code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import warnings
from .generation import TFGenerationMixin
class __lowercase ( lowerCamelCase__ ):
# warning at import time
warnings.warn(
'''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
... | 676 |
from maths.prime_check import is_prime
def A ( snake_case__ : int ) -> int:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
__snake_case = f"Input value of [number={number}] must be an integer"
raise TypeError(snake_ca... | 676 | 1 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.... | 676 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.... | 676 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCAmelCase__ : str = logging.get_l... | 676 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, ... | 676 | 1 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __lowercase ( lowerCamelCase__ ):
_... | 676 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 676 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __lowercase ( lowerCamelCase__ ... | 676 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import t... | 676 | 1 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase__ : Dict = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Mult... | 676 |
from __future__ import annotations
UpperCAmelCase__ : Dict = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def A ( snake_case__ : list[list[int]] , snake_case__ : list[int] , snake_case__ : list[in... | 676 | 1 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
UpperCAmelCase__ : str = logging.get_logger... | 676 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCAmelCase__ : Any = logging.getLogger()
@unittest.skip... | 676 | 1 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-d... | 676 |
def A ( snake_case__ : int , snake_case__ : list[int] , snake_case__ : int ) -> int:
'''simple docstring'''
def count_of_possible_combinations(snake_case__ : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 676 | 1 |
import re
def A ( snake_case__ : str ) -> bool:
'''simple docstring'''
__snake_case = re.compile(
r'^(?:0|94|\+94|0{2}94)' r'7(0|1|2|4|5|6|7|8)' r'(-| |)' r'\d{7}$' )
return bool(re.search(snake_case__ , snake_case__ ) )
... | 676 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require... | 676 | 1 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Con... | 676 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A ( snake_case__ : Dataset , snake_case__ : Dict[str, str] ) -> Op... | 676 | 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 __lowercase ( datasets.BuilderConfig ):
__UpperCAmelCase = None
... | 676 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-d... | 676 | 1 |
from statistics import mean
import numpy as np
def A ( snake_case__ : list , snake_case__ : list , snake_case__ : list , snake_case__ : int ) -> list:
'''simple docstring'''
__snake_case = 0
# Number of processes finis... | 676 |
from datetime import datetime
import requests
def A ( snake_case__ : str ) -> bytes:
'''simple docstring'''
__snake_case = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
__snake_case = requests.get(base_url + url ).json... | 676 | 1 |
def A ( snake_case__ : str , snake_case__ : str ) -> str:
'''simple docstring'''
__snake_case = len(snake_case__ )
__snake_case = len(snake_case__ )
__snake_case = (
first_str_length if first_str_length > second_str_... | 676 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Con... | 676 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase__ : Optional[Any] = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolv... | 676 |
def A ( snake_case__ : int ) -> bool:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
__snake_case = f"Input value of [number={number}] must be an integer"
raise TypeError(snake_case__ )
if number < 0:
return Fal... | 676 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 676 |
import numpy as np
def A ( snake_case__ : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def A ( snake_case__ : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(s... | 676 | 1 |
from __future__ import annotations
class __lowercase :
def __init__( self , lowercase_) -> None:
__snake_case = data
__snake_case = None
__snake_case = None
def A ( snake_case__ : ... | 676 |
def A ( snake_case__ : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
__snake_case = 4
__snake_case = (1 << p) - 1
for _ in range(p - 2 ):
__snake_cas... | 676 | 1 |
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=lowerCamelCase__ ):
__UpperCAmelCase = ['''speech''']
def __init__( self , *lowercase_ , **lowercase_) -> List[str]:
requires_backends(self , ... | 676 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ : Optional[Any] = {
... | 676 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def A ( snake_case__ : Tuple , snake_case__ : Tuple ) -> Dict:
'''simple docstring'''
# ===== initializati... | 676 |
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 OptionalDependencyNotAvailable... | 676 | 1 |
from datetime import datetime
import requests
def A ( snake_case__ : str ) -> bytes:
'''simple docstring'''
__snake_case = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
__snake_case = requests.get(base_url + url ).json... | 676 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def A ( snake_case__ : List[Any] ) -> ... | 676 | 1 |
import sys
from collections import defaultdict
class __lowercase :
def __init__( self) -> List[Any]:
__snake_case = []
def _a ( self , lowercase_) -> Tuple:
return self.node_position[vertex]
... | 676 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCAmelCase__ : int = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_AR... | 676 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Tuple = logging.get_logger(__name__)
UpperCAmelCase__ : str = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class ... | 676 |
from __future__ import annotations
class __lowercase :
def __init__( self , lowercase_) -> None:
__snake_case = data
__snake_case = None
__snake_case = None
def A ( snake_case__ : ... | 676 | 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 r... | 676 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : str = logging.ge... | 676 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class __lowercase ( unittest.TestCase ... | 676 |
from maths.prime_check import is_prime
def A ( snake_case__ : int ) -> int:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
__snake_case = f"Input value of [number={number}] must be an integer"
raise TypeError(snake_ca... | 676 | 1 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
UpperCAmelCase__ : Dict = logging.get_logger(__name__)
class __lowercase ( lowerCamelCase__ ):
def __init__( self , *low... | 676 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.... | 676 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 676 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, ... | 676 | 1 |
from string import ascii_uppercase
UpperCAmelCase__ : str = {char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase__ : Optional[int] = dict(enumerate(ascii_uppercase))
def A ( snake_case__ : str , snake_case__ : str ... | 676 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 676 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require... | 676 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import t... | 676 | 1 |
from __future__ import annotations
UpperCAmelCase__ : Optional[int] = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class __lowercase :
... | 676 |
from __future__ import annotations
UpperCAmelCase__ : Dict = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def A ( snake_case__ : list[list[int]] , snake_case__ : list[int] , snake_case__ : list[in... | 676 | 1 |
from math import pow, sqrt
def A ( *snake_case__ : float ) -> bool:
'''simple docstring'''
__snake_case = len(snake_case__ ) > 0 and all(value > 0.0 for value in values )
return result
def A ( snake_case__ : float , snake... | 676 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCAmelCase__ : Any = logging.getLogger()
@unittest.skip... | 676 | 1 |
def A ( snake_case__ : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
__snake_case = 4
__snake_case = (1 << p) - 1
for _ in range(p - 2 ):
__snake_cas... | 676 |
def A ( snake_case__ : int , snake_case__ : list[int] , snake_case__ : int ) -> int:
'''simple docstring'''
def count_of_possible_combinations(snake_case__ : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 676 | 1 |
def A ( snake_case__ : int ) -> int:
'''simple docstring'''
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def A ( snake_case__ : int ) -> bool:
'''simple docstring'''
__snake_case = 0
__snake_case ... | 676 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require... | 676 | 1 |
from typing import List
import numpy as np
def A ( snake_case__ : dict ) -> int:
'''simple docstring'''
__snake_case = {key: len(snake_case__ ) for key, value in gen_kwargs.items() if isinstance(snake_case__ , snake_case__ )}
if len(set(... | 676 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A ( snake_case__ : Dataset , snake_case__ : Dict[str, str] ) -> Op... | 676 | 1 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobert... | 676 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-d... | 676 | 1 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
fr... | 676 |
from datetime import datetime
import requests
def A ( snake_case__ : str ) -> bytes:
'''simple docstring'''
__snake_case = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
__snake_case = requests.get(base_url + url ).json... | 676 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ : Union[str, Any] = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfi... | 676 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Con... | 676 | 1 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.tes... | 676 |
def A ( snake_case__ : int ) -> bool:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
__snake_case = f"Input value of [number={number}] must be an integer"
raise TypeError(snake_case__ )
if number < 0:
return Fal... | 676 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .... | 676 |
import numpy as np
def A ( snake_case__ : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def A ( snake_case__ : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(s... | 676 | 1 |
def A ( snake_case__ : int = 1000 ) -> int:
'''simple docstring'''
__snake_case , __snake_case = 1, 1
__snake_case = []
for i in range(1 , n + 1 ):
__snake_case = prev_numerator + 2 * prev_denominator
__snake_case ... | 676 |
def A ( snake_case__ : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
__snake_case = 4
__snake_case = (1 << p) - 1
for _ in range(p - 2 ):
__snake_cas... | 676 | 1 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 676 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ : Optional[Any] = {
... | 676 | 1 |
import numpy as np
def A ( snake_case__ : np.ndarray , snake_case__ : np.ndarray , snake_case__ : float = 1e-12 , snake_case__ : int = 100 , ) -> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(snake_case__ ... | 676 |
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 OptionalDependencyNotAvailable... | 676 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nl... | 676 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def A ( snake_case__ : List[Any] ) -> ... | 676 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def A ( snake_case__ : str ) -> None:
'''simple docstring'''
__snake_case , __snake_case = analyze_text(snake_case__ )
__snak... | 676 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCAmelCase__ : int = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_AR... | 676 | 1 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 676 |
from __future__ import annotations
class __lowercase :
def __init__( self , lowercase_) -> None:
__snake_case = data
__snake_case = None
__snake_case = None
def A ( snake_case__ : ... | 676 | 1 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def A ( snake_case__ : Tuple ) -> Union[str, Any]:
'''simple docstring'''
return x + 2
class __lowercase ( unitte... | 676 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : str = logging.ge... | 676 | 1 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.du... | 676 |
from maths.prime_check import is_prime
def A ( snake_case__ : int ) -> int:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
__snake_case = f"Input value of [number={number}] must be an integer"
raise TypeError(snake_ca... | 676 | 1 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def A ( snake_case__ : List[Any] ) -> ... | 676 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.... | 676 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def A ... | 676 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, ... | 676 | 1 |
def A ( snake_case__ : Union[str, Any] ) -> bool:
'''simple docstring'''
__snake_case = 0
for ch in input_str:
__snake_case = ord(SCREAMING_SNAKE_CASE_ )
__snake_case = pow(2 , SCREAMING_SNAKE_CASE_ )
# If we already t... | 700 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 676 | 0 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import ... | 701 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import t... | 676 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data i... | 702 |
from __future__ import annotations
UpperCAmelCase__ : Dict = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def A ( snake_case__ : list[list[int]] , snake_case__ : list[int] , snake_case__ : list[in... | 676 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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... | 703 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCAmelCase__ : Any = logging.getLogger()
@unittest.skip... | 676 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : ... | 704 |
def A ( snake_case__ : int , snake_case__ : list[int] , snake_case__ : int ) -> int:
'''simple docstring'''
def count_of_possible_combinations(snake_case__ : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 676 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase__ )
class __lowercase ( lowercase__ ):
__UpperCAmelCase = field(default='''summarizatio... | 705 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require... | 676 | 0 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __lowercase :
__UpperCAmelCase = 42
__UpperCAmelCase = None
__UpperCAmelCase = None
UpperCAmelCase__ : Any ... | 706 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A ( snake_case__ : Dataset , snake_case__ : Dict[str, str] ) -> Op... | 676 | 0 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 ... | 707 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-d... | 676 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
UpperCAmelCase__ : Tuple = logging.get_logger(__name__)
def A ( snake_case__ : List[str]=None , snake_case... | 708 |
from datetime import datetime
import requests
def A ( snake_case__ : str ) -> bytes:
'''simple docstring'''
__snake_case = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
__snake_case = requests.get(base_url + url ).json... | 676 | 0 |
'''simple docstring'''
def A ( snake_case__ : list[list[float]] ) -> int:
'''simple docstring'''
__snake_case = []
for data in source_data:
for i, el in enumerate(__lowerCAmelCase ):
if len(__lowerCAmelCase ) < i + 1:
data_lists.a... | 709 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Con... | 676 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPi... | 710 |
def A ( snake_case__ : int ) -> bool:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
__snake_case = f"Input value of [number={number}] must be an integer"
raise TypeError(snake_case__ )
if number < 0:
return Fal... | 676 | 0 |
def A ( snake_case__ : int ) -> Optional[int]:
'''simple docstring'''
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multi... | 711 |
import numpy as np
def A ( snake_case__ : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def A ( snake_case__ : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(s... | 676 | 0 |
import unittest
import numpy as np
def A ( snake_case__ : Any , snake_case__ : Any , snake_case__ : str , snake_case__ : List[str] = None , ) -> np.ndarray:
'''simple docstring'''
__snake_case = np.shape(__lowerCAmelCa... | 712 |
def A ( snake_case__ : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
__snake_case = 4
__snake_case = (1 << p) - 1
for _ in range(p - 2 ):
__snake_cas... | 676 | 0 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
... | 713 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ : Optional[Any] = {
... | 676 | 0 |
from __future__ import annotations
import math
def A ( snake_case__ : Tuple , snake_case__ : Tuple , snake_case__ : Optional[int] , snake_case__ : Union[str, Any] , snake_case__ : Tuple ) -> Optional[int]:
'''simple docstri... | 714 |
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 OptionalDependencyNotAvailable... | 676 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from t... | 715 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def A ( snake_case__ : List[Any] ) -> ... | 676 | 0 |
def A ( snake_case__ : Union[str, Any] , snake_case__ : List[str] , snake_case__ : Dict ) -> int:
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCamelCase ... | 716 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCAmelCase__ : int = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_AR... | 676 | 0 |
import requests
UpperCAmelCase__ : List[Any] = "" # <-- Put your OpenWeatherMap appid here!
UpperCAmelCase__ : Optional[Any] = "https://api.openweathermap.org/data/2.5/"
def A ( snake_case__ : str = "Chicago" , snake_case__ : List[st... | 717 |
from __future__ import annotations
class __lowercase :
def __init__( self , lowercase_) -> None:
__snake_case = data
__snake_case = None
__snake_case = None
def A ( snake_case__ : ... | 676 | 0 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def A (... | 718 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : str = logging.ge... | 676 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ : List[str] = {
'''configuration_longformer''': [
'''LONGFO... | 719 |
from maths.prime_check import is_prime
def A ( snake_case__ : int ) -> int:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
__snake_case = f"Input value of [number={number}] must be an integer"
raise TypeError(snake_ca... | 676 | 0 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
UpperCAmelCase__ : Tuple = """__DUMMY_TRANSFORMERS_USER__"""
UpperCAmelCase__ : Dict = """Dummy User"... | 720 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.... | 676 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def A ( *snake_case__ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if not isinstance(__UpperCamelCase , __UpperCamelC... | 721 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, ... | 676 | 0 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
imp... | 700 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 676 | 0 |
'''simple docstring'''
import math
def A ( snake_case__ : Any ) -> list[int]:
'''simple docstring'''
__snake_case = []
__snake_case = 2
__snake_case = int(math.sqrt(__SCREAMING_SNAKE_CASE ) ) # Size of every segment
... | 701 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import t... | 676 | 0 |
from collections.abc import Callable
import numpy as np
def A ( snake_case__ : Callable , snake_case__ : float , snake_case__ : float , snake_case__ : float , snake_case__ : float ) -> Union[str, Any]:
'''simple docst... | 702 |
from __future__ import annotations
UpperCAmelCase__ : Dict = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def A ( snake_case__ : list[list[int]] , snake_case__ : list[int] , snake_case__ : list[in... | 676 | 0 |
import math
def A ( snake_case__ : list , snake_case__ : int = 0 , snake_case__ : int = 0 ) -> int:
'''simple docstring'''
__snake_case = end or len(snake_case_ )
for i in range(snake_case_ , snake_case_ ):
__sn... | 703 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCAmelCase__ : Any = logging.getLogger()
@unittest.skip... | 676 | 0 |
'''simple docstring'''
def A ( snake_case__ : int = 10 , snake_case__ : int = 1000 , snake_case__ : bool = True ) -> List[Any]:
'''simple docstring'''
assert (
isinstance(_lowerCamelCase , _lowerCamelCase )
and i... | 704 |
def A ( snake_case__ : int , snake_case__ : list[int] , snake_case__ : int ) -> int:
'''simple docstring'''
def count_of_possible_combinations(snake_case__ : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 676 | 0 |
def A ( snake_case__ : int ) -> str:
'''simple docstring'''
__snake_case = generate_pascal_triangle(snake_case__ )
for row_idx in range(snake_case__ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=' ' )
... | 705 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require... | 676 | 0 |
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
UpperCAmelCase__ : str = logging.get_logger(__n... | 706 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A ( snake_case__ : Dataset , snake_case__ : Dict[str, str] ) -> Op... | 676 | 0 |
from __future__ import annotations
lowerCAmelCase : Any = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class __lowercase :
def _... | 707 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-d... | 676 | 0 |
def A ( ) -> Any:
'''simple docstring'''
__snake_case = 0
for i in range(1 , 1001 ):
total += i**i
return str(UpperCAmelCase__ )[-10:]
if __name__ == "__main__":
print(solution())
| 708 |
from datetime import datetime
import requests
def A ( snake_case__ : str ) -> bytes:
'''simple docstring'''
__snake_case = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
__snake_case = requests.get(base_url + url ).json... | 676 | 0 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipe... | 709 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Con... | 676 | 0 |
def A ( snake_case__ : int ) -> Tuple:
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
__snake_case = f"Input value of [number={number}] must be an integer"
raise TypeError(UpperCAmelCase__ )
if number < 0... | 710 |
def A ( snake_case__ : int ) -> bool:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
__snake_case = f"Input value of [number={number}] must be an integer"
raise TypeError(snake_case__ )
if number < 0:
return Fal... | 676 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
f... | 711 |
import numpy as np
def A ( snake_case__ : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def A ( snake_case__ : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(s... | 676 | 0 |
import inspect
import unittest
class __lowercase ( unittest.TestCase ):
def _a ( self) -> str:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def _a ( self) -> ... | 712 |
def A ( snake_case__ : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
__snake_case = 4
__snake_case = (1 << p) - 1
for _ in range(p - 2 ):
__snake_cas... | 676 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase__ : List[Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/faceboo... | 713 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ : Optional[Any] = {
... | 676 | 0 |
def A ( snake_case__ : List[Any] , snake_case__ : List[str] ) -> List[Any]:
'''simple docstring'''
__snake_case = len(snake_case__ )
__snake_case = len(snake_case__ )
__snake_case = [[False for _ in range(m + 1 )] for _ in... | 714 |
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 OptionalDependencyNotAvailable... | 676 | 0 |
def A ( snake_case__ : Any ) -> Any:
'''simple docstring'''
stooge(SCREAMING_SNAKE_CASE_ , 0 , len(SCREAMING_SNAKE_CASE_ ) - 1 )
return arr
def A ( snake_case__ : Optional[int] , snake_case__ : Tuple , snake_case__ ... | 715 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def A ( snake_case__ : List[Any] ) -> ... | 676 | 0 |
from math import asin, atan, cos, radians, sin, sqrt, tan
UpperCAmelCase__ : Optional[int] = 6_37_81_37.0
UpperCAmelCase__ : Tuple = 6_35_67_52.31_42_45
UpperCAmelCase__ : Tuple = 6_37_81_37
def A ( snake_case__ : Optional[int]... | 716 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCAmelCase__ : int = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_AR... | 676 | 0 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 717 |
from __future__ import annotations
class __lowercase :
def __init__( self , lowercase_) -> None:
__snake_case = data
__snake_case = None
__snake_case = None
def A ( snake_case__ : ... | 676 | 0 |
UpperCAmelCase__ : str = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"h... | 718 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : str = logging.ge... | 676 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
UpperCAmelCase__ : List[Any] = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
UpperCAmelCase__ ... | 719 |
from maths.prime_check import is_prime
def A ( snake_case__ : int ) -> int:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
__snake_case = f"Input value of [number={number}] must be an integer"
raise TypeError(snake_ca... | 676 | 0 |
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class __lowercase ( __lowercase ):
__UpperCAmelCase = '''philschmid/bart-large-cnn-samsum'''
__UpperCAmelCase = (
'''This is a tool that summarizes an Englis... | 720 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.... | 676 | 0 |
from torch import nn
def A ( snake_case__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError... | 721 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, ... | 676 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ : List[str] = logging.get_logger(__name__)
UpperCAmelCase__ : Optional[int] ... | 700 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 676 | 0 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
UpperCAmelCase__ : List[str] = ... | 701 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import t... | 676 | 0 |
from typing import Any
def A ( snake_case__ : str ) -> Optional[Any]:
'''simple docstring'''
if not input_list:
return []
__snake_case = [input_list.count(lowercase__ ) for value in input_list]
__snake_case = max(lowercase__ ) # Gets ... | 702 |
from __future__ import annotations
UpperCAmelCase__ : Dict = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def A ( snake_case__ : list[list[int]] , snake_case__ : list[int] , snake_case__ : list[in... | 676 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ : str = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_... | 703 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCAmelCase__ : Any = logging.getLogger()
@unittest.skip... | 676 | 0 |
'''simple docstring'''
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
UpperCAmelCase__ : int = "."
if __name__ == "__main__":
UpperCAmelCase__ : Dict ... | 704 |
def A ( snake_case__ : int , snake_case__ : list[int] , snake_case__ : int ) -> int:
'''simple docstring'''
def count_of_possible_combinations(snake_case__ : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 676 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ : int = logging.get_logger(__name__)
UpperCAmelCase__ : O... | 705 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require... | 676 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Any = logging.get_logger(__name__)
UpperCAmelCase__ : Dict = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/... | 706 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A ( snake_case__ : Dataset , snake_case__ : Dict[str, str] ) -> Op... | 676 | 0 |
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 .tokenizati... | 707 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-d... | 676 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase__ : Tuple = logging.get_logger(__name__)
UpperCAmelCase__ : int = {
"face... | 708 |
from datetime import datetime
import requests
def A ( snake_case__ : str ) -> bytes:
'''simple docstring'''
__snake_case = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
__snake_case = requests.get(base_url + url ).json... | 676 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.