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 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONF... | 715 |
from string import ascii_uppercase
lowercase_ = {str(ord(c) - 55): c for c in ascii_uppercase}
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('int()... | 45 | 0 |
from __future__ import annotations
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resista... | 716 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_v... | 45 | 0 |
# flake8: noqa
# Lint as: python3
lowercase_ = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_pro... | 717 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[Any]:
stooge(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) - 1 )
return arr
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if i >... | 45 | 0 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = "x" , _SCREAMING_SNAKE_CASE = 10**-10 , _SCREAMING_SNAKE_CASE = 1 , ) -> complex:
lowercase__ ... | 718 |
from scipy.stats import spearmanr
import datasets
lowercase_ = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlati... | 45 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils im... | 719 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 50 ) -> int:
lowercase__ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number... | 45 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""MIT/ast-finetuned-audioset-10-10-0.4593""": (
"""https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/c... | 720 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transforme... | 45 | 0 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCOND... | 721 |
import argparse
import json
import subprocess
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
lowercase__ = []
lowercase__ = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: B... | 45 | 0 |
lowercase_ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[str]:
# Make sure the supplied data is a bytes-like object
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
lowercase__ ... | 700 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : Tuple = 'ClapFeatureExtractor'
_UpperCamelCase : Union[str, Any] = ('RobertaTokenizer', 'Robert... | 45 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.... | 701 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 45 | 0 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class SCREAMING_SNAKE_CASE :
_UpperCamelCase : Any = field(
metada... | 702 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_dev... | 45 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# 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_ ... | 703 |
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_ = logging.get_logger(__name__)
lowercase_ = {
"""fac... | 45 | 0 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class SCREAMING_SNAKE_CASE (_UpperCAmelCase ):
_UpperCamelCase : List[Any] ... | 704 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=7 ) -> List[Any]:
lowercase__ = None
if token is not None:
lowercase... | 45 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets... | 705 |
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_ = False
class SCREAMING_SNAKE_CASE (unittes... | 45 | 0 |
from manim import *
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
def SCREAMING_SNAKE_CASE_ ( self : Dict )-> Tuple:
"""simple docstring"""
lowercase__ = Rectangle(height=0.5 , width=0.5 )
lowercase__ ... | 706 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> bool:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError('Input list must be a n... | 45 | 0 |
from __future__ import annotations
from collections import deque
class SCREAMING_SNAKE_CASE :
def __init__( self : int , a : Tuple )-> Tuple:
"""simple docstring"""
lowercase__ = []
self.adlist.append(
... | 707 |
from __future__ import annotations
import math
from collections.abc import Callable
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 100 , ) -> float:
lowercase__ = x_start
lowercase__ ... | 45 | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import To... | 708 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
... | 45 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class SCREAMING_SNAKE_CASE (a__ ):
... | 709 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 50 ) -> int:
lowercase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different... | 45 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
cente... | 710 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
def __init__( ... | 45 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[Tuple[... | 711 |
from PIL import Image
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Image:
def brightness(_SCREAMING_SNAKE_CASE ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('level must be between ... | 45 | 0 |
import os
import pytest
from attr import dataclass
lowercase_ = """us-east-1""" # defaults region
@dataclass
class SCREAMING_SNAKE_CASE :
_UpperCamelCase : Optional[Any] = 42
_UpperCamelCase : Any = 'arn:aws:iam::558105141721:role/sagemaker_e... | 712 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 45 | 0 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space... | 713 |
import math
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_SCREAMING_SNAKE_CASE )
else:
if x == 0: # 0 raised to an... | 45 | 0 |
from math import sqrt
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 1000000 ) -> int:
lowercase__ = 0
lowercase__ = 0
lowercase__ = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 ... | 714 |
class SCREAMING_SNAKE_CASE : # Public class to implement a graph
def __init__( self : int , a : int , a : int , a : list[list[bool]] )-> None:
"""simple docstring"""
lowercase__ = row
... | 45 | 0 |
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.stable_diffusio... | 715 |
from string import ascii_uppercase
lowercase_ = {str(ord(c) - 55): c for c in ascii_uppercase}
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('int()... | 45 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__UpperCAmelCase )
class SCREAMING_SNAKE_CASE (__UpperCAmelCase ):
_UpperCamelCase : Any ... | 716 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_v... | 45 | 0 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
lowercase_ = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": ["export", "v... | 717 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[Any]:
stooge(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) - 1 )
return arr
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if i >... | 45 | 0 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDI... | 718 |
from scipy.stats import spearmanr
import datasets
lowercase_ = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlati... | 45 | 0 |
from __future__ import annotations
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> Any:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )
elif el... | 719 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 50 ) -> int:
lowercase__ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number... | 45 | 0 |
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_ = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.... | 720 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transforme... | 45 | 0 |
def __UpperCamelCase () -> int:
lowercase__ = 0
for i in range(1 , 1001 ):
total += i**i
return str(_SCREAMING_SNAKE_CASE )[-10:]
if __name__ == "__main__":
print(solution())
| 721 |
import argparse
import json
import subprocess
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
lowercase__ = []
lowercase__ = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: B... | 45 | 0 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigT... | 700 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : Tuple = 'ClapFeatureExtractor'
_UpperCamelCase : Union[str, Any] = ('RobertaTokenizer', 'Robert... | 45 | 0 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
lowercase_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8... | 701 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 45 | 0 |
lowercase_ = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": """--""", """N""": """-... | 702 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_dev... | 45 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""shi-labs/dinat-mini-in1k-224""": "... | 703 |
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_ = logging.get_logger(__name__)
lowercase_ = {
"""fac... | 45 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_ge... | 704 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=7 ) -> List[Any]:
lowercase__ = None
if token is not None:
lowercase... | 45 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Dict:
lowercase__ = os.path.join(args.tf_mode... | 705 |
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_ = False
class SCREAMING_SNAKE_CASE (unittes... | 45 | 0 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __UpperCamelCase (_SCREAMING... | 706 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> bool:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError('Input list must be a n... | 45 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> str:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
... | 707 |
from __future__ import annotations
import math
from collections.abc import Callable
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 100 , ) -> float:
lowercase__ = x_start
lowercase__ ... | 45 | 0 |
from math import isqrt
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(_SCREAMING_SNAKE_CASE ) + 1 ) )
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 10**6 ) -> int:
lower... | 708 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
... | 45 | 0 |
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 (
IMAGENET_STANDARD_MEA... | 709 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 50 ) -> int:
lowercase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different... | 45 | 0 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
... | 710 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
def __init__( ... | 45 | 0 |
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_ = logging.get_logger(__name__)
lowercase_ = {
"""fac... | 711 |
from PIL import Image
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Image:
def brightness(_SCREAMING_SNAKE_CASE ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('level must be between ... | 45 | 0 |
from collections.abc import Sequence
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = None ) -> int:
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
lowercase__ = nums[0]
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) )... | 712 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 45 | 0 |
import unittest
from knapsack import greedy_knapsack as kp
class SCREAMING_SNAKE_CASE (unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] )-> Dict:
"""simple docstring"""
lowercase__ = [10, 20,... | 713 |
import math
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_SCREAMING_SNAKE_CASE )
else:
if x == 0: # 0 raised to an... | 45 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"""configuration_roc_bert""": ["""ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoCBertConfig"""],
"""tokenization_roc_bert... | 714 |
class SCREAMING_SNAKE_CASE : # Public class to implement a graph
def __init__( self : int , a : int , a : int , a : list[list[bool]] )-> None:
"""simple docstring"""
lowercase__ = row
... | 45 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"""configuration_xlm_roberta_xl""": [
"""XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XLMRobertaXLConfig""",
"""XLMRobe... | 715 |
from string import ascii_uppercase
lowercase_ = {str(ord(c) - 55): c for c in ascii_uppercase}
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('int()... | 45 | 0 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 50 ) -> int:
lowercase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different... | 716 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_v... | 45 | 0 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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_... | 717 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[Any]:
stooge(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) - 1 )
return arr
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if i >... | 45 | 0 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowercase_ = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": operator.ge,
""">""": operat... | 718 |
from scipy.stats import spearmanr
import datasets
lowercase_ = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlati... | 45 | 0 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> np.ndarray:
return input_array.reshape((input_array.size, 1) )
def __UpperCamelCase ... | 719 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 50 ) -> int:
lowercase__ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number... | 45 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : Tuple = 'ClapFeatureExtractor'
_UpperCamelCase : Union[str, Any] = ('RobertaTokenizer', 'Robert... | 720 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transforme... | 45 | 0 |
# Algorithm for the pigeonhole sorting
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> int:
lowercase__ = min(_SCREAMING_SNAKE_CASE ) # min() finds the minimum value
lowercase__ = max(_SCREAMING_SNAKE_CASE ) # max() finds the maximum val... | 721 |
import argparse
import json
import subprocess
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
lowercase__ = []
lowercase__ = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: B... | 45 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class SCREAMING_SNAKE_CASE (u... | 700 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : Tuple = 'ClapFeatureExtractor'
_UpperCamelCase : Union[str, Any] = ('RobertaTokenizer', 'Robert... | 45 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase_ = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDepe... | 701 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 45 | 0 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
... | 702 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_dev... | 45 | 0 |
lowercase_ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
... | 703 |
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_ = logging.get_logger(__name__)
lowercase_ = {
"""fac... | 45 | 0 |
lowercase_ = {
"""joule""": 1.0,
"""kilojoule""": 1_000,
"""megajoule""": 1_000_000,
"""gigajoule""": 1_000_000_000,
"""wattsecond""": 1.0,
"""watthour""": 3_600,
"""kilowatthour""": 3_600_000,
"""newtonmeter""": 1.0,
"""calorie_nutr""": 4_186.8,
""... | 704 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=7 ) -> List[Any]:
lowercase__ = None
if token is not None:
lowercase... | 45 | 0 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
... | 705 |
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_ = False
class SCREAMING_SNAKE_CASE (unittes... | 45 | 0 |
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_availab... | 706 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> bool:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError('Input list must be a n... | 45 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.funct... | 707 |
from __future__ import annotations
import math
from collections.abc import Callable
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 100 , ) -> float:
lowercase__ = x_start
lowercase__ ... | 45 | 0 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common im... | 708 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
... | 45 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder impo... | 709 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 50 ) -> int:
lowercase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different... | 45 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowercase_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[A... | 710 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
def __init__( ... | 45 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
... | 711 |
from PIL import Image
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Image:
def brightness(_SCREAMING_SNAKE_CASE ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('level must be between ... | 45 | 0 |
from random import shuffle
import tensorflow as tf
from numpy import array
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
lowercase__ = int(_SCREAMING_SNAKE_CASE )
assert noofclusters < len(_SCREAMING_SNAKE_CASE )
# F... | 712 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 45 | 0 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 50 ) -> int:
lowercase__ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number... | 713 |
import math
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_SCREAMING_SNAKE_CASE )
else:
if x == 0: # 0 raised to an... | 45 | 0 |
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_common im... | 714 |
class SCREAMING_SNAKE_CASE : # Public class to implement a graph
def __init__( self : int , a : int , a : int , a : list[list[bool]] )-> None:
"""simple docstring"""
lowercase__ = row
... | 45 | 0 |
from __future__ import annotations
import bisect
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = -1 ) -> int:
if hi < 0:
lowercase__ = len(_SCREAMING_SNAKE_CASE )
while lo < hi... | 715 |
from string import ascii_uppercase
lowercase_ = {str(ord(c) - 55): c for c in ascii_uppercase}
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('int()... | 45 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase_ = logging.get_logger(__name__)
lowerca... | 716 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_v... | 45 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
... | 717 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[Any]:
stooge(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) - 1 )
return arr
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if i >... | 45 | 0 |
from __future__ import annotations
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> int:
if not nums:
return 0
lowercase__ = nums[0]
lowercase__ = 0
for num in nums[1:]:
lowercase__ , lowercase__ = (
max_excluding + n... | 718 |
from scipy.stats import spearmanr
import datasets
lowercase_ = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlati... | 45 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
lowercase_ = 6_378_137.0
lowercase_ = 6_356_752.314_245
lowercase_ = 6_378_137
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ... | 719 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 50 ) -> int:
lowercase__ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number... | 45 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.te... | 720 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transforme... | 45 | 0 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
... | 721 |
import argparse
import json
import subprocess
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
lowercase__ = []
lowercase__ = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: B... | 45 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : Dic... | 700 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : Tuple = 'ClapFeatureExtractor'
_UpperCamelCase : Union[str, Any] = ('RobertaTokenizer', 'Robert... | 45 | 0 |
from __future__ import annotations
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> list[int]:
lowercase__ = 0
lowercase__ = len(_SCREAMING_SNAKE_CASE ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nu... | 701 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 45 | 0 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , a : Collection[float] | None = None )-> None:
... | 702 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_dev... | 45 | 0 |
from scipy.stats import spearmanr
import datasets
lowercase_ = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlati... | 703 |
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_ = logging.get_logger(__name__)
lowercase_ = {
"""fac... | 45 | 0 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase_ = get_tests_dir("""fixtures/test_sentencepiece_with_byte... | 704 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=7 ) -> List[Any]:
lowercase__ = None
if token is not None:
lowercase... | 45 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils imp... | 705 |
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_ = False
class SCREAMING_SNAKE_CASE (unittes... | 45 | 0 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
lowercase__ = len(_SCREAMING_SNAKE_CASE )
print('The following activities are selected:' )
# The first activity is always selected
lowercase__ = 0
print(_SCREAMING_SNAK... | 706 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> bool:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError('Input list must be a n... | 45 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
lowercase_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
def __init__( self : Dict , *a : Dict , **a ... | 707 |
from __future__ import annotations
import math
from collections.abc import Callable
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 100 , ) -> float:
lowercase__ = x_start
lowercase__ ... | 45 | 0 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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 TokenizerTesterM... | 708 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
... | 45 | 0 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class SCREAMING_SNAKE_CASE :
def __init__( self : int , a : Tuple )-> Tuple:
"""simple docstrin... | 709 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 50 ) -> int:
lowercase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different... | 45 | 0 |
import argparse
import json
import subprocess
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
lowercase__ = []
lowercase__ = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: B... | 710 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
def __init__( ... | 45 | 0 |
from __future__ import annotations
import math
from collections.abc import Callable
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 100 , ) -> float:
lowercase__ = x_start
lowercase__ ... | 711 |
from PIL import Image
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Image:
def brightness(_SCREAMING_SNAKE_CASE ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('level must be between ... | 45 | 0 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
def __init__( self : List[str] , a : int , a : int , a : float = 0 )-> None:
"""simple docstring"""
lowerc... | 712 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 45 | 0 |
from __future__ import annotations
lowercase_ = 8.988E9 # units = N * m^s * C^-2
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> dict[str, float]:
lowercase__ = abs(chargea * ch... | 713 |
import math
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_SCREAMING_SNAKE_CASE )
else:
if x == 0: # 0 raised to an... | 45 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",
... | 714 |
class SCREAMING_SNAKE_CASE : # Public class to implement a graph
def __init__( self : int , a : int , a : int , a : list[list[bool]] )-> None:
"""simple docstring"""
lowercase__ = row
... | 45 | 0 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str | Literal[False]:
lowercase__ = list(_SCREAMING_SNAKE_CASE )
lowercase__ = ... | 715 |
from string import ascii_uppercase
lowercase_ = {str(ord(c) - 55): c for c in ascii_uppercase}
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('int()... | 45 | 0 |
from __future__ import annotations
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , ) -> tuple[int, float, str]:
lowercase__ = cipher_alphabet or [chr(_SCREAMING_SNAKE_CAS... | 716 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_v... | 45 | 0 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test... | 717 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[Any]:
stooge(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) - 1 )
return arr
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if i >... | 45 | 0 |
from datetime import datetime
import requests
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> bytes:
lowercase__ = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
lowercase__ = requests.get(base_url + url ).json()[0]['urls'][0]['src'... | 718 |
from scipy.stats import spearmanr
import datasets
lowercase_ = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlati... | 45 | 0 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> str:
if version.parse(hfh.__version__ ).release < version.parse... | 719 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE = 50 ) -> int:
lowercase__ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number... | 45 | 0 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
lowercase_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
def __init__( self : str , *a : Any , **a... | 720 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transforme... | 45 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
# See all XGLM models at https://... | 721 |
import argparse
import json
import subprocess
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
lowercase__ = []
lowercase__ = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: B... | 45 | 0 |
def __UpperCamelCase () -> int:
return 1
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> int:
return 0 if x < 0 else five_pe... | 700 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : Tuple = 'ClapFeatureExtractor'
_UpperCamelCase : Union[str, Any] = ('RobertaTokenizer', 'Robert... | 45 | 0 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> int:
lowercase__ = test_file.split(os.path.sep )
... | 701 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 45 | 0 |
import torch
def __UpperCamelCase () -> Dict:
if torch.cuda.is_available():
lowercase__ = torch.cuda.device_count()
else:
lowercase__ = 0
print(F"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__main__":
main()
| 702 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_dev... | 45 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[Any]:
lowercase__ = {}
low... | 703 |
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_ = logging.get_logger(__name__)
lowercase_ = {
"""fac... | 45 | 0 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
lowercase__ = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
lowercase__ = n - k
# Calculate C(n,k)
for i in range(_SCREAMING_SNAKE_CASE ... | 704 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=7 ) -> List[Any]:
lowercase__ = None
if token is not None:
lowercase... | 45 | 0 |
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