code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
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_atten... | 667 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class A__ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=1 , UpperCamelCase__=Fa... | 667 | 1 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAm... | 667 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAm... | 667 | 1 |
'''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
__lowerCamelCase = logging.getLogger(__name__)
__lowerCamelCase... | 667 |
'''simple docstring'''
import os
__lowerCamelCase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = 0
A_ = 0
while index < len(UpperCAm... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_mode... | 667 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 667 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> str:
A_ = [[] for _ in range(UpperCAmelCase__ )]
A_ = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" ... | 667 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_ava... | 667 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
assert (
isinstance(UpperCAmelCase__, UpperCAmelCase__ ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_... | 667 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaP... | 667 | 1 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> datetime:
A_ = year % 19
A_ = year % 4
A_ = year % 7
A_ = math.floor(year / 1_00 )
A_ ... | 667 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( _snake_case ):
lowercase = (IPNDMScheduler,)
lowercase = (("num_inference_steps", 50),)
def snake_case_ (... | 667 | 1 |
'''simple docstring'''
import os
def UpperCAmelCase__ ( ) -> Union[str, Any]:
with open(os.path.dirname(UpperCAmelCase__ ) + """/grid.txt""" ) as f:
A_ = [] # noqa: E741
for _ in range(20 ):
l.append([int(UpperCAmelCa... | 667 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pyto... | 667 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=_snake_case ):
lowercase = ["keras_nlp"]
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[Any]:
'''simple docs... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
assert (
isinstance(UpperCAmelCase__, UpperCAmelCase__ ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_... | 667 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
return str(UpperCAmelCase__ ) == str(UpperCAmelCase__ )[::-1]
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
return int(UpperCAmelCase__ ) + int(str(UpperCAmelCase__ ... | 667 | 1 |
'''simple docstring'''
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
i... | 667 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Optional[int]:
# This defines a "chinese character" as anything in the C... | 667 | 1 |
'''simple docstring'''
from math import isqrt
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> list[int]:
A_ = [True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, U... | 667 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 667 | 1 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
Sta... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ = 0, UpperCAmelCase__ = 0 ) -> int:
A_ = right or len(UpperCAmelCase__ ) - 1
if left > right:
return -1
elif list_data[left] ... | 667 | 1 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_ava... | 667 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = FileLock(str(tmpdir / """foo.lock""" ) )
A_ = FileLock(str(tmpdir / """foo.lock"... | 667 | 1 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterM... | 667 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, ge... | 667 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A__ ( _snake_case ):
@staticmethod
@abstractmethod
def snake_case_ ( UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
... | 667 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCAmelCase__ ( ... | 667 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> list:
if any(not isinstance(UpperCAmelCase__, UpperCAmelCase__ ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in rang... | 667 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A__ ( _snake_case ):
lowercase = "ClapFeatureExtractor"
lowercase = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self ... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> None:
create_state_space_tree(UpperCAmelCase__, [], 0, [0 for i in range(len(UpperCAmelCase__ ) )] )
def UpperCAmelCase__ ( UpperCAmelCase_... | 667 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia a... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> float:
_validate_point(UpperCAmelCase__ )
_validate_point(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError("""B... | 667 | 1 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Optional[... | 667 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase = logging.get_logger(__name__)
class A__ ( _snake_case ):
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ... | 667 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, ge... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if num < 0:
return False
A_ = num
A_ = 0
while num > 0:
A_ = rev_num * 10 + (num % 10)
num //= 10
return nu... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 667 |
'''simple docstring'''
__lowerCamelCase = range(2, 20 + 1)
__lowerCamelCase = [10**k for k in range(ks[-1] + 1)]
__lowerCamelCase = {}
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Tuple... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}... | 667 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class A__ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=1 , UpperCamelCase__=Fa... | 667 | 1 |
'''simple docstring'''
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization ... | 667 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAm... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ = 1_00, ) -> float:
A_ = x_start
A_ = fnc(Upp... | 667 |
'''simple docstring'''
import os
__lowerCamelCase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = 0
A_ = 0
while index < len(UpperCAm... | 667 | 1 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ... | 667 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 667 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> tuple[float, list[float]]:
A_ = list(range(len(UpperCAmelCase__ ) ) )
A_ = [v / w for v, w in zi... | 667 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_ava... | 667 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
... | 667 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaP... | 667 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 667 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( _snake_case ):
lowercase = (IPNDMScheduler,)
lowercase = (("num_inference_steps", 50),)
def snake_case_ (... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__lowerCamelCase = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFI... | 667 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pyto... | 667 | 1 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class A__ ( unittest.TestCase ):
def snake_case_ ( self ) -> Any:
'''simple docstring'''
A_ = 0
A_ = [0]
A_ =... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
assert (
isinstance(UpperCAmelCase__, UpperCAmelCase__ ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
return str(UpperCAmelCase__ ) == str(UpperCAmelCase__ )[::-1]
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
return int(UpperCAmelCase__ ) + int(str(UpperCAmelCase__ ... | 667 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> "list[int]":
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
A_ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
A_ ... | 667 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Optional[int]:
# This defines a "chinese character" as anything in the C... | 667 | 1 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or nu... | 667 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 667 | 1 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class A__ ( _snake_case ):
@require_torch
def snake_case_ ( self ) ... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ = 0, UpperCAmelCase__ = 0 ) -> int:
A_ = right or len(UpperCAmelCase__ ) - 1
if left > right:
return -1
elif list_data[left] ... | 667 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__lowerCamelCase = logging.get_logger(__name__)
class A__ ( _snake_case ):
lowercase = "upernet"
... | 667 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = FileLock(str(tmpdir / """foo.lock""" ) )
A_ = FileLock(str(tmpdir / """foo.lock"... | 667 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class A__ ( datasets.BuilderConfig ):
lowercase = None
class A__ ... | 667 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, ge... | 667 | 1 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> np.array:
A_ = int(np.ceil((x_end - xa) / step_size ... | 667 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCAmelCase__ ( ... | 667 | 1 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
__lowerCamelCase = get_logger(__name__)
class A__ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=None ) -> List[Any]:
'''simple doc... | 667 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A__ ( _snake_case ):
lowercase = "ClapFeatureExtractor"
lowercase = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self ... | 667 | 1 |
'''simple docstring'''
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... | 667 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia a... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__lowerCamelCase = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> float:
_validate_point(UpperCAmelCase__ )
_validate_point(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError("""B... | 667 | 1 |
'''simple docstring'''
import argparse
import datetime
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> str:
A_ = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": "... | 667 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase = logging.get_logger(__name__)
class A__ ( _snake_case ):
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ... | 667 | 1 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPM... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if num < 0:
return False
A_ = num
A_ = 0
while num > 0:
A_ = rev_num * 10 + (num % 10)
num //= 10
return nu... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 667 |
'''simple docstring'''
__lowerCamelCase = range(2, 20 + 1)
__lowerCamelCase = [10**k for k in range(ks[-1] + 1)]
__lowerCamelCase = {}
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Tuple... | 667 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
Au... | 667 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class A__ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=1 , UpperCamelCase__=Fa... | 667 | 1 |
'''simple docstring'''
import math
import sys
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if number != int(UpperCAmelCase__ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the v... | 667 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAm... | 667 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 667 |
'''simple docstring'''
import os
__lowerCamelCase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = 0
A_ = 0
while index < len(UpperCAm... | 667 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
A_ = 4
A_ = (1 << p) - 1
for _ in range... | 667 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 667 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueErro... | 667 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_ava... | 667 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 667 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaP... | 667 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, l... | 667 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( _snake_case ):
lowercase = (IPNDMScheduler,)
lowercase = (("num_inference_steps", 50),)
def snake_case_ (... | 667 | 1 |
'''simple docstring'''
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadat... | 667 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pyto... | 667 | 1 |
'''simple docstring'''
import math
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
A_ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ = 1 / 1_... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
assert (
isinstance(UpperCAmelCase__, UpperCAmelCase__ ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_... | 667 | 1 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
# TODO Update this
__lowerCamelCase = {
'''facebook/esm-1b'... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
return str(UpperCAmelCase__ ) == str(UpperCAmelCase__ )[::-1]
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
return int(UpperCAmelCase__ ) + int(str(UpperCAmelCase__ ... | 667 | 1 |
'''simple docstring'''
class A__ : # Public class to implement a graph
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> None:
'''simple docstring'''
A_ = row
A_ = col
... | 667 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Optional[int]:
# This defines a "chinese character" as anything in the C... | 667 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCA... | 667 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 667 | 1 |
'''simple docstring'''
__lowerCamelCase = range(2, 20 + 1)
__lowerCamelCase = [10**k for k in range(ks[-1] + 1)]
__lowerCamelCase = {}
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Tuple... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ = 0, UpperCAmelCase__ = 0 ) -> int:
A_ = right or len(UpperCAmelCase__ ) - 1
if left > right:
return -1
elif list_data[left] ... | 667 | 1 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class A__ ( nn.Module ):
def __init__( self , UpperCamelCase__ = 16 , UpperCamelCase__ = 88 , UpperCamelCase__ = None , ... | 667 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = FileLock(str(tmpdir / """foo.lock""" ) )
A_ = FileLock(str(tmpdir / """foo.lock"... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from .... | 667 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, ge... | 667 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class A__ ( un... | 667 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCAmelCase__ ( ... | 667 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json... | 667 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A__ ( _snake_case ):
lowercase = "ClapFeatureExtractor"
lowercase = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self ... | 667 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
__lowerCa... | 667 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia a... | 667 | 1 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class A__ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=0.2 , UpperCamelCase_... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> float:
_validate_point(UpperCAmelCase__ )
_validate_point(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError("""B... | 667 | 1 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 667 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase = logging.get_logger(__name__)
class A__ ( _snake_case ):
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ... | 667 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''nielsr/canine-s''': 2048,
}
# Unicode defines 1,1... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if num < 0:
return False
A_ = num
A_ = 0
while num > 0:
A_ = rev_num * 10 + (num % 10)
num //= 10
return nu... | 667 | 1 |
'''simple docstring'''
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 PreTrainedTokeni... | 667 |
'''simple docstring'''
__lowerCamelCase = range(2, 20 + 1)
__lowerCamelCase = [10**k for k in range(ks[-1] + 1)]
__lowerCamelCase = {}
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Tuple... | 667 | 1 |
'''simple docstring'''
from collections import deque
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Optional[Any]:
A_ = len(UpperCAmelCase__ )
A_ = deque()
A_ = [False for _ in range(UpperCAmelCase__ )]
A_ = [-1 for _ ... | 667 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class A__ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=1 , UpperCamelCase__=Fa... | 667 | 1 |
'''simple docstring'''
import os
__lowerCamelCase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = 0
A_ = 0
while index < len(UpperCAm... | 667 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAm... | 667 | 1 |
'''simple docstring'''
__lowerCamelCase = 8.314462 # Unit - J mol-1 K-1
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter p... | 667 |
'''simple docstring'''
import os
__lowerCamelCase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = 0
A_ = 0
while index < len(UpperCAm... | 667 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
fro... | 667 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 667 | 1 |
'''simple docstring'''
import math
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> int:
A_ = len(UpperCAmelCase__ )
A_ = int(math.floor(math.sqrt(UpperCAmelCase__ ) ) )
A_ = 0
while arr[min(UpperCAme... | 667 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_ava... | 667 | 1 |
'''simple docstring'''
import operator as op
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Any:
A_ = []
A_ = lambda UpperCAmelCase__, UpperCAmelCase__ : int(x / y ) # noqa: E731 integer division operation
A_ = {
"""^... | 667 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaP... | 667 | 1 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class ... | 667 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( _snake_case ):
lowercase = (IPNDMScheduler,)
lowercase = (("num_inference_steps", 50),)
def snake_case_ (... | 667 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddi... | 667 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pyto... | 667 | 1 |
'''simple docstring'''
import cmath
import math
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> complex:
A_ = math.radians(UpperCAmelCase__ )
A_ = math.radians(UpperCAmelCase__ )
... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
assert (
isinstance(UpperCAmelCase__, UpperCAmelCase__ ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_... | 667 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A__ ( _snake_case ):
@staticmethod
@abstractmethod
def snake_case_ ( UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
return str(UpperCAmelCase__ ) == str(UpperCAmelCase__ )[::-1]
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
return int(UpperCAmelCase__ ) + int(str(UpperCAmelCase__ ... | 667 | 1 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pyto... | 667 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Optional[int]:
# This defines a "chinese character" as anything in the C... | 667 | 1 |
'''simple docstring'''
import requests
__lowerCamelCase = '''''' # <-- Put your OpenWeatherMap appid here!
__lowerCamelCase = '''https://api.openweathermap.org/data/2.5/'''
def UpperCAmelCase__ ( UpperCAmelCase__ = "Chicago", UpperCAmelCase__ = APPID ) -> dict... | 667 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 667 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A_ = 1
A_ = 1
while repunit:
A_ = (10 * repunit + 1) % divisor
repu... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ = 0, UpperCAmelCase__ = 0 ) -> int:
A_ = right or len(UpperCAmelCase__ ) - 1
if left > right:
return -1
elif list_data[left] ... | 667 | 1 |
'''simple docstring'''
import math
__lowerCamelCase = 10
__lowerCamelCase = 7
__lowerCamelCase = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCAmelCase__ ( UpperCAmelCase__ = 20 ) -> str:
A_ = math.comb(UpperCAmelCase__, UpperCAmelCase__ ... | 667 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = FileLock(str(tmpdir / """foo.lock""" ) )
A_ = FileLock(str(tmpdir / """foo.lock"... | 667 | 1 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import B... | 667 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, ge... | 667 | 1 |
'''simple docstring'''
import sys
__lowerCamelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
... | 667 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCAmelCase__ ( ... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__lowerCamelCase = 10
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase_... | 667 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A__ ( _snake_case ):
lowercase = "ClapFeatureExtractor"
lowercase = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self ... | 667 | 1 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():... | 667 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia a... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> float:
_validate_point(UpperCAmelCase__ )
_validate_point(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError("""B... | 667 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( _s... | 667 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase = logging.get_logger(__name__)
class A__ ( _snake_case ):
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ... | 667 | 1 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class A__ ( _snake_case ):
lowercase = Di... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if num < 0:
return False
A_ = num
A_ = 0
while num > 0:
A_ = rev_num * 10 + (num % 10)
num //= 10
return nu... | 667 | 1 |
'''simple docstring'''
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCamelCase = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingf... | 667 |
'''simple docstring'''
__lowerCamelCase = range(2, 20 + 1)
__lowerCamelCase = [10**k for k in range(ks[-1] + 1)]
__lowerCamelCase = {}
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Tuple... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tens... | 700 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class A__ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=1 , UpperCamelCase__=Fa... | 667 | 0 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""", [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""", ["""default""", 0, 1_00 * 2**20, 9_00 * 2... | 701 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAm... | 667 | 0 |
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenizatio... | 702 |
'''simple docstring'''
import os
__lowerCamelCase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = 0
A_ = 0
while index < len(UpperCAm... | 667 | 0 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,... | 703 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 667 | 0 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class A__ ( unittest.TestCase ):
def snake_case_ ( self ) -> Any:
'''simple docstring'''
A_ = [10, 20, 30, 40, 50, 60]
A_ =... | 704 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_ava... | 667 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def UpperCAmelCase__ ( UpperCAmelCase... | 705 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaP... | 667 | 0 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager imp... | 706 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( _snake_case ):
lowercase = (IPNDMScheduler,)
lowercase = (("num_inference_steps", 50),)
def snake_case_ (... | 667 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig... | 707 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pyto... | 667 | 0 |
'''simple docstring'''
from PIL import Image
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Image:
def brightness(UpperCAmelCase__ ) -> float:
return 1_28 + level + (c - 1_28)
if not -255.0 <= level <= 255.0:
... | 708 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
assert (
isinstance(UpperCAmelCase__, UpperCAmelCase__ ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_... | 667 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__lowerCamelCase = {
'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}
try:
... | 709 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
return str(UpperCAmelCase__ ) == str(UpperCAmelCase__ )[::-1]
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
return int(UpperCAmelCase__ ) + int(str(UpperCAmelCase__ ... | 667 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class A__ ( _snake_case ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> ... | 710 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Optional[int]:
# This defines a "chinese character" as anything in the C... | 667 | 0 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class A__ ( _snake_case ):
lowercase = Di... | 711 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 667 | 0 |
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