code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from __future__ import annotations
import requests
def __UpperCamelCase ( _lowercase ) -> dict:
_lowercase : Optional[int] = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_lowercase ).jso... | 4 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 4 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> str:
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _lowercase ) -> None:
_lowercase , _lowercase : List[Any] = analyze_text(_lowercase )
_lowercase ... | 4 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline... | 4 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sent... | 4 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@... | 4 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : int =logging.get_logger(__name_... | 4 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Any ={
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Infor... | 4 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 4 | 1 |
'''simple docstring'''
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCamelCase_ : list ) -> None:
'''simple docstring'''
_lowercase : Any = set_counts
_lowercase : Optiona... | 4 |
'''simple docstring'''
_A : Dict ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A : ... | 4 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTeste... | 4 |
'''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,
Bert... | 4 | 1 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
_A : List[str] =[
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def __UpperCamel... | 4 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : int ) -> Any:
'''simple docstring'''
_lowercase ... | 4 | 1 |
'''simple docstring'''
import qiskit
def __UpperCamelCase ( _lowercase, _lowercase ) -> qiskit.result.counts.Counts:
_lowercase : Tuple = qiskit.Aer.get_backend('aer_simulator' )
_lowercase : Optional[Any] = qiskit.QuantumCircuit(4, 2 )... | 4 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[Any] ={'''configuration_xlnet''': ['''XLNE... | 4 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_A : Any =logging.get_logger(__name__)
_A ... | 4 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.... | 4 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, sl... | 4 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _lowercase ... | 4 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .toke... | 4 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> list:
_lowercase : List[str] = word.split()
def justify(_lowercase, _lowercase, _lowercase ) -> str:
_lowercase : Dict = max_width - width
_lowercase : Tupl... | 4 | 1 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_A : Optional[int] =logging.get_logger('''transformers.models.speecht5''')
def __UpperCamelCase ( _l... | 4 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCamelCase ( _lowercase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(_lowercase ):
_lowercase : Optional[int] = [d for d in dir_names if d != 'scripts' and ... | 4 | 1 |
'''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
_A : int =logging.get_logger(__name__)
_A : List[Any] ... | 4 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Union[str, Any] ={'''configuration_reformer''': ['''REFORMER_PRETRAINED_... | 4 | 1 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 4 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 4 | 1 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
_A : Optional[Any] =TypeVar('''T''')
def __UpperCamelCase ( _lowercase ) -> int:
return (position - 1) // 2
def __UpperCamelCase ( ... | 4 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 4 | 1 |
'''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,
Bert... | 4 |
'''simple docstring'''
from __future__ import annotations
import requests
def __UpperCamelCase ( _lowercase ) -> dict:
_lowercase : Optional[int] = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_lowercase ).jso... | 4 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def __UpperCamelCase ( _lowercase = 200_0000 ) -> int:
_lowercase : list[int] = [0]
_lowercase : int
for idx in range(1, ceil(sqrt(target * 2 ) * 1.1 )... | 4 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict =logging.get_logger(__name__)
_A : Dict ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__ ( A... | 4 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolv... | 4 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __UpperCamelCase ( _lowercase ) -> List[Any]:
_lowercase : Tuple = args.pruning_method
_lowercase : ... | 4 | 1 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_A : Tuple =datasets.logging.get_logger(__name__)
_A : Optional[Any] ='''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for T... | 4 |
'''simple docstring'''
_A : Optional[Any] ='''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __UpperCamelCase ( _lowercase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_lowercase, _lowercase ):
_lo... | 4 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_A : int ={
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_av... | 4 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> bool:
return str(_lowercase ) == str(_lowercase )[::-1]
def __UpperCamelCase ( _lowercase ) -> int:
return int(_lowercase ) + int(str(_lowercase )[::-1] )
def __UpperCam... | 4 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _lowercase ) -> None:
_lowercase , _lowercase : List[Any] = analyze_text(_lowercase )
_lowercase ... | 4 |
'''simple docstring'''
import argparse
from collections import defaultdict
def __UpperCamelCase ( _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ) -> int:
_lowercase : Optional[int] = f'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
... | 4 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A )
class lowerCamelCase__ ( A ):
'''simple docstring'''
A_ = field(defaul... | 4 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 4 | 1 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from... | 4 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _lowercase ) -> None:
_lowercase , _lowercase : List[Any] = analyze_text(_lowercase )
_lowercase ... | 4 | 1 |
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __UpperCamelCase ( _lowercase, _lowerca... | 4 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sent... | 4 | 1 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __UpperCamelCase ( ) -> List[str]:
_lowercase , _lowercase : Optional[int] = 9, 14 # noqa: F841
_lowercase : int = ... | 4 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : int =logging.get_logger(__name_... | 4 | 1 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCamelCase__ ( A , unittest.T... | 4 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 4 | 1 |
'''simple docstring'''
from __future__ import annotations
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self : Dict , UpperCamelCase_ : str , UpperCamelCase_ : str ) -> Tuple:
'''simple docstring'''
_lowercase , _lowe... | 4 |
'''simple docstring'''
_A : Dict ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A : ... | 4 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, 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 .tokenizat... | 4 |
'''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,
Bert... | 4 | 1 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
_A : Any =logging.get_... | 4 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : int ) -> Any:
'''simple docstring'''
_lowercase ... | 4 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_A : Optional[int] =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_copies # noqa... | 4 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[Any] ={'''configuration_xlnet''': ['''XLNE... | 4 | 1 |
'''simple docstring'''
from __future__ import annotations
_A : str ='''#'''
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self : Any ) -> None:
'''simple docstring'''
_lowercase : dict = {}
def ... | 4 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.... | 4 | 1 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin... | 4 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _lowercase ... | 4 | 1 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def __UpperCamelCase ( _lowercase ) -> List[Any]:
_lowercase : List[Any] = min(_lowercase ) # min() finds the minimum value
_lowercase : List[str] = max(_lowercase ) # max() fin... | 4 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> list:
_lowercase : List[str] = word.split()
def justify(_lowercase, _lowercase, _lowercase ) -> str:
_lowercase : Dict = max_width - width
_lowercase : Tupl... | 4 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
_A : Union[str, Any] ={
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''Patch... | 4 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCamelCase ( _lowercase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(_lowercase ):
_lowercase : Optional[int] = [d for d in dir_names if d != 'scripts' and ... | 4 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : int ={
'''configuration_electra''': ['''ELECTRA_PRETRAIN... | 4 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Union[str, Any] ={'''configuration_reformer''': ['''REFORMER_PRETRAINED_... | 4 | 1 |
'''simple docstring'''
import math
import os
import sys
def __UpperCamelCase ( _lowercase ) -> str:
_lowercase : Dict = ''
try:
with open(_lowercase, 'rb' ) as binary_file:
_lowercase : List[Any] = binary_file.read()
for dat in data:
... | 4 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 4 | 1 |
'''simple docstring'''
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_A : Any =logging.get_logger(__name__)
def __UpperCamelCase ( _lowercase, ... | 4 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 4 | 1 |
'''simple docstring'''
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self : str , UpperCamelCase_ : int ) -> None:
'''simple docstring'''
_lowercase : List[Any] = size
_lowercase : int = [0] *... | 4 |
'''simple docstring'''
from __future__ import annotations
import requests
def __UpperCamelCase ( _lowercase ) -> dict:
_lowercase : Optional[int] = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_lowercase ).jso... | 4 | 1 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_A : str =logging.get_logger(__name__)
... | 4 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict =logging.get_logger(__name__)
_A : Dict ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__ ( A... | 4 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowerCamelCase__ ( A ):
'''simple docstring'''
def __init__( self : int , UpperCamelCase_ : int , UpperCam... | 4 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __UpperCamelCase ( _lowercase ) -> List[Any]:
_lowercase : Tuple = args.pruning_method
_lowercase : ... | 4 | 1 |
'''simple docstring'''
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCamelCase_ : Dict ) -> Tuple:
'''simple docstring'''
_lowercase : List[str] = arr.split(',' )
def ... | 4 |
'''simple docstring'''
_A : Optional[Any] ='''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __UpperCamelCase ( _lowercase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_lowercase, _lowercase ):
_lo... | 4 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : Optional[int] =logging.get_logger(__name__)
_A : Optional[int] ={
'''ber... | 4 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> bool:
return str(_lowercase ) == str(_lowercase )[::-1]
def __UpperCamelCase ( _lowercase ) -> int:
return int(_lowercase ) + int(str(_lowercase )[::-1] )
def __UpperCam... | 4 | 1 |
'''simple docstring'''
from typing import Any
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self : str , UpperCamelCase_ : Any ) -> Union[str, Any]:
'''simple docstring'''
_lowercase : Optional[int] = data
... | 4 |
'''simple docstring'''
import argparse
from collections import defaultdict
def __UpperCamelCase ( _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ) -> int:
_lowercase : Optional[int] = f'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
... | 4 | 1 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_F... | 4 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 4 | 1 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def __UpperCamelCase ( _lowercase, _lowercase ) -> Union[str, Any]:
_lowercase : int ... | 4 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _lowercase ) -> None:
_lowercase , _lowercase : List[Any] = analyze_text(_lowercase )
_lowercase ... | 4 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Union[str, Any] ={'''configuration_reformer''': ['''REFORMER_PRETRAINED_... | 4 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sent... | 4 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axi... | 4 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : int =logging.get_logger(__name_... | 4 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda... | 4 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 4 | 1 |
'''simple docstring'''
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 (
ChannelDi... | 4 |
'''simple docstring'''
_A : Dict ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A : ... | 4 | 1 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
_A : Optional[Any] =yaml.safe_load(
'''\
name: ""
allow_empty: false
allow_empty_text: true
subsect... | 4 |
'''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,
Bert... | 4 | 1 |
'''simple docstring'''
import os
def __UpperCamelCase ( _lowercase = "input.txt" ) -> int:
with open(os.path.join(os.path.dirname(_lowercase ), _lowercase ) ) as input_file:
_lowercase : Optional[Any] = [
[int(_lowercase ) for element in lin... | 4 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : int ) -> Any:
'''simple docstring'''
_lowercase ... | 4 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common im... | 4 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[Any] ={'''configuration_xlnet''': ['''XLNE... | 4 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Any ={'''configuration_xglm'... | 4 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.... | 4 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[Any] ={'''configuration_xlnet''': ['''XLNE... | 4 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _lowercase ... | 4 | 1 |
'''simple docstring'''
import math
def __UpperCamelCase ( _lowercase, _lowercase ) -> float:
if (
not isinstance(_lowercase, (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('power_factor must be a valid float value between -1 and ... | 4 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> list:
_lowercase : List[str] = word.split()
def justify(_lowercase, _lowercase, _lowercase ) -> str:
_lowercase : Dict = max_width - width
_lowercase : Tupl... | 4 | 1 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_A : Optional[Any] =[
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''laye... | 4 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCamelCase ( _lowercase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(_lowercase ):
_lowercase : Optional[int] = [d for d in dir_names if d != 'scripts' and ... | 4 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A ):
'''simple docstring'''
A_ = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *UpperCamelCase_ : Tuple , ... | 4 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Union[str, Any] ={'''configuration_reformer''': ['''REFORMER_PRETRAINED_... | 4 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCamelCase__ ( ... | 4 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 4 | 1 |
'''simple docstring'''
from __future__ import annotations
from cmath import sqrt
def __UpperCamelCase ( _lowercase, _lowercase, _lowercase ) -> tuple[complex, complex]:
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_lowercase : Tuple ... | 4 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 4 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCamelCase ( ) -> None:
assert or_gate(0, 0 ) == 0
assert or_gate(0, 1 ) == 1
assert or_gate(1, 0 ... | 4 |
'''simple docstring'''
from __future__ import annotations
import requests
def __UpperCamelCase ( _lowercase ) -> dict:
_lowercase : Optional[int] = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_lowercase ).jso... | 4 | 1 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
_A : List[Any] =[int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def __UpperCamelCase ( ) -> Optional[Any]:
_lowercase : Dict = os.path.dirname(os.path.realpath(_low... | 4 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict =logging.get_logger(__name__)
_A : Dict ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__ ( A... | 4 | 1 |
'''simple docstring'''
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess... | 4 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __UpperCamelCase ( _lowercase ) -> List[Any]:
_lowercase : Tuple = args.pruning_method
_lowercase : ... | 4 | 1 |
'''simple docstring'''
from collections import deque
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self : Any , UpperCamelCase_ : str , UpperCamelCase_ : int , UpperCamelCase_ : int ) -> None:
'''simple docstring'''
... | 4 |
'''simple docstring'''
_A : Optional[Any] ='''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __UpperCamelCase ( _lowercase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_lowercase, _lowercase ):
_lo... | 4 | 1 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> Union[str, Any]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_lowercase, int(b / 2 ) ) * actual_power(_lowercase, int(b / 2 ) )
else:
return a * actual_power(_lowercase, ... | 4 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> bool:
return str(_lowercase ) == str(_lowercase )[::-1]
def __UpperCamelCase ( _lowercase ) -> int:
return int(_lowercase ) + int(str(_lowercase )[::-1] )
def __UpperCam... | 4 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : str ={
'''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''... | 4 |
'''simple docstring'''
import argparse
from collections import defaultdict
def __UpperCamelCase ( _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ) -> int:
_lowercase : Optional[int] = f'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
... | 4 | 1 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase ( _lowercase, _lowercase ... | 4 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 4 | 1 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCamelCase__ ( tf.keras.layers.Layer ):
... | 4 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _lowercase ) -> None:
_lowercase , _lowercase : List[Any] = analyze_text(_lowercase )
_lowercase ... | 4 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import req... | 4 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sent... | 4 | 1 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, 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 ModelTest... | 4 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : int =logging.get_logger(__name_... | 4 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_A : List[str] =logging.get_logger(__name__)
_A : Any ={
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/m... | 4 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 4 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Union[str, Any] ={
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip2QF... | 4 |
'''simple docstring'''
_A : Dict ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A : ... | 4 | 1 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
_A : Optional[Any] ='''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relie... | 4 |
'''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,
Bert... | 4 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict =logging.get_logger(__name__)
_A : Dict ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__ ( A... | 4 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : int ) -> Any:
'''simple docstring'''
_lowercase ... | 4 | 1 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_A : int =(
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS ... | 4 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[Any] ={'''configuration_xlnet''': ['''XLNE... | 4 | 1 |
'''simple docstring'''
import os
from typing import Dict, List, Tuple, TypeVar, Union
_A : str =TypeVar('''T''')
_A : int =Union[List[T], Tuple[T, ...]]
_A : Dict =Union[T, List[T], Dict[str, T]]
_A : Any =Union[str, bytes, os.PathLike]
| 4 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.... | 4 | 1 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust... | 4 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _lowercase ... | 4 | 1 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def __UpperCamelCase ( _lowercase ) -> Optional[Any]:
_lowercase : Optiona... | 4 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> list:
_lowercase : List[str] = word.split()
def justify(_lowercase, _lowercase, _lowercase ) -> str:
_lowercase : Dict = max_width - width
_lowercase : Tupl... | 4 | 1 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load... | 4 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCamelCase ( _lowercase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(_lowercase ):
_lowercase : Optional[int] = [d for d in dir_names if d != 'scripts' and ... | 4 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase__ ( _lowerCam... | 700 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Union[str, Any] ={'''configuration_reformer''': ['''REFORMER_PRETRAINED_... | 4 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : i... | 701 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 4 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __UpperCamelCase ( *_lowercase ) -> int:
if not isinstance(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE ):
_lowercase : List[Any] = list(_SCREAM... | 702 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 4 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cu... | 703 |
'''simple docstring'''
from __future__ import annotations
import requests
def __UpperCamelCase ( _lowercase ) -> dict:
_lowercase : Optional[int] = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_lowercase ).jso... | 4 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
_A : Dict =list[list[float | int]]
def __UpperCamelCase ( _lowercase, _lowercase ) -> Dict:
_lowercase : int = len(_A )
_lowercase : Ma... | 704 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict =logging.get_logger(__name__)
_A : Dict ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCamelCase__ ( A... | 4 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
A_ = 42
A_ = 42
class lowerC... | 705 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __UpperCamelCase ( _lowercase ) -> List[Any]:
_lowercase : Tuple = args.pruning_method
_lowercase : ... | 4 | 0 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> list:
_lowercase : List[Any] = [0] * len(_lowercase )
for i in range(1, len(_lowercase ) ):
# use last results for better performance - dynamic programming
_lowercase : Dict = pref... | 706 |
'''simple docstring'''
_A : Optional[Any] ='''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __UpperCamelCase ( _lowercase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_lowercase, _lowercase ):
_lo... | 4 | 0 |
'''simple docstring'''
from __future__ import annotations
import queue
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self : Optional[int] , UpperCamelCase_ : List[str] ) -> Optional[int]:
'''simple docstring'''
_lowercase : ... | 707 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase ) -> bool:
return str(_lowercase ) == str(_lowercase )[::-1]
def __UpperCamelCase ( _lowercase ) -> int:
return int(_lowercase ) + int(str(_lowercase )[::-1] )
def __UpperCam... | 4 | 0 |
'''simple docstring'''
_A : Tuple ='0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_... | 708 |
'''simple docstring'''
import argparse
from collections import defaultdict
def __UpperCamelCase ( _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ) -> int:
_lowercase : Optional[int] = f'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
... | 4 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : str =logging.get_logger(__name__)
_A : Optional[int] ={
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/ma... | 709 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 4 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPr... | 710 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _lowercase ) -> None:
_lowercase , _lowercase : List[Any] = analyze_text(_lowercase )
_lowercase ... | 4 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeli... | 711 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sent... | 4 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_A : int ={}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass... | 712 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : int =logging.get_logger(__name_... | 4 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( A ):
'''simple docstring'''
A_ = ["""image_processor""", """tokenizer"""]
A_ = "... | 713 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 4 | 0 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
_A : Union[str, Any] =False
class lowerCamelCase__... | 714 |
'''simple docstring'''
_A : Dict ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A : ... | 4 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_A : List[str] =logging.get_logger(__name__)
_A : List[str] ={
'''googl... | 715 |
'''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,
Bert... | 4 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availabl... | 716 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : int ) -> Any:
'''simple docstring'''
_lowercase ... | 4 | 0 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase__ ( _UpperCAmelCase ):
'''simple docstring'''
A_ = """M-CLIP"""
def __init__( self : Optional[Any] , UpperCamelCas... | 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[Any] ={'''configuration_xlnet''': ['''XLNE... | 4 | 0 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __UpperCamelCase ( _lowercase ) -> Tuple:
return 1 / (1 + np.exp(-z ))
def __UpperCamelCase ( _lowercase, _lowercase ) -> int:
return (-y ... | 718 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.... | 4 | 0 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def __UpperCamelCase ( _lowercase ) -> Dict:
return DownloadCommand(args.model, args.cache_dir, args.force, args.trust_remote_code )
class lowerCamelCase__ ( _a ):
'... | 719 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _lowercase ... | 4 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusio... | 720 |
'''simple docstring'''
def __UpperCamelCase ( _lowercase, _lowercase ) -> list:
_lowercase : List[str] = word.split()
def justify(_lowercase, _lowercase, _lowercase ) -> str:
_lowercase : Dict = max_width - width
_lowercase : Tupl... | 4 | 0 |
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