code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils impo... | 443 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 665 | 0 |
def snake_case__ ( lowercase = 3 , lowercase = 7 , lowercase = 1000000 ):
lowerCAmelCase_: str = 0
lowerCAmelCase_: List[str] = 1
for current_denominator in range(1 , limit + 1 ):
lowerCAmelCase_: Union[str, Any] = current_denomin... | 613 |
'''simple docstring'''
import functools
def lowerCamelCase ( lowerCamelCase : list[int] , lowerCamelCase : list[int]):
# Validation
if not isinstance(lowerCamelCase , lowerCamelCase) or not all(isinstance(lowerCamelCase , lowerCamelCase) for day in days):
... | 665 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate imp... | 563 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase ( lowerCamelCase : NDArray[floataa] , lowerCamelCase : NDArray[floataa] , lowerCamelCase : list[int] , lowerCamelCase : ... | 665 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_availa... | 294 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
A_ : Any = len(lowerCamelCase)
A_ : Optional[Any] = len(lowerCamelCase)
A_ : Optional[int] = [[False for _ in range(m + 1)] for _ in range(n + 1)... | 665 | 0 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
@register_to_config
def __init... | 651 |
'''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 __lowerCAmelCase... | 665 | 0 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class A__ :
@property
def UpperCamelCase__ ( self ):
return self.ge... | 681 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 10**9):
A_ : Optional[int] = 1
A_ : int = 2
A_ : List[Any] = 0
A_ : Optional[Any] = 0
A_ : str = 0
while perimeter <= max_perimet... | 665 | 0 |
'''simple docstring'''
import os
import re
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 logging
lowercase_ = logging.get_logger(__name__)
lowe... | 314 |
'''simple docstring'''
# Copyright 2021 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
#
# ... | 665 | 0 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...... | 534 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltC... | 665 | 0 |
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
_lowercase : List[Any] =logging.getLogger(__name__)
_lowercase : O... | 136 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_visi... | 665 | 0 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase__ = '''\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav... | 186 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Debe... | 665 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ =logging.get_logger(__name__)
lowercase__ ={
'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/confi... | 263 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : Tuple):
A_ : str = [0] * len(lowerCamelCase)
A_ : Union[str, Any] = []
A_ : Union[str, Any] = []
A_ : Tuple = 0
for values in graph.values():
f... | 665 | 0 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_... | 443 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common ... | 665 | 0 |
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 i... | 613 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__magic_name__ = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kada... | 665 | 0 |
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
UpperCAmelCase : int = logging.get_logger(__name__)
UpperCAme... | 563 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__magic_name__ = logging.get_logger(__name__)
# TODO: upload to AWS
__magic_name__ = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-unca... | 665 | 0 |
'''simple docstring'''
def _UpperCamelCase ( lowerCAmelCase__: dict ) -> Any:
SCREAMING_SNAKE_CASE_ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CASE_ = set()
return any(
nod... | 294 |
'''simple docstring'''
import os
import re
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 logging
__magic_name__ = logging.get_logger(__name__)
__magic_n... | 665 | 0 |
from __future__ import annotations
from typing import TypedDict
class SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase : str =42
lowerCamelCase : int =42
def snake_case_ (__A : str ) -> ... | 651 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 665 | 0 |
from collections import defaultdict
def _a ( lowerCamelCase, lowerCamelCase ):
lowerCamelCase : List[Any] = first_str.lower().strip()
lowerCamelCase : Union[str, Any] = second_str.lower().strip()
# Remove whitespace
lowerCamelCase : Union[... | 681 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
... | 665 | 0 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class a_ ( __SCREAMING_SNAKE_CASE ):
'''simple docstri... | 314 |
'''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
__magic_name__ = logging.get_logger(__name__)
__magic_name__ ... | 665 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__a : str = TypeVar("""T""")
class _UpperCamelCase ( Generic[T] ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
... | 534 |
'''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
__magic_name__ = logging.get_logger(__name__)
... | 665 | 0 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def lowerCAmelCase_ ( _l... | 136 |
'''simple docstring'''
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... | 665 | 0 |
'''simple docstring'''
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : list ):
"""simple docstring"""
def merge(_SCREAMING_SNAKE_CASE : list,_SCREAMING_SNAKE_CASE : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[... | 186 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 665 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
lowercase__ =True
except (ImportError, ModuleNotFoundError):
lowercase__ =False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def UpperCamelCase_ ( ... | 263 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils im... | 665 | 0 |
from jiwer import compute_measures
import datasets
_lowercase = """\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measur... | 443 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 665 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a : Optional[int] = {
"""configuration_conditional_detr""": [
"""CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ConditionalDetrConf... | 613 |
'''simple docstring'''
import functools
def lowerCamelCase ( lowerCamelCase : list[int] , lowerCamelCase : list[int]):
# Validation
if not isinstance(lowerCamelCase , lowerCamelCase) or not all(isinstance(lowerCamelCase , lowerCamelCase) for day in days):
... | 665 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNot... | 563 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase ( lowerCamelCase : NDArray[floataa] , lowerCamelCase : NDArray[floataa] , lowerCamelCase : list[int] , lowerCamelCase : ... | 665 | 0 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must ... | 294 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
A_ : Any = len(lowerCamelCase)
A_ : Optional[Any] = len(lowerCamelCase)
A_ : Optional[int] = [[False for _ in range(m + 1)] for _ in range(n + 1)... | 665 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 651 |
'''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 __lowerCAmelCase... | 665 | 0 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
_lowerCamelCase ={
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
"""convert""": ["""ex... | 681 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 10**9):
A_ : Optional[int] = 1
A_ : int = 2
A_ : List[Any] = 0
A_ : Optional[Any] = 0
A_ : str = 0
while perimeter <= max_perimet... | 665 | 0 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ ... | 314 |
'''simple docstring'''
# Copyright 2021 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
#
# ... | 665 | 0 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ... | 534 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltC... | 665 | 0 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common im... | 136 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_visi... | 665 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/m... | 186 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Debe... | 665 | 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_available
f... | 263 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : Tuple):
A_ : str = [0] * len(lowerCamelCase)
A_ : Union[str, Any] = []
A_ : Union[str, Any] = []
A_ : Tuple = 0
for values in graph.values():
f... | 665 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.ut... | 443 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common ... | 665 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 613 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__magic_name__ = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kada... | 665 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,... | 563 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__magic_name__ = logging.get_logger(__name__)
# TODO: upload to AWS
__magic_name__ = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-unca... | 665 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://h... | 294 |
'''simple docstring'''
import os
import re
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 logging
__magic_name__ = logging.get_logger(__name__)
__magic_n... | 665 | 0 |
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
... | 651 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 665 | 0 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 681 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
... | 665 | 0 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class a_ :
'''simple docstring'''
pass
| 314 |
'''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
__magic_name__ = logging.get_logger(__name__)
__magic_name__ ... | 665 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
"XCLIPVisionConfig"... | 666 | import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( _Upp... | 666 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: list[str] | None = None , lowerCAmelCase_: dict[str, float] | None = None , lowerCAmelCase_: bool = False , ):
snake_case_ : List[str] = ... | 666 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
... | 666 | 1 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import Fr... | 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = ... | 666 | 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()... | 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
... | 666 | 1 |
UpperCAmelCase = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD,... | 666 | 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_roberta import RobertaTokenizer... | 666 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_... | 666 | from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRo... | 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
snake_case_ : str = len(lowerCAmelCase_ )
snake_case_ : Optional[int] = len(lowerCAmelCase_ )
snake_case_ : Any = [[False for _ in range(m + 1 )] for _ in... | 666 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 666 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
... | 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),... | 666 | 1 |
from math import loga
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Input value must be a 'int' ... | 666 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | 1 |
import warnings
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_axis_dim... | 666 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : ... | 666 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( _Upp... | 666 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 666 | 1 |
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 SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: ... | 666 | import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenizat... | 666 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],... | 666 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
Auto... | 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,... | 666 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | 1 |
from __future__ import annotations
import os
from collections.abc import Mapping
UpperCAmelCase = tuple[int, int]
class snake_case__ :
def __init__( self : Optional[Any] , A__ : set[int] , A__ : Mapping[EdgeT, int] ) -> None:
... | 666 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusio... | 666 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 1 |
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 666 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRA... | 666 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_avai... | 666 | from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas... | 666 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] ):
snake_case_ : str = len(lowerCAmelCase_ ) // 2
# choose the middle 3 elements
snake_case_ : int = lst[m - 1 : m + 2]
# if middle element ... | 666 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ... | 666 | 1 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokeniz... | 666 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Dict , lowerCAmelCase_: Dict ):
snake_case_ : int = int(lowerCAmelCase_ )
assert noofclusters < len(lowerCAmelCase_ )
#... | 666 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCA... | 666 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
UpperCAmelCase =... | 666 | import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( _Upp... | 666 | 1 |
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
UpperCAmelCase = "\nArgs:\n predictions (... | 666 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
... | 666 | 1 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: NDArray[floataa] , lowerCAmelCase_: NDArray[floataa] , lowerCAmelCase_: list[int] , lowerCAmelCase_: i... | 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = ... | 666 | 1 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: dict ):
snake_case_ : List[Any] = BeautifulSoup(requests.get(lowerCAmelCase_ , params=lowerCAmelCase_ ).content , "html.parser" )
... | 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
... | 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: dict ):
snake_case_ : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
snake_case_ : set[int] = set()
return any(
node not in visited and depth... | 666 | 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_roberta import RobertaTokenizer... | 666 | 1 |
import random
class snake_case__ :
@staticmethod
def UpperCAmelCase__ ( A__ : str ) -> tuple[list[int], list[int]]:
'''simple docstring'''
snake_case_ : Optional[Any] = [ord(A__ ) for i in text]
snake_case... | 666 | from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRo... | 666 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class snake_case__ ( _UpperCamelCa... | 666 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 666 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
UpperCAmelCase = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n auth... | 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),... | 666 | 1 |
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_m... | 666 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2form... | 666 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : ... | 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int , lowerCAmelCase_: int ):
snake_case_ : Tuple = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
snake_case_ : Optional[int] = n - k
# Calculate C(n... | 666 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 666 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ... | 666 | import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenizat... | 666 | 1 |
import re
import string
import numpy as np
import datasets
UpperCAmelCase = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
UpperCAmelCase = "\nArgs:\n predicti... | 666 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class snake_case__ ... | 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 1 |
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
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = ... | 666 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | 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_sch... | 666 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "Reform... | 666 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 1 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_... | 666 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRA... | 666 | 1 |
class snake_case__ :
def __init__( self : Dict , A__ : str , A__ : Tuple , A__ : Union[str, Any] ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = None
snake_case_ : Tuple = ... | 666 | from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas... | 666 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
... | 666 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ... | 666 | 1 |
UpperCAmelCase = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609344,
"knot": 1.852,
}
UpperCAmelCase = {
"km/h": 1.0,
"m/s": 0.277777778,
"mph": 0.621371192,
"knot": 0.539956803,
}
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_:... | 666 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] ): # This function is recursive
snake_case_ : int = len(lowerCAmelCase_ )
# If the array contains only one element, we return it (it's the stop condition of
# recur... | 666 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCA... | 666 | 1 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( _Upp... | 666 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisi... | 666 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
... | 666 | 1 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class snake_case__ ( _UpperCamelCase ):
def __init... | 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = ... | 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
... | 666 | 1 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import P... | 666 | 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_roberta import RobertaTokenizer... | 666 | 1 |
from __future__ import annotations
import unittest
from transformers import 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_... | 666 | from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRo... | 666 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: List[Any] , lowerCAmelCase_: List[... | 666 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 666 | 1 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.conf... | 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),... | 666 | 1 |
UpperCAmelCase = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
UpperCAmelCase = ... | 666 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHori... | 666 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : ... | 666 | 1 |
# 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 a... | 666 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 666 | 1 |
from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenizat... | 666 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAG... | 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 1 |
from __future__ import annotations
class snake_case__ :
def __init__( self : Union[str, Any] , A__ : int = 0 ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = key
def UpperCAmelCase__ ( ... | 666 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), f"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
snake_case_ : str = f"The input... | 666 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.