self.config.max_decoder_noise_time, ).to(dtype=self.dtype)
__lowerCAmelCase = self.conditioning_emb(_lowercase).unsqueeze(1)
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
__lowerCAmelCase = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
__lowerCAmelCase = torch.broadcast_to(
torch.arange(_lowercase, device=decoder_input_tokens.device), (batch, seq_length), )
__lowerCAmelCase = self.position_encoding(_lowercase)
__lowerCAmelCase = self.continuous_inputs_projection(_lowercase)
inputs += position_encodings
__lowerCAmelCase = self.dropout(_lowercase)
# decoder: No padding present.
__lowerCAmelCase = torch.ones(
decoder_input_tokens.shape[:2], device=decoder_input_tokens.device, dtype=inputs.dtype)
# Translate encoding masks to encoder-decoder masks.
__lowerCAmelCase = [(x, self.encoder_decoder_mask(_lowercase, _lowercase)) for x, y in encodings_and_masks]
# cross attend style: concat encodings
__lowerCAmelCase = torch.cat([x[0] for x in encodings_and_encdec_masks], dim=1)
__lowerCAmelCase = torch.cat([x[1] for x in encodings_and_encdec_masks], dim=-1)
for lyr in self.decoders:
__lowerCAmelCase = lyr(
_lowercase, conditioning_emb=_lowercase, encoder_hidden_states=_lowercase, encoder_attention_mask=_lowercase, )[0]
__lowerCAmelCase = self.decoder_norm(_lowercase)
__lowerCAmelCase = self.post_dropout(_lowercase)
__lowerCAmelCase = self.spec_out(_lowercase)
return spec_out
class lowercase_ ( nn.Module ):
def __init__( self: Optional[Any], _lowercase: Optional[int], _lowercase: Any, _lowercase: Optional[int], _lowercase: Optional[Any], _lowercase: Union[str, Any], _lowercase: List[Any]=1e-6):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=_lowercase, d_kv=_lowercase, num_heads=_lowercase, dropout_rate=_lowercase))
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=_lowercase, d_kv=_lowercase, num_heads=_lowercase, dropout_rate=_lowercase, layer_norm_epsilon=_lowercase, ))
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=_lowercase, d_ff=_lowercase, dropout_rate=_lowercase, layer_norm_epsilon=_lowercase))
def _lowercase ( self: Union[str, Any], _lowercase: List[Any], _lowercase: Optional[Any]=None, _lowercase: Optional[int]=None, _lowercase: str=None, _lowercase: List[str]=None, _lowercase: Dict=None, ):
'''simple docstring'''
__lowerCAmelCase = self.layer[0](
_lowercase, conditioning_emb=_lowercase, attention_mask=_lowercase, )
if encoder_hidden_states is not None:
__lowerCAmelCase = torch.where(encoder_attention_mask > 0, 0, -1e10).to(
encoder_hidden_states.dtype)
__lowerCAmelCase = self.layer[1](
_lowercase, key_value_states=_lowercase, attention_mask=_lowercase, )
# Apply Film Conditional Feed Forward layer
__lowerCAmelCase = self.layer[-1](_lowercase, _lowercase)
return (hidden_states,)
class lowercase_ ( nn.Module ):
def __init__( self: int, _lowercase: int, _lowercase: Tuple, _lowercase: Union[str, Any], _lowercase: Optional[int]):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = TaLayerNorm(_lowercase)
__lowerCAmelCase = TaFiLMLayer(in_features=d_model * 4, out_features=_lowercase)
__lowerCAmelCase = Attention(query_dim=_lowercase, heads=_lowercase, dim_head=_lowercase, out_bias=_lowercase, scale_qk=_lowercase)
__lowerCAmelCase = nn.Dropout(_lowercase)
def _lowercase ( self: int, _lowercase: Union[str, Any], _lowercase: Union[str, Any]=None, _lowercase: Tuple=None, ):
'''simple docstring'''
__lowerCAmelCase = self.layer_norm(_lowercase)
if conditioning_emb is not None:
__lowerCAmelCase = self.FiLMLayer(_lowercase, _lowercase)
# Self-attention block
__lowerCAmelCase = self.attention(_lowercase)
__lowerCAmelCase = hidden_states + self.dropout(_lowercase)
return hidden_states
class lowercase_ ( nn.Module ):
def __init__( self: Optional[int], _lowercase: List[Any], _lowercase: Union[str, Any], _lowercase: List[str], _lowercase: List[Any], _lowercase: Optional[int]):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = Attention(query_dim=_lowercase, heads=_lowercase, dim_head=_lowercase, out_bias=_lowercase, scale_qk=_lowercase)
__lowerCAmelCase = TaLayerNorm(_lowercase, eps=_lowercase)
__lowerCAmelCase = nn.Dropout(_lowercase)
def _lowercase ( self: List[str], _lowercase: Any, _lowercase: Union[str, Any]=None, _lowercase: List[str]=None, ):
'''simple docstring'''
__lowerCAmelCase = self.layer_norm(_lowercase)
__lowerCAmelCase = self.attention(
_lowercase, encoder_hidden_states=_lowercase, attention_mask=attention_mask.squeeze(1), )
__lowerCAmelCase = hidden_states + self.dropout(_lowercase)
return layer_output
class lowercase_ ( nn.Module ):
def __init__( self: Tuple, _lowercase: Union[str, Any], _lowercase: Optional[int], _lowercase: Dict, _lowercase: str):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = TaDenseGatedActDense(d_model=_lowercase, d_ff=_lowercase, dropout_rate=_lowercase)
__lowerCAmelCase = TaFiLMLayer(in_features=d_model * 4, out_features=_lowercase)
__lowerCAmelCase = TaLayerNorm(_lowercase, eps=_lowercase)
__lowerCAmelCase = nn.Dropout(_lowercase)
def _lowercase ( self: Optional[Any], _lowercase: List[Any], _lowercase: Optional[int]=None):
'''simple docstring'''
__lowerCAmelCase = self.layer_norm(_lowercase)
if conditioning_emb is not None:
__lowerCAmelCase = self.film(_lowercase, _lowercase)
__lowerCAmelCase = self.DenseReluDense(_lowercase)
__lowerCAmelCase = hidden_states + self.dropout(_lowercase)
return hidden_states
class lowercase_ ( nn.Module ):
def __init__( self: Any, _lowercase: Optional[int], _lowercase: Union[str, Any], _lowercase: List[str]):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = nn.Linear(_lowercase, _lowercase, bias=_lowercase)
__lowerCAmelCase = nn.Linear(_lowercase, _lowercase, bias=_lowercase)
__lowerCAmelCase = nn.Linear(_lowercase, _lowercase, bias=_lowercase)
__lowerCAmelCase = nn.Dropout(_lowercase)
__lowerCAmelCase = NewGELUActivation()
def _lowercase ( self: str, _lowercase: Union[str, Any]):
'''simple docstring'''
__lowerCAmelCase = self.act(self.wi_a(_lowercase))
__lowerCAmelCase = self.wi_a(_lowercase)
__lowerCAmelCase = hidden_gelu * hidden_linear
__lowerCAmelCase = self.dropout(_lowercase)
__lowerCAmelCase = self.wo(_lowercase)
return hidden_states
class lowercase_ ( nn.Module ):
def __init__( self: Dict, _lowercase: Optional[Any], _lowercase: List[str]=1e-6):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = nn.Parameter(torch.ones(_lowercase))
__lowerCAmelCase = eps
def _lowercase ( self: Any, _lowercase: Optional[Any]):
'''simple docstring'''
__lowerCAmelCase = hidden_states.to(torch.floataa).pow(2).mean(-1, keepdim=_lowercase)
__lowerCAmelCase = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
__lowerCAmelCase = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states
class lowercase_ ( nn.Module ):
def _lowercase ( self: Optional[int], _lowercase: torch.Tensor):
'''simple docstring'''
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044_715 * torch.pow(_lowercase, 3.0))))
class lowercase_ ( nn.Module ):
def __init__( self: List[str], _lowercase: Optional[int], _lowercase: List[str]):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = nn.Linear(_lowercase, out_features * 2, bias=_lowercase)
def _lowercase ( self: List[str], _lowercase: Tuple, _lowercase: List[Any]):
'''simple docstring'''
__lowerCAmelCase = self.scale_bias(_lowercase)
__lowerCAmelCase , __lowerCAmelCase = torch.chunk(_lowercase, 2, -1)
__lowerCAmelCase = x * (1 + scale) + shift
return x
| 334 | 1 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__lowerCamelCase = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
__lowerCamelCase = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(F'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def UpperCamelCase ( __lowerCamelCase : str ):
if "://" in dataset_path:
snake_case : Dict = dataset_path.split("://" )[1]
return dataset_path
def UpperCamelCase ( __lowerCamelCase : fsspec.AbstractFileSystem ):
if fs is not None and fs.protocol != "file":
return True
else:
return False
def UpperCamelCase ( __lowerCamelCase : fsspec.AbstractFileSystem , __lowerCamelCase : str , __lowerCamelCase : str ):
snake_case : Dict = not is_remote_filesystem(a_ )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(a_ ) , fs._strip_protocol(a_ ) )
else:
fs.mv(a_ , a_ , recursive=a_ )
def UpperCamelCase ( ):
if hasattr(fsspec.asyn , "reset_lock" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
snake_case : List[Any] = None
snake_case : Union[str, Any] = None
snake_case : List[str] = threading.Lock()
| 204 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger()
@dataclass
class __magic_name__ :
__A : nn.Module
__A : List[nn.Module] = field(default_factory=__UpperCAmelCase )
__A : list = field(default_factory=__UpperCAmelCase )
def __snake_case ( self : List[str] , snake_case__ : List[str] , snake_case__ : Tensor , snake_case__ : Tensor ):
'''simple docstring'''
lowercase :List[str] = len(list(m.modules() ) ) == 1 or isinstance(snake_case__ , nn.Convad ) or isinstance(snake_case__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(snake_case__ )
def __call__( self : int , snake_case__ : Tensor ):
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(snake_case__ )
[x.remove() for x in self.handles]
return self
@property
def __snake_case ( self : int ):
'''simple docstring'''
return list(filter(lambda snake_case__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class __magic_name__ :
__A : nn.Module
__A : nn.Module
__A : int = 0
__A : List = field(default_factory=__UpperCAmelCase )
__A : List = field(default_factory=__UpperCAmelCase )
def __call__( self : Dict , snake_case__ : Tensor ):
'''simple docstring'''
lowercase :Dict = Tracker(self.dest )(snake_case__ ).parametrized
lowercase :Optional[Any] = Tracker(self.src )(snake_case__ ).parametrized
lowercase :List[str] = list(filter(lambda snake_case__ : type(snake_case__ ) not in self.src_skip , snake_case__ ) )
lowercase :Tuple = list(filter(lambda snake_case__ : type(snake_case__ ) not in self.dest_skip , snake_case__ ) )
if len(snake_case__ ) != len(snake_case__ ):
raise Exception(
f"""Numbers of operations are different. Source module has {len(snake_case__ )} operations while"""
f""" destination module has {len(snake_case__ )}.""" )
for dest_m, src_m in zip(snake_case__ , snake_case__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f"""Transfered from={src_m} to={dest_m}""" )
def lowerCamelCase (a_ :str , a_ :ResNetConfig , a_ :Path , a_ :bool = True) -> Optional[Any]:
print(F"""Converting {name}...""")
with torch.no_grad():
lowercase :Union[str, Any] = timm.create_model(a_ , pretrained=a_).eval()
lowercase :Tuple = ResNetForImageClassification(a_).eval()
lowercase :int = ModuleTransfer(src=a_ , dest=a_)
lowercase :List[Any] = torch.randn((1, 3, 224, 224))
module_transfer(a_)
assert torch.allclose(from_model(a_) , our_model(a_).logits), "The model logits don't match the original one."
lowercase :List[Any] = F"""resnet{'-'.join(name.split('resnet'))}"""
print(a_)
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add model''' , use_temp_dir=a_ , )
# we can use the convnext one
lowercase :Any = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''')
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add image processor''' , use_temp_dir=a_ , )
print(F"""Pushed {checkpoint_name}""")
def lowerCamelCase (a_ :Path , a_ :str = None , a_ :bool = True) -> int:
lowercase :Optional[Any] = '''imagenet-1k-id2label.json'''
lowercase :Union[str, Any] = 1000
lowercase :Any = (1, num_labels)
lowercase :Tuple = '''huggingface/label-files'''
lowercase :List[str] = num_labels
lowercase :Union[str, Any] = json.load(open(hf_hub_download(a_ , a_ , repo_type='''dataset''') , '''r'''))
lowercase :Any = {int(a_): v for k, v in idalabel.items()}
lowercase :str = idalabel
lowercase :Any = {v: k for k, v in idalabel.items()}
lowercase :Union[str, Any] = partial(a_ , num_labels=a_ , idalabel=a_ , labelaid=a_)
lowercase :Optional[int] = {
'''resnet18''': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[64, 128, 256, 512] , layer_type='''basic'''),
'''resnet26''': ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck'''),
'''resnet34''': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[64, 128, 256, 512] , layer_type='''basic'''),
'''resnet50''': ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck'''),
'''resnet101''': ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck'''),
'''resnet152''': ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type='''bottleneck'''),
}
if model_name:
convert_weight_and_push(a_ , names_to_config[model_name] , a_ , a_)
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(a_ , a_ , a_ , a_)
return config, expected_shape
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help=(
'''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=Path,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
default=True,
type=bool,
required=False,
help='''If True, push model and image processor to the hub.''',
)
UpperCAmelCase = parser.parse_args()
UpperCAmelCase = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 677 | 0 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
A : str = HfApi()
A : List[str] = {}
# fmt: off
A : Optional[Any] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189,
-1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839,
0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557
])
A : Dict = torch.tensor([
-2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436,
1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208,
-2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948,
2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365
])
A : Union[str, Any] = torch.tensor([
-0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869,
-0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304,
-0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925,
0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943
])
A : str = torch.tensor([
0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172,
-0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309,
0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805,
-0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505
])
A : Optional[Any] = torch.tensor([
0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133,
-0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395,
0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559,
-0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386
])
A : List[Any] = torch.tensor([
0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078,
-0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330,
0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683,
-0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431
])
A : Optional[int] = torch.tensor([
0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042,
-0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398,
0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574,
-0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390
])
A : Tuple = torch.tensor([
0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042,
-0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290,
0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746,
-0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473
])
A : Any = torch.tensor([
-1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330,
1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243,
-2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810,
1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251])
A : Union[str, Any] = torch.tensor([
-1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324,
0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181,
-2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259,
1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266
])
A : Tuple = torch.tensor([
-1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212,
0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027,
-2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131,
1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355
])
A : Dict = torch.tensor([
-2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959,
1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351,
-3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341,
3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066
])
A : Tuple = torch.tensor([
-2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740,
1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398,
-2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395,
2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243
])
A : List[str] = torch.tensor([
-2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336,
1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908,
-3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560,
3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343
])
A : Union[str, Any] = torch.tensor([
-1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344,
1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391,
-2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439,
1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219
])
# fmt: on
A : Any = api.list_models(filter='''diffusers''')
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
A : Union[str, Any] = '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]
print(F'''Started running {mod.modelId}!!!''')
if mod.modelId.startswith('''CompVis'''):
A : List[str] = UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
else:
A : List[str] = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
A : int = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
A : Optional[int] = torch.tensor([1_0] * noise.shape[0])
with torch.no_grad():
A : Any = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :3_0], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1e-3
)
print(F'''{mod.modelId} has passed successfully!!!''')
| 711 |
from ..utils import DummyObject, requires_backends
class A (metaclass=SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCamelCase : Any = ['''keras_nlp''']
def __init__( self : Any , *__lowerCAmelCase : Any , **__lowerCAmelCase : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ["""keras_nlp"""] )
| 247 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : Optional[int] = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
),
}
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = '''dpr'''
def __init__( self :Dict , __magic_name__ :Optional[int]=3_0522 , __magic_name__ :Optional[Any]=768 , __magic_name__ :Optional[int]=12 , __magic_name__ :int=12 , __magic_name__ :List[Any]=3072 , __magic_name__ :int="gelu" , __magic_name__ :Optional[int]=0.1 , __magic_name__ :Optional[Any]=0.1 , __magic_name__ :List[str]=512 , __magic_name__ :List[str]=2 , __magic_name__ :str=0.02 , __magic_name__ :List[Any]=1E-1_2 , __magic_name__ :Dict=0 , __magic_name__ :List[Any]="absolute" , __magic_name__ :int = 0 , **__magic_name__ :Any , ):
'''simple docstring'''
super().__init__(pad_token_id=__magic_name__ , **__magic_name__ )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = projection_dim
a = position_embedding_type
| 468 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __A ( __lowerCamelCase , __lowerCamelCase=None ) -> Tuple:
a = None
if token is not None:
a = {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}
a = f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
a = requests.get(__lowerCamelCase , headers=__lowerCamelCase ).json()
a = {}
try:
job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
a = math.ceil((result["""total_count"""] - 100) / 100 )
for i in range(__lowerCamelCase ):
a = requests.get(url + f'&page={i + 2}' , headers=__lowerCamelCase ).json()
job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
return job_links
except Exception:
print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
def __A ( __lowerCamelCase , __lowerCamelCase=None ) -> Dict:
a = None
if token is not None:
a = {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}
a = f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
a = requests.get(__lowerCamelCase , headers=__lowerCamelCase ).json()
a = {}
try:
artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} )
a = math.ceil((result["""total_count"""] - 100) / 100 )
for i in range(__lowerCamelCase ):
a = requests.get(url + f'&page={i + 2}' , headers=__lowerCamelCase ).json()
artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} )
return artifacts
except Exception:
print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[int]:
a = None
if token is not None:
a = {"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}
a = requests.get(__lowerCamelCase , headers=__lowerCamelCase , allow_redirects=__lowerCamelCase )
a = result.headers["""Location"""]
a = requests.get(__lowerCamelCase , allow_redirects=__lowerCamelCase )
a = os.path.join(__lowerCamelCase , f'{artifact_name}.zip' )
with open(__lowerCamelCase , """wb""" ) as fp:
fp.write(response.content )
def __A ( __lowerCamelCase , __lowerCamelCase=None ) -> Tuple:
a = []
a = []
a = None
with zipfile.ZipFile(__lowerCamelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(__lowerCamelCase ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(__lowerCamelCase ) as f:
for line in f:
a = line.decode("""UTF-8""" ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
a = line[: line.index(""": """ )]
a = line[line.index(""": """ ) + len(""": """ ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith("""FAILED """ ):
# `test` is the test method that failed
a = line[len("""FAILED """ ) :]
failed_tests.append(__lowerCamelCase )
elif filename == "job_name.txt":
a = line
if len(__lowerCamelCase ) != len(__lowerCamelCase ):
raise ValueError(
f'`errors` and `failed_tests` should have the same number of elements. Got {len(__lowerCamelCase )} for `errors` '
f'and {len(__lowerCamelCase )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
""" problem.""" )
a = None
if job_name and job_links:
a = job_links.get(__lowerCamelCase , __lowerCamelCase )
# A list with elements of the form (line of error, error, failed test)
a = [x + [y] + [job_link] for x, y in zip(__lowerCamelCase , __lowerCamelCase )]
return result
def __A ( __lowerCamelCase , __lowerCamelCase=None ) -> Dict:
a = []
a = [os.path.join(__lowerCamelCase , __lowerCamelCase ) for p in os.listdir(__lowerCamelCase ) if p.endswith(""".zip""" )]
for p in paths:
errors.extend(get_errors_from_single_artifact(__lowerCamelCase , job_links=__lowerCamelCase ) )
return errors
def __A ( __lowerCamelCase , __lowerCamelCase=None ) -> Tuple:
a = Counter()
counter.update([x[1] for x in logs] )
a = counter.most_common()
a = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
a = {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}
a = dict(sorted(r.items() , key=lambda __lowerCamelCase : item[1]["count"] , reverse=__lowerCamelCase ) )
return r
def __A ( __lowerCamelCase ) -> List[str]:
a = test.split("""::""" )[0]
if test.startswith("""tests/models/""" ):
a = test.split("""/""" )[2]
else:
a = None
return test
def __A ( __lowerCamelCase , __lowerCamelCase=None ) -> Any:
a = [(x[0], x[1], get_model(x[2] )) for x in logs]
a = [x for x in logs if x[2] is not None]
a = {x[2] for x in logs}
a = {}
for test in tests:
a = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
a = counter.most_common()
a = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
a = sum(error_counts.values() )
if n_errors > 0:
a = {"""count""": n_errors, """errors""": error_counts}
a = dict(sorted(r.items() , key=lambda __lowerCamelCase : item[1]["count"] , reverse=__lowerCamelCase ) )
return r
def __A ( __lowerCamelCase ) -> Optional[int]:
a = """| no. | error | status |"""
a = """|-:|:-|:-|"""
a = [header, sep]
for error in reduced_by_error:
a = reduced_by_error[error]["""count"""]
a = f'| {count} | {error[:100]} | |'
lines.append(__lowerCamelCase )
return "\n".join(__lowerCamelCase )
def __A ( __lowerCamelCase ) -> int:
a = """| model | no. of errors | major error | count |"""
a = """|-:|-:|-:|-:|"""
a = [header, sep]
for model in reduced_by_model:
a = reduced_by_model[model]["""count"""]
a , a = list(reduced_by_model[model]["""errors"""].items() )[0]
a = f'| {model} | {count} | {error[:60]} | {_count} |'
lines.append(__lowerCamelCase )
return "\n".join(__lowerCamelCase )
if __name__ == "__main__":
__UpperCamelCase : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Where to store the downloaded artifacts and other result files.",
)
parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
__UpperCamelCase : Any = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
__UpperCamelCase : Optional[Any] = get_job_links(args.workflow_run_id, token=args.token)
__UpperCamelCase : str = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
__UpperCamelCase : List[str] = k.find(" / ")
__UpperCamelCase : List[Any] = k[index + len(" / ") :]
__UpperCamelCase : List[str] = v
with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
__UpperCamelCase : Tuple = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
__UpperCamelCase : int = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
__UpperCamelCase : Optional[Any] = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
__UpperCamelCase : Any = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
__UpperCamelCase : Union[str, Any] = reduce_by_error(errors)
__UpperCamelCase : Dict = reduce_by_model(errors)
__UpperCamelCase : Union[str, Any] = make_github_table(reduced_by_error)
__UpperCamelCase : Union[str, Any] = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
fp.write(sa)
| 468 | 1 |
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def lowercase__ ( self : Optional[Any] , __A : Optional[int] , __A : List[Any] , __A : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
self.assertEqual(len(__A ) , len(__A ) )
for a, b in zip(__A , __A ):
self.assertAlmostEqual(__A , __A , delta=__A )
def lowercase__ ( self : str ) -> Tuple:
'''simple docstring'''
lowerCAmelCase__ = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(__A ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1E-2 )
def lowercase__ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
lowerCAmelCase__ = None
ops.enable_eager_execution_internal()
lowerCAmelCase__ = tf.config.list_physical_devices("""CPU""" )
if len(__A ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
lowerCAmelCase__ = tf.config.list_logical_devices(device_type="""CPU""" )
lowerCAmelCase__ = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
lowerCAmelCase__ = GradientAccumulator()
lowerCAmelCase__ = tf.Variable([4.0, 3.0] )
lowerCAmelCase__ ,lowerCAmelCase__ = create_optimizer(5E-5 , 10 , 5 )
lowerCAmelCase__ = tf.Variable([0.0, 0.0] , trainable=__A )
def accumulate_on_replica(__A : str ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(__A : List[Any] , __A : Tuple ):
with strategy.scope():
lowerCAmelCase__ = strategy.experimental_local_results(__A )
local_variables[0].assign(__A )
local_variables[1].assign(__A )
strategy.run(__A , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(__A )
def _check_local_values(__A : Any , __A : List[Any] ):
lowerCAmelCase__ = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , __A , tol=1E-2 )
self.assertListAlmostEqual(values[1].value() , __A , tol=1E-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 211 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def lowercase__ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase__ = tempfile.mkdtemp()
lowerCAmelCase__ = BlipImageProcessor()
lowerCAmelCase__ = GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""" )
lowerCAmelCase__ = BlipaProcessor(__A , __A )
processor.save_pretrained(self.tmpdirname )
def lowercase__ ( self : Optional[Any] , **__A : Optional[int] ) -> List[Any]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **__A ).tokenizer
def lowercase__ ( self : Dict , **__A : List[Any] ) -> Dict:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **__A ).image_processor
def lowercase__ ( self : Any ) -> Any:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowercase__ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowerCAmelCase__ = [Image.fromarray(np.moveaxis(__A , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase__ ( self : Dict ) -> List[str]:
'''simple docstring'''
lowerCAmelCase__ = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowerCAmelCase__ = self.get_image_processor(do_normalize=__A , padding_value=1.0 )
lowerCAmelCase__ = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=__A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __A )
def lowercase__ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = BlipaProcessor(tokenizer=__A , image_processor=__A )
lowerCAmelCase__ = self.prepare_image_inputs()
lowerCAmelCase__ = image_processor(__A , return_tensors="""np""" )
lowerCAmelCase__ = processor(images=__A , 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 lowercase__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = BlipaProcessor(tokenizer=__A , image_processor=__A )
lowerCAmelCase__ = """lower newer"""
lowerCAmelCase__ = processor(text=__A )
lowerCAmelCase__ = tokenizer(__A , return_token_type_ids=__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase__ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = BlipaProcessor(tokenizer=__A , image_processor=__A )
lowerCAmelCase__ = """lower newer"""
lowerCAmelCase__ = self.prepare_image_inputs()
lowerCAmelCase__ = processor(text=__A , images=__A )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
# test if it raises when no input is passed
with pytest.raises(__A ):
processor()
def lowercase__ ( self : Tuple ) -> str:
'''simple docstring'''
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = BlipaProcessor(tokenizer=__A , image_processor=__A )
lowerCAmelCase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase__ = processor.batch_decode(__A )
lowerCAmelCase__ = tokenizer.batch_decode(__A )
self.assertListEqual(__A , __A )
def lowercase__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = BlipaProcessor(tokenizer=__A , image_processor=__A )
lowerCAmelCase__ = """lower newer"""
lowerCAmelCase__ = self.prepare_image_inputs()
lowerCAmelCase__ = processor(text=__A , images=__A )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """input_ids""", """attention_mask"""] )
| 211 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase = logging.get_logger(__name__)
def a__ ( lowerCAmelCase__ ):
# initialize config
if "resnet-50" in model_name:
UpperCAmelCase_ = ResNetConfig.from_pretrained("microsoft/resnet-50" )
elif "resnet-101" in model_name:
UpperCAmelCase_ = ResNetConfig.from_pretrained("microsoft/resnet-101" )
else:
raise ValueError("Model name should include either resnet50 or resnet101" )
UpperCAmelCase_ = DetrConfig(use_timm_backbone=lowerCAmelCase__ , backbone_config=lowerCAmelCase__ )
# set label attributes
UpperCAmelCase_ = "panoptic" in model_name
if is_panoptic:
UpperCAmelCase_ = 250
else:
UpperCAmelCase_ = 91
UpperCAmelCase_ = "huggingface/label-files"
UpperCAmelCase_ = "coco-detection-id2label.json"
UpperCAmelCase_ = json.load(open(hf_hub_download(lowerCAmelCase__ , lowerCAmelCase__ , repo_type="dataset" ) , "r" ) )
UpperCAmelCase_ = {int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
UpperCAmelCase_ = idalabel
UpperCAmelCase_ = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def a__ ( lowerCAmelCase__ ):
# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase_ = []
# stem
# fmt: off
rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight") )
rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight") )
rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias") )
rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean") )
rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var") )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
) )
# fmt: on
for i in range(config.encoder_layers ):
# 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 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.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"),
] )
return rename_keys
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = state_dict.pop(lowerCAmelCase__ )
UpperCAmelCase_ = val
def a__ ( lowerCAmelCase__ , lowerCAmelCase__=False ):
UpperCAmelCase_ = ""
if is_panoptic:
UpperCAmelCase_ = "detr."
# 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)
UpperCAmelCase_ = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
UpperCAmelCase_ = 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
UpperCAmelCase_ = in_proj_weight[:256, :]
UpperCAmelCase_ = in_proj_bias[:256]
UpperCAmelCase_ = in_proj_weight[256:512, :]
UpperCAmelCase_ = in_proj_bias[256:512]
UpperCAmelCase_ = in_proj_weight[-256:, :]
UpperCAmelCase_ = 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
UpperCAmelCase_ = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
UpperCAmelCase_ = 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
UpperCAmelCase_ = in_proj_weight[:256, :]
UpperCAmelCase_ = in_proj_bias[:256]
UpperCAmelCase_ = in_proj_weight[256:512, :]
UpperCAmelCase_ = in_proj_bias[256:512]
UpperCAmelCase_ = in_proj_weight[-256:, :]
UpperCAmelCase_ = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
UpperCAmelCase_ = state_dict.pop(
f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
UpperCAmelCase_ = 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
UpperCAmelCase_ = in_proj_weight_cross_attn[:256, :]
UpperCAmelCase_ = in_proj_bias_cross_attn[:256]
UpperCAmelCase_ = in_proj_weight_cross_attn[256:512, :]
UpperCAmelCase_ = in_proj_bias_cross_attn[256:512]
UpperCAmelCase_ = in_proj_weight_cross_attn[-256:, :]
UpperCAmelCase_ = in_proj_bias_cross_attn[-256:]
def a__ ( ):
UpperCAmelCase_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
UpperCAmelCase_ = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw )
return im
@torch.no_grad()
def a__ ( lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=False ):
UpperCAmelCase_ , UpperCAmelCase_ = get_detr_config(lowerCAmelCase__ )
# load original model from torch hub
UpperCAmelCase_ = {
"detr-resnet-50": "detr_resnet50",
"detr-resnet-101": "detr_resnet101",
}
logger.info(f"""Converting model {model_name}...""" )
UpperCAmelCase_ = torch.hub.load("facebookresearch/detr" , model_name_to_original_name[model_name] , pretrained=lowerCAmelCase__ ).eval()
UpperCAmelCase_ = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(lowerCAmelCase__ ):
if is_panoptic:
UpperCAmelCase_ = "detr." + src
rename_key(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCAmelCase__ , is_panoptic=lowerCAmelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCAmelCase_ = "detr.model." if is_panoptic else "model."
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("detr" )
and not key.startswith("class_labels_classifier" )
and not key.startswith("bbox_predictor" )
):
UpperCAmelCase_ = state_dict.pop(lowerCAmelCase__ )
UpperCAmelCase_ = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
UpperCAmelCase_ = state_dict.pop(lowerCAmelCase__ )
UpperCAmelCase_ = val
elif key.startswith("bbox_attention" ) or key.startswith("mask_head" ):
continue
else:
UpperCAmelCase_ = state_dict.pop(lowerCAmelCase__ )
UpperCAmelCase_ = val
else:
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
UpperCAmelCase_ = state_dict.pop(lowerCAmelCase__ )
UpperCAmelCase_ = val
# finally, create HuggingFace model and load state dict
UpperCAmelCase_ = DetrForSegmentation(lowerCAmelCase__ ) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__ )
model.load_state_dict(lowerCAmelCase__ )
model.eval()
# verify our conversion on an image
UpperCAmelCase_ = "coco_panoptic" if is_panoptic else "coco_detection"
UpperCAmelCase_ = DetrImageProcessor(format=lowerCAmelCase__ )
UpperCAmelCase_ = processor(images=prepare_img() , return_tensors="pt" )
UpperCAmelCase_ = encoding["pixel_values"]
UpperCAmelCase_ = detr(lowerCAmelCase__ )
UpperCAmelCase_ = model(lowerCAmelCase__ )
assert torch.allclose(outputs.logits , original_outputs["pred_logits"] , atol=1e-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs["pred_boxes"] , atol=1e-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["pred_masks"] , 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(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
model.save_pretrained(lowerCAmelCase__ )
processor.save_pretrained(lowerCAmelCase__ )
if push_to_hub:
# Upload model and image processor to the hub
logger.info("Uploading PyTorch model and image processor to the hub..." )
model.push_to_hub(f"""nielsr/{model_name}""" )
processor.push_to_hub(f"""nielsr/{model_name}""" )
if __name__ == "__main__":
lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""detr-resnet-50""",
type=str,
choices=["""detr-resnet-50""", """detr-resnet-101"""],
help="""Name of the DETR model 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 to push the model to the hub or not.""")
lowerCamelCase = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 82 |
"""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 : Union[str, Any] , lowerCAmelCase : Optional[int] , lowerCAmelCase : List[str]=13 , lowerCAmelCase : Optional[Any]=32 , lowerCAmelCase : Optional[int]=3 , lowerCAmelCase : List[Any]=4 , lowerCAmelCase : str=[10, 20, 30, 40] , lowerCAmelCase : Any=[2, 2, 3, 2] , lowerCAmelCase : Any=True , lowerCAmelCase : List[Any]=True , lowerCAmelCase : Optional[Any]=37 , lowerCAmelCase : int="gelu" , lowerCAmelCase : List[str]=10 , lowerCAmelCase : List[str]=0.02 , lowerCAmelCase : Tuple=["stage2", "stage3", "stage4"] , lowerCAmelCase : str=[2, 3, 4] , lowerCAmelCase : Union[str, Any]=None , ):
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = image_size
lowerCAmelCase = num_channels
lowerCAmelCase = num_stages
lowerCAmelCase = hidden_sizes
lowerCAmelCase = depths
lowerCAmelCase = is_training
lowerCAmelCase = use_labels
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = num_labels
lowerCAmelCase = initializer_range
lowerCAmelCase = out_features
lowerCAmelCase = out_indices
lowerCAmelCase = scope
def __lowercase ( self : str ):
lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase = None
if self.use_labels:
lowerCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def __lowercase ( self : Dict ):
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=lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def __lowercase ( self : Union[str, Any] , lowerCAmelCase : int , lowerCAmelCase : Dict , lowerCAmelCase : Dict ):
lowerCAmelCase = ConvNextVaModel(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCAmelCase = model(lowerCAmelCase )
# 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 __lowercase ( self : Dict , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : List[str] , lowerCAmelCase : str ):
lowerCAmelCase = ConvNextVaForImageClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCAmelCase = model(lowerCAmelCase , labels=lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase ( self : int , lowerCAmelCase : Optional[Any] , lowerCAmelCase : List[str] , lowerCAmelCase : str ):
lowerCAmelCase = ConvNextVaBackbone(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCAmelCase = model(lowerCAmelCase )
# 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
lowerCAmelCase = None
lowerCAmelCase = ConvNextVaBackbone(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCAmelCase = model(lowerCAmelCase )
# 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 __lowercase ( self : Optional[Any] ):
lowerCAmelCase = self.prepare_config_and_inputs()
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = config_and_inputs
lowerCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
def __lowercase ( self : Tuple ):
lowerCAmelCase = self.prepare_config_and_inputs()
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = config_and_inputs
lowerCAmelCase = {"""pixel_values""": pixel_values, """labels""": labels}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( _a , _a , unittest.TestCase ):
_a = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
_a = (
{'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
_a = False
_a = False
_a = False
_a = False
_a = False
def __lowercase ( self : Any ):
lowerCAmelCase = ConvNextVaModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=lowerCAmelCase , has_text_modality=lowerCAmelCase , hidden_size=37 )
def __lowercase ( self : Optional[int] ):
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 __lowercase ( self : Dict ):
return
@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds""" )
def __lowercase ( self : Dict ):
pass
@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings""" )
def __lowercase ( self : Optional[Any] ):
pass
@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking""" )
def __lowercase ( self : Dict ):
pass
def __lowercase ( self : Union[str, Any] ):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowerCAmelCase , lowerCAmelCase = self.model_tester.prepare_config_and_inputs_with_labels()
lowerCAmelCase = True
if model_class.__name__ in [
*get_values(lowerCAmelCase ),
*get_values(lowerCAmelCase ),
]:
continue
lowerCAmelCase = model_class(lowerCAmelCase )
model.to(lowerCAmelCase )
model.train()
lowerCAmelCase = self._prepare_for_class(lowerCAmelCase , lowerCAmelCase , return_labels=lowerCAmelCase )
lowerCAmelCase = model(**lowerCAmelCase ).loss
loss.backward()
def __lowercase ( self : Optional[int] ):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
lowerCAmelCase , lowerCAmelCase = self.model_tester.prepare_config_and_inputs_with_labels()
lowerCAmelCase = False
lowerCAmelCase = True
if (
model_class.__name__
in [*get_values(lowerCAmelCase ), *get_values(lowerCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
lowerCAmelCase = model_class(lowerCAmelCase )
model.to(lowerCAmelCase )
model.gradient_checkpointing_enable()
model.train()
lowerCAmelCase = self._prepare_for_class(lowerCAmelCase , lowerCAmelCase , return_labels=lowerCAmelCase )
lowerCAmelCase = model(**lowerCAmelCase ).loss
loss.backward()
def __lowercase ( self : Dict ):
lowerCAmelCase , lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase = model_class(lowerCAmelCase )
lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase = [*signature.parameters.keys()]
lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCAmelCase )
def __lowercase ( self : str ):
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase )
def __lowercase ( self : Union[str, Any] ):
def check_hidden_states_output(lowerCAmelCase : List[Any] , lowerCAmelCase : Any , lowerCAmelCase : List[Any] ):
lowerCAmelCase = model_class(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase = model(**self._prepare_for_class(lowerCAmelCase , lowerCAmelCase ) )
lowerCAmelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCAmelCase = self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase ) , 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] , )
lowerCAmelCase , lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase = True
check_hidden_states_output(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase = True
check_hidden_states_output(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def __lowercase ( self : int ):
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase )
@slow
def __lowercase ( self : Any ):
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = ConvNextVaModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
def lowercase () -> List[str]:
'''simple docstring'''
lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@cached_property
def __lowercase ( self : int ):
return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224""" ) if is_vision_available() else None
@slow
def __lowercase ( self : int ):
lowerCAmelCase = ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224""" ).to(lowerCAmelCase )
lowerCAmelCase = self.default_image_processor
lowerCAmelCase = prepare_img()
lowerCAmelCase = preprocessor(images=lowerCAmelCase , return_tensors="""pt""" ).to(lowerCAmelCase )
# forward pass
with torch.no_grad():
lowerCAmelCase = model(**lowerCAmelCase )
# verify the logits
lowerCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase )
lowerCAmelCase = torch.tensor([0.9996, 0.1966, -0.4386] ).to(lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCAmelCase , atol=1e-4 ) )
| 169 | 0 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( lowerCAmelCase , unittest.TestCase ):
UpperCAmelCase_ = ProphetNetTokenizer
UpperCAmelCase_ = False
def UpperCAmelCase_ ( self :Union[str, Any] ) -> str:
super().setUp()
UpperCAmelCase__ = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
UpperCAmelCase__ = 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] ) )
def UpperCAmelCase_ ( self :List[Any] , lowerCamelCase :int ) -> Dict:
UpperCAmelCase__ = "UNwant\u00E9d,running"
UpperCAmelCase__ = "unwanted, running"
return input_text, output_text
def UpperCAmelCase_ ( self :Tuple ) -> Optional[Any]:
UpperCAmelCase__ = self.tokenizer_class(self.vocab_file )
UpperCAmelCase__ = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(lowerCamelCase , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase ) , [9, 6, 7, 12, 10, 11] )
def UpperCAmelCase_ ( self :List[str] ) -> Any:
UpperCAmelCase__ = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def UpperCAmelCase_ ( self :Dict ) -> Optional[Any]:
UpperCAmelCase__ = BasicTokenizer(do_lower_case=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCAmelCase_ ( self :List[str] ) -> Dict:
UpperCAmelCase__ = BasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def UpperCAmelCase_ ( self :Any ) -> int:
UpperCAmelCase__ = BasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCAmelCase_ ( self :Tuple ) -> Union[str, Any]:
UpperCAmelCase__ = BasicTokenizer(do_lower_case=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCAmelCase_ ( self :Optional[int] ) -> Any:
UpperCAmelCase__ = BasicTokenizer(do_lower_case=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCAmelCase_ ( self :Dict ) -> int:
UpperCAmelCase__ = BasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCAmelCase_ ( self :Optional[Any] ) -> List[str]:
UpperCAmelCase__ = BasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCAmelCase_ ( self :Tuple ) -> Optional[int]:
UpperCAmelCase__ = BasicTokenizer(do_lower_case=lowerCamelCase , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def UpperCAmelCase_ ( self :str ) -> Any:
UpperCAmelCase__ = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
UpperCAmelCase__ = {}
for i, token in enumerate(lowerCamelCase ):
UpperCAmelCase__ = i
UpperCAmelCase__ = WordpieceTokenizer(vocab=lowerCamelCase , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("unwanted running" ) , ["un", "##want", "##ed", "runn", "##ing"] )
self.assertListEqual(tokenizer.tokenize("unwantedX running" ) , ["[UNK]", "runn", "##ing"] )
@require_torch
def UpperCAmelCase_ ( self :Optional[int] ) -> str:
UpperCAmelCase__ = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased" )
UpperCAmelCase__ = ["A long paragraph for summarization.", "Another paragraph for summarization."]
UpperCAmelCase__ = [1037, 2146, 2_0423, 2005, 7680, 7849, 3989, 1012, 102]
UpperCAmelCase__ = tokenizer(lowerCamelCase , padding=lowerCamelCase , return_tensors="pt" )
self.assertIsInstance(lowerCamelCase , lowerCamelCase )
UpperCAmelCase__ = list(batch.input_ids.numpy()[0] )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def UpperCAmelCase_ ( self :int ) -> int:
self.assertTrue(_is_whitespace(" " ) )
self.assertTrue(_is_whitespace("\t" ) )
self.assertTrue(_is_whitespace("\r" ) )
self.assertTrue(_is_whitespace("\n" ) )
self.assertTrue(_is_whitespace("\u00A0" ) )
self.assertFalse(_is_whitespace("A" ) )
self.assertFalse(_is_whitespace("-" ) )
def UpperCAmelCase_ ( self :List[str] ) -> int:
self.assertTrue(_is_control("\u0005" ) )
self.assertFalse(_is_control("A" ) )
self.assertFalse(_is_control(" " ) )
self.assertFalse(_is_control("\t" ) )
self.assertFalse(_is_control("\r" ) )
def UpperCAmelCase_ ( self :int ) -> int:
self.assertTrue(_is_punctuation("-" ) )
self.assertTrue(_is_punctuation("$" ) )
self.assertTrue(_is_punctuation("`" ) )
self.assertTrue(_is_punctuation("." ) )
self.assertFalse(_is_punctuation("A" ) )
self.assertFalse(_is_punctuation(" " ) )
@slow
def UpperCAmelCase_ ( self :str ) -> str:
UpperCAmelCase__ = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased" )
UpperCAmelCase__ = tokenizer.encode("sequence builders" , add_special_tokens=lowerCamelCase )
UpperCAmelCase__ = tokenizer.encode("multi-sequence build" , add_special_tokens=lowerCamelCase )
UpperCAmelCase__ = tokenizer.build_inputs_with_special_tokens(lowerCamelCase )
UpperCAmelCase__ = tokenizer.build_inputs_with_special_tokens(lowerCamelCase , lowerCamelCase )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 364 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_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 ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class _UpperCamelCase :
def __init__( self :Any , lowerCamelCase :List[str] , lowerCamelCase :Optional[int]=13 , lowerCamelCase :Optional[Any]=2 , lowerCamelCase :Any=24 , lowerCamelCase :Union[str, Any]=16 , lowerCamelCase :Any=True , lowerCamelCase :int=True , lowerCamelCase :Optional[Any]=32 , lowerCamelCase :Union[str, Any]=5 , lowerCamelCase :Tuple=4 , lowerCamelCase :Optional[Any]=37 , lowerCamelCase :Optional[Any]="gelu" , lowerCamelCase :int=0.1 , lowerCamelCase :Tuple=0.1 , lowerCamelCase :List[str]=10 , lowerCamelCase :Optional[Any]=0.02 , lowerCamelCase :Optional[int]=None , lowerCamelCase :Optional[Any]=2 , lowerCamelCase :List[Any]=2 , ) -> Union[str, Any]:
UpperCAmelCase__ = parent
UpperCAmelCase__ = batch_size
UpperCAmelCase__ = patch_size
UpperCAmelCase__ = max_length
UpperCAmelCase__ = num_mel_bins
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__ = frequency_stride
UpperCAmelCase__ = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
UpperCAmelCase__ = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
UpperCAmelCase__ = (self.max_length - self.patch_size) // self.time_stride + 1
UpperCAmelCase__ = frequency_out_dimension * time_out_dimension
UpperCAmelCase__ = num_patches + 2
def UpperCAmelCase_ ( self :int ) -> List[str]:
UpperCAmelCase__ = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
UpperCAmelCase__ = None
if self.use_labels:
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ = self.get_config()
return config, input_values, labels
def UpperCAmelCase_ ( self :List[Any] ) -> Any:
return ASTConfig(
patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , 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=lowerCamelCase , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def UpperCAmelCase_ ( self :List[str] , lowerCamelCase :List[str] , lowerCamelCase :List[str] , lowerCamelCase :List[str] ) -> Optional[Any]:
UpperCAmelCase__ = ASTModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
UpperCAmelCase__ = model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self :List[Any] ) -> str:
UpperCAmelCase__ = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) = config_and_inputs
UpperCAmelCase__ = {"input_values": input_values}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase ):
UpperCAmelCase_ = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
UpperCAmelCase_ = (
{"""audio-classification""": ASTForAudioClassification, """feature-extraction""": ASTModel}
if is_torch_available()
else {}
)
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = False
def UpperCAmelCase_ ( self :List[str] , lowerCamelCase :Optional[int] , lowerCamelCase :List[Any] , lowerCamelCase :str , lowerCamelCase :List[Any] , lowerCamelCase :int ) -> str:
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def UpperCAmelCase_ ( self :List[str] ) -> int:
UpperCAmelCase__ = ASTModelTester(self )
UpperCAmelCase__ = ConfigTester(self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase , hidden_size=37 )
def UpperCAmelCase_ ( self :Tuple ) -> str:
self.config_tester.run_common_tests()
@unittest.skip(reason="AST does not use inputs_embeds" )
def UpperCAmelCase_ ( self :List[str] ) -> Optional[Any]:
pass
def UpperCAmelCase_ ( self :Optional[int] ) -> Any:
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ = model_class(lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase , nn.Linear ) )
def UpperCAmelCase_ ( self :Tuple ) -> List[str]:
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ = model_class(lowerCamelCase )
UpperCAmelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ = [*signature.parameters.keys()]
UpperCAmelCase__ = ["input_values"]
self.assertListEqual(arg_names[:1] , lowerCamelCase )
def UpperCAmelCase_ ( self :int ) -> Any:
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
@slow
def UpperCAmelCase_ ( self :int ) -> Optional[Any]:
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ = ASTModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def lowerCAmelCase ( ):
"""simple docstring"""
UpperCAmelCase__ = hf_hub_download(
repo_id="nielsr/audio-spectogram-transformer-checkpoint" , filename="sample_audio.flac" , repo_type="dataset" )
UpperCAmelCase__ , UpperCAmelCase__ = torchaudio.load(_lowerCAmelCase )
return audio, sampling_rate
@require_torch
@require_torchaudio
class _UpperCamelCase ( unittest.TestCase ):
@cached_property
def UpperCAmelCase_ ( self :str ) -> Dict:
return (
ASTFeatureExtractor.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593" )
if is_torchaudio_available()
else None
)
@slow
def UpperCAmelCase_ ( self :str ) -> Optional[int]:
UpperCAmelCase__ = self.default_feature_extractor
UpperCAmelCase__ = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593" ).to(lowerCamelCase )
UpperCAmelCase__ = self.default_feature_extractor
UpperCAmelCase__ , UpperCAmelCase__ = prepare_audio()
UpperCAmelCase__ = audio.squeeze().numpy()
UpperCAmelCase__ = feature_extractor(lowerCamelCase , sampling_rate=lowerCamelCase , return_tensors="pt" ).to(lowerCamelCase )
# forward pass
with torch.no_grad():
UpperCAmelCase__ = model(**lowerCamelCase )
# verify the logits
UpperCAmelCase__ = torch.Size((1, 527) )
self.assertEqual(outputs.logits.shape , lowerCamelCase )
UpperCAmelCase__ = torch.tensor([-0.87_60, -7.00_42, -8.66_02] ).to(lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase , atol=1e-4 ) )
| 364 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_snake_case = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_snake_case = """\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""
_snake_case = """\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
'google_bleu': google_bleu score
Examples:
Example 1:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results[\"google_bleu\"], 2))
0.44
Example 2:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results[\"google_bleu\"], 2))
0.61
Example 3:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results[\"google_bleu\"], 2))
0.53
Example 4:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results[\"google_bleu\"], 2))
0.4
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _snake_case ( datasets.Metric ):
def _lowerCamelCase ( self: str ) -> Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ) , id="references" ),
} ) , )
def _lowerCamelCase ( self: Union[str, Any] , __lowerCamelCase: Any , __lowerCamelCase: Dict , __lowerCamelCase: Optional[int] = 1 , __lowerCamelCase: Optional[int] = 4 , ) -> Any:
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=lowerCamelCase__ , hypotheses=lowerCamelCase__ , min_len=lowerCamelCase__ , max_len=lowerCamelCase__ )
}
| 382 |
def __UpperCamelCase ( _lowerCAmelCase = 1000 ) -> int:
"""simple docstring"""
A , A : str = 1, 1
A : List[Any] = []
for i in range(1 , n + 1 ):
A : Optional[int] = prev_numerator + 2 * prev_denominator
A : Any = prev_numerator + prev_denominator
if len(str(_lowerCAmelCase ) ) > len(str(_lowerCAmelCase ) ):
result.append(_lowerCAmelCase )
A : int = numerator
A : int = denominator
return len(_lowerCAmelCase )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 662 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
UpperCamelCase__ : Tuple = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
" Distillation"
)
)
parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
parser.add_argument("--model_name", default="roberta-large", type=str)
parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
parser.add_argument("--vocab_transform", action="store_true")
UpperCamelCase__ : Optional[Any] = parser.parse_args()
if args.model_type == "roberta":
UpperCamelCase__ : Tuple = RobertaForMaskedLM.from_pretrained(args.model_name)
UpperCamelCase__ : List[str] = "roberta"
elif args.model_type == "gpt2":
UpperCamelCase__ : Any = GPTaLMHeadModel.from_pretrained(args.model_name)
UpperCamelCase__ : List[str] = "transformer"
UpperCamelCase__ : Tuple = model.state_dict()
UpperCamelCase__ : Tuple = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
UpperCamelCase__ : Dict = state_dict[f"""{prefix}.{param_name}"""]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
UpperCamelCase__ : List[str] = f"""{prefix}.embeddings.{w}.weight"""
UpperCamelCase__ : List[Any] = state_dict[param_name]
for w in ["weight", "bias"]:
UpperCamelCase__ : Optional[Any] = f"""{prefix}.embeddings.LayerNorm.{w}"""
UpperCamelCase__ : Union[str, Any] = state_dict[param_name]
# Transformer Blocks #
UpperCamelCase__ : List[Any] = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
UpperCamelCase__ : Tuple = state_dict[
f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
]
UpperCamelCase__ : List[str] = state_dict[f"""{prefix}.h.{teacher_idx}.attn.bias"""]
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
UpperCamelCase__ : Optional[Any] = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
UpperCamelCase__ : Any = state_dict[f"""{layer}"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
UpperCamelCase__ : Dict = state_dict[f"""lm_head.dense.{w}"""]
UpperCamelCase__ : int = state_dict[f"""lm_head.layer_norm.{w}"""]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
UpperCamelCase__ : Dict = state_dict[f"""{prefix}.ln_f.{w}"""]
UpperCamelCase__ : Optional[Any] = state_dict["lm_head.weight"]
print(f"""N layers selected for distillation: {std_idx}""")
print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=99 , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=16 , A__=2 , A__=0.02 , A__=3 , A__=4 , A__=None , ) -> int:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = 13
_SCREAMING_SNAKE_CASE = 7
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = 99
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = 37
_SCREAMING_SNAKE_CASE = """gelu"""
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 5_12
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 0.02
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=A__ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModel(config=A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_SCREAMING_SNAKE_CASE = [input_ids, input_mask]
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> str:
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = TFRoFormerForCausalLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )["""logits"""]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForSequenceClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = TFRoFormerForMultipleChoice(config=A__ )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForTokenClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = TFRoFormerForQuestionAnswering(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> str:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" )
self.assertIsNotNone(A__ )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
_SCREAMING_SNAKE_CASE = tf.constant([[0, 1, 2, 3, 4, 5]] )
_SCREAMING_SNAKE_CASE = model(A__ )[0]
# TODO Replace vocab size
_SCREAMING_SNAKE_CASE = 5_00_00
_SCREAMING_SNAKE_CASE = [1, 6, vocab_size]
self.assertEqual(output.shape , A__ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
_SCREAMING_SNAKE_CASE = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , A__ , atol=1E-4 )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant([[4, 10]] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
_SCREAMING_SNAKE_CASE = emba(input_ids.shape )
_SCREAMING_SNAKE_CASE = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12 )
emba([2, 16, 5_12] )
_SCREAMING_SNAKE_CASE = emba.weight[:3, :5]
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> int:
# 2,12,16,64
_SCREAMING_SNAKE_CASE = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
_SCREAMING_SNAKE_CASE = embed_positions([2, 16, 7_68] )[None, None, :, :]
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
A__ , A__ , A__ )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
| 0 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
"""microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}
class lowercase__ ( A_ ):
__UpperCAmelCase = '''markuplm'''
def __init__( self , SCREAMING_SNAKE_CASE=3_0522 , SCREAMING_SNAKE_CASE=768 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=3072 , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=512 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=1e-1_2 , SCREAMING_SNAKE_CASE=0 , SCREAMING_SNAKE_CASE=0 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=256 , SCREAMING_SNAKE_CASE=1024 , SCREAMING_SNAKE_CASE=216 , SCREAMING_SNAKE_CASE=1001 , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=50 , SCREAMING_SNAKE_CASE="absolute" , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE , ) -> int:
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE , bos_token_id=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
_lowerCamelCase : str = vocab_size
_lowerCamelCase : Union[str, Any] = hidden_size
_lowerCamelCase : List[Any] = num_hidden_layers
_lowerCamelCase : Any = num_attention_heads
_lowerCamelCase : Union[str, Any] = hidden_act
_lowerCamelCase : Optional[int] = intermediate_size
_lowerCamelCase : Optional[int] = hidden_dropout_prob
_lowerCamelCase : Union[str, Any] = attention_probs_dropout_prob
_lowerCamelCase : List[Any] = max_position_embeddings
_lowerCamelCase : Optional[int] = type_vocab_size
_lowerCamelCase : int = initializer_range
_lowerCamelCase : Tuple = layer_norm_eps
_lowerCamelCase : str = position_embedding_type
_lowerCamelCase : List[Any] = use_cache
_lowerCamelCase : Tuple = classifier_dropout
# additional properties
_lowerCamelCase : Tuple = max_depth
_lowerCamelCase : List[Any] = max_xpath_tag_unit_embeddings
_lowerCamelCase : Optional[int] = max_xpath_subs_unit_embeddings
_lowerCamelCase : Tuple = tag_pad_id
_lowerCamelCase : int = subs_pad_id
_lowerCamelCase : Optional[int] = xpath_unit_hidden_size
| 88 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class lowercase__ ( A_ ):
def __init__( self , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE) -> None:
warnings.warn(
"""The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use ImageGPTImageProcessor instead.""" , SCREAMING_SNAKE_CASE , )
super().__init__(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE)
| 88 | 1 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__snake_case = '''.'''
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__snake_case = [
'''Assert''',
'''AssignVariableOp''',
'''EmptyTensorList''',
'''MergeV2Checkpoints''',
'''ReadVariableOp''',
'''ResourceGather''',
'''RestoreV2''',
'''SaveV2''',
'''ShardedFilename''',
'''StatefulPartitionedCall''',
'''StaticRegexFullMatch''',
'''VarHandleOp''',
]
def A_ ( _lowerCAmelCase : List[Any], _lowerCAmelCase : Tuple, _lowerCAmelCase : str ):
"""simple docstring"""
_a = SavedModel()
_a = []
with open(os.path.join(lowerCAmelCase__, '''utils''', '''tf_ops''', '''onnx.json''' ) ) as f:
_a = json.load(lowerCAmelCase__ )['''opsets''']
for i in range(1, opset + 1 ):
onnx_ops.extend(onnx_opsets[str(lowerCAmelCase__ )] )
with open(lowerCAmelCase__, '''rb''' ) as f:
saved_model.ParseFromString(f.read() )
_a = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
_a = sorted(lowerCAmelCase__ )
_a = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(lowerCAmelCase__ )
if strict and len(lowerCAmelCase__ ) > 0:
raise Exception(f'Found the following incompatible ops for the opset {opset}:\n' + incompatible_ops )
elif len(lowerCAmelCase__ ) > 0:
print(f'Found the following incompatible ops for the opset {opset}:' )
print(*lowerCAmelCase__, sep='''\n''' )
else:
print(f'The saved model {saved_model_path} can properly be converted with ONNX.' )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
parser.add_argument(
'''--opset''', default=12, type=int, help='''The ONNX opset against which the model has to be tested.'''
)
parser.add_argument(
'''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
)
parser.add_argument(
'''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
)
__snake_case = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset) | 714 |
"""simple docstring"""
from typing import Any
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
if not input_list:
return []
_a = [input_list.count(_lowerCAmelCase ) for value in input_list]
_a = max(_lowerCAmelCase ) # Gets the maximum count in the input list.
# Gets values of modes
return sorted({input_list[i] for i, value in enumerate(_lowerCAmelCase ) if value == y} )
if __name__ == "__main__":
import doctest
doctest.testmod() | 285 | 0 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[str] = ort.SessionOptions()
__snake_case : Tuple = False
return options
def UpperCAmelCase ( self ) -> str:
'''simple docstring'''
__snake_case : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
__snake_case : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
__snake_case : List[str] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy" )
# using the PNDM scheduler by default
__snake_case : List[str] = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="onnx" , safety_checker=UpperCAmelCase , feature_extractor=UpperCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
__snake_case : Tuple = "A red cat sitting on a park bench"
__snake_case : List[str] = np.random.RandomState(0 )
__snake_case : Dict = pipe(
prompt=UpperCAmelCase , image=UpperCAmelCase , mask_image=UpperCAmelCase , strength=0.75 , guidance_scale=7.5 , num_inference_steps=15 , generator=UpperCAmelCase , output_type="np" , )
__snake_case : int = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 1E-2
| 243 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
class _lowerCamelCase ( a ):
"""simple docstring"""
UpperCAmelCase_ : Dict =["input_ids", "attention_mask"]
def __init__( self , UpperCAmelCase="</s>" , UpperCAmelCase="<unk>" , UpperCAmelCase="<pad>" , UpperCAmelCase=125 , UpperCAmelCase=None , **UpperCAmelCase , ) -> None:
'''simple docstring'''
if extra_ids > 0 and additional_special_tokens is None:
__snake_case : Union[str, Any] = [F"""<extra_id_{i}>""" for i in range(UpperCAmelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__snake_case : Optional[Any] = len(set(filter(lambda UpperCAmelCase : bool("extra_id" in str(UpperCAmelCase ) ) , UpperCAmelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
" extra_ids tokens" )
__snake_case : List[str] = AddedToken(UpperCAmelCase , lstrip=UpperCAmelCase , rstrip=UpperCAmelCase ) if isinstance(UpperCAmelCase , UpperCAmelCase ) else pad_token
__snake_case : List[str] = AddedToken(UpperCAmelCase , lstrip=UpperCAmelCase , rstrip=UpperCAmelCase ) if isinstance(UpperCAmelCase , UpperCAmelCase ) else eos_token
__snake_case : int = AddedToken(UpperCAmelCase , lstrip=UpperCAmelCase , rstrip=UpperCAmelCase ) if isinstance(UpperCAmelCase , UpperCAmelCase ) else unk_token
super().__init__(
eos_token=UpperCAmelCase , unk_token=UpperCAmelCase , pad_token=UpperCAmelCase , extra_ids=UpperCAmelCase , additional_special_tokens=UpperCAmelCase , **UpperCAmelCase , )
__snake_case : str = extra_ids
__snake_case : List[Any] = 2**8 # utf is 8 bits
# define special tokens dict
__snake_case : Dict[int, str] = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
__snake_case : List[Any] = len(self.special_tokens_encoder )
__snake_case : Optional[int] = len(UpperCAmelCase )
for i, token in enumerate(UpperCAmelCase ):
__snake_case : Tuple = self.vocab_size + i - n
__snake_case : Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def UpperCAmelCase ( self ) -> str:
'''simple docstring'''
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase = None , UpperCAmelCase = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase , token_ids_a=UpperCAmelCase , already_has_special_tokens=UpperCAmelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(UpperCAmelCase )) + [1]
return ([0] * len(UpperCAmelCase )) + [1] + ([0] * len(UpperCAmelCase )) + [1]
def UpperCAmelCase ( self , UpperCAmelCase ) -> List[int]:
'''simple docstring'''
if len(UpperCAmelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
" eos tokens being added." )
return token_ids
else:
return token_ids + [self.eos_token_id]
def UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase = None ) -> List[int]:
'''simple docstring'''
__snake_case : Tuple = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase = None ) -> List[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = self._add_eos_if_not_present(UpperCAmelCase )
if token_ids_a is None:
return token_ids_a
else:
__snake_case : Optional[Any] = self._add_eos_if_not_present(UpperCAmelCase )
return token_ids_a + token_ids_a
def UpperCAmelCase ( self , UpperCAmelCase ) -> List[str]:
'''simple docstring'''
__snake_case : List[str] = [chr(UpperCAmelCase ) for i in text.encode("utf-8" )]
return tokens
def UpperCAmelCase ( self , UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
if token in self.special_tokens_encoder:
__snake_case : Union[str, Any] = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
__snake_case : Tuple = self.added_tokens_encoder[token]
elif len(UpperCAmelCase ) != 1:
__snake_case : List[str] = self.unk_token_id
else:
__snake_case : Any = ord(UpperCAmelCase ) + self._num_special_tokens
return token_id
def UpperCAmelCase ( self , UpperCAmelCase ) -> Any:
'''simple docstring'''
if index in self.special_tokens_decoder:
__snake_case : str = self.special_tokens_decoder[index]
else:
__snake_case : Optional[int] = chr(index - self._num_special_tokens )
return token
def UpperCAmelCase ( self , UpperCAmelCase ) -> str:
'''simple docstring'''
__snake_case : Dict = B""
for token in tokens:
if token in self.special_tokens_decoder:
__snake_case : Tuple = self.special_tokens_decoder[token].encode("utf-8" )
elif token in self.added_tokens_decoder:
__snake_case : Optional[int] = self.special_tokens_decoder[token].encode("utf-8" )
elif token in self.special_tokens_encoder:
__snake_case : List[Any] = token.encode("utf-8" )
elif token in self.added_tokens_encoder:
__snake_case : str = token.encode("utf-8" )
else:
__snake_case : str = bytes([ord(UpperCAmelCase )] )
bstring += tok_string
__snake_case : int = bstring.decode("utf-8" , errors="ignore" )
return string
def UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase = None ) -> Tuple[str]:
'''simple docstring'''
return ()
| 243 | 1 |
"""simple docstring"""
def _A ( _a : int , _a : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
A = str(bin(_a ) )[2:] # remove the leading "0b"
A = str(bin(_a ) )[2:] # remove the leading "0b"
A = max(len(_a ) , len(_a ) )
return "0b" + "".join(
str(int(char_a == """1""" and char_b == """1""" ) )
for char_a, char_b in zip(a_binary.zfill(_a ) , b_binary.zfill(_a ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 255 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _A ( _a : np.ndarray , _a : int , _a : int ):
"""simple docstring"""
A = np.array(_a )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array is not a square matrix""" )
A = 0
A = 0
A = 0
A = 0
# compute the shape of the output matrix
A = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape maxpool_shape
A = np.zeros((maxpool_shape, maxpool_shape) )
while i < arr.shape[0]:
if i + size > arr.shape[0]:
# if the end of the matrix is reached, break
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the maximum of the pooling matrix
A = np.max(arr[i : i + size, j : j + size] )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
A = 0
A = 0
return updated_arr
def _A ( _a : np.ndarray , _a : int , _a : int ):
"""simple docstring"""
A = np.array(_a )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array is not a square matrix""" )
A = 0
A = 0
A = 0
A = 0
# compute the shape of the output matrix
A = (arr.shape[0] - size) // stride + 1
# initialize the output matrix with zeros of shape avgpool_shape
A = np.zeros((avgpool_shape, avgpool_shape) )
while i < arr.shape[0]:
# if the end of the matrix is reached, break
if i + size > arr.shape[0]:
break
while j < arr.shape[1]:
# if the end of the matrix is reached, break
if j + size > arr.shape[1]:
break
# compute the average of the pooling matrix
A = int(np.average(arr[i : i + size, j : j + size] ) )
# shift the pooling matrix by stride of column pixels
j += stride
mat_j += 1
# shift the pooling matrix by stride of row pixels
i += stride
mat_i += 1
# reset the column index to 0
A = 0
A = 0
return updated_arr
# Main Function
if __name__ == "__main__":
from doctest import testmod
testmod(name="avgpooling", verbose=True)
# Loading the image
UpperCAmelCase =Image.open("path_to_image")
# Converting the image to numpy array and maxpooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
# Converting the image to numpy array and averagepooling, displaying the result
# Ensure that the image is a square matrix
Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
| 255 | 1 |
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
a_ = {
'vocab_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
a_ = {
'vocab_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
),
},
}
a_ = {
'vocab_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
),
},
}
a_ = {
'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
a_ = {
'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
a_ = {
'facebook/dpr-reader-single-nq-base': 5_1_2,
'facebook/dpr-reader-multiset-base': 5_1_2,
}
a_ = {
'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
a_ = {
'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
a_ = {
'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class UpperCAmelCase_ ( snake_case ):
UpperCamelCase =VOCAB_FILES_NAMES
UpperCamelCase =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class UpperCAmelCase_ ( snake_case ):
UpperCamelCase =VOCAB_FILES_NAMES
UpperCamelCase =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
a_ = collections.namedtuple(
'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)
a_ = collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])
a_ = r'\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n ```\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n ```\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n - `\'np\'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Returns:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n '
@add_start_docstrings(snake_case )
class UpperCAmelCase_ :
def __call__( self , UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = False , UpperCamelCase_ = False , UpperCamelCase_ = None , UpperCamelCase_ = None , UpperCamelCase_ = None , **UpperCamelCase_ , ) -> BatchEncoding:
if titles is None and texts is None:
return super().__call__(
UpperCamelCase_ , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=UpperCamelCase_ , return_tensors=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , **UpperCamelCase_ , )
elif titles is None or texts is None:
__lowercase : int = titles if texts is None else texts
return super().__call__(
UpperCamelCase_ , UpperCamelCase_ , padding=UpperCamelCase_ , truncation=UpperCamelCase_ , max_length=UpperCamelCase_ , return_tensors=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , **UpperCamelCase_ , )
__lowercase : Optional[int] = titles if not isinstance(UpperCamelCase_ , UpperCamelCase_ ) else [titles]
__lowercase : Optional[int] = texts if not isinstance(UpperCamelCase_ , UpperCamelCase_ ) else [texts]
__lowercase : str = len(UpperCamelCase_ )
__lowercase : List[Any] = questions if not isinstance(UpperCamelCase_ , UpperCamelCase_ ) else [questions] * n_passages
if len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
raise ValueError(
F"""There should be as many titles than texts but got {len(UpperCamelCase_ )} titles and {len(UpperCamelCase_ )} texts.""" )
__lowercase : int = super().__call__(UpperCamelCase_ , UpperCamelCase_ , padding=UpperCamelCase_ , truncation=UpperCamelCase_ )['''input_ids''']
__lowercase : List[Any] = super().__call__(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , padding=UpperCamelCase_ , truncation=UpperCamelCase_ )['''input_ids''']
__lowercase : Optional[Any] = {
'''input_ids''': [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(UpperCamelCase_ , UpperCamelCase_ )
]
}
if return_attention_mask is not False:
__lowercase : str = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
__lowercase : List[str] = attention_mask
return self.pad(UpperCamelCase_ , padding=UpperCamelCase_ , max_length=UpperCamelCase_ , return_tensors=UpperCamelCase_ )
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = 16 , UpperCamelCase_ = 64 , UpperCamelCase_ = 4 , ) -> List[DPRSpanPrediction]:
__lowercase : List[Any] = reader_input['''input_ids''']
__lowercase ,__lowercase ,__lowercase : List[str] = reader_output[:3]
__lowercase : Optional[int] = len(UpperCamelCase_ )
__lowercase : Any = sorted(range(UpperCamelCase_ ) , reverse=UpperCamelCase_ , key=relevance_logits.__getitem__ )
__lowercase : List[DPRReaderOutput] = []
for doc_id in sorted_docs:
__lowercase : Any = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
__lowercase : Tuple = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
__lowercase : Optional[Any] = sequence_ids.index(self.pad_token_id )
else:
__lowercase : List[Any] = len(UpperCamelCase_ )
__lowercase : List[str] = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=UpperCamelCase_ , top_spans=UpperCamelCase_ , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=UpperCamelCase_ , start_index=UpperCamelCase_ , end_index=UpperCamelCase_ , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(UpperCamelCase_ ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ) -> List[DPRSpanPrediction]:
__lowercase : Tuple = []
for start_index, start_score in enumerate(UpperCamelCase_ ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
__lowercase : int = sorted(UpperCamelCase_ , key=lambda UpperCamelCase_ : x[1] , reverse=UpperCamelCase_ )
__lowercase : Optional[Any] = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""" )
__lowercase : Any = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(F"""Span is too long: {length} > {max_answer_length}""" )
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(UpperCamelCase_ ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(snake_case )
class UpperCAmelCase_ ( snake_case , snake_case ):
UpperCamelCase =VOCAB_FILES_NAMES
UpperCamelCase =READER_PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase =READER_PRETRAINED_INIT_CONFIGURATION
UpperCamelCase =["input_ids", "attention_mask"]
| 76 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"nvidia/segformer-b0-finetuned-ade-512-512": (
"https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class __lowercase ( __magic_name__ ):
_a = """segformer"""
def __init__( self , UpperCamelCase=3 , UpperCamelCase=4 , UpperCamelCase=[2, 2, 2, 2] , UpperCamelCase=[8, 4, 2, 1] , UpperCamelCase=[32, 64, 160, 256] , UpperCamelCase=[7, 3, 3, 3] , UpperCamelCase=[4, 2, 2, 2] , UpperCamelCase=[1, 2, 5, 8] , UpperCamelCase=[4, 4, 4, 4] , UpperCamelCase="gelu" , UpperCamelCase=0.0 , UpperCamelCase=0.0 , UpperCamelCase=0.1 , UpperCamelCase=0.02 , UpperCamelCase=0.1 , UpperCamelCase=1e-6 , UpperCamelCase=256 , UpperCamelCase=255 , **UpperCamelCase , ) -> int:
super().__init__(**UpperCamelCase )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
' removed, as the behaviour will default to that of reshape_last_stage = True.' , UpperCamelCase , )
__a = num_channels
__a = num_encoder_blocks
__a = depths
__a = sr_ratios
__a = hidden_sizes
__a = patch_sizes
__a = strides
__a = mlp_ratios
__a = num_attention_heads
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = classifier_dropout_prob
__a = initializer_range
__a = drop_path_rate
__a = layer_norm_eps
__a = decoder_hidden_size
__a = kwargs.get('reshape_last_stage' , UpperCamelCase )
__a = semantic_loss_ignore_index
class __lowercase ( __magic_name__ ):
_a = version.parse("""1.11""" )
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def UpperCamelCase__ ( self ) -> float:
return 1e-4
@property
def UpperCamelCase__ ( self ) -> int:
return 12
| 539 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
smartaaa_timesteps,
smartaaa_timesteps,
superaa_timesteps,
superaa_timesteps,
superaaa_timesteps,
)
@dataclass
class snake_case_ ( __A ):
'''simple docstring'''
lowerCamelCase = 42
lowerCamelCase = 42
lowerCamelCase = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_if import IFPipeline
from .pipeline_if_imgaimg import IFImgaImgPipeline
from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
from .pipeline_if_inpainting import IFInpaintingPipeline
from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
from .pipeline_if_superresolution import IFSuperResolutionPipeline
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
| 253 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : List[Any] = logging.get_logger(__name__)
snake_case_ : Dict = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW models at https://huggingface.co/models?filter=sew
}
class snake_case_ ( __A ):
'''simple docstring'''
lowerCamelCase = "sew"
def __init__( self : Optional[Any] , __magic_name__ : List[Any]=32 , __magic_name__ : str=768 , __magic_name__ : int=12 , __magic_name__ : int=12 , __magic_name__ : Optional[int]=3072 , __magic_name__ : Optional[Any]=2 , __magic_name__ : int="gelu" , __magic_name__ : Any=0.1 , __magic_name__ : str=0.1 , __magic_name__ : str=0.1 , __magic_name__ : Optional[int]=0.0 , __magic_name__ : Tuple=0.1 , __magic_name__ : List[Any]=0.1 , __magic_name__ : int=0.02 , __magic_name__ : List[Any]=1e-5 , __magic_name__ : List[Any]="group" , __magic_name__ : List[Any]="gelu" , __magic_name__ : str=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , __magic_name__ : Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , __magic_name__ : Dict=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , __magic_name__ : Union[str, Any]=False , __magic_name__ : List[str]=128 , __magic_name__ : str=16 , __magic_name__ : Tuple=True , __magic_name__ : Optional[int]=0.05 , __magic_name__ : int=10 , __magic_name__ : Union[str, Any]=2 , __magic_name__ : str=0.0 , __magic_name__ : Optional[Any]=10 , __magic_name__ : Optional[Any]=0 , __magic_name__ : int="mean" , __magic_name__ : str=False , __magic_name__ : int=False , __magic_name__ : List[str]=256 , __magic_name__ : List[Any]=0 , __magic_name__ : Tuple=1 , __magic_name__ : Dict=2 , **__magic_name__ : List[Any] , ) -> Tuple:
super().__init__(**__magic_name__ , pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ )
lowerCamelCase_ : str = hidden_size
lowerCamelCase_ : Union[str, Any] = feat_extract_norm
lowerCamelCase_ : List[str] = feat_extract_activation
lowerCamelCase_ : int = list(__magic_name__ )
lowerCamelCase_ : List[str] = list(__magic_name__ )
lowerCamelCase_ : Optional[int] = list(__magic_name__ )
lowerCamelCase_ : Optional[Any] = conv_bias
lowerCamelCase_ : Union[str, Any] = num_conv_pos_embeddings
lowerCamelCase_ : Optional[int] = num_conv_pos_embedding_groups
lowerCamelCase_ : Union[str, Any] = len(self.conv_dim )
lowerCamelCase_ : List[str] = num_hidden_layers
lowerCamelCase_ : List[Any] = intermediate_size
lowerCamelCase_ : List[Any] = squeeze_factor
lowerCamelCase_ : Tuple = hidden_act
lowerCamelCase_ : Tuple = num_attention_heads
lowerCamelCase_ : int = hidden_dropout
lowerCamelCase_ : Optional[Any] = attention_dropout
lowerCamelCase_ : List[Any] = activation_dropout
lowerCamelCase_ : Dict = feat_proj_dropout
lowerCamelCase_ : List[str] = final_dropout
lowerCamelCase_ : Any = layerdrop
lowerCamelCase_ : List[Any] = layer_norm_eps
lowerCamelCase_ : Union[str, Any] = initializer_range
lowerCamelCase_ : Any = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect."
"It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
F"but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"
F"= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`." )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCamelCase_ : Optional[int] = apply_spec_augment
lowerCamelCase_ : Union[str, Any] = mask_time_prob
lowerCamelCase_ : Optional[int] = mask_time_length
lowerCamelCase_ : str = mask_time_min_masks
lowerCamelCase_ : List[str] = mask_feature_prob
lowerCamelCase_ : List[Any] = mask_feature_length
lowerCamelCase_ : List[Any] = mask_feature_min_masks
# ctc loss
lowerCamelCase_ : str = ctc_loss_reduction
lowerCamelCase_ : Union[str, Any] = ctc_zero_infinity
# sequence classification
lowerCamelCase_ : List[Any] = use_weighted_layer_sum
lowerCamelCase_ : Optional[Any] = classifier_proj_size
@property
def __SCREAMING_SNAKE_CASE ( self : Tuple ) -> Dict:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 253 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : int = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Optional[Any] = [
'''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FocalNetForImageClassification''',
'''FocalNetForMaskedImageModeling''',
'''FocalNetBackbone''',
'''FocalNetModel''',
'''FocalNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 454 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
# See all BART models at https://huggingface.co/models?filter=bart
_lowerCAmelCase : int = {
'''vocab_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
},
'''merges_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
},
}
_lowerCAmelCase : Optional[int] = {
'''facebook/bart-base''': 1_024,
'''facebook/bart-large''': 1_024,
'''facebook/bart-large-mnli''': 1_024,
'''facebook/bart-large-cnn''': 1_024,
'''facebook/bart-large-xsum''': 1_024,
'''yjernite/bart_eli5''': 1_024,
}
class __magic_name__ ( lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ['''input_ids''', '''attention_mask''']
__UpperCamelCase = BartTokenizer
def __init__( self :List[str] , snake_case :Tuple=None , snake_case :Union[str, Any]=None , snake_case :Union[str, Any]=None , snake_case :List[Any]="replace" , snake_case :List[str]="<s>" , snake_case :Optional[int]="</s>" , snake_case :Union[str, Any]="</s>" , snake_case :Optional[Any]="<s>" , snake_case :List[Any]="<unk>" , snake_case :Optional[Any]="<pad>" , snake_case :Dict="<mask>" , snake_case :int=False , snake_case :List[Any]=True , **snake_case :Union[str, Any] , ):
'''simple docstring'''
super().__init__(
snake_case , snake_case , tokenizer_file=snake_case , errors=snake_case , bos_token=snake_case , eos_token=snake_case , sep_token=snake_case , cls_token=snake_case , unk_token=snake_case , pad_token=snake_case , mask_token=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case , **snake_case , )
A_ : Optional[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , snake_case ) != add_prefix_space:
A_ : Dict = getattr(snake_case , pre_tok_state.pop("type" ) )
A_ : List[str] = add_prefix_space
A_ : int = pre_tok_class(**snake_case )
A_ : Any = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
A_ : Tuple = "post_processor"
A_ : Union[str, Any] = getattr(self.backend_tokenizer , snake_case , snake_case )
if tokenizer_component_instance:
A_ : Any = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
A_ : List[Any] = tuple(state["sep"] )
if "cls" in state:
A_ : str = tuple(state["cls"] )
A_ : int = False
if state.get("add_prefix_space" , snake_case ) != add_prefix_space:
A_ : List[Any] = add_prefix_space
A_ : Union[str, Any] = True
if state.get("trim_offsets" , snake_case ) != trim_offsets:
A_ : int = trim_offsets
A_ : str = True
if changes_to_apply:
A_ : Tuple = getattr(snake_case , state.pop("type" ) )
A_ : Union[str, Any] = component_class(**snake_case )
setattr(self.backend_tokenizer , snake_case , snake_case )
@property
def SCREAMING_SNAKE_CASE ( self :List[Any] ):
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def SCREAMING_SNAKE_CASE ( self :Tuple , snake_case :Dict ):
'''simple docstring'''
A_ : Optional[int] = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else value
A_ : List[Any] = value
def SCREAMING_SNAKE_CASE ( self :Tuple , *snake_case :str , **snake_case :str ):
'''simple docstring'''
A_ : Tuple = kwargs.get("is_split_into_words" , snake_case )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs." )
return super()._batch_encode_plus(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE ( self :Union[str, Any] , *snake_case :Any , **snake_case :Optional[Any] ):
'''simple docstring'''
A_ : List[Any] = kwargs.get("is_split_into_words" , snake_case )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs." )
return super()._encode_plus(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE ( self :int , snake_case :str , snake_case :Optional[str] = None ):
'''simple docstring'''
A_ : Any = self._tokenizer.model.save(snake_case , name=snake_case )
return tuple(snake_case )
def SCREAMING_SNAKE_CASE ( self :Dict , snake_case :List[str] , snake_case :Optional[int]=None ):
'''simple docstring'''
A_ : str = [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 SCREAMING_SNAKE_CASE ( self :int , snake_case :List[int] , snake_case :Optional[List[int]] = None ):
'''simple docstring'''
A_ : Dict = [self.sep_token_id]
A_ : 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 + sep + token_ids_a + sep ) * [0]
| 454 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> list:
if len(lowerCamelCase__ ) < 2:
return collection
def circle_sort_util(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> bool:
__lowerCamelCase : Dict = False
if low == high:
return swapped
__lowerCamelCase : Dict = low
__lowerCamelCase : Tuple = high
while left < right:
if collection[left] > collection[right]:
__lowerCamelCase , __lowerCamelCase : List[str] = (
collection[right],
collection[left],
)
__lowerCamelCase : Any = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
__lowerCamelCase , __lowerCamelCase : List[str] = (
collection[right + 1],
collection[left],
)
__lowerCamelCase : Optional[int] = True
__lowerCamelCase : int = low + int((high - low) / 2 )
__lowerCamelCase : List[str] = circle_sort_util(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : str = circle_sort_util(lowerCamelCase__ , mid + 1 , lowerCamelCase__ )
return swapped or left_swap or right_swap
__lowerCamelCase : List[str] = True
while is_not_sorted is True:
__lowerCamelCase : int = circle_sort_util(lowerCamelCase__ , 0 , len(lowerCamelCase__ ) - 1 )
return collection
if __name__ == "__main__":
a =input("""Enter numbers separated by a comma:\n""").strip()
a =[int(item) for item in user_input.split(""",""")]
print(circle_sort(unsorted))
| 337 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
a =transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Optional[int]:
if isinstance(lowerCamelCase__ , torch.Tensor ):
return image
elif isinstance(lowerCamelCase__ , PIL.Image.Image ):
__lowerCamelCase : List[Any] = [image]
__lowerCamelCase : int = [trans(img.convert('RGB' ) ) for img in image]
__lowerCamelCase : Optional[Any] = torch.stack(lowerCamelCase__ )
return image
class A_ ( SCREAMING_SNAKE_CASE ):
def __init__( self : str ,SCREAMING_SNAKE_CASE__ : str ,SCREAMING_SNAKE_CASE__ : Any):
super().__init__()
# make sure scheduler can always be converted to DDIM
__lowerCamelCase : Union[str, Any] = DDIMScheduler.from_config(scheduler.config)
self.register_modules(unet=SCREAMING_SNAKE_CASE__ ,scheduler=SCREAMING_SNAKE_CASE__)
def lowerCAmelCase ( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : Any):
if strength < 0 or strength > 1:
raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}")
def lowerCAmelCase ( self : Any ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : str ,SCREAMING_SNAKE_CASE__ : Dict):
# get the original timestep using init_timestep
__lowerCamelCase : List[Any] = min(int(num_inference_steps * strength) ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = max(num_inference_steps - init_timestep ,0)
__lowerCamelCase : Dict = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowerCAmelCase ( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Dict ,SCREAMING_SNAKE_CASE__ : List[Any] ,SCREAMING_SNAKE_CASE__ : Any ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : str ,SCREAMING_SNAKE_CASE__ : List[Any]=None):
if not isinstance(SCREAMING_SNAKE_CASE__ ,(torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(SCREAMING_SNAKE_CASE__)}")
__lowerCamelCase : int = image.to(device=SCREAMING_SNAKE_CASE__ ,dtype=SCREAMING_SNAKE_CASE__)
if isinstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__) and len(SCREAMING_SNAKE_CASE__) != batch_size:
raise ValueError(
F"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__)}, but requested an effective batch"
F" size of {batch_size}. Make sure the batch size matches the length of the generators.")
__lowerCamelCase : Dict = init_latents.shape
__lowerCamelCase : List[str] = randn_tensor(SCREAMING_SNAKE_CASE__ ,generator=SCREAMING_SNAKE_CASE__ ,device=SCREAMING_SNAKE_CASE__ ,dtype=SCREAMING_SNAKE_CASE__)
# get latents
print('add noise to latents at timestep' ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Union[str, Any] = self.scheduler.add_noise(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[int] = init_latents
return latents
@torch.no_grad()
def __call__( self : int ,SCREAMING_SNAKE_CASE__ : Union[torch.FloatTensor, PIL.Image.Image] = None ,SCREAMING_SNAKE_CASE__ : float = 0.8 ,SCREAMING_SNAKE_CASE__ : int = 1 ,SCREAMING_SNAKE_CASE__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,SCREAMING_SNAKE_CASE__ : float = 0.0 ,SCREAMING_SNAKE_CASE__ : int = 5_0 ,SCREAMING_SNAKE_CASE__ : Optional[bool] = None ,SCREAMING_SNAKE_CASE__ : Optional[str] = "pil" ,SCREAMING_SNAKE_CASE__ : bool = True ,):
self.check_inputs(SCREAMING_SNAKE_CASE__)
# 2. Preprocess image
__lowerCamelCase : List[str] = preprocess(SCREAMING_SNAKE_CASE__)
# 3. set timesteps
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ ,device=self.device)
__lowerCamelCase , __lowerCamelCase : Union[str, Any] = self.get_timesteps(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,self.device)
__lowerCamelCase : Tuple = timesteps[:1].repeat(SCREAMING_SNAKE_CASE__)
# 4. Prepare latent variables
__lowerCamelCase : Dict = self.prepare_latents(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,self.unet.dtype ,self.device ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = latents
# 5. Denoising loop
for t in self.progress_bar(SCREAMING_SNAKE_CASE__):
# 1. predict noise model_output
__lowerCamelCase : int = self.unet(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
__lowerCamelCase : Optional[int] = self.scheduler.step(
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,eta=SCREAMING_SNAKE_CASE__ ,use_clipped_model_output=SCREAMING_SNAKE_CASE__ ,generator=SCREAMING_SNAKE_CASE__ ,).prev_sample
__lowerCamelCase : Union[str, Any] = (image / 2 + 0.5).clamp(0 ,1)
__lowerCamelCase : str = image.cpu().permute(0 ,2 ,3 ,1).numpy()
if output_type == "pil":
__lowerCamelCase : Union[str, Any] = self.numpy_to_pil(SCREAMING_SNAKE_CASE__)
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__)
| 337 | 1 |
def a (lowerCAmelCase__ ):
assert column_title.isupper()
__a = 0
__a = len(lowerCAmelCase__ ) - 1
__a = 0
while index >= 0:
__a = (ord(column_title[index] ) - 64) * pow(26 , lowerCAmelCase__ )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 99 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}
class __UpperCAmelCase ( __A ):
"""simple docstring"""
_lowerCamelCase = """luke"""
def __init__( self , __A=50267 , __A=500000 , __A=768 , __A=256 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.02 , __A=1E-12 , __A=True , __A=None , __A=1 , __A=0 , __A=2 , **__A , ):
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
__a = vocab_size
__a = entity_vocab_size
__a = hidden_size
__a = entity_emb_size
__a = num_hidden_layers
__a = num_attention_heads
__a = hidden_act
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = type_vocab_size
__a = initializer_range
__a = layer_norm_eps
__a = use_entity_aware_attention
__a = classifier_dropout
| 99 | 1 |
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
lowercase_ : List[str] = 3_0_0 # TEMPERATURE (unit = K)
def A__ ( snake_case_ : float , snake_case_ : float , snake_case_ : float , ):
if donor_conc <= 0:
raise ValueError('''Donor concentration should be positive''' )
elif acceptor_conc <= 0:
raise ValueError('''Acceptor concentration should be positive''' )
elif intrinsic_conc <= 0:
raise ValueError('''Intrinsic concentration should be positive''' )
elif donor_conc <= intrinsic_conc:
raise ValueError(
'''Donor concentration should be greater than intrinsic concentration''' )
elif acceptor_conc <= intrinsic_conc:
raise ValueError(
'''Acceptor concentration should be greater than intrinsic concentration''' )
else:
return (
Boltzmann
* T
* log((donor_conc * acceptor_conc) / intrinsic_conc**2 )
/ physical_constants["electron volt"][0]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 707 | def A__ ( snake_case_ : str ):
if not head:
return True
# split the list to two parts
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: Optional[int]= head.next, head
while fast and fast.next:
SCREAMING_SNAKE_CASE__: Dict= fast.next.next
SCREAMING_SNAKE_CASE__: Union[str, Any]= slow.next
SCREAMING_SNAKE_CASE__: Union[str, Any]= slow.next
SCREAMING_SNAKE_CASE__: Union[str, Any]= None # Don't forget here! But forget still works!
# reverse the second part
SCREAMING_SNAKE_CASE__: Optional[int]= None
while second:
SCREAMING_SNAKE_CASE__: Any= second.next
SCREAMING_SNAKE_CASE__: int= node
SCREAMING_SNAKE_CASE__: Optional[Any]= second
SCREAMING_SNAKE_CASE__: Any= nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
SCREAMING_SNAKE_CASE__: Tuple= node.next
SCREAMING_SNAKE_CASE__: Optional[int]= head.next
return True
def A__ ( snake_case_ : Optional[Any] ):
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
SCREAMING_SNAKE_CASE__: List[Any]= head
while fast and fast.next:
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: Any= fast.next.next, slow.next
# 2. Push the second half into the stack
SCREAMING_SNAKE_CASE__: Optional[Any]= [slow.val]
while slow.next:
SCREAMING_SNAKE_CASE__: Optional[int]= slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
SCREAMING_SNAKE_CASE__: Tuple= cur.next
return True
def A__ ( snake_case_ : Any ):
if not head or not head.next:
return True
SCREAMING_SNAKE_CASE__: Optional[int]= {}
SCREAMING_SNAKE_CASE__: Union[str, Any]= 0
while head:
if head.val in d:
d[head.val].append(snake_case_ )
else:
SCREAMING_SNAKE_CASE__: Optional[int]= [pos]
SCREAMING_SNAKE_CASE__: Dict= head.next
pos += 1
SCREAMING_SNAKE_CASE__: Dict= pos - 1
SCREAMING_SNAKE_CASE__: str= 0
for v in d.values():
if len(snake_case_ ) % 2 != 0:
middle += 1
else:
SCREAMING_SNAKE_CASE__: List[Any]= 0
for i in range(0 , len(snake_case_ ) ):
if v[i] + v[len(snake_case_ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 107 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Optional[int] = logging.get_logger()
def _lowerCAmelCase ( __snake_case : int , __snake_case : str , __snake_case : LevitConfig , __snake_case : Path , __snake_case : bool = True ) -> int:
print(f'Converting {name}...' )
with torch.no_grad():
if hidden_sizes == 1_28:
if name[-1] == "S":
__A : Tuple = timm.create_model('levit_128s' , pretrained=__snake_case )
else:
__A : Any = timm.create_model('levit_128' , pretrained=__snake_case )
if hidden_sizes == 1_92:
__A : Union[str, Any] = timm.create_model('levit_192' , pretrained=__snake_case )
if hidden_sizes == 2_56:
__A : List[Any] = timm.create_model('levit_256' , pretrained=__snake_case )
if hidden_sizes == 3_84:
__A : List[str] = timm.create_model('levit_384' , pretrained=__snake_case )
from_model.eval()
__A : Tuple = LevitForImageClassificationWithTeacher(__snake_case ).eval()
__A : List[Any] = OrderedDict()
__A : int = from_model.state_dict()
__A : List[Any] = list(from_model.state_dict().keys() )
__A : Optional[int] = list(our_model.state_dict().keys() )
print(len(__snake_case ) , len(__snake_case ) )
for i in range(len(__snake_case ) ):
__A : Optional[int] = weights[og_keys[i]]
our_model.load_state_dict(__snake_case )
__A : Tuple = torch.randn((2, 3, 2_24, 2_24) )
__A : Optional[int] = from_model(__snake_case )
__A : List[str] = our_model(__snake_case ).logits
assert torch.allclose(__snake_case , __snake_case ), "The model logits don't match the original one."
__A : List[Any] = name
print(__snake_case )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
__A : Optional[int] = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(f'Pushed {checkpoint_name}' )
def _lowerCAmelCase ( __snake_case : Path , __snake_case : str = None , __snake_case : bool = True ) -> List[str]:
__A : Tuple = 'imagenet-1k-id2label.json'
__A : List[str] = 10_00
__A : Any = (1, num_labels)
__A : Dict = 'huggingface/label-files'
__A : Optional[Any] = num_labels
__A : str = json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type='dataset' ) , 'r' ) )
__A : Union[str, Any] = {int(__snake_case ): v for k, v in idalabel.items()}
__A : Tuple = idalabel
__A : Dict = {v: k for k, v in idalabel.items()}
__A : List[Any] = partial(__snake_case , num_labels=__snake_case , idalabel=__snake_case , labelaid=__snake_case )
__A : Optional[Any] = {
'levit-128S': 1_28,
'levit-128': 1_28,
'levit-192': 1_92,
'levit-256': 2_56,
'levit-384': 3_84,
}
__A : Optional[Any] = {
'levit-128S': ImageNetPreTrainedConfig(
hidden_sizes=[1_28, 2_56, 3_84] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'levit-128': ImageNetPreTrainedConfig(
hidden_sizes=[1_28, 2_56, 3_84] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'levit-192': ImageNetPreTrainedConfig(
hidden_sizes=[1_92, 2_88, 3_84] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'levit-256': ImageNetPreTrainedConfig(
hidden_sizes=[2_56, 3_84, 5_12] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'levit-384': ImageNetPreTrainedConfig(
hidden_sizes=[3_84, 5_12, 7_68] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , __snake_case , names_to_config[model_name] , __snake_case , __snake_case )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , __snake_case , __snake_case , __snake_case , __snake_case )
return config, expected_shape
if __name__ == "__main__":
lowercase__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''levit-dump-folder/''',
type=Path,
required=False,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
parser.add_argument(
'''--no-push_to_hub''',
dest='''push_to_hub''',
action='''store_false''',
help='''Do not push model and image processor to the hub''',
)
lowercase__ : Optional[Any] = parser.parse_args()
lowercase__ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub) | 8 |
'''simple docstring'''
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def a_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=0 ) -> Dict:
"""simple docstring"""
if name is None:
snake_case: Any =None
else:
snake_case: Any ='.' * max(0 , spaces - 2 ) + '# {:' + str(50 - spaces ) + 's}'
snake_case: Optional[int] =fmt.format(__UpperCAmelCase )
# Print and recurse (if needed).
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
if msg is not None:
print(__UpperCAmelCase )
for k in val.keys():
recursive_print(__UpperCAmelCase , val[k] , spaces + 2 )
elif isinstance(__UpperCAmelCase , torch.Tensor ):
print(__UpperCAmelCase , ':' , val.size() )
else:
print(__UpperCAmelCase , ':' , __UpperCAmelCase )
def a_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> int:
"""simple docstring"""
snake_case: Any =param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
snake_case: Tuple =(num_heads, hidden_size, num_splits) + input_shape[1:]
snake_case: Tuple =param.view(*__UpperCAmelCase )
snake_case: List[Any] =param.transpose(0 , 2 )
snake_case: Union[str, Any] =param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
snake_case: Any =(num_heads, num_splits, hidden_size) + input_shape[1:]
snake_case: str =param.view(*__UpperCAmelCase )
snake_case: Optional[Any] =param.transpose(0 , 1 ).contiguous()
snake_case: Any =param.view(*__UpperCAmelCase )
return param
def a_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case: Optional[Any] ={}
# old versions did not store training args
snake_case: Dict =input_state_dict.get('args' , __UpperCAmelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
snake_case: List[Any] =ds_args.padded_vocab_size
snake_case: List[Any] =ds_args.max_position_embeddings
snake_case: str =ds_args.hidden_size
snake_case: Any =ds_args.num_layers
snake_case: Dict =ds_args.num_attention_heads
snake_case: Dict =ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
snake_case: Any =config.n_head
# The hidden_size per head.
snake_case: Union[str, Any] =config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
snake_case: Any =input_state_dict['checkpoint_version']
else:
snake_case: Optional[int] =0.0
# The model.
snake_case: List[str] =input_state_dict['model']
# The language model.
snake_case: List[Any] =model['language_model']
# The embeddings.
snake_case: Union[str, Any] =lm['embedding']
# The word embeddings.
snake_case: List[Any] =embeddings['word_embeddings']['weight']
# Truncate the embedding table to vocab_size rows.
snake_case: Dict =word_embeddings[: config.vocab_size, :]
snake_case: List[str] =word_embeddings
# The position embeddings.
snake_case: str =embeddings['position_embeddings']['weight']
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
snake_case: Dict =pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
f'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
snake_case: Any =pos_embeddings
# The transformer.
snake_case: Union[str, Any] =lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']
# The regex to extract layer names.
snake_case: Union[str, Any] =re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)' )
# The simple map of names for "automated" rules.
snake_case: List[str] ={
'attention.dense': '.attn.c_proj.',
'self_attention.dense': '.attn.c_proj.',
'mlp.dense_h_to_4h': '.mlp.c_fc.',
'mlp.dense_4h_to_h': '.mlp.c_proj.',
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
snake_case: Union[str, Any] =layer_re.match(__UpperCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
snake_case: str =int(m.group(1 ) )
# The name of the operation.
snake_case: Optional[Any] =m.group(2 )
# Is it a weight or a bias?
snake_case: Any =m.group(3 )
# The name of the layer.
snake_case: Tuple =f'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith('layernorm' ):
snake_case: Union[str, Any] ='ln_1' if op_name.startswith('input' ) else 'ln_2'
snake_case: List[str] =val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
snake_case: Optional[Any] =torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __UpperCAmelCase , __UpperCAmelCase )
snake_case: int =causal_mask
# Insert a "dummy" tensor for masked_bias.
snake_case: Dict =torch.tensor(-1e4 , dtype=torch.floataa )
snake_case: Optional[Any] =masked_bias
snake_case: Dict =fix_query_key_value_ordering(__UpperCAmelCase , __UpperCAmelCase , 3 , __UpperCAmelCase , __UpperCAmelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
snake_case: Dict =out_val.transpose(0 , 1 ).contiguous()
# Store.
snake_case: Optional[Any] =out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
snake_case: Dict =fix_query_key_value_ordering(__UpperCAmelCase , __UpperCAmelCase , 3 , __UpperCAmelCase , __UpperCAmelCase )
# Store. No change of shape.
snake_case: str =out_val
# Transpose the weights.
elif weight_or_bias == "weight":
snake_case: Optional[int] =megatron_to_transformers[op_name]
snake_case: str =val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
snake_case: int =megatron_to_transformers[op_name]
snake_case: Dict =val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
snake_case: Optional[int] =transformer['final_layernorm.weight']
snake_case: Optional[Any] =transformer['final_layernorm.bias']
# For LM head, transformers' wants the matrix to weight embeddings.
snake_case: Union[str, Any] =word_embeddings
# It should be done!
return output_state_dict
def a_ ( ) -> Tuple:
"""simple docstring"""
snake_case: List[str] =argparse.ArgumentParser()
parser.add_argument('--print-checkpoint-structure' , action='store_true' )
parser.add_argument(
'path_to_checkpoint' , type=__UpperCAmelCase , help='Path to the checkpoint file (.zip archive or direct .pt file)' , )
parser.add_argument(
'--config_file' , default='' , type=__UpperCAmelCase , help='An optional config json file describing the pre-trained model.' , )
snake_case: List[Any] =parser.parse_args()
# Extract the basename.
snake_case: Any =os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(f'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith('.zip' ):
with zipfile.ZipFile(args.path_to_checkpoint , 'r' ) as checkpoint:
with checkpoint.open('release/mp_rank_00/model_optim_rng.pt' ) as pytorch_dict:
snake_case: List[Any] =torch.load(__UpperCAmelCase , map_location='cpu' )
else:
snake_case: Dict =torch.load(args.path_to_checkpoint , map_location='cpu' )
snake_case: Optional[Any] =input_state_dict.get('args' , __UpperCAmelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
snake_case: List[Any] ='gelu_fast'
elif ds_args.openai_gelu:
snake_case: Optional[int] ='gelu_new'
else:
snake_case: Any ='gelu'
else:
# in the very early days this used to be "gelu_new"
snake_case: Dict ='gelu_new'
# Spell out all parameters in case the defaults change.
snake_case: Union[str, Any] =GPTaConfig(
vocab_size=5_02_57 , n_positions=10_24 , n_embd=10_24 , n_layer=24 , n_head=16 , n_inner=40_96 , activation_function=__UpperCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1e-5 , initializer_range=0.02 , summary_type='cls_index' , summary_use_proj=__UpperCAmelCase , summary_activation=__UpperCAmelCase , summary_proj_to_labels=__UpperCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__UpperCAmelCase , use_cache=__UpperCAmelCase , bos_token_id=5_02_56 , eos_token_id=5_02_56 , )
else:
snake_case: Optional[Any] =GPTaConfig.from_json_file(args.config_file )
snake_case: int =['GPT2LMHeadModel']
# Convert.
print('Converting' )
snake_case: str =convert_megatron_checkpoint(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__UpperCAmelCase , __UpperCAmelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
snake_case: Dict =ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
snake_case: Tuple ='gpt2'
elif tokenizer_type == "PretrainedFromHF":
snake_case: int =ds_args.tokenizer_name_or_path
else:
raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
snake_case: Optional[Any] ='gpt2'
snake_case: List[Any] =AutoTokenizer.from_pretrained(__UpperCAmelCase )
snake_case: Any =type(__UpperCAmelCase ).__name__
snake_case: Optional[Any] =tokenizer_class
# Store the config to file.
print('Saving config' )
config.save_pretrained(__UpperCAmelCase )
# Save tokenizer based on args
print(f'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__UpperCAmelCase )
# Store the state_dict to file.
snake_case: int =os.path.join(__UpperCAmelCase , 'pytorch_model.bin' )
print(f'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__UpperCAmelCase , __UpperCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 350 | 0 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowercase = int(lowerCAmelCase_ )
assert noofclusters < len(lowerCAmelCase_ )
# Find out the dimensionality
lowercase = len(vectors[0] )
# Will help select random centroids from among the available vectors
lowercase = list(range(len(lowerCAmelCase_ ) ) )
shuffle(lowerCAmelCase_ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
lowercase = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
lowercase = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
lowercase = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(lowerCAmelCase_ )
]
##These nodes will assign the centroid Variables the appropriate
##values
lowercase = tf.placeholder("float64" , [dim] )
lowercase = []
for centroid in centroids:
cent_assigns.append(tf.assign(lowerCAmelCase_ , lowerCAmelCase_ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
lowercase = [tf.Variable(0 ) for i in range(len(lowerCAmelCase_ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
lowercase = tf.placeholder("int32" )
lowercase = []
for assignment in assignments:
cluster_assigns.append(tf.assign(lowerCAmelCase_ , lowerCAmelCase_ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
lowercase = tf.placeholder("float" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
lowercase = tf.reduce_mean(lowerCAmelCase_ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
lowercase = tf.placeholder("float" , [dim] )
lowercase = tf.placeholder("float" , [dim] )
lowercase = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowerCAmelCase_ , lowerCAmelCase_ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
lowercase = tf.placeholder("float" , [noofclusters] )
lowercase = tf.argmin(lowerCAmelCase_ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
lowercase = tf.initialize_all_variables()
# Initialize all variables
sess.run(lowerCAmelCase_ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
lowercase = 100
for _ in range(lowerCAmelCase_ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(lowerCAmelCase_ ) ):
lowercase = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
lowercase = [
sess.run(lowerCAmelCase_ , feed_dict={va: vect, va: sess.run(lowerCAmelCase_ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
lowercase = sess.run(
lowerCAmelCase_ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(lowerCAmelCase_ ):
# Collect all the vectors assigned to this cluster
lowercase = [
vectors[i]
for i in range(len(lowerCAmelCase_ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
lowercase = sess.run(
lowerCAmelCase_ , feed_dict={mean_input: array(lowerCAmelCase_ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
lowercase = sess.run(lowerCAmelCase_ )
lowercase = sess.run(lowerCAmelCase_ )
return centroids, assignments
| 459 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__lowerCamelCase : Any = "\\n@inproceedings{popovic-2015-chrf,\n title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n author = \"Popovi{\'c}, Maja\",\n booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n month = sep,\n year = \"2015\",\n address = \"Lisbon, Portugal\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W15-3049\",\n doi = \"10.18653/v1/W15-3049\",\n pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n title = \"chr{F}++: words helping character n-grams\",\n author = \"Popovi{\'c}, Maja\",\n booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n month = sep,\n year = \"2017\",\n address = \"Copenhagen, Denmark\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W17-4770\",\n doi = \"10.18653/v1/W17-4770\",\n pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n author = \"Post, Matt\",\n booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n month = oct,\n year = \"2018\",\n address = \"Belgium, Brussels\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W18-6319\",\n pages = \"186--191\",\n}\n"
__lowerCamelCase : Dict = "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"
__lowerCamelCase : List[Any] = "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n predictions (list of str): The predicted sentences.\n references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n char_order (int): Character n-gram order. Defaults to `6`.\n word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n to reference chrF++.py, NLTK and Moses implementations. If `False`,\n it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n 'score' (float): The chrF (chrF++) score,\n 'char_order' (int): The character n-gram order,\n 'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n 'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n Example 1--a simple example of calculating chrF:\n >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n >>> chrf = datasets.load_metric(\"chrf\")\n >>> results = chrf.compute(predictions=prediction, references=reference)\n >>> print(results)\n {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n >>> chrf = datasets.load_metric(\"chrf\")\n >>> results = chrf.compute(predictions=prediction,\n ... references=reference,\n ... word_order=2)\n >>> print(results)\n {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n >>> chrf = datasets.load_metric(\"chrf\")\n >>> results = chrf.compute(predictions=prediction,\n ... references=reference,\n ... word_order=2,\n ... lowercase=True)\n >>> print(results)\n {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
def UpperCAmelCase__ (self : List[str] ) -> Union[str, Any]:
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="https://github.com/mjpost/sacreBLEU#chrf--chrf" , 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#chrf--chrf"] , reference_urls=[
"https://github.com/m-popovic/chrF",
] , )
def UpperCAmelCase__ (self : Tuple , A__ : Dict , A__ : Tuple , A__ : int = CHRF.CHAR_ORDER , A__ : int = CHRF.WORD_ORDER , A__ : int = CHRF.BETA , A__ : bool = False , A__ : bool = False , A__ : bool = False , ) -> List[str]:
lowercase = len(references[0] )
if any(len(A__ ) != references_per_prediction for refs in references ):
raise ValueError("Sacrebleu requires the same number of references for each prediction" )
lowercase = [[refs[i] for refs in references] for i in range(A__ )]
lowercase = CHRF(A__ , A__ , A__ , A__ , A__ , A__ )
lowercase = sb_chrf.corpus_score(A__ , A__ )
return {
"score": output.score,
"char_order": output.char_order,
"word_order": output.word_order,
"beta": output.beta,
}
| 459 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : int , lowerCAmelCase : int ):
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(lowerCAmelCase , x % y )
def lowerCamelCase ( lowerCAmelCase : int , lowerCAmelCase : int ):
"""simple docstring"""
return (x * y) // greatest_common_divisor(lowerCAmelCase , lowerCAmelCase )
def lowerCamelCase ( lowerCAmelCase : int = 20 ):
"""simple docstring"""
__magic_name__ : Dict = 1
for i in range(1 , n + 1 ):
__magic_name__ : Dict = lcm(lowerCAmelCase , lowerCAmelCase )
return g
if __name__ == "__main__":
print(F'{solution() = }') | 561 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
A_ : Optional[Any] = ViTImageProcessor if is_vision_available() else None
@property
def __lowerCAmelCase ( self : str ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self : List[str] ) -> Optional[Any]:
__magic_name__ : Dict = (3, 32, 128)
__magic_name__ : Any = tempfile.mkdtemp()
# fmt: off
__magic_name__ : Optional[int] = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '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']
# fmt: on
__magic_name__ : Union[str, Any] = dict(zip(_A , range(len(_A ) ) ) )
__magic_name__ : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(_A ) + '\n' )
__magic_name__ : Tuple = {
'do_normalize': False,
'do_resize': True,
'image_processor_type': 'ViTImageProcessor',
'resample': 3,
'size': {'height': 32, 'width': 128},
}
__magic_name__ : Union[str, Any] = os.path.join(self.tmpdirname , _A )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(_A , _A )
def __lowerCAmelCase ( self : str , **_A : Optional[int] ) -> List[str]:
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_A )
def __lowerCAmelCase ( self : int , **_A : Optional[int] ) -> List[Any]:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_A )
def __lowerCAmelCase ( self : Dict ) -> Tuple:
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self : Dict ) -> Any:
__magic_name__ : str = np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )
__magic_name__ : List[Any] = Image.fromarray(np.moveaxis(_A , 0 , -1 ) )
return image_input
def __lowerCAmelCase ( self : List[str] ) -> List[Any]:
__magic_name__ : Union[str, Any] = self.get_tokenizer()
__magic_name__ : Union[str, Any] = self.get_image_processor()
__magic_name__ : List[str] = MgpstrProcessor(tokenizer=_A , image_processor=_A )
processor.save_pretrained(self.tmpdirname )
__magic_name__ : Optional[Any] = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=_A )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , _A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _A )
def __lowerCAmelCase ( self : List[str] ) -> Optional[int]:
__magic_name__ : int = self.get_tokenizer()
__magic_name__ : int = self.get_image_processor()
__magic_name__ : int = MgpstrProcessor(tokenizer=_A , image_processor=_A )
processor.save_pretrained(self.tmpdirname )
__magic_name__ : Any = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__magic_name__ : Optional[Any] = self.get_image_processor(do_normalize=_A , padding_value=1.0 )
__magic_name__ : List[str] = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=_A , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , _A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _A )
def __lowerCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
__magic_name__ : Any = self.get_image_processor()
__magic_name__ : Optional[Any] = self.get_tokenizer()
__magic_name__ : Optional[int] = MgpstrProcessor(tokenizer=_A , image_processor=_A )
__magic_name__ : List[str] = self.prepare_image_inputs()
__magic_name__ : str = image_processor(_A , return_tensors='np' )
__magic_name__ : Tuple = processor(images=_A , return_tensors='np' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __lowerCAmelCase ( self : Optional[Any] ) -> List[Any]:
__magic_name__ : Optional[int] = self.get_image_processor()
__magic_name__ : int = self.get_tokenizer()
__magic_name__ : Optional[int] = MgpstrProcessor(tokenizer=_A , image_processor=_A )
__magic_name__ : Union[str, Any] = 'test'
__magic_name__ : Optional[Any] = processor(text=_A )
__magic_name__ : int = tokenizer(_A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowerCAmelCase ( self : int ) -> int:
__magic_name__ : Union[str, Any] = self.get_image_processor()
__magic_name__ : str = self.get_tokenizer()
__magic_name__ : List[Any] = MgpstrProcessor(tokenizer=_A , image_processor=_A )
__magic_name__ : Union[str, Any] = 'test'
__magic_name__ : str = self.prepare_image_inputs()
__magic_name__ : Dict = processor(text=_A , images=_A )
self.assertListEqual(list(inputs.keys() ) , ['pixel_values', 'labels'] )
# test if it raises when no input is passed
with pytest.raises(_A ):
processor()
def __lowerCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
__magic_name__ : Dict = self.get_image_processor()
__magic_name__ : Optional[Any] = self.get_tokenizer()
__magic_name__ : Optional[Any] = MgpstrProcessor(tokenizer=_A , image_processor=_A )
__magic_name__ : Any = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
__magic_name__ : str = processor.char_decode(_A )
__magic_name__ : Tuple = tokenizer.batch_decode(_A )
__magic_name__ : Union[str, Any] = [seq.replace(' ' , '' ) for seq in decoded_tok]
self.assertListEqual(_A , _A )
def __lowerCAmelCase ( self : Optional[Any] ) -> Any:
__magic_name__ : int = self.get_image_processor()
__magic_name__ : Tuple = self.get_tokenizer()
__magic_name__ : Any = MgpstrProcessor(tokenizer=_A , image_processor=_A )
__magic_name__ : int = None
__magic_name__ : Tuple = self.prepare_image_inputs()
__magic_name__ : Dict = processor(text=_A , images=_A )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def __lowerCAmelCase ( self : List[str] ) -> Dict:
__magic_name__ : Any = self.get_image_processor()
__magic_name__ : Tuple = self.get_tokenizer()
__magic_name__ : List[str] = MgpstrProcessor(tokenizer=_A , image_processor=_A )
__magic_name__ : List[str] = torch.randn(1 , 27 , 38 )
__magic_name__ : Optional[Any] = torch.randn(1 , 27 , 50257 )
__magic_name__ : Optional[int] = torch.randn(1 , 27 , 30522 )
__magic_name__ : List[Any] = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds'] ) | 561 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class __lowercase ( _A ):
lowercase = 'vivit'
def __init__( self : Any , __lowerCamelCase : List[str]=2_24 , __lowerCamelCase : List[Any]=32 , __lowerCamelCase : int=[2, 16, 16] , __lowerCamelCase : Tuple=3 , __lowerCamelCase : int=7_68 , __lowerCamelCase : Dict=12 , __lowerCamelCase : Union[str, Any]=12 , __lowerCamelCase : int=30_72 , __lowerCamelCase : Union[str, Any]="gelu_fast" , __lowerCamelCase : Dict=0.0 , __lowerCamelCase : Any=0.0 , __lowerCamelCase : Optional[int]=0.02 , __lowerCamelCase : str=1E-06 , __lowerCamelCase : Tuple=True , **__lowerCamelCase : Optional[int] , ) -> int:
'''simple docstring'''
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 = initializer_range
lowercase = layer_norm_eps
lowercase = image_size
lowercase = num_frames
lowercase = tubelet_size
lowercase = num_channels
lowercase = qkv_bias
super().__init__(**__lowerCamelCase )
| 479 | from __future__ import annotations
from collections.abc import MutableSequence
class __lowercase :
def __init__( self : Optional[Any] , __lowerCamelCase : int , __lowerCamelCase : MutableSequence[float] ) -> None:
'''simple docstring'''
if len(__lowerCamelCase ) != degree + 1:
raise ValueError(
'''The number of coefficients should be equal to the degree + 1.''' )
lowercase = list(__lowerCamelCase )
lowercase = degree
def __add__( self : Any , __lowerCamelCase : Polynomial ) -> Polynomial:
'''simple docstring'''
if self.degree > polynomial_a.degree:
lowercase = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , __lowerCamelCase )
else:
lowercase = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , __lowerCamelCase )
def __sub__( self : str , __lowerCamelCase : Polynomial ) -> Polynomial:
'''simple docstring'''
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : str ) -> Polynomial:
'''simple docstring'''
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : List[str] , __lowerCamelCase : Polynomial ) -> Polynomial:
'''simple docstring'''
lowercase = [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 , __lowerCamelCase )
def __a ( self : List[str] , __lowerCamelCase : int | float ) -> int | float:
'''simple docstring'''
lowercase = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : str ) -> str:
'''simple docstring'''
lowercase = ''''''
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(__lowerCamelCase )
return polynomial
def __repr__( self : Tuple ) -> str:
'''simple docstring'''
return self.__str__()
def __a ( self : Union[str, Any] ) -> Polynomial:
'''simple docstring'''
lowercase = [0] * self.degree
for i in range(self.degree ):
lowercase = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , __lowerCamelCase )
def __a ( self : Union[str, Any] , __lowerCamelCase : int | float = 0 ) -> Polynomial:
'''simple docstring'''
lowercase = [0] * (self.degree + 2)
lowercase = constant
for i in range(self.degree + 1 ):
lowercase = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , __lowerCamelCase )
def __eq__( self : Tuple , __lowerCamelCase : object ) -> bool:
'''simple docstring'''
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
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 : Tuple , __lowerCamelCase : object ) -> bool:
'''simple docstring'''
return not self.__eq__(__lowerCamelCase )
| 479 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(1 ) != 0 )
def _snake_case ( ) -> None:
'''simple docstring'''
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 7 |
import math
def _UpperCAmelCase (UpperCamelCase__ : int ):
return math.sqrt(UpperCamelCase__ ) * math.sqrt(UpperCamelCase__ ) == num
def _UpperCAmelCase (UpperCamelCase__ : int ):
_A : Dict = 0
_A : Dict = n
while left <= right:
_A : Optional[int] = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
_A : Optional[Any] = mid - 1
else:
_A : str = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 503 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None ) -> Optional[int]:
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
_a = nn.Parameter(_UpperCamelCase )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
_a = nn.Parameter(_UpperCamelCase )
def __snake_case ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> str:
# set torch weights for 1-to-1 comparison
_a = np.asarray(weights[0] )
_a = np.asarray(weights[1] )
_a = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(_UpperCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCamelCase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(_UpperCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCamelCase ) , )
set_param(
torch_layer.output.dense , torch.tensor(_UpperCamelCase ).view(-1 , _UpperCamelCase ).contiguous().transpose(0 , 1 ) , )
def __snake_case ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Union[str, Any]:
# set torch weights for 1-to-1 comparison
_a = np.asarray(weights[0] )
_a = np.asarray(weights[1] )
_a = np.asarray(weights[2] )
_a = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(_UpperCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCamelCase ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(_UpperCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCamelCase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(_UpperCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCamelCase ) , )
set_param(
torch_layer.output.dense , torch.tensor(_UpperCamelCase ).view(-1 , _UpperCamelCase ).contiguous().transpose(0 , 1 ) , )
def __snake_case ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
# layernorm 1
_a = weights[0][0][0]
_a = np.asarray(layer_norm_a[0] )
_a = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(_UpperCamelCase ) , torch.tensor(_UpperCamelCase ) , )
# lsh weights + output
_a = weights[0][1]
if len(_UpperCamelCase ) < 4:
set_layer_weights_in_torch_lsh(_UpperCamelCase , torch_block.attention , _UpperCamelCase )
else:
set_layer_weights_in_torch_local(_UpperCamelCase , torch_block.attention , _UpperCamelCase )
# intermediate weighs
_a = weights[2][0][1][2]
# Chunked Feed Forward
if len(_UpperCamelCase ) == 4:
_a = intermediate_weights[2]
# layernorm 2
_a = np.asarray(intermediate_weights[0][0] )
_a = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(_UpperCamelCase ) , torch.tensor(_UpperCamelCase ) , )
# intermediate dense
_a = np.asarray(intermediate_weights[1][0] )
_a = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(_UpperCamelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(_UpperCamelCase ) , )
# intermediate out
_a = np.asarray(intermediate_weights[4][0] )
_a = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(_UpperCamelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(_UpperCamelCase ) , )
def __snake_case ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> List[Any]:
# reformer model
_a = torch_model.reformer
# word embeds
_a = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(_UpperCamelCase ) , )
if isinstance(weights[3] , _UpperCamelCase ):
_a = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
_a = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), f"{position_embeddings[emb_idx]} emb does not match"
_a = nn.Parameter(torch.tensor(_UpperCamelCase ) )
_a = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
_UpperCamelCase ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
_a = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# output layer norm
_a = np.asarray(weights[7][0] )
_a = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(_UpperCamelCase ) , torch.tensor(_UpperCamelCase ) , )
# output embeddings
_a = np.asarray(weights[9][0] )
_a = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(_UpperCamelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(_UpperCamelCase ) , )
def __snake_case ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> int:
# Initialise PyTorch model
_a = ReformerConfig.from_json_file(_UpperCamelCase )
print(f"Building PyTorch model from configuration: {config}" )
_a = ReformerModelWithLMHead(_UpperCamelCase )
with open(_UpperCamelCase , '''rb''' ) as f:
_a = pickle.load(_UpperCamelCase )['''weights''']
set_model_weights_in_torch(_UpperCamelCase , _UpperCamelCase , config.hidden_size )
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , _UpperCamelCase )
if __name__ == "__main__":
lowerCamelCase :List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained Reformer model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
lowerCamelCase :Optional[Any] = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 708 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :List[str] = logging.get_logger(__name__)
lowerCamelCase :List[str] = {}
class UpperCAmelCase ( __snake_case ):
a: str = "llama"
a: List[str] = ["past_key_values"]
def __init__( self: Tuple , __UpperCamelCase: Optional[Any]=3_2000 , __UpperCamelCase: Optional[int]=4096 , __UpperCamelCase: Union[str, Any]=1_1008 , __UpperCamelCase: str=32 , __UpperCamelCase: List[str]=32 , __UpperCamelCase: Tuple=None , __UpperCamelCase: Dict="silu" , __UpperCamelCase: Any=2048 , __UpperCamelCase: Optional[int]=0.0_2 , __UpperCamelCase: int=1E-6 , __UpperCamelCase: List[Any]=True , __UpperCamelCase: List[str]=0 , __UpperCamelCase: Union[str, Any]=1 , __UpperCamelCase: str=2 , __UpperCamelCase: int=1 , __UpperCamelCase: Optional[Any]=False , __UpperCamelCase: int=None , **__UpperCamelCase: Optional[int] , ):
_a = vocab_size
_a = max_position_embeddings
_a = hidden_size
_a = intermediate_size
_a = num_hidden_layers
_a = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
_a = num_attention_heads
_a = num_key_value_heads
_a = hidden_act
_a = initializer_range
_a = rms_norm_eps
_a = pretraining_tp
_a = use_cache
_a = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , tie_word_embeddings=__UpperCamelCase , **__UpperCamelCase , )
def _A ( self: Any ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __UpperCamelCase ) or len(self.rope_scaling ) != 2:
raise ValueError(
'''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
f"got {self.rope_scaling}" )
_a = self.rope_scaling.get('''type''' , __UpperCamelCase )
_a = self.rope_scaling.get('''factor''' , __UpperCamelCase )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" )
if rope_scaling_factor is None or not isinstance(__UpperCamelCase , __UpperCamelCase ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 346 | 0 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
lowerCamelCase__ : Optional[Any] = """\
@inproceedings{pillutla-etal:mauve:neurips2021,
title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
booktitle = {NeurIPS},
year = {2021}
}
"""
lowerCamelCase__ : int = """\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.
MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.
For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).
This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
"""
lowerCamelCase__ : Any = """
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
predictions: list of generated text to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Optional Args:
num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer
pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
kmeans_max_iter: maximum number of k-means iterations. Default 500
featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].
device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
max_text_length: maximum number of tokens to consider. Default 1024
divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
mauve_scaling_factor: \"c\" from the paper. Default 5.
verbose: If True (default), print running time updates
seed: random seed to initialize k-means cluster assignments.
Returns:
mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
q_hist: same as above, but with q_text.
Examples:
>>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
>>> import datasets
>>> mauve = datasets.load_metric('mauve')
>>> predictions = [\"hello there\", \"general kenobi\"]
>>> references = [\"hello there\", \"general kenobi\"]
>>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
>>> print(out.mauve) # doctest: +SKIP
1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __magic_name__ (datasets.Metric ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self:int ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='''https://github.com/krishnap25/mauve''' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/krishnap25/mauve'''] , reference_urls=[
'''https://arxiv.org/abs/2102.01454''',
'''https://github.com/krishnap25/mauve''',
] , )
def SCREAMING_SNAKE_CASE__ ( self:List[str] , _a:Any , _a:Tuple , _a:str=None , _a:str=None , _a:List[Any]=None , _a:Dict=None , _a:List[Any]="auto" , _a:Optional[int]=-1 , _a:int=0.9 , _a:str=5 , _a:List[str]=5_00 , _a:Tuple="gpt2-large" , _a:Union[str, Any]=-1 , _a:Optional[int]=10_24 , _a:Optional[Any]=25 , _a:Optional[Any]=5 , _a:Optional[Any]=True , _a:List[str]=25 , ):
snake_case__ = compute_mauve(
p_text=_a , q_text=_a , p_features=_a , q_features=_a , p_tokens=_a , q_tokens=_a , num_buckets=_a , pca_max_data=_a , kmeans_explained_var=_a , kmeans_num_redo=_a , kmeans_max_iter=_a , featurize_model_name=_a , device_id=_a , max_text_length=_a , divergence_curve_discretization_size=_a , mauve_scaling_factor=_a , verbose=_a , seed=_a , )
return out
| 33 |
'''simple docstring'''
def lowerCAmelCase_ ( a : int ):
a__ = generate_pascal_triangle(a )
for row_idx in range(a ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=' ' )
# Print row values
for col_idx in range(row_idx + 1 ):
if col_idx != row_idx:
print(triangle[row_idx][col_idx] , end=' ' )
else:
print(triangle[row_idx][col_idx] , end='' )
print()
def lowerCAmelCase_ ( a : int ):
if not isinstance(a , a ):
raise TypeError('The input value of \'num_rows\' should be \'int\'' )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
'The input value of \'num_rows\' should be greater than or equal to 0' )
a__ = []
for current_row_idx in range(a ):
a__ = populate_current_row(a , a )
triangle.append(a )
return triangle
def lowerCAmelCase_ ( a : list[list[int]] , a : int ):
a__ = [-1] * (current_row_idx + 1)
# first and last elements of current row are equal to 1
a__ , a__ = 1, 1
for current_col_idx in range(1 , a ):
calculate_current_element(
a , a , a , a )
return current_row
def lowerCAmelCase_ ( a : list[list[int]] , a : list[int] , a : int , a : int , ):
a__ = triangle[current_row_idx - 1][current_col_idx - 1]
a__ = triangle[current_row_idx - 1][current_col_idx]
a__ = above_to_left_elt + above_to_right_elt
def lowerCAmelCase_ ( a : int ):
if not isinstance(a , a ):
raise TypeError('The input value of \'num_rows\' should be \'int\'' )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
'The input value of \'num_rows\' should be greater than or equal to 0' )
a__ = [[1]]
for row_index in range(1 , a ):
a__ = [0] + result[-1] + [0]
a__ = row_index + 1
# Calculate the number of distinct elements in a row
a__ = sum(divmod(a , 2 ) )
a__ = [
temp_row[i - 1] + temp_row[i] for i in range(1 , distinct_elements + 1 )
]
a__ = row_first_half[: (row_index + 1) // 2]
row_second_half.reverse()
a__ = row_first_half + row_second_half
result.append(a )
return result
def lowerCAmelCase_ ( ):
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(a : Callable , a : int ) -> None:
a__ = f'''{func.__name__}({value})'''
a__ = timeit(f'''__main__.{call}''' , setup='import __main__' )
# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
print(f'''{call:38} -- {timing:.4f} seconds''' )
for value in range(15 ): # (1, 7, 14):
for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
benchmark_a_function(a , a )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 394 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt'}
a_ = {
'vocab_file': {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
}
}
a_ = {
'YituTech/conv-bert-base': 5_1_2,
'YituTech/conv-bert-medium-small': 5_1_2,
'YituTech/conv-bert-small': 5_1_2,
}
a_ = {
'YituTech/conv-bert-base': {'do_lower_case': True},
'YituTech/conv-bert-medium-small': {'do_lower_case': True},
'YituTech/conv-bert-small': {'do_lower_case': True},
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_INIT_CONFIGURATION
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ConvBertTokenizer
def __init__( self : int , __lowercase : int=None , __lowercase : int=None , __lowercase : Dict=True , __lowercase : Optional[int]="[UNK]" , __lowercase : str="[SEP]" , __lowercase : Union[str, Any]="[PAD]" , __lowercase : str="[CLS]" , __lowercase : Dict="[MASK]" , __lowercase : List[str]=True , __lowercase : Dict=None , **__lowercase : Dict , ) -> List[Any]:
super().__init__(
__lowercase , tokenizer_file=__lowercase , do_lower_case=__lowercase , unk_token=__lowercase , sep_token=__lowercase , pad_token=__lowercase , cls_token=__lowercase , mask_token=__lowercase , tokenize_chinese_chars=__lowercase , strip_accents=__lowercase , **__lowercase , )
SCREAMING_SNAKE_CASE__ : Any =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __lowercase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __lowercase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __lowercase ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE__ : int =getattr(__lowercase , normalizer_state.pop('''type''' ) )
SCREAMING_SNAKE_CASE__ : List[str] =do_lower_case
SCREAMING_SNAKE_CASE__ : Dict =strip_accents
SCREAMING_SNAKE_CASE__ : Any =tokenize_chinese_chars
SCREAMING_SNAKE_CASE__ : List[str] =normalizer_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =do_lower_case
def __magic_name__ ( self : int , __lowercase : Optional[Any] , __lowercase : Optional[int]=None ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[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 __magic_name__ ( self : Union[str, Any] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ : Any =[self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple =[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 __magic_name__ ( self : int , __lowercase : str , __lowercase : Optional[str] = None ) -> Tuple[str]:
SCREAMING_SNAKE_CASE__ : str =self._tokenizer.model.save(__lowercase , name=__lowercase )
return tuple(__lowercase ) | 665 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """gpt_bigcode"""
snake_case_ = ["""past_key_values"""]
snake_case_ = {
"""hidden_size""": """n_embd""",
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Any , __lowercase : Any=5_02_57 , __lowercase : int=10_24 , __lowercase : List[str]=7_68 , __lowercase : Optional[int]=12 , __lowercase : Dict=12 , __lowercase : List[str]=None , __lowercase : int="gelu_pytorch_tanh" , __lowercase : Union[str, Any]=0.1 , __lowercase : Optional[int]=0.1 , __lowercase : Optional[int]=0.1 , __lowercase : Optional[Any]=1e-5 , __lowercase : List[str]=0.02 , __lowercase : Tuple=True , __lowercase : Optional[Any]=True , __lowercase : Union[str, Any]=5_02_56 , __lowercase : List[Any]=5_02_56 , __lowercase : Union[str, Any]=True , __lowercase : List[str]=True , __lowercase : Dict=True , **__lowercase : List[Any] , ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =n_positions
SCREAMING_SNAKE_CASE__ : Dict =n_embd
SCREAMING_SNAKE_CASE__ : Dict =n_layer
SCREAMING_SNAKE_CASE__ : Union[str, Any] =n_head
SCREAMING_SNAKE_CASE__ : List[str] =n_inner
SCREAMING_SNAKE_CASE__ : List[str] =activation_function
SCREAMING_SNAKE_CASE__ : List[Any] =resid_pdrop
SCREAMING_SNAKE_CASE__ : List[Any] =embd_pdrop
SCREAMING_SNAKE_CASE__ : List[str] =attn_pdrop
SCREAMING_SNAKE_CASE__ : Dict =layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : List[str] =initializer_range
SCREAMING_SNAKE_CASE__ : List[Any] =scale_attn_weights
SCREAMING_SNAKE_CASE__ : Union[str, Any] =use_cache
SCREAMING_SNAKE_CASE__ : Dict =attention_softmax_in_fpaa
SCREAMING_SNAKE_CASE__ : int =scale_attention_softmax_in_fpaa
SCREAMING_SNAKE_CASE__ : Dict =multi_query
SCREAMING_SNAKE_CASE__ : Optional[Any] =bos_token_id
SCREAMING_SNAKE_CASE__ : Optional[Any] =eos_token_id
super().__init__(bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase ) | 665 | 1 |
import math
from collections.abc import Callable
def __a ( A__ : Callable[[float], float] , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = xa
SCREAMING_SNAKE_CASE = xa
while True:
if x_n == x_na or function(A__ ) == function(A__ ):
raise ZeroDivisionError("float division by zero, could not find root" )
SCREAMING_SNAKE_CASE = x_na - (
function(A__ ) / ((function(A__ ) - function(A__ )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
SCREAMING_SNAKE_CASE = x_na
SCREAMING_SNAKE_CASE = x_na
def __a ( A__ : float ):
return math.pow(A__ , 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5)) | 16 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ["""EncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ["""TFEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = ["""FlaxEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
UpperCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 590 | 0 |
'''simple docstring'''
def __snake_case (__UpperCAmelCase , __UpperCAmelCase = 0 ):
"""simple docstring"""
lowerCamelCase_ : List[Any] = length or len(__UpperCAmelCase )
lowerCamelCase_ : Tuple = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
lowerCamelCase_ , lowerCamelCase_ : Union[str, Any] = list_data[i + 1], list_data[i]
lowerCamelCase_ : str = True
return list_data if not swapped else bubble_sort(__UpperCAmelCase , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 418 |
'''simple docstring'''
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
lowerCamelCase_ : Union[str, Any] = sorted(string.lower() )
return len(__UpperCAmelCase ) == len(set(__UpperCAmelCase ) )
if __name__ == "__main__":
__lowerCamelCase : Tuple = input("""Enter a string """).strip()
__lowerCamelCase : Union[str, Any] = is_isogram(input_str)
print(f"""{input_str} is {'an' if isogram else 'not an'} isogram.""")
| 418 | 1 |
from math import isqrt
def lowerCAmelCase ( UpperCAmelCase ) ->bool:
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2, isqrt(UpperCAmelCase ) + 1 ) )
def lowerCAmelCase ( UpperCAmelCase = 10**6 ) ->int:
"""simple docstring"""
__magic_name__ : Any = 0
__magic_name__ : Union[str, Any] = 1
__magic_name__ : Any = 7
while prime_candidate < max_prime:
primes_count += is_prime(UpperCAmelCase )
cube_index += 1
prime_candidate += 6 * cube_index
return primes_count
if __name__ == "__main__":
print(f"{solution() = }")
| 154 |
from math import isqrt
def lowerCAmelCase ( UpperCAmelCase ) ->bool:
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2, isqrt(UpperCAmelCase ) + 1 ) )
def lowerCAmelCase ( UpperCAmelCase = 10**6 ) ->int:
"""simple docstring"""
__magic_name__ : Any = 0
__magic_name__ : Union[str, Any] = 1
__magic_name__ : Any = 7
while prime_candidate < max_prime:
primes_count += is_prime(UpperCAmelCase )
cube_index += 1
prime_candidate += 6 * cube_index
return primes_count
if __name__ == "__main__":
print(f"{solution() = }")
| 154 | 1 |
def UpperCamelCase_( __magic_name__ : int = 10**9 ):
"""simple docstring"""
_lowerCAmelCase :List[Any] = 1
_lowerCAmelCase :List[str] = 2
_lowerCAmelCase :Optional[int] = 0
_lowerCAmelCase :List[str] = 0
_lowerCAmelCase :str = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
_lowerCAmelCase :Union[str, Any] = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(F'''{solution() = }''') | 382 |
a = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
a = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
a = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
} | 382 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = list(SCREAMING_SNAKE_CASE )
lowercase__ = list(SCREAMING_SNAKE_CASE )
lowercase__ = 0
for i in range(len(SCREAMING_SNAKE_CASE ) ):
if lista[i] != lista[i]:
count += 1
lowercase__ = '''_'''
if count > 1:
return False
else:
return "".join(SCREAMING_SNAKE_CASE )
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = []
while True:
lowercase__ = ['''$'''] * len(SCREAMING_SNAKE_CASE )
lowercase__ = []
for i in range(len(SCREAMING_SNAKE_CASE ) ):
for j in range(i + 1 , len(SCREAMING_SNAKE_CASE ) ):
lowercase__ = compare_string(binary[i] , binary[j] )
if k is False:
lowercase__ = '''*'''
lowercase__ = '''*'''
temp.append('''X''' )
for i in range(len(SCREAMING_SNAKE_CASE ) ):
if checka[i] == "$":
pi.append(binary[i] )
if len(SCREAMING_SNAKE_CASE ) == 0:
return pi
lowercase__ = list(set(SCREAMING_SNAKE_CASE ) )
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = []
for minterm in minterms:
lowercase__ = ''''''
for _ in range(SCREAMING_SNAKE_CASE ):
lowercase__ = str(minterm % 2 ) + string
minterm //= 2
temp.append(SCREAMING_SNAKE_CASE )
return temp
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = list(SCREAMING_SNAKE_CASE )
lowercase__ = list(SCREAMING_SNAKE_CASE )
lowercase__ = 0
for i in range(len(SCREAMING_SNAKE_CASE ) ):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = []
lowercase__ = [0] * len(SCREAMING_SNAKE_CASE )
for i in range(len(chart[0] ) ):
lowercase__ = 0
lowercase__ = -1
for j in range(len(SCREAMING_SNAKE_CASE ) ):
if chart[j][i] == 1:
count += 1
lowercase__ = j
if count == 1:
lowercase__ = 1
for i in range(len(SCREAMING_SNAKE_CASE ) ):
if select[i] == 1:
for j in range(len(chart[0] ) ):
if chart[i][j] == 1:
for k in range(len(SCREAMING_SNAKE_CASE ) ):
lowercase__ = 0
temp.append(prime_implicants[i] )
while True:
lowercase__ = 0
lowercase__ = -1
lowercase__ = 0
for i in range(len(SCREAMING_SNAKE_CASE ) ):
lowercase__ = chart[i].count(1 )
if count_n > max_n:
lowercase__ = count_n
lowercase__ = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem] )
for i in range(len(chart[0] ) ):
if chart[rem][i] == 1:
for j in range(len(SCREAMING_SNAKE_CASE ) ):
lowercase__ = 0
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = [[0 for x in range(len(SCREAMING_SNAKE_CASE ) )] for x in range(len(SCREAMING_SNAKE_CASE ) )]
for i in range(len(SCREAMING_SNAKE_CASE ) ):
lowercase__ = prime_implicants[i].count('''_''' )
for j in range(len(SCREAMING_SNAKE_CASE ) ):
if is_for_table(prime_implicants[i] , binary[j] , SCREAMING_SNAKE_CASE ):
lowercase__ = 1
return chart
def _a ( ):
"""simple docstring"""
lowercase__ = int(input('''Enter the no. of variables\n''' ) )
lowercase__ = [
float(SCREAMING_SNAKE_CASE )
for x in input(
'''Enter the decimal representation of Minterms \'Spaces Separated\'\n''' ).split()
]
lowercase__ = decimal_to_binary(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ = check(SCREAMING_SNAKE_CASE )
print('''Prime Implicants are:''' )
print(SCREAMING_SNAKE_CASE )
lowercase__ = prime_implicant_chart(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ = selection(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
print('''Essential Prime Implicants are:''' )
print(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 43 |
from __future__ import annotations
import math
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(SCREAMING_SNAKE_CASE ) == 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 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , minimax(depth + 1 , node_index * 2 + 1 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , )
return min(
minimax(depth + 1 , node_index * 2 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , minimax(depth + 1 , node_index * 2 + 1 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , )
def _a ( ):
"""simple docstring"""
lowercase__ = [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
lowercase__ = math.log(len(SCREAMING_SNAKE_CASE ) , 2 )
print('''Optimal value : ''' , end='''''' )
print(minimax(0 , 0 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 43 | 1 |
"""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
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
SCREAMING_SNAKE_CASE_ = {
"""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"""
)
},
}
SCREAMING_SNAKE_CASE_ = {
"""facebook/blenderbot_small-90M""": 5_12,
}
class snake_case_ ( a_ ):
__lowerCAmelCase = VOCAB_FILES_NAMES
__lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase = BlenderbotSmallTokenizer
def __init__( self , a_=None , a_=None , a_="<|endoftext|>" , a_="<|endoftext|>" , a_="<|endoftext|>" , a_=False , a_=True , **a_ , ):
super().__init__(
ByteLevelBPETokenizer(
vocab=a_ , merges=a_ , add_prefix_space=a_ , trim_offsets=a_ , ) , bos_token=a_ , eos_token=a_ , unk_token=a_ , **a_ , )
a_ : Dict = add_prefix_space
def snake_case_ ( self , a_ , a_=None ):
a_ : str = [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 snake_case_ ( self , a_ , a_ = None ):
a_ : str = [self.sep_token_id]
a_ : Dict = [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] | 702 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ = 10, SCREAMING_SNAKE_CASE__ = 1_000, SCREAMING_SNAKE_CASE__ = True ) -> int:
assert (
isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError("Invalid value for min_val or max_val (min_value < max_value)" )
return min_val if option else max_val
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) -> int:
return int((number_a + number_a) / 2 )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) -> None:
assert (
isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
), 'argument values must be type of "int"'
if lower > higher:
raise ValueError("argument value for lower and higher must be(lower > higher)" )
if not lower < to_guess < higher:
raise ValueError(
"guess value must be within the range of lower and higher value" )
def answer(SCREAMING_SNAKE_CASE__ ) -> str:
if number > to_guess:
return "high"
elif number < to_guess:
return "low"
else:
return "same"
print("started..." )
a_ : List[str] = lower
a_ : Dict = higher
a_ : str = []
while True:
a_ : List[str] = get_avg(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
last_numbers.append(SCREAMING_SNAKE_CASE__ )
if answer(SCREAMING_SNAKE_CASE__ ) == "low":
a_ : Optional[Any] = number
elif answer(SCREAMING_SNAKE_CASE__ ) == "high":
a_ : Union[str, Any] = number
else:
break
print(F"""guess the number : {last_numbers[-1]}""" )
print(F"""details : {last_numbers!s}""" )
def lowerCAmelCase_ ( ) -> None:
a_ : str = int(input("Enter lower value : " ).strip() )
a_ : Dict = int(input("Enter high value : " ).strip() )
a_ : Optional[Any] = int(input("Enter value to guess : " ).strip() )
guess_the_number(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
main() | 370 | 0 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def UpperCamelCase ( a , a=1 ) -> Optional[int]:
'''simple docstring'''
if n_shave_prefix_segments >= 0:
return ".".join(path.split('''.''' )[n_shave_prefix_segments:] )
else:
return ".".join(path.split('''.''' )[:n_shave_prefix_segments] )
def UpperCamelCase ( a , a=0 ) -> Optional[int]:
'''simple docstring'''
__magic_name__ = []
for old_item in old_list:
__magic_name__ = old_item.replace('''in_layers.0''' , '''norm1''' )
__magic_name__ = new_item.replace('''in_layers.2''' , '''conv1''' )
__magic_name__ = new_item.replace('''out_layers.0''' , '''norm2''' )
__magic_name__ = new_item.replace('''out_layers.3''' , '''conv2''' )
__magic_name__ = new_item.replace('''emb_layers.1''' , '''time_emb_proj''' )
__magic_name__ = new_item.replace('''skip_connection''' , '''conv_shortcut''' )
__magic_name__ = shave_segments(a , n_shave_prefix_segments=a )
mapping.append({'''old''': old_item, '''new''': new_item} )
return mapping
def UpperCamelCase ( a , a=0 ) -> Tuple:
'''simple docstring'''
__magic_name__ = []
for old_item in old_list:
__magic_name__ = old_item
__magic_name__ = new_item.replace('''norm.weight''' , '''group_norm.weight''' )
__magic_name__ = new_item.replace('''norm.bias''' , '''group_norm.bias''' )
__magic_name__ = new_item.replace('''proj_out.weight''' , '''proj_attn.weight''' )
__magic_name__ = new_item.replace('''proj_out.bias''' , '''proj_attn.bias''' )
__magic_name__ = shave_segments(a , n_shave_prefix_segments=a )
mapping.append({'''old''': old_item, '''new''': new_item} )
return mapping
def UpperCamelCase ( a , a , a , a=None , a=None , a=None ) -> str:
'''simple docstring'''
assert isinstance(a , a ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
__magic_name__ = old_checkpoint[path]
__magic_name__ = old_tensor.shape[0] // 3
__magic_name__ = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
__magic_name__ = old_tensor.shape[0] // config['''num_head_channels'''] // 3
__magic_name__ = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
__magic_name__ , __magic_name__ , __magic_name__ = old_tensor.split(channels // num_heads , dim=1 )
__magic_name__ = query.reshape(a )
__magic_name__ = key.reshape(a )
__magic_name__ = value.reshape(a )
for path in paths:
__magic_name__ = path['''new''']
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
__magic_name__ = new_path.replace('''middle_block.0''' , '''mid_block.resnets.0''' )
__magic_name__ = new_path.replace('''middle_block.1''' , '''mid_block.attentions.0''' )
__magic_name__ = new_path.replace('''middle_block.2''' , '''mid_block.resnets.1''' )
if additional_replacements is not None:
for replacement in additional_replacements:
__magic_name__ = new_path.replace(replacement['''old'''] , replacement['''new'''] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
__magic_name__ = old_checkpoint[path['''old''']][:, :, 0]
else:
__magic_name__ = old_checkpoint[path['''old''']]
def UpperCamelCase ( a , a ) -> str:
'''simple docstring'''
__magic_name__ = {}
__magic_name__ = checkpoint['''time_embed.0.weight''']
__magic_name__ = checkpoint['''time_embed.0.bias''']
__magic_name__ = checkpoint['''time_embed.2.weight''']
__magic_name__ = checkpoint['''time_embed.2.bias''']
__magic_name__ = checkpoint['''input_blocks.0.0.weight''']
__magic_name__ = checkpoint['''input_blocks.0.0.bias''']
__magic_name__ = checkpoint['''out.0.weight''']
__magic_name__ = checkpoint['''out.0.bias''']
__magic_name__ = checkpoint['''out.2.weight''']
__magic_name__ = checkpoint['''out.2.bias''']
# Retrieves the keys for the input blocks only
__magic_name__ = len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''input_blocks''' in layer} )
__magic_name__ = {
layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
for layer_id in range(a )
}
# Retrieves the keys for the middle blocks only
__magic_name__ = len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''middle_block''' in layer} )
__magic_name__ = {
layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
for layer_id in range(a )
}
# Retrieves the keys for the output blocks only
__magic_name__ = len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in checkpoint if '''output_blocks''' in layer} )
__magic_name__ = {
layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
for layer_id in range(a )
}
for i in range(1 , a ):
__magic_name__ = (i - 1) // (config['''num_res_blocks'''] + 1)
__magic_name__ = (i - 1) % (config['''num_res_blocks'''] + 1)
__magic_name__ = [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
__magic_name__ = [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
__magic_name__ = checkpoint[
F'''input_blocks.{i}.0.op.weight'''
]
__magic_name__ = checkpoint[
F'''input_blocks.{i}.0.op.bias'''
]
continue
__magic_name__ = renew_resnet_paths(a )
__magic_name__ = {'''old''': F'''input_blocks.{i}.0''', '''new''': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
__magic_name__ = {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
assign_to_checkpoint(
a , a , a , additional_replacements=[meta_path, resnet_op] , config=a )
if len(a ):
__magic_name__ = renew_attention_paths(a )
__magic_name__ = {
'''old''': F'''input_blocks.{i}.1''',
'''new''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
}
__magic_name__ = {
F'''input_blocks.{i}.1.qkv.bias''': {
'''key''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
'''query''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
'''value''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
},
F'''input_blocks.{i}.1.qkv.weight''': {
'''key''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
'''query''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
'''value''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
},
}
assign_to_checkpoint(
a , a , a , additional_replacements=[meta_path] , attention_paths_to_split=a , config=a , )
__magic_name__ = middle_blocks[0]
__magic_name__ = middle_blocks[1]
__magic_name__ = middle_blocks[2]
__magic_name__ = renew_resnet_paths(a )
assign_to_checkpoint(a , a , a , config=a )
__magic_name__ = renew_resnet_paths(a )
assign_to_checkpoint(a , a , a , config=a )
__magic_name__ = renew_attention_paths(a )
__magic_name__ = {
'''middle_block.1.qkv.bias''': {
'''key''': '''mid_block.attentions.0.key.bias''',
'''query''': '''mid_block.attentions.0.query.bias''',
'''value''': '''mid_block.attentions.0.value.bias''',
},
'''middle_block.1.qkv.weight''': {
'''key''': '''mid_block.attentions.0.key.weight''',
'''query''': '''mid_block.attentions.0.query.weight''',
'''value''': '''mid_block.attentions.0.value.weight''',
},
}
assign_to_checkpoint(
a , a , a , attention_paths_to_split=a , config=a )
for i in range(a ):
__magic_name__ = i // (config['''num_res_blocks'''] + 1)
__magic_name__ = i % (config['''num_res_blocks'''] + 1)
__magic_name__ = [shave_segments(a , 2 ) for name in output_blocks[i]]
__magic_name__ = {}
for layer in output_block_layers:
__magic_name__ , __magic_name__ = layer.split('''.''' )[0], shave_segments(a , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(a )
else:
__magic_name__ = [layer_name]
if len(a ) > 1:
__magic_name__ = [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
__magic_name__ = [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
__magic_name__ = renew_resnet_paths(a )
__magic_name__ = renew_resnet_paths(a )
__magic_name__ = {'''old''': F'''output_blocks.{i}.0''', '''new''': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
if ["conv.weight", "conv.bias"] in output_block_list.values():
__magic_name__ = list(output_block_list.values() ).index(['''conv.weight''', '''conv.bias'''] )
__magic_name__ = checkpoint[
F'''output_blocks.{i}.{index}.conv.weight'''
]
__magic_name__ = checkpoint[
F'''output_blocks.{i}.{index}.conv.bias'''
]
# Clear attentions as they have been attributed above.
if len(a ) == 2:
__magic_name__ = []
if len(a ):
__magic_name__ = renew_attention_paths(a )
__magic_name__ = {
'''old''': F'''output_blocks.{i}.1''',
'''new''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
}
__magic_name__ = {
F'''output_blocks.{i}.1.qkv.bias''': {
'''key''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
'''query''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
'''value''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
},
F'''output_blocks.{i}.1.qkv.weight''': {
'''key''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
'''query''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
'''value''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
},
}
assign_to_checkpoint(
a , a , a , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions ) else None , config=a , )
else:
__magic_name__ = renew_resnet_paths(a , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
__magic_name__ = '''.'''.join(['''output_blocks''', str(a ), path['''old''']] )
__magic_name__ = '''.'''.join(['''up_blocks''', str(a ), '''resnets''', str(a ), path['''new''']] )
__magic_name__ = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the architecture.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
_lowerCAmelCase = parser.parse_args()
_lowerCAmelCase = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
_lowerCAmelCase = json.loads(f.read())
_lowerCAmelCase = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
_lowerCAmelCase = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
_lowerCAmelCase = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
_lowerCAmelCase = VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))
_lowerCAmelCase = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 432 |
'''simple docstring'''
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class _SCREAMING_SNAKE_CASE ( __a ):
def __init__( self : int , a__ : Optional[int] , a__ : Union[str, Any]=768 ):
super().__init__(a__ )
__magic_name__ = proj_size
__magic_name__ = CLIPVisionModel(a__ )
__magic_name__ = PaintByExampleMapper(a__ )
__magic_name__ = nn.LayerNorm(config.hidden_size )
__magic_name__ = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
__magic_name__ = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def snake_case__ ( self : Tuple , a__ : Any , a__ : List[str]=False ):
__magic_name__ = self.model(pixel_values=a__ )
__magic_name__ = clip_output.pooler_output
__magic_name__ = self.mapper(latent_states[:, None] )
__magic_name__ = self.final_layer_norm(a__ )
__magic_name__ = self.proj_out(a__ )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self : Any , a__ : Dict ):
super().__init__()
__magic_name__ = (config.num_hidden_layers + 1) // 5
__magic_name__ = config.hidden_size
__magic_name__ = 1
__magic_name__ = nn.ModuleList(
[
BasicTransformerBlock(a__ , a__ , a__ , activation_fn='''gelu''' , attention_bias=a__ )
for _ in range(a__ )
] )
def snake_case__ ( self : List[str] , a__ : List[Any] ):
for block in self.blocks:
__magic_name__ = block(a__ )
return hidden_states
| 432 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase):
lowerCAmelCase_ = """encoder-decoder"""
lowerCAmelCase_ = True
def __init__( self , **A_ )-> List[Any]:
'''simple docstring'''
super().__init__(**UpperCAmelCase_ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
UpperCamelCase = kwargs.pop('encoder' )
UpperCamelCase = encoder_config.pop('model_type' )
UpperCamelCase = kwargs.pop('decoder' )
UpperCamelCase = decoder_config.pop('model_type' )
from ..auto.configuration_auto import AutoConfig
UpperCamelCase = AutoConfig.for_model(UpperCAmelCase_ , **UpperCAmelCase_ )
UpperCamelCase = AutoConfig.for_model(UpperCAmelCase_ , **UpperCAmelCase_ )
UpperCamelCase = True
@classmethod
def UpperCAmelCase_ ( cls , A_ , A_ , **A_ )-> Tuple:
'''simple docstring'''
logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
UpperCamelCase = True
UpperCamelCase = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **UpperCAmelCase_ )
def UpperCAmelCase_ ( self )-> Dict:
'''simple docstring'''
UpperCamelCase = copy.deepcopy(self.__dict__ )
UpperCamelCase = self.encoder.to_dict()
UpperCamelCase = self.decoder.to_dict()
UpperCamelCase = self.__class__.model_type
return output
| 711 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def A_( A : Tuple):
UpperCamelCase = torch.exp(A)
UpperCamelCase = torch.sum(A , dim=1) # sum of exp(x_i)
UpperCamelCase = torch.sum(x * exp_x , dim=1) # sum of x_i * exp(x_i)
return torch.log(A) - B / A
class SCREAMING_SNAKE_CASE__ ( nn.Module):
def __init__( self , A_ )-> List[Any]:
'''simple docstring'''
super().__init__()
UpperCamelCase = config.output_attentions
UpperCamelCase = config.output_hidden_states
UpperCamelCase = nn.ModuleList([BertLayer(A_ ) for _ in range(config.num_hidden_layers )] )
UpperCamelCase = nn.ModuleList([BertHighway(A_ ) for _ in range(config.num_hidden_layers )] )
UpperCamelCase = [-1 for _ in range(config.num_hidden_layers )]
def UpperCAmelCase_ ( self , A_ )-> str:
'''simple docstring'''
if (type(A_ ) is float) or (type(A_ ) is int):
for i in range(len(self.early_exit_entropy ) ):
UpperCamelCase = x
else:
UpperCamelCase = x
def UpperCAmelCase_ ( self , A_ )-> Dict:
'''simple docstring'''
UpperCamelCase = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def UpperCAmelCase_ ( self , A_ , A_=None , A_=None , A_=None , A_=None , )-> Tuple:
'''simple docstring'''
UpperCamelCase = ()
UpperCamelCase = ()
UpperCamelCase = ()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
UpperCamelCase = all_hidden_states + (hidden_states,)
UpperCamelCase = layer_module(
A_ , A_ , head_mask[i] , A_ , A_ )
UpperCamelCase = layer_outputs[0]
if self.output_attentions:
UpperCamelCase = all_attentions + (layer_outputs[1],)
UpperCamelCase = (hidden_states,)
if self.output_hidden_states:
UpperCamelCase = current_outputs + (all_hidden_states,)
if self.output_attentions:
UpperCamelCase = current_outputs + (all_attentions,)
UpperCamelCase = self.highway[i](A_ )
# logits, pooled_output
if not self.training:
UpperCamelCase = highway_exit[0]
UpperCamelCase = entropy(A_ )
UpperCamelCase = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
UpperCamelCase = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
UpperCamelCase = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(A_ , i + 1 )
else:
UpperCamelCase = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
UpperCamelCase = all_hidden_states + (hidden_states,)
UpperCamelCase = (hidden_states,)
if self.output_hidden_states:
UpperCamelCase = outputs + (all_hidden_states,)
if self.output_attentions:
UpperCamelCase = outputs + (all_attentions,)
UpperCamelCase = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
"""The Bert Model transformer with early exiting (DeeBERT). """ , snake_case_ , )
class SCREAMING_SNAKE_CASE__ ( snake_case_):
def __init__( self , A_ )-> Dict:
'''simple docstring'''
super().__init__(A_ )
UpperCamelCase = config
UpperCamelCase = BertEmbeddings(A_ )
UpperCamelCase = DeeBertEncoder(A_ )
UpperCamelCase = BertPooler(A_ )
self.init_weights()
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
self.encoder.init_highway_pooler(self.pooler )
def UpperCAmelCase_ ( self )-> List[str]:
'''simple docstring'''
return self.embeddings.word_embeddings
def UpperCAmelCase_ ( self , A_ )-> Optional[Any]:
'''simple docstring'''
UpperCamelCase = value
def UpperCAmelCase_ ( self , A_ )-> List[Any]:
'''simple docstring'''
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(A_ )
@add_start_docstrings_to_model_forward(A_ )
def UpperCAmelCase_ ( self , A_=None , A_=None , A_=None , A_=None , A_=None , A_=None , A_=None , A_=None , )-> List[Any]:
'''simple docstring'''
if input_ids is not None and inputs_embeds is not None:
raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
elif input_ids is not None:
UpperCamelCase = input_ids.size()
elif inputs_embeds is not None:
UpperCamelCase = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds' )
UpperCamelCase = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
UpperCamelCase = torch.ones(A_ , device=A_ )
if encoder_attention_mask is None:
UpperCamelCase = torch.ones(A_ , device=A_ )
if token_type_ids is None:
UpperCamelCase = torch.zeros(A_ , dtype=torch.long , device=A_ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
UpperCamelCase = self.get_extended_attention_mask(A_ , A_ , A_ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
UpperCamelCase = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
UpperCamelCase = encoder_attention_mask[:, None, None, :]
UpperCamelCase = encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
UpperCamelCase = (1.0 - encoder_extended_attention_mask) * -10_000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
UpperCamelCase = self.get_head_mask(A_ , self.config.num_hidden_layers )
UpperCamelCase = self.embeddings(
input_ids=A_ , position_ids=A_ , token_type_ids=A_ , inputs_embeds=A_ )
UpperCamelCase = self.encoder(
A_ , attention_mask=A_ , head_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , )
UpperCamelCase = encoder_outputs[0]
UpperCamelCase = self.pooler(A_ )
UpperCamelCase = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class SCREAMING_SNAKE_CASE__ ( snake_case_):
def __init__( self , A_ , A_ )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = message
UpperCamelCase = exit_layer # start from 1!
class SCREAMING_SNAKE_CASE__ ( nn.Module):
def __init__( self , A_ )-> Dict:
'''simple docstring'''
super().__init__()
UpperCamelCase = BertPooler(A_ )
UpperCamelCase = nn.Dropout(config.hidden_dropout_prob )
UpperCamelCase = nn.Linear(config.hidden_size , config.num_labels )
def UpperCAmelCase_ ( self , A_ )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = encoder_outputs[0]
UpperCamelCase = self.pooler(A_ )
# "return" pooler_output
# BertModel
UpperCamelCase = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
UpperCamelCase = bmodel_output[1]
UpperCamelCase = self.dropout(A_ )
UpperCamelCase = self.classifier(A_ )
return logits, pooled_output
@add_start_docstrings(
"""Bert Model (with early exiting - DeeBERT) with a classifier on top,
also takes care of multi-layer training. """ , snake_case_ , )
class SCREAMING_SNAKE_CASE__ ( snake_case_):
def __init__( self , A_ )-> Tuple:
'''simple docstring'''
super().__init__(A_ )
UpperCamelCase = config.num_labels
UpperCamelCase = config.num_hidden_layers
UpperCamelCase = DeeBertModel(A_ )
UpperCamelCase = nn.Dropout(config.hidden_dropout_prob )
UpperCamelCase = nn.Linear(config.hidden_size , self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(A_ )
def UpperCAmelCase_ ( self , A_=None , A_=None , A_=None , A_=None , A_=None , A_=None , A_=None , A_=-1 , A_=False , )-> Tuple:
'''simple docstring'''
UpperCamelCase = self.num_layers
try:
UpperCamelCase = self.bert(
A_ , attention_mask=A_ , token_type_ids=A_ , position_ids=A_ , head_mask=A_ , inputs_embeds=A_ , )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
UpperCamelCase = outputs[1]
UpperCamelCase = self.dropout(A_ )
UpperCamelCase = self.classifier(A_ )
UpperCamelCase = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
UpperCamelCase = e.message
UpperCamelCase = e.exit_layer
UpperCamelCase = outputs[0]
if not self.training:
UpperCamelCase = entropy(A_ )
UpperCamelCase = []
UpperCamelCase = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
UpperCamelCase = MSELoss()
UpperCamelCase = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
UpperCamelCase = CrossEntropyLoss()
UpperCamelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
UpperCamelCase = []
for highway_exit in outputs[-1]:
UpperCamelCase = highway_exit[0]
if not self.training:
highway_logits_all.append(A_ )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
UpperCamelCase = MSELoss()
UpperCamelCase = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
UpperCamelCase = CrossEntropyLoss()
UpperCamelCase = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(A_ )
if train_highway:
UpperCamelCase = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
UpperCamelCase = (loss,) + outputs
if not self.training:
UpperCamelCase = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
UpperCamelCase = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 432 | 0 |
"""simple docstring"""
def lowercase (snake_case__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase = [1]
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 0, 0, 0
lowerCAmelCase = ugly_nums[ia] * 2
lowerCAmelCase = ugly_nums[ia] * 3
lowerCAmelCase = ugly_nums[ia] * 5
for _ in range(1 , __lowercase ):
lowerCAmelCase = min(__lowercase , __lowercase , __lowercase )
ugly_nums.append(__lowercase )
if next_num == next_a:
ia += 1
lowerCAmelCase = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
lowerCAmelCase = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
lowerCAmelCase = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f"""{ugly_numbers(2_0_0) = }""")
| 169 |
import math
from datetime import datetime, timedelta
def _snake_case (__lowercase):
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = math.floor(year / 100)
UpperCamelCase_ = math.floor((13 + 8 * leap_day_inhibits) / 25)
UpperCamelCase_ = leap_day_inhibits / 4
UpperCamelCase_ = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
UpperCamelCase_ = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
UpperCamelCase_ = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
UpperCamelCase_ = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(__lowercase , 4 , 19)
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(__lowercase , 4 , 18)
else:
return datetime(__lowercase , 3 , 22) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday))
if __name__ == "__main__":
for year in (1_9_9_4, 2_0_0_0, 2_0_1_0, 2_0_2_1, 2_0_2_3):
snake_case__ : Dict = """will be""" if year > datetime.now().year else """was"""
print(f'Easter in {year} {tense} {gauss_easter(year)}')
| 23 | 0 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class __a ( __lowerCamelCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] ,_UpperCamelCase : Callable ,_UpperCamelCase : Optional[Features] = None ,_UpperCamelCase : str = None ,_UpperCamelCase : bool = False ,_UpperCamelCase : bool = False ,_UpperCamelCase : Optional[dict] = None ,_UpperCamelCase : Optional[int] = None ,**_UpperCamelCase : Union[str, Any] ,) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
features=_UpperCamelCase ,cache_dir=_UpperCamelCase ,keep_in_memory=_UpperCamelCase ,streaming=_UpperCamelCase ,num_proc=_UpperCamelCase ,**_UpperCamelCase ,)
SCREAMING_SNAKE_CASE__ =Generator(
cache_dir=_UpperCamelCase ,features=_UpperCamelCase ,generator=_UpperCamelCase ,gen_kwargs=_UpperCamelCase ,**_UpperCamelCase ,)
def __A ( self : str ) -> int:
'''simple docstring'''
if self.streaming:
SCREAMING_SNAKE_CASE__ =self.builder.as_streaming_dataset(split="""train""" )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE__ =None
SCREAMING_SNAKE_CASE__ =None
SCREAMING_SNAKE_CASE__ =None
SCREAMING_SNAKE_CASE__ =None
self.builder.download_and_prepare(
download_config=_UpperCamelCase ,download_mode=_UpperCamelCase ,verification_mode=_UpperCamelCase ,base_path=_UpperCamelCase ,num_proc=self.num_proc ,)
SCREAMING_SNAKE_CASE__ =self.builder.as_dataset(
split="""train""" ,verification_mode=_UpperCamelCase ,in_memory=self.keep_in_memory )
return dataset
| 588 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.g4dn.xlarge",
"results": {"train_runtime": 6_50, "eval_accuracy": 0.6, "eval_loss": 0.9},
},
{
"framework": "tensorflow",
"script": "run_tf.py",
"model_name_or_path": "distilbert-base-cased",
"instance_type": "ml.g4dn.xlarge",
"results": {"train_runtime": 6_00, "eval_accuracy": 0.3, "eval_loss": 0.9},
},
] )
class __a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() ,encoding="""utf-8""" ,check=_UpperCamelCase ,)
assert hasattr(self ,"""env""" )
def __A ( self : List[str] ,_UpperCamelCase : int=1 ) -> int:
'''simple docstring'''
return HuggingFace(
entry_point=self.script ,source_dir=self.env.test_path ,role=self.env.role ,image_uri=self.env.image_uri ,base_job_name=f"""{self.env.base_job_name}-single""" ,instance_count=_UpperCamelCase ,instance_type=self.instance_type ,debugger_hook_config=_UpperCamelCase ,hyperparameters={**self.env.hyperparameters, """model_name_or_path""": self.model_name_or_path} ,metric_definitions=self.env.metric_definitions ,py_version="""py36""" ,)
def __A ( self : Tuple ,_UpperCamelCase : Optional[int] ) -> List[Any]:
'''simple docstring'''
TrainingJobAnalytics(_UpperCamelCase ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
def __A ( self : Dict ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ =self.create_estimator()
# run training
estimator.fit()
# result dataframe
SCREAMING_SNAKE_CASE__ =TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
SCREAMING_SNAKE_CASE__ =list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
SCREAMING_SNAKE_CASE__ =list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
SCREAMING_SNAKE_CASE__ =(
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" ,9_9_9_9_9_9 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" ,"""w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} ,_UpperCamelCase )
| 588 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
snake_case__ = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowercase_):
def __init__( self : List[Any] , __A : str=None , **__A : Optional[int] ) ->Optional[Any]:
"""simple docstring"""
warnings.warn(
"`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
"instead." , UpperCamelCase_ , )
super().__init__(args=UpperCamelCase_ , **UpperCamelCase_ )
| 395 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( __lowercase : list[int] , __lowercase : int ) -> bool:
"""simple docstring"""
if len(__lowercase ) == 0:
return False
__A = len(__lowercase ) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , __lowercase )
else:
return binary_search(a_list[midpoint + 1 :] , __lowercase )
if __name__ == "__main__":
__a : Tuple = input("Enter numbers separated by comma:\n").strip()
__a : Any = [int(item.strip()) for item in user_input.split(",")]
__a : List[Any] = int(input("Enter the number to be found in the list:\n").strip())
__a : Optional[int] = "" if binary_search(sequence, target) else "not "
print(f"""{target} was {not_str}found in {sequence}""")
| 637 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class a_ ( snake_case ):
def __init__( self : Optional[Any] , a_ : Union[str, Any] , a_ : Optional[int]=1_3 , a_ : Optional[Any]=7 , a_ : List[Any]=True , a_ : Union[str, Any]=True , a_ : str=True , a_ : int=True , a_ : Optional[Any]=9_9 , a_ : Dict=3_2 , a_ : str=5 , a_ : Optional[int]=4 , a_ : Optional[int]=3_7 , a_ : int="gelu" , a_ : Optional[int]=0.1 , a_ : Optional[Any]=0.1 , a_ : Optional[int]=5_1_2 , a_ : Any=1_6 , a_ : List[Any]=2 , a_ : Union[str, Any]=0.0_2 , a_ : List[Any]=False , a_ : Any=True , a_ : int="None" , a_ : Optional[int]=3 , a_ : Optional[Any]=4 , a_ : List[str]=None , ) -> str:
snake_case: Union[str, Any] =parent
snake_case: Optional[Any] =batch_size
snake_case: List[str] =seq_length
snake_case: str =is_training
snake_case: Optional[int] =use_input_mask
snake_case: Union[str, Any] =use_token_type_ids
snake_case: Dict =use_labels
snake_case: Optional[Any] =vocab_size
snake_case: List[Any] =hidden_size
snake_case: Any =num_hidden_layers
snake_case: Tuple =num_attention_heads
snake_case: Any =intermediate_size
snake_case: int =hidden_act
snake_case: Optional[Any] =hidden_dropout_prob
snake_case: int =attention_probs_dropout_prob
snake_case: Dict =max_position_embeddings
snake_case: Tuple =type_vocab_size
snake_case: Tuple =type_sequence_label_size
snake_case: Any =initializer_range
snake_case: Dict =num_labels
snake_case: Union[str, Any] =num_choices
snake_case: int =relative_attention
snake_case: List[str] =position_biased_input
snake_case: Dict =pos_att_type
snake_case: Any =scope
def UpperCamelCase ( self : Optional[int] ) -> Tuple:
snake_case: List[str] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case: Optional[int] =None
if self.use_input_mask:
snake_case: Optional[int] =ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
snake_case: List[Any] =None
if self.use_token_type_ids:
snake_case: List[str] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case: Optional[Any] =None
snake_case: str =None
snake_case: int =None
if self.use_labels:
snake_case: str =ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case: Optional[int] =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case: Dict =ids_tensor([self.batch_size] , self.num_choices )
snake_case: Optional[int] =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self : int ) -> Union[str, Any]:
return DebertaVaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def UpperCamelCase ( self : Union[str, Any] , a_ : Optional[int] ) -> Any:
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def UpperCamelCase ( self : Union[str, Any] , a_ : Any , a_ : Optional[int] , a_ : Any , a_ : Dict , a_ : List[Any] , a_ : Optional[Any] , a_ : Optional[int] ) -> str:
snake_case: Optional[Any] =DebertaVaModel(config=a_ )
model.to(a_ )
model.eval()
snake_case: Union[str, Any] =model(a_ , attention_mask=a_ , token_type_ids=a_ )[0]
snake_case: Union[str, Any] =model(a_ , token_type_ids=a_ )[0]
snake_case: Optional[Any] =model(a_ )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def UpperCamelCase ( self : Optional[int] , a_ : Dict , a_ : Union[str, Any] , a_ : Tuple , a_ : Union[str, Any] , a_ : Tuple , a_ : Any , a_ : Any ) -> Tuple:
snake_case: Optional[Any] =DebertaVaForMaskedLM(config=a_ )
model.to(a_ )
model.eval()
snake_case: Tuple =model(a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self : List[Any] , a_ : List[Any] , a_ : Union[str, Any] , a_ : Any , a_ : Optional[int] , a_ : Union[str, Any] , a_ : List[Any] , a_ : Tuple ) -> List[str]:
snake_case: Any =self.num_labels
snake_case: Optional[Any] =DebertaVaForSequenceClassification(a_ )
model.to(a_ )
model.eval()
snake_case: Any =model(a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(a_ )
def UpperCamelCase ( self : Union[str, Any] , a_ : int , a_ : Dict , a_ : Tuple , a_ : str , a_ : Tuple , a_ : Union[str, Any] , a_ : str ) -> Optional[Any]:
snake_case: Union[str, Any] =self.num_labels
snake_case: int =DebertaVaForTokenClassification(config=a_ )
model.to(a_ )
model.eval()
snake_case: int =model(a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self : Optional[int] , a_ : str , a_ : List[Any] , a_ : Dict , a_ : str , a_ : Tuple , a_ : Tuple , a_ : Optional[int] ) -> Dict:
snake_case: Tuple =DebertaVaForQuestionAnswering(config=a_ )
model.to(a_ )
model.eval()
snake_case: Union[str, Any] =model(
a_ , attention_mask=a_ , token_type_ids=a_ , start_positions=a_ , end_positions=a_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self : Optional[Any] , a_ : Any , a_ : Union[str, Any] , a_ : List[Any] , a_ : Dict , a_ : List[Any] , a_ : List[Any] , a_ : List[Any] ) -> Dict:
snake_case: List[str] =DebertaVaForMultipleChoice(config=a_ )
model.to(a_ )
model.eval()
snake_case: Any =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case: List[str] =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case: List[str] =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case: int =model(
a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self : List[Any] ) -> Union[str, Any]:
snake_case: str =self.prepare_config_and_inputs()
(
(
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) , (
snake_case
) ,
): Union[str, Any] =config_and_inputs
snake_case: Any ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a_ ( snake_case , snake_case , unittest.TestCase ):
UpperCAmelCase : int = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
UpperCAmelCase : List[Any] = (
{
"""feature-extraction""": DebertaVaModel,
"""fill-mask""": DebertaVaForMaskedLM,
"""question-answering""": DebertaVaForQuestionAnswering,
"""text-classification""": DebertaVaForSequenceClassification,
"""token-classification""": DebertaVaForTokenClassification,
"""zero-shot""": DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase : Optional[int] = True
UpperCAmelCase : Optional[Any] = False
UpperCAmelCase : List[Any] = False
UpperCAmelCase : Union[str, Any] = False
UpperCAmelCase : Any = False
def UpperCamelCase ( self : str ) -> Any:
snake_case: Optional[int] =DebertaVaModelTester(self )
snake_case: Dict =ConfigTester(self , config_class=a_ , hidden_size=3_7 )
def UpperCamelCase ( self : Union[str, Any] ) -> str:
self.config_tester.run_common_tests()
def UpperCamelCase ( self : int ) -> Tuple:
snake_case: int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*a_ )
def UpperCamelCase ( self : List[str] ) -> int:
snake_case: str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*a_ )
def UpperCamelCase ( self : str ) -> List[Any]:
snake_case: Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*a_ )
def UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
snake_case: Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*a_ )
def UpperCamelCase ( self : Optional[Any] ) -> Any:
snake_case: str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*a_ )
def UpperCamelCase ( self : str ) -> Union[str, Any]:
snake_case: str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*a_ )
@slow
def UpperCamelCase ( self : List[str] ) -> Optional[Any]:
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case: Optional[int] =DebertaVaModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class a_ ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet' )
def UpperCamelCase ( self : str ) -> Dict:
pass
@slow
def UpperCamelCase ( self : List[str] ) -> Tuple:
snake_case: Dict =DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge' )
snake_case: str =torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] )
snake_case: Dict =torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
snake_case: Any =model(a_ , attention_mask=a_ )[0]
# compare the actual values for a slice.
snake_case: List[str] =torch.tensor(
[[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a_ , atol=1E-4 ) , F'''{output[:, 1:4, 1:4]}''' )
| 347 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
a = logging.getLogger(__name__)
class a_ ( snake_case ):
UpperCAmelCase : Any = """sequence-classification"""
def __init__( self : int , a_ : str ) -> str:
if type(a_ ) == dict:
snake_case: List[Any] =Namespace(**a_ )
snake_case: Tuple =glue_output_modes[hparams.task]
snake_case: Any =glue_tasks_num_labels[hparams.task]
super().__init__(a_ , a_ , self.mode )
def UpperCamelCase ( self : Tuple , **a_ : Tuple ) -> Union[str, Any]:
return self.model(**a_ )
def UpperCamelCase ( self : int , a_ : Union[str, Any] , a_ : Optional[int] ) -> Optional[int]:
snake_case: Any ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
snake_case: Optional[int] =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
snake_case: Optional[int] =self(**a_ )
snake_case: Any =outputs[0]
snake_case: Union[str, Any] =self.trainer.lr_schedulers[0]['scheduler']
snake_case: str ={'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def UpperCamelCase ( self : str ) -> Tuple:
snake_case: int =self.hparams
snake_case: Union[str, Any] =processors[args.task]()
snake_case: Union[str, Any] =processor.get_labels()
for mode in ["train", "dev"]:
snake_case: Optional[Any] =self._feature_file(a_ )
if os.path.exists(a_ ) and not args.overwrite_cache:
logger.info('Loading features from cached file %s' , a_ )
else:
logger.info('Creating features from dataset file at %s' , args.data_dir )
snake_case: int =(
processor.get_dev_examples(args.data_dir )
if mode == 'dev'
else processor.get_train_examples(args.data_dir )
)
snake_case: Tuple =convert_examples_to_features(
a_ , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('Saving features into cached file %s' , a_ )
torch.save(a_ , a_ )
def UpperCamelCase ( self : List[Any] , a_ : str , a_ : int , a_ : bool = False ) -> DataLoader:
snake_case: List[Any] ='dev' if mode == 'test' else mode
snake_case: Union[str, Any] =self._feature_file(a_ )
logger.info('Loading features from cached file %s' , a_ )
snake_case: Dict =torch.load(a_ )
snake_case: Union[str, Any] =torch.tensor([f.input_ids for f in features] , dtype=torch.long )
snake_case: List[Any] =torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
snake_case: str =torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
snake_case: Optional[Any] =torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
snake_case: Union[str, Any] =torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(a_ , a_ , a_ , a_ ) , batch_size=a_ , shuffle=a_ , )
def UpperCamelCase ( self : List[str] , a_ : Optional[int] , a_ : Any ) -> Dict:
snake_case: int ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
snake_case: Tuple =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
snake_case: List[str] =self(**a_ )
snake_case , snake_case: str =outputs[:2]
snake_case: Any =logits.detach().cpu().numpy()
snake_case: Union[str, Any] =inputs['labels'].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def UpperCamelCase ( self : int , a_ : Union[str, Any] ) -> tuple:
snake_case: Optional[Any] =torch.stack([x['val_loss'] for x in outputs] ).mean().detach().cpu().item()
snake_case: str =np.concatenate([x['pred'] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
snake_case: Union[str, Any] =np.argmax(a_ , axis=1 )
elif self.hparams.glue_output_mode == "regression":
snake_case: Optional[Any] =np.squeeze(a_ )
snake_case: Tuple =np.concatenate([x['target'] for x in outputs] , axis=0 )
snake_case: Any =[[] for _ in range(out_label_ids.shape[0] )]
snake_case: str =[[] for _ in range(out_label_ids.shape[0] )]
snake_case: int ={**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task , a_ , a_ )}
snake_case: Union[str, Any] =dict(results.items() )
snake_case: Dict =results
return ret, preds_list, out_label_list
def UpperCamelCase ( self : str , a_ : list ) -> dict:
snake_case , snake_case , snake_case: Union[str, Any] =self._eval_end(a_ )
snake_case: Optional[Any] =ret['log']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def UpperCamelCase ( self : Tuple , a_ : Tuple ) -> dict:
snake_case , snake_case , snake_case: int =self._eval_end(a_ )
snake_case: List[Any] =ret['log']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def UpperCamelCase ( a_ : Optional[int] , a_ : Dict ) -> Tuple:
BaseTransformer.add_model_specific_args(a_ , a_ )
parser.add_argument(
'--max_seq_length' , default=1_2_8 , type=a_ , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--task' , default='' , type=a_ , required=a_ , help='The GLUE task to run' , )
parser.add_argument(
'--gpus' , default=0 , type=a_ , help='The number of GPUs allocated for this, it is by default 0 meaning none' , )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
return parser
def a_ ( ) -> Any:
"""simple docstring"""
snake_case: Tuple =argparse.ArgumentParser()
add_generic_args(__UpperCAmelCase , os.getcwd() )
snake_case: List[Any] =GLUETransformer.add_model_specific_args(__UpperCAmelCase , os.getcwd() )
snake_case: Optional[int] =parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
snake_case: Optional[int] =os.path.join(
'./results' , f'''{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}''' , )
os.makedirs(args.output_dir )
snake_case: str =GLUETransformer(__UpperCAmelCase )
snake_case: Tuple =generic_train(__UpperCAmelCase , __UpperCAmelCase )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
snake_case: str =sorted(glob.glob(os.path.join(args.output_dir , 'checkpoint-epoch=*.ckpt' ) , recursive=__UpperCAmelCase ) )
snake_case: int =model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(__UpperCAmelCase )
if __name__ == "__main__":
main()
| 347 | 1 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,
set_seed,
)
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument('--model_ckpt' , type=_UpperCAmelCase , default='microsoft/unixcoder-base-nine' )
parser.add_argument('--num_epochs' , type=_UpperCAmelCase , default=5 )
parser.add_argument('--batch_size' , type=_UpperCAmelCase , default=6 )
parser.add_argument('--gradient_accumulation_steps' , type=_UpperCAmelCase , default=1 )
parser.add_argument('--freeze' , type=_UpperCAmelCase , default=_UpperCAmelCase )
parser.add_argument('--learning_rate' , type=_UpperCAmelCase , default=5e-4 )
parser.add_argument('--seed' , type=_UpperCAmelCase , default=0 )
parser.add_argument('--lr_scheduler_type' , type=_UpperCAmelCase , default='cosine' )
parser.add_argument('--num_warmup_steps' , type=_UpperCAmelCase , default=10 )
parser.add_argument('--weight_decay' , type=_UpperCAmelCase , default=0.01 )
parser.add_argument('--output_dir' , type=_UpperCAmelCase , default='./results' )
return parser.parse_args()
__UpperCamelCase : Tuple = load('''accuracy''')
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[Any] ):
lowerCAmelCase ,lowerCAmelCase = eval_pred
lowerCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
return metric.compute(predictions=_UpperCAmelCase , references=_UpperCAmelCase )
class a ( a__ ):
def __init__( self , _snake_case ):
"""simple docstring"""
super().__init__()
lowerCAmelCase = trainer
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , **_snake_case ):
"""simple docstring"""
if control.should_evaluate:
lowerCAmelCase = deepcopy(_snake_case )
self._trainer.evaluate(eval_dataset=self._trainer.train_dataset , metric_key_prefix='train' )
return control_copy
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = get_args()
set_seed(args.seed )
lowerCAmelCase = load_dataset('codeparrot/codecomplex' , split='train' )
lowerCAmelCase = dataset.train_test_split(test_size=0.2 )
lowerCAmelCase = train_test['test'].train_test_split(test_size=0.5 )
lowerCAmelCase = DatasetDict(
{
'train': train_test['train'],
'test': test_validation['train'],
'valid': test_validation['test'],
} )
print('Loading tokenizer and model' )
lowerCAmelCase = AutoTokenizer.from_pretrained(args.model_ckpt )
lowerCAmelCase = tokenizer.eos_token
lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained(args.model_ckpt , num_labels=7 )
lowerCAmelCase = model.config.eos_token_id
if args.freeze:
for param in model.roberta.parameters():
lowerCAmelCase = False
lowerCAmelCase = ClassLabel(num_classes=7 , names=list(set(train_test_validation['train']['complexity'] ) ) )
def tokenize(_UpperCAmelCase : Optional[Any] ):
lowerCAmelCase = tokenizer(example['src'] , truncation=_UpperCAmelCase , max_length=1024 )
lowerCAmelCase = labels.straint(example['complexity'] )
return {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"label": label,
}
lowerCAmelCase = train_test_validation.map(
_UpperCAmelCase , batched=_UpperCAmelCase , remove_columns=train_test_validation['train'].column_names , )
lowerCAmelCase = DataCollatorWithPadding(tokenizer=_UpperCAmelCase )
lowerCAmelCase = TrainingArguments(
output_dir=args.output_dir , learning_rate=args.learning_rate , lr_scheduler_type=args.lr_scheduler_type , evaluation_strategy='epoch' , save_strategy='epoch' , logging_strategy='epoch' , per_device_train_batch_size=args.batch_size , per_device_eval_batch_size=args.batch_size , num_train_epochs=args.num_epochs , gradient_accumulation_steps=args.gradient_accumulation_steps , weight_decay=0.01 , metric_for_best_model='accuracy' , run_name='complexity-java' , report_to='wandb' , )
lowerCAmelCase = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=tokenized_datasets['train'] , eval_dataset=tokenized_datasets['valid'] , tokenizer=_UpperCAmelCase , data_collator=_UpperCAmelCase , compute_metrics=_UpperCAmelCase , )
print('Training...' )
trainer.add_callback(CustomCallback(_UpperCAmelCase ) )
trainer.train()
if __name__ == "__main__":
main()
| 4 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , ):
"""simple docstring"""
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = seq_length
lowerCAmelCase = is_training
lowerCAmelCase = use_token_type_ids
lowerCAmelCase = use_labels
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = type_vocab_size
lowerCAmelCase = type_sequence_label_size
lowerCAmelCase = initializer_range
lowerCAmelCase = num_labels
lowerCAmelCase = num_choices
lowerCAmelCase = scope
lowerCAmelCase = self.vocab_size - 1
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase = None
if self.use_token_type_ids:
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = None
if self.use_labels:
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
lowerCAmelCase = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTModel(config=_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , head_mask=_snake_case )
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTLMHeadModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTDoubleHeadsModel(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , *_snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = OpenAIGPTForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = model(_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class a ( a__ , a__ , a__ , unittest.TestCase ):
snake_case__ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
snake_case__ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
snake_case__ = (
{
'''feature-extraction''': OpenAIGPTModel,
'''text-classification''': OpenAIGPTForSequenceClassification,
'''text-generation''': OpenAIGPTLMHeadModel,
'''zero-shot''': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case=False ):
"""simple docstring"""
lowerCAmelCase = super()._prepare_for_class(_snake_case , _snake_case , return_labels=_snake_case )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowerCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=_snake_case , )
lowerCAmelCase = inputs_dict['labels']
lowerCAmelCase = inputs_dict['labels']
lowerCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=_snake_case , )
lowerCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_snake_case )
return inputs_dict
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , n_embd=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = OpenAIGPTModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@require_torch
class a ( unittest.TestCase ):
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(_snake_case )
lowerCAmelCase = torch.tensor([[4_81, 47_35, 5_44]] , dtype=torch.long , device=_snake_case ) # the president is
lowerCAmelCase = [
4_81,
47_35,
5_44,
2_46,
9_63,
8_70,
7_62,
2_39,
2_44,
4_04_77,
2_44,
2_49,
7_19,
8_81,
4_87,
5_44,
2_40,
2_44,
6_03,
4_81,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowerCAmelCase = model.generate(_snake_case , do_sample=_snake_case )
self.assertListEqual(output_ids[0].tolist() , _snake_case )
| 4 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class A_ ( a_ , a_ ):
_SCREAMING_SNAKE_CASE = """pixel_values"""
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = TimmBackboneConfig
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : List[Any] ):
requires_backends(self , "timm" )
super().__init__(__SCREAMING_SNAKE_CASE )
__a = config
if config.backbone is None:
raise ValueError("backbone is not set in the config. Please set it to a timm model name." )
if config.backbone not in timm.list_models():
raise ValueError(f"""backbone {config.backbone} is not supported by timm.""" )
if hasattr(__SCREAMING_SNAKE_CASE , "out_features" ) and config.out_features is not None:
raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead." )
__a = getattr(__SCREAMING_SNAKE_CASE , "use_pretrained_backbone" , __SCREAMING_SNAKE_CASE )
if pretrained is None:
raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False." )
# We just take the final layer by default. This matches the default for the transformers models.
__a = config.out_indices if getattr(__SCREAMING_SNAKE_CASE , "out_indices" , __SCREAMING_SNAKE_CASE ) is not None else (-1,)
__a = timm.create_model(
config.backbone , pretrained=__SCREAMING_SNAKE_CASE , features_only=config.features_only , in_chans=config.num_channels , out_indices=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
__a = self._backbone.return_layers
__a = {layer["module"]: str(__SCREAMING_SNAKE_CASE ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(__SCREAMING_SNAKE_CASE )
@classmethod
def _UpperCAmelCase ( cls : Dict , __SCREAMING_SNAKE_CASE : Dict , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Tuple ):
requires_backends(cls , ["vision", "timm"] )
from ...models.timm_backbone import TimmBackboneConfig
__a = kwargs.pop("config" , TimmBackboneConfig() )
__a = kwargs.pop("use_timm_backbone" , __SCREAMING_SNAKE_CASE )
if not use_timm:
raise ValueError("use_timm_backbone must be True for timm backbones" )
__a = kwargs.pop("num_channels" , config.num_channels )
__a = kwargs.pop("features_only" , config.features_only )
__a = kwargs.pop("use_pretrained_backbone" , config.use_pretrained_backbone )
__a = kwargs.pop("out_indices" , config.out_indices )
__a = TimmBackboneConfig(
backbone=__SCREAMING_SNAKE_CASE , num_channels=__SCREAMING_SNAKE_CASE , features_only=__SCREAMING_SNAKE_CASE , use_pretrained_backbone=__SCREAMING_SNAKE_CASE , out_indices=__SCREAMING_SNAKE_CASE , )
return super()._from_config(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any] ):
pass
def _UpperCAmelCase ( self : Dict , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[int]=None , __SCREAMING_SNAKE_CASE : Optional[Any]=None , __SCREAMING_SNAKE_CASE : Tuple=None , **__SCREAMING_SNAKE_CASE : List[Any] ):
__a = return_dict if return_dict is not None else self.config.use_return_dict
__a = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__a = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError("Cannot output attentions for timm backbones at the moment" )
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
__a = self._all_layers
__a = self._backbone(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__a = self._return_layers
__a = tuple(hidden_states[i] for i in self.out_indices )
else:
__a = self._backbone(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__a = None
__a = tuple(__SCREAMING_SNAKE_CASE )
__a = tuple(__SCREAMING_SNAKE_CASE ) if hidden_states is not None else None
if not return_dict:
__a = (feature_maps,)
if output_hidden_states:
__a = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=__SCREAMING_SNAKE_CASE , hidden_states=__SCREAMING_SNAKE_CASE , attentions=__SCREAMING_SNAKE_CASE )
| 704 | from ... import PretrainedConfig
SCREAMING_SNAKE_CASE : Any = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class A_ ( a_ ):
_SCREAMING_SNAKE_CASE = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
_SCREAMING_SNAKE_CASE = """nezha"""
def __init__( self : Any , __SCREAMING_SNAKE_CASE : List[str]=2_11_28 , __SCREAMING_SNAKE_CASE : Dict=7_68 , __SCREAMING_SNAKE_CASE : List[Any]=12 , __SCREAMING_SNAKE_CASE : str=12 , __SCREAMING_SNAKE_CASE : Optional[Any]=30_72 , __SCREAMING_SNAKE_CASE : Any="gelu" , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : List[str]=0.1 , __SCREAMING_SNAKE_CASE : int=5_12 , __SCREAMING_SNAKE_CASE : Union[str, Any]=64 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : int=0.02 , __SCREAMING_SNAKE_CASE : Optional[Any]=1E-12 , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : Any=0 , __SCREAMING_SNAKE_CASE : Any=2 , __SCREAMING_SNAKE_CASE : Optional[Any]=3 , __SCREAMING_SNAKE_CASE : List[Any]=True , **__SCREAMING_SNAKE_CASE : List[str] , ):
super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE , bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = hidden_act
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = max_relative_position
__a = type_vocab_size
__a = initializer_range
__a = layer_norm_eps
__a = classifier_dropout
__a = use_cache
| 525 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class UpperCamelCase__ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ = """dandelin/vilt-b32-finetuned-vqa"""
UpperCAmelCase__ = (
"""This is a tool that answers a question about an image. It takes an input named `image` which should be the """
"""image containing the information, as well as a `question` which should be the question in English. It """
"""returns a text that is the answer to the question."""
)
UpperCAmelCase__ = """image_qa"""
UpperCAmelCase__ = AutoProcessor
UpperCAmelCase__ = AutoModelForVisualQuestionAnswering
UpperCAmelCase__ = ["""image""", """text"""]
UpperCAmelCase__ = ["""text"""]
def __init__( self : int , *__A : int , **__A : List[Any] ):
"""simple docstring"""
requires_backends(self , ["vision"] )
super().__init__(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def snake_case ( self : Any , __A : "Image" , __A : str ):
"""simple docstring"""
return self.pre_processor(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , return_tensors="pt" )
def snake_case ( self : int , __A : Union[str, Any] ):
"""simple docstring"""
with torch.no_grad():
return self.model(**SCREAMING_SNAKE_CASE__ ).logits
def snake_case ( self : Any , __A : Optional[Any] ):
"""simple docstring"""
_lowercase = outputs.argmax(-1 ).item()
return self.model.config.idalabel[idx]
| 497 |
import cmath
import math
def __magic_name__ ( __lowerCAmelCase : float , __lowerCAmelCase : float , __lowerCAmelCase : float , __lowerCAmelCase : float ) -> complex:
__lowerCamelCase = math.radians(__lowerCAmelCase )
__lowerCamelCase = math.radians(__lowerCAmelCase )
# Convert voltage and current to rectangular form
__lowerCamelCase = cmath.rect(__lowerCAmelCase , __lowerCAmelCase )
__lowerCamelCase = cmath.rect(__lowerCAmelCase , __lowerCAmelCase )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 298 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def _lowercase ( ):
__A : List[str] = 'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
__A : List[str] = Image.open(requests.get(UpperCamelCase__, stream=UpperCamelCase__ ).raw ).convert('RGB' )
return image
def _lowercase ( UpperCamelCase__ : Dict ):
__A : List[Any] = []
# fmt: off
# vision encoder
rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding') )
rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding') )
rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight') )
rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias') )
rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight') )
rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.weight""", f"""vision_model.encoder.layers.{i}.layer_norm1.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.bias""", f"""vision_model.encoder.layers.{i}.layer_norm1.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.weight""", f"""vision_model.encoder.layers.{i}.layer_norm2.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.bias""", f"""vision_model.encoder.layers.{i}.layer_norm2.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.attn.qkv.weight""", f"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.weight""", f"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",) )
rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.bias""", f"""vision_model.encoder.layers.{i}.self_attn.projection.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc1.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc1.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc2.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc2.bias""") )
# QFormer
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight') )
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias') )
# fmt: on
return rename_keys
def _lowercase ( UpperCamelCase__ : List[str], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : int ):
__A : Union[str, Any] = dct.pop(UpperCamelCase__ )
__A : Dict = val
def _lowercase ( UpperCamelCase__ : str, UpperCamelCase__ : Any ):
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
__A : Optional[Any] = state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias""" )
__A : Optional[Any] = state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias""" )
# next, set bias in the state dict
__A : List[Any] = torch.cat((q_bias, torch.zeros_like(UpperCamelCase__, requires_grad=UpperCamelCase__ ), v_bias) )
__A : Any = qkv_bias
def _lowercase ( UpperCamelCase__ : List[str] ):
__A : Tuple = 364 if 'coco' in model_name else 224
__A : str = InstructBlipVisionConfig(image_size=UpperCamelCase__ ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
__A : str = TaConfig.from_pretrained('google/flan-t5-xl', dense_act_fn='gelu', bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
__A : Tuple = TaConfig.from_pretrained('google/flan-t5-xxl', dense_act_fn='gelu', bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
__A : Optional[int] = LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf', vocab_size=32001 ).to_dict()
elif "vicuna-13b" in model_name:
__A : Union[str, Any] = LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf', vocab_size=32001 ).to_dict()
else:
raise ValueError('Model name not supported' )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
__A : List[str] = InstructBlipQFormerConfig(vocab_size=30523 ).to_dict()
__A : int = InstructBlipConfig(vision_config=UpperCamelCase__, text_config=UpperCamelCase__, qformer_config=UpperCamelCase__ )
return config, image_size
@torch.no_grad()
def _lowercase ( UpperCamelCase__ : Any, UpperCamelCase__ : int=None, UpperCamelCase__ : str=False ):
__A : Optional[int] = AutoTokenizer.from_pretrained('bert-base-uncased', truncation_side='left' )
qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'} )
if "t5" in model_name:
__A : Optional[Any] = TaTokenizerFast.from_pretrained('google/flan-t5-xl', truncation_side='left' )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
__A : Union[str, Any] = LlamaTokenizerFast.from_pretrained(
'huggyllama/llama-7b', truncation_side='left', bos_token='</s>', unk_token='</s>' )
tokenizer.add_special_tokens({'pad_token': '[PAD]'} )
__A ,__A : List[str] = get_blipa_config(UpperCamelCase__ )
__A : Union[str, Any] = InstructBlipForConditionalGeneration(UpperCamelCase__ ).eval()
__A : List[str] = {
'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
}
__A ,__A : Any = model_name_to_original[model_name]
# load original model
print('Loading original model...' )
__A : List[Any] = 'cuda:1' if torch.cuda.is_available() else 'cpu'
__A : Union[str, Any] = 'cuda:2' if torch.cuda.is_available() else 'cpu'
__A ,__A ,__A : Any = load_model_and_preprocess(
name=UpperCamelCase__, model_type=UpperCamelCase__, is_eval=UpperCamelCase__, device=UpperCamelCase__ )
original_model.eval()
print('Done!' )
# update state dict keys
__A : List[str] = original_model.state_dict()
__A : Union[str, Any] = create_rename_keys(UpperCamelCase__ )
for src, dest in rename_keys:
rename_key(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
__A : str = state_dict.pop(UpperCamelCase__ )
if key.startswith('Qformer.bert' ):
__A : Dict = key.replace('Qformer.bert', 'qformer' )
if "attention.self" in key:
__A : Tuple = key.replace('self', 'attention' )
if "llm_proj" in key:
__A : Tuple = key.replace('llm_proj', 'language_projection' )
if "t5_proj" in key:
__A : Union[str, Any] = key.replace('t5_proj', 'language_projection' )
if key.startswith('llm_model' ):
__A : str = key.replace('llm_model', 'language_model' )
if key.startswith('t5' ):
__A : Optional[Any] = key.replace('t5', 'language' )
__A : str = val
# read in qv biases
read_in_q_v_bias(UpperCamelCase__, UpperCamelCase__ )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(UpperCamelCase__, strict=UpperCamelCase__ )
__A : str = load_demo_image()
__A : Any = 'What is unusual about this image?'
# create processor
__A : List[str] = BlipImageProcessor(
size={'height': image_size, 'width': image_size}, image_mean=UpperCamelCase__, image_std=UpperCamelCase__ )
__A : int = InstructBlipProcessor(
image_processor=UpperCamelCase__, tokenizer=UpperCamelCase__, qformer_tokenizer=UpperCamelCase__, )
__A : Union[str, Any] = processor(images=UpperCamelCase__, text=UpperCamelCase__, return_tensors='pt' ).to(UpperCamelCase__ )
# make sure processor creates exact same pixel values
__A : Any = vis_processors['eval'](UpperCamelCase__ ).unsqueeze(0 ).to(UpperCamelCase__ )
__A : List[str] = inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ), UpperCamelCase__ )
original_model.to(UpperCamelCase__ )
hf_model.to(UpperCamelCase__ )
with torch.no_grad():
if "vicuna" in model_name:
__A : Tuple = original_model({'image': original_pixel_values, 'text_input': [prompt]} ).logits
__A : Dict = hf_model(**UpperCamelCase__ ).logits
else:
__A : int = original_model(
{'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']} ).logits
__A : Optional[int] = tokenizer('\n', return_tensors='pt' ).input_ids.to(UpperCamelCase__ )
__A : Optional[int] = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id, -100 )
__A : Optional[int] = hf_model(**UpperCamelCase__, labels=UpperCamelCase__ ).logits
print('First values of original logits:', original_logits[0, :3, :3] )
print('First values of HF logits:', logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
__A : int = 1E-4 if 'vicuna' in model_name else 1E-5
assert torch.allclose(original_logits.to(logits.device ), UpperCamelCase__, atol=UpperCamelCase__ )
print('Looks ok!' )
print('Generating with original model...' )
__A : Optional[int] = original_model.generate({'image': original_pixel_values, 'prompt': prompt}, num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print('Generating with HF model...' )
__A : List[Any] = hf_model.generate(
**UpperCamelCase__, do_sample=UpperCamelCase__, num_beams=5, max_length=256, min_length=1, top_p=0.9, repetition_penalty=1.5, length_penalty=1.0, temperature=1, )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
__A : Union[str, Any] = 2
print('Original generation:', UpperCamelCase__ )
__A : List[str] = processor.batch_decode(UpperCamelCase__, skip_special_tokens=UpperCamelCase__ )
__A : List[str] = [text.strip() for text in output_text]
print('HF generation:', UpperCamelCase__ )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(UpperCamelCase__ )
hf_model.save_pretrained(UpperCamelCase__ )
if push_to_hub:
processor.push_to_hub(f"""Salesforce/{model_name}""" )
hf_model.push_to_hub(f"""Salesforce/{model_name}""" )
if __name__ == "__main__":
UpperCAmelCase_ : List[str] = argparse.ArgumentParser()
UpperCAmelCase_ : str = [
'instructblip-vicuna-7b',
'instructblip-vicuna-13b',
'instructblip-flan-t5-xl',
'instructblip-flan-t5-xxl',
]
parser.add_argument(
'--model_name',
default='instructblip-flan-t5-xl',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub after converting',
)
UpperCAmelCase_ : Optional[int] = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 540 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
UpperCAmelCase_ : str = 'naver-clova-ix/donut-base'
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def snake_case__ ( self ):
"""simple docstring"""
__A : List[str] = DonutProcessor.from_pretrained(__lowercase )
def snake_case__ ( self ):
"""simple docstring"""
__A : List[str] = {
'name': 'John Doe',
'age': '99',
'city': 'Atlanta',
'state': 'GA',
'zip': '30301',
'phone': '123-4567',
'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
}
__A : Optional[Any] = (
'<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
'<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
'<s_nicknames><s_nickname>Johnny</s_nickname>'
'<sep/><s_nickname>JD</s_nickname></s_nicknames>'
)
__A : List[Any] = self.processor.tokenajson(__lowercase )
self.assertDictEqual(__lowercase , __lowercase )
| 540 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class __lowerCamelCase (unittest.TestCase ):
def __init__( self: List[Any],A_: Dict,A_: List[str]=7,A_: Dict=3,A_: int=18,A_: Optional[Any]=30,A_: Dict=400,A_: int=True,A_: Tuple=None,A_: Any=True,):
'''simple docstring'''
__UpperCamelCase = size if size is not None else {'height': 18, 'width': 18}
__UpperCamelCase = parent
__UpperCamelCase = batch_size
__UpperCamelCase = num_channels
__UpperCamelCase = image_size
__UpperCamelCase = min_resolution
__UpperCamelCase = max_resolution
__UpperCamelCase = do_resize
__UpperCamelCase = size
__UpperCamelCase = apply_ocr
def snake_case_ ( self: Dict ):
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class __lowerCamelCase (_a , unittest.TestCase ):
_lowercase = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def snake_case_ ( self: Tuple ):
'''simple docstring'''
__UpperCamelCase = LayoutLMvaImageProcessingTester(self )
@property
def snake_case_ ( self: List[str] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case_ ( self: List[Any] ):
'''simple docstring'''
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A_,'do_resize' ) )
self.assertTrue(hasattr(A_,'size' ) )
self.assertTrue(hasattr(A_,'apply_ocr' ) )
def snake_case_ ( self: Union[str, Any] ):
'''simple docstring'''
__UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size,{'height': 18, 'width': 18} )
__UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict,size=42 )
self.assertEqual(image_processor.size,{'height': 42, 'width': 42} )
def snake_case_ ( self: Union[str, Any] ):
'''simple docstring'''
pass
def snake_case_ ( self: Any ):
'''simple docstring'''
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__UpperCamelCase = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_ )
for image in image_inputs:
self.assertIsInstance(A_,Image.Image )
# Test not batched input
__UpperCamelCase = image_processing(image_inputs[0],return_tensors='pt' )
self.assertEqual(
encoding.pixel_values.shape,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
),)
self.assertIsInstance(encoding.words,A_ )
self.assertIsInstance(encoding.boxes,A_ )
# Test batched
__UpperCamelCase = image_processing(A_,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.size['height'],
self.image_processor_tester.size['width'],
),)
def snake_case_ ( self: Optional[Any] ):
'''simple docstring'''
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__UpperCamelCase = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_,numpify=A_ )
for image in image_inputs:
self.assertIsInstance(A_,np.ndarray )
# Test not batched input
__UpperCamelCase = 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.size['height'],
self.image_processor_tester.size['width'],
),)
# Test batched
__UpperCamelCase = image_processing(A_,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.size['height'],
self.image_processor_tester.size['width'],
),)
def snake_case_ ( self: Dict ):
'''simple docstring'''
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__UpperCamelCase = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_,torchify=A_ )
for image in image_inputs:
self.assertIsInstance(A_,torch.Tensor )
# Test not batched input
__UpperCamelCase = 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.size['height'],
self.image_processor_tester.size['width'],
),)
# Test batched
__UpperCamelCase = image_processing(A_,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.size['height'],
self.image_processor_tester.size['width'],
),)
def snake_case_ ( self: Tuple ):
'''simple docstring'''
__UpperCamelCase = LayoutLMvaImageProcessor()
from datasets import load_dataset
__UpperCamelCase = load_dataset('hf-internal-testing/fixtures_docvqa',split='test' )
__UpperCamelCase = Image.open(ds[0]['file'] ).convert('RGB' )
__UpperCamelCase = image_processing(A_,return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape,(1, 3, 224, 224) )
self.assertEqual(len(encoding.words ),len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
__UpperCamelCase = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
__UpperCamelCase = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words,A_ )
self.assertListEqual(encoding.boxes,A_ )
# with apply_OCR = False
__UpperCamelCase = LayoutLMvaImageProcessor(apply_ocr=A_ )
__UpperCamelCase = image_processing(A_,return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape,(1, 3, 224, 224) )
| 1 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""facebook/deit-base-distilled-patch16-224""": (
"""https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
),
# See all DeiT models at https://huggingface.co/models?filter=deit
}
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''deit'''
def __init__( self , lowercase=7_6_8 , lowercase=1_2 , lowercase=1_2 , lowercase=3_0_7_2 , lowercase="gelu" , lowercase=0.0 , lowercase=0.0 , lowercase=0.02 , lowercase=1E-12 , lowercase=2_2_4 , lowercase=1_6 , lowercase=3 , lowercase=True , lowercase=1_6 , **lowercase , ):
"""simple docstring"""
super().__init__(**lowercase )
A_ : Dict = hidden_size
A_ : List[Any] = num_hidden_layers
A_ : Optional[int] = num_attention_heads
A_ : List[str] = intermediate_size
A_ : int = hidden_act
A_ : Optional[int] = hidden_dropout_prob
A_ : str = attention_probs_dropout_prob
A_ : List[str] = initializer_range
A_ : List[Any] = layer_norm_eps
A_ : List[str] = image_size
A_ : str = patch_size
A_ : str = num_channels
A_ : Dict = qkv_bias
A_ : Optional[Any] = encoder_stride
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = version.parse('''1.11''' )
@property
def lowerCAmelCase_ ( self ):
"""simple docstring"""
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCAmelCase_ ( self ):
"""simple docstring"""
return 1E-4
| 558 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs (`Dict[str, Any]`, *optional*):
Additional stopping criteria specific kwargs.
Return:
`bool`. `False` indicates we should continue, `True` indicates we should stop.
"""
class A__ ( lowerCAmelCase__ ):
@add_start_docstrings(_UpperCAmelCase )
def __call__( self : List[Any] , _UpperCAmelCase : torch.LongTensor , _UpperCAmelCase : torch.FloatTensor , **_UpperCAmelCase : List[Any] ) -> bool:
"""simple docstring"""
raise NotImplementedError('StoppingCriteria needs to be subclassed' )
class A__ ( lowerCAmelCase__ ):
def __init__( self : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int] = None ) -> Optional[Any]:
"""simple docstring"""
__lowercase = max_length
__lowercase = max_position_embeddings
@add_start_docstrings(_UpperCAmelCase )
def __call__( self : Optional[int] , _UpperCAmelCase : torch.LongTensor , _UpperCAmelCase : torch.FloatTensor , **_UpperCAmelCase : Optional[int] ) -> bool:
"""simple docstring"""
__lowercase = input_ids.shape[-1]
__lowercase = cur_len >= self.max_length
if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
logger.warning_once(
'This is a friendly reminder - the current text generation call will exceed the model\'s predefined '
f"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
'exceptions, performance degradation, or nothing at all.' )
return is_done
class A__ ( lowerCAmelCase__ ):
def __init__( self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> List[str]:
"""simple docstring"""
warnings.warn(
'The class `MaxNewTokensCriteria` is deprecated. '
f"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
'with `max_length = start_length + max_new_tokens` instead.' , _UpperCAmelCase , )
__lowercase = start_length
__lowercase = max_new_tokens
__lowercase = start_length + max_new_tokens
@add_start_docstrings(_UpperCAmelCase )
def __call__( self : Dict , _UpperCAmelCase : torch.LongTensor , _UpperCAmelCase : torch.FloatTensor , **_UpperCAmelCase : Dict ) -> bool:
"""simple docstring"""
return input_ids.shape[-1] >= self.max_length
class A__ ( lowerCAmelCase__ ):
def __init__( self : List[str] , _UpperCAmelCase : float , _UpperCAmelCase : Optional[float] = None ) -> int:
"""simple docstring"""
__lowercase = max_time
__lowercase = time.time() if initial_timestamp is None else initial_timestamp
@add_start_docstrings(_UpperCAmelCase )
def __call__( self : Tuple , _UpperCAmelCase : torch.LongTensor , _UpperCAmelCase : torch.FloatTensor , **_UpperCAmelCase : List[Any] ) -> bool:
"""simple docstring"""
return time.time() - self.initial_timestamp > self.max_time
class A__ ( lowerCAmelCase__ ):
@add_start_docstrings(_UpperCAmelCase )
def __call__( self : Tuple , _UpperCAmelCase : torch.LongTensor , _UpperCAmelCase : torch.FloatTensor , **_UpperCAmelCase : Dict ) -> bool:
"""simple docstring"""
return any(criteria(_UpperCAmelCase , _UpperCAmelCase ) for criteria in self )
@property
def a__ ( self : Any ) -> Optional[int]:
"""simple docstring"""
for stopping_criterium in self:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
return stopping_criterium.max_length
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
return stopping_criterium.max_length
return None
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : StoppingCriteriaList , SCREAMING_SNAKE_CASE : int ) -> StoppingCriteriaList:
__lowercase = stopping_criteria.max_length
__lowercase = deepcopy(SCREAMING_SNAKE_CASE )
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn('You set different `max_length` for stopping criteria and `max_length` parameter' , SCREAMING_SNAKE_CASE )
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=SCREAMING_SNAKE_CASE ) )
return new_stopping_criteria
| 702 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class A__ ( lowerCAmelCase__ ):
lowerCAmelCase__ : List[str] = ["pixel_values"]
def __init__( self : Tuple , _UpperCAmelCase : bool = True , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , _UpperCAmelCase : bool = True , _UpperCAmelCase : Union[int, float] = 1 / 2_55 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : bool = True , **_UpperCAmelCase : str , ) -> None:
"""simple docstring"""
super().__init__(**_UpperCAmelCase )
__lowercase = size if size is not None else {'height': 3_84, 'width': 3_84}
__lowercase = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
__lowercase = do_resize
__lowercase = size
__lowercase = resample
__lowercase = do_rescale
__lowercase = rescale_factor
__lowercase = do_normalize
__lowercase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__lowercase = image_std if image_std is not None else OPENAI_CLIP_STD
__lowercase = do_convert_rgb
def a__ ( self : int , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : int , ) -> np.ndarray:
"""simple docstring"""
__lowercase = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
__lowercase = (size['height'], size['width'])
return resize(_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def a__ ( self : Optional[int] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[int, float] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : Any , ) -> str:
"""simple docstring"""
return rescale(_UpperCAmelCase , scale=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def a__ ( self : str , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : List[str] , ) -> np.ndarray:
"""simple docstring"""
return normalize(_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def a__ ( self : int , _UpperCAmelCase : ImageInput , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[Dict[str, int]] = None , _UpperCAmelCase : PILImageResampling = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[float] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : ChannelDimension = ChannelDimension.FIRST , **_UpperCAmelCase : int , ) -> PIL.Image.Image:
"""simple docstring"""
__lowercase = do_resize if do_resize is not None else self.do_resize
__lowercase = resample if resample is not None else self.resample
__lowercase = do_rescale if do_rescale is not None else self.do_rescale
__lowercase = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowercase = do_normalize if do_normalize is not None else self.do_normalize
__lowercase = image_mean if image_mean is not None else self.image_mean
__lowercase = image_std if image_std is not None else self.image_std
__lowercase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__lowercase = size if size is not None else self.size
__lowercase = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
__lowercase = 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_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__lowercase = [convert_to_rgb(_UpperCAmelCase ) for image in images]
# All transformations expect numpy arrays.
__lowercase = [to_numpy_array(_UpperCAmelCase ) for image in images]
if do_resize:
__lowercase = [self.resize(image=_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase ) for image in images]
if do_rescale:
__lowercase = [self.rescale(image=_UpperCAmelCase , scale=_UpperCAmelCase ) for image in images]
if do_normalize:
__lowercase = [self.normalize(image=_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase ) for image in images]
__lowercase = [to_channel_dimension_format(_UpperCAmelCase , _UpperCAmelCase ) for image in images]
__lowercase = BatchFeature(data={'pixel_values': images} , tensor_type=_UpperCAmelCase )
return encoded_outputs
| 688 | 0 |
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