code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from functools import lru_cache
@lru_cache
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> int:
'''simple docstring'''
if num < 0:
raise ValueError("Number should not be negative.")
return 1 if num in (0, 1) else num * facto... | 232 |
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCas... | 232 | 1 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[int]) -> int:
'''simple docstring'''
if not nums:
return 0
__UpperCamelCase : Optional[int] = nums[0]
__UpperCamelCase : str ... | 232 |
import math
def _SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
__UpperCamelCase : List[Any] = input("Enter message: ")
__UpperCamelCase : Optional[int] = int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
... | 232 | 1 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowercase : Any = {
... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Union[str, Any] ) -> None:
__UpperCamelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCamelCase : List[str] = False
def _lowerCa... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : list) -> List[Any]:
'''simple docstring'''
_enforce_args(_lowerCamelCase , _lowerCamelCase)
if n == 0:
return 0
__UpperCamelCase : str = fl... | 232 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, Pr... | 232 | 1 |
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 diffusers.utils i... | 232 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar('T')
class lowerCamelCase__ ( Generic[T]):
'''simple docstring'''
_A = 42 # Cache store ... | 232 | 1 |
lowercase : Optional[Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
lowercase : Union[str, Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : dict[int, list[int]] , _lowerCamelCase : i... | 232 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalD... | 232 | 1 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowercase : Optional[int] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5S 9S AC'... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :int ) -> Dict:
__UpperCamelCase : Union[str, Any] = {}
def _lowerCamelCase ( self :str ) -> None:
print(self.vertex )
for i in self.vert... | 232 | 1 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase : List[Any] = get_tests_di... | 232 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
lowercase : int = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamplin... | 232 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
... | 232 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamel... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> int:
'''simple docstring'''
if n == 1 or not isinstance(_lowerCamelCase , _lowerCamelCase):
return 0
elif n == 2:
return 1
else:
__UpperCamelCas... | 232 |
import argparse
import datetime
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wed... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 2_000_000) -> int:
'''simple docstring'''
__UpperCamelCase : Any = [0 for i in range(n + 1)]
__UpperCamelCase : int = 1
__UpperCamelCase : List[str] = 1
f... | 232 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 232 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 232 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 232 | 1 |
import argparse
from collections import defaultdict
import yaml
lowercase : Optional[Any] = 'docs/source/en/_toctree.yml'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[str]) -> int:
'''simple docstring'''
__UpperCamelCa... | 232 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageR... | 232 | 1 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import Onn... | 232 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase : Optional[int] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # ... | 232 | 1 |
import os
def _SCREAMING_SNAKE_CASE ( ) -> Any:
'''simple docstring'''
with open(os.path.dirname(_lowerCamelCase) + "/grid.txt") as f:
__UpperCamelCase : List[Any] = [] # noqa: E741
for _ in range(20):
... | 232 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Any) -> Dict:
'''simple docstring'''
__UpperCamelCase : Op... | 232 | 1 |
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:
from ... | 232 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__U... | 232 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 232 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 232 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowercase : int... | 232 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCamelCase__ ( __lowercase):
... | 232 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
loggin... | 232 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> float:
'''simple docstring'''
return base * power(_lowerCamelCase , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of e... | 232 | 1 |
import math
import sys
import cva
import numpy as np
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : np.ndarray , _lowerCamelCase : float) -> np.ndarray:
'''simple docstring'''
__UpperCamelCase : Any = math.sqrt(_lowerCamelCase)
... | 232 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_fl... | 232 | 1 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
lowercase : int = logging.get_logger(__name__)
class lowerCamelCase__ :
'''simple docstring'''
_A = None
... | 232 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, ... | 232 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 232 |
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 torch
lowercase : ... | 232 | 1 |
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_available():
imp... | 232 |
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCas... | 232 | 1 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Any) -> Dict:
'''simple docstring'''
__UpperCamelCase : Op... | 232 |
import math
def _SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
__UpperCamelCase : List[Any] = input("Enter message: ")
__UpperCamelCase : Optional[int] = int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any]) -> List[Any]: # noqa: E741
'''simple docstring'''
__UpperCamelCase : Optional[Any] = len(_lowerCamelCase)
__UpperCamelCase : int = 0
__UpperCamelCase : Tuple ... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Union[str, Any] ) -> None:
__UpperCamelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCamelCase : List[str] = False
def _lowerCa... | 232 | 1 |
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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 232 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, Pr... | 232 | 1 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 Co... | 232 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar('T')
class lowerCamelCase__ ( Generic[T]):
'''simple docstring'''
_A = 42 # Cache store ... | 232 | 1 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_fil... | 232 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalD... | 232 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class lowerCamelCase__... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :int ) -> Dict:
__UpperCamelCase : Union[str, Any] = {}
def _lowerCamelCase ( self :str ) -> None:
print(self.vertex )
for i in self.vert... | 232 | 1 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCamelCase__ ( __lowercase):
... | 232 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
lowercase : int = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamplin... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any]) -> Union[str, Any]:
'''simple docstring'''
stooge(_lowerCamelCase , 0 , len(_lowerCamelCase) - 1)
return arr
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[str] ... | 232 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamel... | 232 | 1 |
import qiskit
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> qiskit.result.counts.Counts:
'''simple docstring'''
__UpperCamelCase : List[Any] = qiskit.Aer.get_backend("aer_simulator")
# Create a Q... | 232 |
import argparse
import datetime
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wed... | 232 | 1 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if ... | 232 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 232 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 232 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 232 | 1 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.util... | 232 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageR... | 232 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase : Optional[int] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # ... | 232 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase : Optional[int] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # ... | 232 | 1 |
import random
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list , _lowerCamelCase : Dict) -> tuple:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase : str = [], [], []
for element in da... | 232 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Any) -> Dict:
'''simple docstring'''
__UpperCamelCase : Op... | 232 | 1 |
from math import factorial
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 100) -> int:
'''simple docstring'''
return sum(int(_lowerCamelCase) for x in str(factorial(_lowerCamelCase)))
if __name__ == "__main__":
print(solution(int(input('Enter ... | 232 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__U... | 232 | 1 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamel... | 232 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str , _lowerCamelCase : str) -> bool:
'''simple docstring'''
__UpperCamelCase : int = len(_lowerCamelCase) + 1
__UpperCamelCase : int = len(_lowerCamelCase) + 1
# dp is a ... | 232 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCamelCase__ ( __lowercase):
... | 232 | 1 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_fl... | 232 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> float:
'''simple docstring'''
return base * power(_lowerCamelCase , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of e... | 232 | 1 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
Musicge... | 232 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_fl... | 232 | 1 |
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.'
) | 232 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, ... | 232 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
... | 232 |
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 torch
lowercase : ... | 232 | 1 |
from math import pi, sqrt, tan
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length*... | 232 |
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCas... | 232 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 232 |
import math
def _SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
__UpperCamelCase : List[Any] = input("Enter message: ")
__UpperCamelCase : Optional[int] = int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
... | 232 | 1 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
lowercase ... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Union[str, Any] ) -> None:
__UpperCamelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCamelCase : List[str] = False
def _lowerCa... | 232 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class lowerCamelCase__ ( unittest.TestCase , __lowercase):
'''simple docstring'''
def _lowerCamelCase ( self :Optional[int] ) -> List[Any]:
... | 232 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, Pr... | 232 | 1 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
lowercase : List[str] = logging.get_logger(__name__)
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
def __init__( self :Dict... | 232 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar('T')
class lowerCamelCase__ ( Generic[T]):
'''simple docstring'''
_A = 42 # Cache store ... | 232 | 1 |
import os
import sys
import unittest
lowercase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_obj... | 232 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalD... | 232 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHo... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :int ) -> Dict:
__UpperCamelCase : Union[str, Any] = {}
def _lowerCamelCase ( self :str ) -> None:
print(self.vertex )
for i in self.vert... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 4_000_000) -> int:
'''simple docstring'''
__UpperCamelCase : str = []
__UpperCamelCase , __UpperCamelCase : int = 0, 1
while b <= n:
if b % 2 == 0:
... | 232 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
lowercase : int = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamplin... | 232 | 1 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any]) -> List[str]:
'''simple docstring'''
__UpperCamelCase : Tuple ... | 232 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamel... | 232 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseSched... | 232 |
import argparse
import datetime
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wed... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError("Input must be a positive integer")
__UpperCamelCase : Union[str, Any] = [True] * (num + 1)
__UpperCa... | 232 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 232 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Any = logging.get_logger(__name__)
lowercase : Any = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# S... | 232 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 232 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase : int = {
'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}
try:
if not is_tor... | 232 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageR... | 232 | 1 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowercase : Optional[int] = '.'
# Interna... | 232 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase : Optional[int] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # ... | 232 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import... | 232 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Any) -> Dict:
'''simple docstring'''
__UpperCamelCase : Op... | 232 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, ... | 232 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__U... | 232 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
_A = ['image_processor', 'tokenizer']
_A = 'ChineseCLIPImageProcessor'
... | 232 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 232 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 232 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCamelCase__ ( __lowercase):
... | 232 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
lowercase : List[Any] = False
class lowerCamelCase__... | 232 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> float:
'''simple docstring'''
return base * power(_lowerCamelCase , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of e... | 232 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils imp... | 232 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_fl... | 232 | 1 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowercase : Tuple = lo... | 232 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, ... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : int) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception("Principal borrowed must be > 0")
if rate_per_annum < 0... | 232 |
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 torch
lowercase : ... | 232 | 1 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.... | 232 |
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCas... | 232 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase : Optional[Any] = {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}
try:
if not is_torch_... | 232 |
import math
def _SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
__UpperCamelCase : List[Any] = input("Enter message: ")
__UpperCamelCase : Optional[int] = int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
... | 232 | 1 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
lowercase : str = ... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Union[str, Any] ) -> None:
__UpperCamelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCamelCase : List[str] = False
def _lowerCa... | 232 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : str = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP... | 232 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, Pr... | 232 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils impor... | 232 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar('T')
class lowerCamelCase__ ( Generic[T]):
'''simple docstring'''
_A = 42 # Cache store ... | 232 | 1 |
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
lowercase : int = logging.get_logger(__name__)
lowercase : Optional[... | 232 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalD... | 232 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImag... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :int ) -> Dict:
__UpperCamelCase : Union[str, Any] = {}
def _lowerCamelCase ( self :str ) -> None:
print(self.vertex )
for i in self.vert... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> Dict:
'''simple docstring'''
__UpperCamelCase : Dict = [0] * len(_lowerCamelCase)
__UpperCamelCase : List[str] = []
__UpperCamelCase : Dict = []
__UpperCa... | 232 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
lowercase : int = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamplin... | 232 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase : Optional[int] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n ... | 232 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamel... | 232 | 1 |
from datetime import datetime
import requests
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> bytes:
'''simple docstring'''
__UpperCamelCase : int = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
__UpperC... | 232 |
import argparse
import datetime
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wed... | 232 | 1 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
... | 232 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> bool:
'''simple docstring'''
return sum(i for i in range(1 , number // 2 + 1) if number % i == 0) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number o... | 232 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 232 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : Any = logging.get_logger(__name__)
lowercase : Optional[int] = {
'... | 232 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageR... | 232 | 1 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageR... | 232 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase : Optional[int] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # ... | 232 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
lowercase : List[Any] ... | 232 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Any) -> Dict:
'''simple docstring'''
__UpperCamelCase : Op... | 232 | 1 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
lowerc... | 232 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__U... | 232 | 1 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar('T')
class lowerCamelCase__ ( Generic[T]):
'''simple docstring'''
_A = 42 # Cache store ... | 232 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 232 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Dict = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
raise ... | 232 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCamelCase__ ( __lowercase):
... | 232 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils impor... | 232 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> float:
'''simple docstring'''
return base * power(_lowerCamelCase , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of e... | 232 | 1 |
from __future__ import annotations
from math import pi
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float) -> dict[str, float]:
'''simple docstring'''
if (inductance, frequency, reacta... | 232 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_fl... | 232 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class lowerCamelCase__ ( __lowercase):
'''simple do... | 232 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, ... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list) -> float:
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = 0
while len(_lowerCamelCase) > 1:
__UpperCamelCase : int = 0
# Consider two files w... | 232 |
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 torch
lowercase : ... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> int:
'''simple docstring'''
assert isinstance(_lowerCamelCase , _lowerCamelCase), F'The input value of [n={number}] is not an integer'
if number == 1:
return 2
elif number ... | 232 |
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCas... | 232 | 1 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : List[str] = logging.get_logger(__name__)
lowercase : int = {
'vocab_... | 232 |
import math
def _SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
__UpperCamelCase : List[Any] = input("Enter message: ")
__UpperCamelCase : Optional[int] = int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
... | 232 | 1 |
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 OptionalDependencyNotAvailable:
from ...uti... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Union[str, Any] ) -> None:
__UpperCamelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCamelCase : List[str] = False
def _lowerCa... | 232 | 1 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
lowercase : Dict = logging.getLogger(__name__)
@dataclass
class lowerCamelCase__ ( __lower... | 232 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, Pr... | 232 | 1 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.model... | 232 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar('T')
class lowerCamelCase__ ( Generic[T]):
'''simple docstring'''
_A = 42 # Cache store ... | 232 | 1 |
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_available():
imp... | 232 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalD... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> float:
'''simple docstring'''
return base * power(_lowerCamelCase , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of e... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :int ) -> Dict:
__UpperCamelCase : Union[str, Any] = {}
def _lowerCamelCase ( self :str ) -> None:
print(self.vertex )
for i in self.vert... | 232 | 1 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :int ) -> Dict:
__UpperCamelCase : Union[str, Any] = {}
def _lowerCamelCase ( self :str ) -> None:
print(self.vertex )
for i in self.vert... | 232 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
lowercase : int = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamplin... | 232 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
i... | 232 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamel... | 232 | 1 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__U... | 232 |
import argparse
import datetime
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wed... | 232 | 1 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
_A = CustomTokenizer
pass | 232 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 232 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, requ... | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 232 | 0 |
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