code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
def _A ( _lowercase ) -> bool:
"""simple docstring"""
return str(UpperCamelCase__ ) == str(UpperCamelCase__ )[::-1]
def _A ( _lowercase ) -> int:
"""simple docstring"""
return int(UpperCamelCase__ ... | 310 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelC... | 273 | 0 |
from __future__ import annotations
import pandas as pd
def lowerCamelCase_ ( _a , _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = [0] * no_of_processes
lowerCAmelCase__ : Optional[int] = [0] * no_... | 131 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImagePro... | 326 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformer... | 273 | 0 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def a__ ( __SCREAMING_SNAKE_CASE ) -> Dict:
__lowerCAmelCase: int = args.pruning_method
__lowerCAmel... | 217 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 0 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
_lowerCAmelCase : Any = {
"n_samples": 64,
"horizon": 32,
"num_inference_steps": 20,
"n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network
"scale_... | 300 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
"configuration_funnel": ["FUNNEL_PRETRAI... | 16 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 273 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 195 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 | 0 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
lowercase__ : List[str] = logging.getLogger(__name__)
class __lowerCAmelCase ( a_ ):
"""simple docstring"""
_snake_case : Optional[int] = 'masked_b... | 324 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
lowerCamelCase_ = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch_availabl... | 244 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 273 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def __lowercase ( __lowerCAmelCase : Optional[Any] ):
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output ... | 240 |
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 A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase_... | 273 | 0 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
... | 298 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 273 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from trans... | 310 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A : Union[str, Any] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETR... | 273 | 0 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowerCamelCase_ ( _a ):
"""simple docstring"""
if "model" in orig_key:
lowerCAmelCase__ : Any = orig_key.replace('''model.''' , '''''' ... | 131 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ ... | 273 | 0 |
from collections.abc import Generator
from math import sin
def lowerCAmelCase__( lowercase : str ) -> bytes:
if len(UpperCamelCase__ ) != 32:
raise ValueError("Input must be of length 32" )
__snake_case : Any = B""
for i in [3, 2, 1, 0]:
little_endian += stri... | 326 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Huggi... | 273 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__A = logging.get_logger(__name__)
class snake_case ( a_ ):
def __init__( self : str , *UpperCamelCase__ : Tuple , *... | 217 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 0 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 300 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 273 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase ) -> set:
lowercase__ : Optional[int] = set()
# edges = list of graph's edges
lowercase__ : Optional[Any] = get_edges(UpperCamelCase__ )
# While there are still elements in edg... | 16 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 | 0 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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... | 195 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCho... | 273 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
_snake_case : Any = 4_2
_snake_case : Optional[Any] = None
... | 324 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 273 | 0 |
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,... | 244 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 273 | 0 |
from ... import PretrainedConfig
snake_case : str = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class snake_case_ (a_ ):
UpperCAmelCase__ : List[str] = NEZHA_PRETRAINED_CONFIG_ARCHIVE_M... | 240 |
import cva
import numpy as np
class A_ :
def __init__( self , _A , _A ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCAmelCase = k
UpperCAmelCase = window_size
else:
raise ValueError('''invalid k value''' )
def __... | 273 | 0 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJob... | 298 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 0 |
def _A ( _lowercase ) -> str:
"""simple docstring"""
__UpperCamelCase = 0
# if input_string is "aba" than new_input_string become "a|b|a"
__UpperCamelCase = ''
__UpperCamelCase = ''
# append each character +... | 310 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelC... | 273 | 0 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowerCamelCase_ ( _a , _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = ('''dense.w... | 131 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 0 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import Dat... | 326 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformer... | 273 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A = {
"configuration_speech_to_text": ["SPE... | 217 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 0 |
def __snake_case ( _lowerCAmelCase : Tuple ) -> bool:
A_ : Dict = 0
for ch in input_str:
A_ : str = ord(UpperCamelCase__ )
A_ : List[Any] = pow(2 , UpperCamelCase__ )
# If we already turned on bit for current character's unicode
... | 300 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> str:
lowercase__ : int = [0 for i in range(r + 1 )]
# nc0 = 1
lowercase__ : Union[str, Any] = 1
for i in range(1 , n + 1 ):
# to co... | 16 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 273 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTa... | 195 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 | 0 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common impo... | 324 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 0 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
neste... | 244 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 273 | 0 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
snake... | 240 |
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 A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase_... | 273 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( a_ ):
... | 298 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 273 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import ... | 310 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A : Union[str, Any] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETR... | 273 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"distilbert-base-uncased": "https://hugging... | 131 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ ... | 273 | 0 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_UpperCamelCase = get_tests_dir()... | 326 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Huggi... | 273 | 0 |
"""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 = logging.get_logger(__name__)
... | 217 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutpu... | 300 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 273 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils i... | 16 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 | 0 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A_ ( a_ , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple ... | 195 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCho... | 273 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase ( ... | 324 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 273 | 0 |
import os
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {"vocab_file": "... | 244 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 273 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environ... | 240 |
import cva
import numpy as np
class A_ :
def __init__( self , _A , _A ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCAmelCase = k
UpperCAmelCase = window_size
else:
raise ValueError('''invalid k value''' )
def __... | 273 | 0 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "m... | 298 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCamelCase (a_ ):
_lowercase = (UnCLIPScheduler,)
def snake_case_ ( self: str,**A_: List[Any] ):
... | 310 |
from __future__ import annotations
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ) -> float:
'''simple docstring'''
UpperCAmelCase = x_start
UpperCAmelC... | 273 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop... | 131 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A : Dict = logging.get_logger(__name__)
__A : An... | 273 | 0 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
_UpperCamelCase = logging.getLogger(__name__)
if __name__ == "__main__":
_... | 326 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformer... | 273 | 0 |
"""simple docstring"""
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,
... | 217 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_lowerCAmelCase : ... | 300 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
... | 16 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 273 | 0 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import a... | 195 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "... | 273 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fr... | 324 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a_ )
class __A( a_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = field(default="""language-modeling""" , metadata={"""inc... | 244 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 273 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case : Tuple = {
"configuration_autoformer": [
"AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AutoformerConfig"... | 240 |
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 A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase_... | 273 | 0 |
'''simple docstring'''
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, TextI... | 298 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 273 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import float... | 310 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__A : Union[str, Any] = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETR... | 273 | 0 |
import math
from collections.abc import Callable
def lowerCamelCase_ ( _a , _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : Any = xa
lowerCAmelCase__ : List[str] = xa
while True:
if x_n == x_na or funct... | 131 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(UpperCamelCase__ ... | 273 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_avail... | 326 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Huggi... | 273 | 0 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A = "src/transformers"
# This... | 217 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 0 |
def __lowerCamelCase ( __a :list[list[int]] , __a :int , __a :int , __a :set ) -> int:
"""simple docstring"""
A__ , A__ = len(__a ), len(grid[0] )
if (
min(__a , __a ) < 0
or row == row_leng... | 274 |
def __lowerCamelCase ( __a :int = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
A__ = limit + 1
A__ = [0] * limit
for first_term in range(1 , __a ):
for n in range(__a , __a , __a ):
A__ = firs... | 274 | 1 |
import os
import time
import numpy as np
import onnxruntime as ort
A : List[Any] = '''1'''
A : Union[str, Any] = '''0'''
A : Union[str, Any] = '''1'''
A : Any = ort.SessionOptions()
A : Optional[int] = ort.GraphOptimizationLevel.ORT... | 274 |
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class A :
'''simple docstring'''
def __init__( self : List[Any] ) -> str:
"""simp... | 274 | 1 |
def __lowerCamelCase ( __a :int = 1_0_0_0 ) -> int:
"""simple docstring"""
A__ , A__ = 1, 1
A__ = 2
while True:
A__ = 0
A__ = fa + fa
A__ , A__ = fa, f
index += 1
for _ in str(... | 274 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from tra... | 274 | 1 |
import math
from collections.abc import Callable
def __lowerCamelCase ( __a :Callable[[float], float] , __a :float , __a :float ) -> float:
"""simple docstring"""
A__ = xa
A__ = xa
while True:
if x_n == ... | 274 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
A : Dict = Lock()
def __lowerCamelCase ( __a :Dict , __a :List[str] , __a :Optional[int] , __a :Optional[int... | 274 | 1 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,... | 274 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __lowerCamelCase ( __a :Dict ) -> Any:
"""simple docstring"""
A__ = [
"""decoder.version"... | 274 | 1 |
def __lowerCamelCase ( __a :str ) -> list:
"""simple docstring"""
A__ = [0] * len(__a )
for i in range(1 , len(__a ) ):
# use last results for better performance - dynamic programming
A__ = prefix_result[i - 1]
w... | 274 |
import unittest
import numpy as np
from transformers import AlbertConfig, 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.numpy... | 274 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
re... | 274 |
from sklearn.metrics import fa_score
import datasets
A : Any = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
A : List[Any] = '''
Args:
predictions (`li... | 274 | 1 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A : List[str] = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'''text-cla... | 274 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : int = {
'''xlm-roberta-base''': '''... | 274 | 1 |
import argparse
import os
import re
import packaging.version
A : Dict = '''examples/'''
A : Optional[Any] = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(R'''^... | 274 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
A : str = 0
A : Any = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0... | 274 | 1 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 274 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class A (unittest.TestCase ):
'''simple docstring'''
def a_ ( self : Any ) -> Union[s... | 274 | 1 |
from collections import deque
class A :
'''simple docstring'''
def __init__( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : int ) -> None:
"""simple docstring"""
... | 274 |
from collections import deque
class A :
'''simple docstring'''
def __init__( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : int ) -> None:
"""simple docstring"""
... | 274 | 1 |
from collections.abc import Callable
import numpy as np
def __lowerCamelCase ( __a :Callable , __a :float , __a :float , __a :float , __a :float ) -> np.array:
"""simple docstring"""
A__ = int(np.ceil((x... | 274 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
... | 274 | 1 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class A (datasets.BuilderConfig ):
'''simple docstring'''
__lowerCamelCase : O... | 274 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A : Optional[Any] = logging... | 274 | 1 |
# 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
#
# Unless r... | 274 |
from __future__ import annotations
from PIL import Image
# Define glider example
A : Any = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0... | 274 | 1 |
from __future__ import annotations
A : Optional[int] = list[tuple[int, int]]
A : str = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
... | 274 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A : List[str] = input('''Enter image url: ''').strip()
print(F'''Downloading image from {url} ...''')
A : Any = BeautifulSoup(requests.get(url).content, '''... | 274 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hu... | 274 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def __lowerCamelCase ( ... | 274 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __lowerCamelCase ( __a :Tuple , __a :str , __a :Union[str, Any] ) -> Dict:... | 274 |
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 .tokeniz... | 274 | 1 |
def __lowerCamelCase ( __a :int , __a :int ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def __lowerCamelCase ( ) -> None:
"""simple docstring"""
print("""Truth Table of NOR Gate... | 274 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
A : Dict = logging.get_logger(__name__)
def __lowerCamelCase ( __a :int=None , __a ... | 274 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
A : str = get_logger(__name__)
class A (enum.Enum ):
'''simple docstring'''
__lowerCamelCase : List[Any] = '''... | 274 |
from math import ceil
def __lowerCamelCase ( __a :int = 1_0_0_1 ) -> int:
"""simple docstring"""
A__ = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
A__ = 2 * i + 1
A__ = 2 * i
A__ =... | 274 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __lowerCamelCase ( __a :str ) -> None:
"""simple docstring"""
A__ , A__ = analyze_text(__a )
A__ = ... | 274 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 274 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCamelCase : int = ['''image_processor''', '''tokenizer''']
__lowerCamelCase : ... | 274 |
import argparse
from collections import defaultdict
import yaml
A : str = '''docs/source/en/_toctree.yml'''
def __lowerCamelCase ( __a :str ) -> List[Any]:
"""simple docstring"""
A__ = defaultdict(__a )
A__ =... | 274 | 1 |
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 .tokeniz... | 274 |
def __lowerCamelCase ( __a :str ) -> list:
"""simple docstring"""
A__ = [0] * len(__a )
for i in range(1 , len(__a ) ):
# use last results for better performance - dynamic programming
A__ = prefix_result[i - 1]
w... | 274 | 1 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
A : Union[str, Any] = logging.getLogger(__name_... | 274 |
def __lowerCamelCase ( __a :int = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
A__ = limit + 1
A__ = [0] * limit
for first_term in range(1 , __a ):
for n in range(__a , __a , __a ):
A__ = firs... | 274 | 1 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impo... | 274 |
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class A :
'''simple docstring'''
def __init__( self : List[Any] ) -> str:
"""simp... | 274 | 1 |
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class A :
'''simple docstring'''
def __init__( self : List[Any] ) -> str:
"""simp... | 274 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from tra... | 274 | 1 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datase... | 274 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
A : Dict = Lock()
def __lowerCamelCase ( __a :Dict , __a :List[str] , __a :Optional[int] , __a :Optional[int... | 274 | 1 |
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 ... | 274 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __lowerCamelCase ( __a :Dict ) -> Any:
"""simple docstring"""
A__ = [
"""decoder.version"... | 274 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Dict = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if n... | 274 |
import unittest
import numpy as np
from transformers import AlbertConfig, 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.numpy... | 274 | 1 |
def __lowerCamelCase ( __a :int , __a :int ) -> int:
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(__a , x % y )
def __lowerCamelCase ( __a :int , __a :int ) -> int:
... | 274 |
from sklearn.metrics import fa_score
import datasets
A : Any = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
A : List[Any] = '''
Args:
predictions (`li... | 274 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class A (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@re... | 274 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : int = {
'''xlm-roberta-base''': '''... | 274 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
A : List[str] = logging.get_logger(__name__)
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Optional[Any] , *__lowe... | 274 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
A : str = 0
A : Any = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0... | 274 | 1 |
from __future__ import annotations
def __lowerCamelCase ( __a :list , __a :int | None = None , __a :int | None = None ) -> None:
"""simple docstring"""
if start is None:
A__ = 0
if end is None:
A__ = ... | 274 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class A (unittest.TestCase ):
'''simple docstring'''
def a_ ( self : Any ) -> Union[s... | 274 | 1 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A : List[str] = input('''Enter image url: ''').strip()
print(F'''Downloading image from {url} ...''')
A : Any = BeautifulSoup(requests.get(url).content, '''... | 274 |
from collections import deque
class A :
'''simple docstring'''
def __init__( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : int ) -> None:
"""simple docstring"""
... | 274 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Optional[int] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',... | 274 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
... | 274 | 1 |
from math import pi
def __lowerCamelCase ( __a :int , __a :int ) -> float:
"""simple docstring"""
return 2 * pi * radius * (angle / 3_6_0)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 274 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A : Optional[Any] = logging... | 274 | 1 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Union[str, Any] = {
'''snap-research/efficientformer-l1-300''': (
'''https://huggingface.co/snap-researc... | 274 |
from __future__ import annotations
from PIL import Image
# Define glider example
A : Any = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0... | 274 | 1 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class A ... | 274 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A : List[str] = input('''Enter image url: ''').strip()
print(F'''Downloading image from {url} ...''')
A : Any = BeautifulSoup(requests.get(url).content, '''... | 274 | 1 |
def __lowerCamelCase ( __a :List[str] ) -> int:
"""simple docstring"""
A__ = []
A__ = []
A__ = {
"""^""": 3,
"""*""": 2,
"""/""": 2,
"""%""": 2,
"""+""": 1,
"""-""": 1,
} # Priority of ea... | 274 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def __lowerCamelCase ( ... | 274 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTester... | 274 |
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 .tokeniz... | 274 | 1 |
def __lowerCamelCase ( __a :List[Any] ) -> List[str]:
"""simple docstring"""
stooge(__a , 0 , len(__a ) - 1 )
return arr
def __lowerCamelCase ( __a :Dict , __a :Optional[int] , __a :List[Any] ... | 274 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
A : Dict = logging.get_logger(__name__)
def __lowerCamelCase ( __a :int=None , __a ... | 274 | 1 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A : List[Any] = '''.'''
if __name__ == "__main__":
A : Optional[Any] = os.path.join(REPO_PATH, '''utils/documenta... | 274 |
from math import ceil
def __lowerCamelCase ( __a :int = 1_0_0_1 ) -> int:
"""simple docstring"""
A__ = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
A__ = 2 * i + 1
A__ = 2 * i
A__ =... | 274 | 1 |
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