code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __a ( _snake... | 717 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1 , __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 0
for divide_by_number in range(__UpperCAm... | 13 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_v... | 718 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import... | 13 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_a... | 719 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
def wrapper(*__UpperCAmel... | 13 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class __a ( __SCREAMING_SNAKE_CASE ):
def __init__( self : List[Any] ):
'''simple docstring'''
self.test()
def UpperCAmelCase__ ( self : List[Any] ... | 720 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a = "__DUMMY_TRANSFORMERS_USER__"
a = "Dummy User"
a = "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"
... | 13 | 0 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
a = (
"""This metric will be removed from the library soo... | 721 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __a ( ... | 13 | 0 |
import torch
def __magic_name__ ( ) -> List[Any]:
'''simple docstring'''
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE = torch.cuda.device_count()
else:
__SCREAMING_SNAKE_CASE = 0
print(f"""Successfully ran on {... | 700 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class __a ( nn.Module ):
__UpperCamelCase : int
__UpperCamelCase : jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SC... | 13 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ )
__SCREAMING_SNAKE_CASE = [[False] * (required_sum ... | 701 |
'''simple docstring'''
import sys
from collections import defaultdict
class __a :
def __init__( self : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : ... | 13 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> list:
'''simple docstring'''
if bit_count < 0:
raise ValueError("""The given input must be positive""" )
# get the generated string sequence
__SCREAMING_SNAKE_CASE = ... | 702 |
'''simple docstring'''
import os
import string
import sys
a = 1 << 8
a = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91... | 13 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1... | 703 |
'''simple docstring'''
from __future__ import annotations
import bisect
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 , __UpperCAmelCase = -1 ) -> int:
'''simple docstring'''
if hi < 0:
__SCREAMING_SNAKE_CAS... | 13 | 0 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 704 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a = logging.get_logger(__name__)
class __a ( _snake_case ):
__UpperCamelCase : int... | 13 | 0 |
'''simple docstring'''
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subproce... | 705 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 13 | 0 |
'''simple docstring'''
import math
def __magic_name__ ( __UpperCAmelCase ) -> Any:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while num > 0:
__SCREAMING_SNAKE_CASE = ... | 706 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase = "AAPL" ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__SCREAMI... | 13 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_snake_case )
class __a ( _snake_case ):
__UpperCamelCase : Dict = field(defau... | 707 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if num < 0:
return False
__SCREAMING_SNAKE_CASE = num
__SCREAMING_SNAKE_CASE = 0
while num > 0:
__SCREAMING_SNAKE_... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...... | 708 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a = list[list[float | int]]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Matrix:
'''simple docstring'''
__SCREAMING_SNAKE_CASE ... | 13 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
# Se... | 709 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 13 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a = logging.getLogger(__name__)
class __a ( snake_case__ ):
... | 710 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 13 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependency... | 711 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 13 | 0 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
a = (
"This metric will be removed from the library soon, met... | 712 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 13 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_avai... | 713 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(__UpperCAmelCase , params... | 13 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vis... | 714 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"camembert-base": "https://huggingface.co/ca... | 13 | 0 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __a ( _snake_case ):
__UpperCamelCase : List[Any] = (CMStochasticIterativeScheduler,)
__UpperCamelCase : Tuple = 10
def UpperCAmelCase__ ( ... | 715 |
'''simple docstring'''
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_t... | 13 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImag... | 716 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See a... | 13 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'google... | 717 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1 , __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 0
for divide_by_number in range(__UpperCAm... | 13 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 718 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import... | 13 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __a ( metaclass=__lowerCamelCase ):
__UpperCamelCase : Optional[Any] = ['torch', 'torchsde']
def __init__( self : Optional[int] ,*lowerCamelCase : Optional[Any] ,**lowerCamelCase :... | 719 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
def wrapper(*__UpperCAmel... | 13 | 0 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a ( _snake_case ):
__UpperCamelCase : Dict = (DDPMScheduler,)
def UpperCAmelCase__ ( self : int ,**lowerCamelCase : Optional[A... | 720 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a = "__DUMMY_TRANSFORMERS_USER__"
a = "Dummy User"
a = "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"
... | 13 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __a ( lowercase__ ):
def __init__( self : Optional[... | 721 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __a ( ... | 13 | 0 |
a = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
__SCREAMING_SNAKE_CASE = ... | 700 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class __a ( nn.Module ):
__UpperCamelCase : int
__UpperCamelCase : jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SC... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor... | 701 |
'''simple docstring'''
import sys
from collections import defaultdict
class __a :
def __init__( self : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : ... | 13 | 0 |
'''simple docstring'''
import random
class __a :
@staticmethod
def UpperCAmelCase__ ( lowerCamelCase : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [ord(_lowercase ) for i in text]
__SCREAMING_SNAKE_CASE = ... | 702 |
'''simple docstring'''
import os
import string
import sys
a = 1 << 8
a = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91... | 13 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from trans... | 703 |
'''simple docstring'''
from __future__ import annotations
import bisect
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 , __UpperCAmelCase = -1 ) -> int:
'''simple docstring'''
if hi < 0:
__SCREAMING_SNAKE_CAS... | 13 | 0 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...t... | 704 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a = logging.get_logger(__name__)
class __a ( _snake_case ):
__UpperCamelCase : int... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> list[tuple[int, int]]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = position
__SCREAMING_SNAKE_CASE = ... | 705 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 13 | 0 |
'''simple docstring'''
import argparse
import struct
import unittest
class __a :
def __init__( self : List[Any] ,lowerCamelCase : bytes ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = data
# Initialize hash values
__SCREAMING_SNA... | 706 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase = "AAPL" ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__SCREAMI... | 13 | 0 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' whe... | 707 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if num < 0:
return False
__SCREAMING_SNAKE_CASE = num
__SCREAMING_SNAKE_CASE = 0
while num > 0:
__SCREAMING_SNAKE_... | 13 | 0 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
a = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
a = requests.get(url, head... | 708 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a = list[list[float | int]]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Matrix:
'''simple docstring'''
__SCREAMING_SNAKE_CASE ... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 709 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
a = list[tuple[int, int]]
a = [
[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... | 710 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 13 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
fr... | 711 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 13 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from t... | 712 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 13 | 0 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __magic_... | 713 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(__UpperCAmelCase , params... | 13 | 0 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import P... | 714 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"camembert-base": "https://huggingface.co/ca... | 13 | 0 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...... | 715 |
'''simple docstring'''
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_t... | 13 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InformerConfi... | 716 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See a... | 13 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"facebook/xlm-roberta-xl": "https://huggingf... | 717 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1 , __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 0
for divide_by_number in range(__UpperCAm... | 13 | 0 |
'''simple docstring'''
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
a = logging.get_logger(__name__)
def ... | 718 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import... | 13 | 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 __a ( lowercase_ ... | 719 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
def wrapper(*__UpperCAmel... | 13 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 200 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [1, 2, 5, 10, 20, 50, 100, 200]
__SCREAMING_SNAKE_CASE = [0] * (pence + 1)
__SCREAMING_SNAKE_CASE ... | 720 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a = "__DUMMY_TRANSFORMERS_USER__"
a = "Dummy User"
a = "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"
... | 13 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone... | 721 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __a ( ... | 13 | 0 |
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = f"""Input value of [number={number}] must be an integer"""
raise TypeError... | 700 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class __a ( nn.Module ):
__UpperCamelCase : int
__UpperCamelCase : jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SC... | 13 | 0 |
'''simple docstring'''
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, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from... | 701 |
'''simple docstring'''
import sys
from collections import defaultdict
class __a :
def __init__( self : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : ... | 13 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
a = [
"word_embeddings_layern... | 702 |
'''simple docstring'''
import os
import string
import sys
a = 1 << 8
a = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91... | 13 | 0 |
'''simple docstring'''
import numpy
# List of input, output pairs
a = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
a = (((515, 22, 13), 555), ((61, 35, 49), 150))
a = [2, 4, 1, 5]
a ... | 703 |
'''simple docstring'''
from __future__ import annotations
import bisect
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 , __UpperCAmelCase = -1 ) -> int:
'''simple docstring'''
if hi < 0:
__SCREAMING_SNAKE_CAS... | 13 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"vocab_file": "vocab.json",
"merges_file":... | 704 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a = logging.get_logger(__name__)
class __a ( _snake_case ):
__UpperCamelCase : int... | 13 | 0 |
'''simple docstring'''
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 ... | 705 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 13 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a = {"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "O... | 706 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase = "AAPL" ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__SCREAMI... | 13 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> float:
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
__SCREAMING_SNAKE_CASE = sum(__UpperCAmelCase... | 707 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if num < 0:
return False
__SCREAMING_SNAKE_CASE = num
__SCREAMING_SNAKE_CASE = 0
while num > 0:
__SCREAMING_SNAKE_... | 13 | 0 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __magic_name__ ( __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
for param in module.parameters():
__SCREAMING_SNAKE_CASE = Fal... | 708 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a = list[list[float | int]]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Matrix:
'''simple docstring'''
__SCREAMING_SNAKE_CASE ... | 13 | 0 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def __magic_name__ ( ) -> Tuple:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 9
__SCREAMING_SNAKE_CASE = [
[0, 1, 4],
[0, 7, 8],
... | 709 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 13 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
neste... | 710 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 13 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 3
__SCREAMING_SNAKE_CASE = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
... | 711 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 13 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
# See all Donut models at https:/... | 712 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 13 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
fro... | 713 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(__UpperCAmelCase , params... | 13 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a = {
"configuration_transfo_xl": ["TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig"],
"tokenization_transfo_xl": ["TransfoXLCorpus", "TransfoXL... | 714 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"camembert-base": "https://huggingface.co/ca... | 13 | 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,
BertTokeniz... | 715 |
'''simple docstring'''
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_t... | 13 | 0 |
'''simple docstring'''
import os
import string
import sys
a = 1 << 8
a = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91... | 716 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See a... | 13 | 0 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torc... | 717 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1 , __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 0
for divide_by_number in range(__UpperCAm... | 13 | 0 |
'''simple docstring'''
from functools import reduce
a = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"668966489... | 718 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import... | 13 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See a... | 719 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
def wrapper(*__UpperCAmel... | 13 | 0 |
'''simple docstring'''
from ....utils import logging
a = logging.get_logger(__name__)
class __a ( _snake_case ):
def __init__( self : Tuple ,lowerCamelCase : List[str] ,lowerCamelCase : Optional[int]=None ,lowerCamelCase : Optional[Any]=2048 ):... | 720 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a = "__DUMMY_TRANSFORMERS_USER__"
a = "Dummy User"
a = "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"
... | 13 | 0 |
'''simple docstring'''
import torch
def __magic_name__ ( ) -> Dict:
'''simple docstring'''
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE = torch.cuda.device_count()
else:
__SCREAMING_SNAKE_CASE = 0
print(f"""Su... | 721 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __a ( ... | 13 | 0 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, Path... | 700 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class __a ( nn.Module ):
__UpperCamelCase : int
__UpperCamelCase : jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SC... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
a = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
a = typing.Union[np.floataa, int, float] # noqa: UP007
def __magic_name__ ... | 701 |
'''simple docstring'''
import sys
from collections import defaultdict
class __a :
def __init__( self : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : ... | 13 | 0 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=1024 , __UpperCAm... | 702 |
'''simple docstring'''
import os
import string
import sys
a = 1 << 8
a = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91... | 13 | 0 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = ... | 703 |
'''simple docstring'''
from __future__ import annotations
import bisect
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 , __UpperCAmelCase = -1 ) -> int:
'''simple docstring'''
if hi < 0:
__SCREAMING_SNAKE_CAS... | 13 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __a ( ... | 704 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a = logging.get_logger(__name__)
class __a ( _snake_case ):
__UpperCamelCase : int... | 13 | 0 |
'''simple docstring'''
import sys
from collections import defaultdict
class __a :
'''simple docstring'''
def __init__( self : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
def UpperCAmelCase__ ( self : List[Any]... | 705 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 13 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
f... | 706 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase = "AAPL" ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__SCREAMI... | 13 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://hug... | 707 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if num < 0:
return False
__SCREAMING_SNAKE_CASE = num
__SCREAMING_SNAKE_CASE = 0
while num > 0:
__SCREAMING_SNAKE_... | 13 | 0 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stabl... | 708 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a = list[list[float | int]]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Matrix:
'''simple docstring'''
__SCREAMING_SNAKE_CASE ... | 13 | 0 |
'''simple docstring'''
from statistics import mean
import numpy as np
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> list:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
#... | 709 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 13 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
... | 710 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 13 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backb... | 711 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 13 | 0 |
'''simple docstring'''
a = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_9344,
"knot": 1.852,
}
a = {
"km/h": 1.0,
"m/s": 0.2_7777_7778,
"mph": 0.6_2137_1192,
"knot": 0.5_3995_6803,
}
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ... | 712 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 13 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = len(__UpperCAmelCase )
__SCREAMING_SNAKE_CASE = len(__UpperCAmelCase )
__SCRE... | 713 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(__UpperCAmelCase , params... | 13 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.ut... | 714 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"camembert-base": "https://huggingface.co/ca... | 13 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils impor... | 715 |
'''simple docstring'''
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_t... | 13 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
a = loggi... | 716 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See a... | 13 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass... | 717 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1 , __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 0
for divide_by_number in range(__UpperCAm... | 13 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
__SCREAMING_SNAKE_CASE = str(... | 718 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import... | 13 | 0 |
'''simple docstring'''
def __magic_name__ ( ) -> int:
'''simple docstring'''
return 1
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def ... | 719 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
def wrapper(*__UpperCAmel... | 13 | 0 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
a = collections.namedtuple("_Datasets", ["tra... | 720 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a = "__DUMMY_TRANSFORMERS_USER__"
a = "Dummy User"
a = "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"
... | 13 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __magic_name__ ( __Upp... | 721 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __a ( ... | 13 | 0 |
from ..utils import DummyObject, requires_backends
class __a ( metaclass=_snake_case ):
__UpperCamelCase : Tuple = ['note_seq']
def __init__( self : Optional[Any] ,*lowerCamelCase : Tuple ,**lowerCamelCase : Any ):
'''simple docstring'''
requ... | 700 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class __a ( nn.Module ):
__UpperCamelCase : int
__UpperCamelCase : jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
__SC... | 13 | 0 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def __magic_name__ ( __UpperCAmelCase ) -> float:
'''simple docstring'''
if num <= 0:
raise ValueError("""math domain error""" )
return quad(__UpperCAmelCase ... | 701 |
'''simple docstring'''
import sys
from collections import defaultdict
class __a :
def __init__( self : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : ... | 13 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transforme... | 702 |
'''simple docstring'''
import os
import string
import sys
a = 1 << 8
a = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91... | 13 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a = {"tokenization_byt5": ["ByT5Tokenizer"]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
a = _LazyModule(__name__, globals()[... | 703 |
'''simple docstring'''
from __future__ import annotations
import bisect
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 , __UpperCAmelCase = -1 ) -> int:
'''simple docstring'''
if hi < 0:
__SCREAMING_SNAKE_CAS... | 13 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
a = logging.get_logger(__name... | 704 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a = logging.get_logger(__name__)
class __a ( _snake_case ):
__UpperCamelCase : int... | 13 | 0 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
a = TypeVar("T")
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
return (position - 1) // 2
def __magic_... | 705 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 13 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,... | 706 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase = "AAPL" ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__SCREAMI... | 13 | 0 |
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