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 collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
... | 712 |
'''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 | 0 |
'''simple docstring'''
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def UpperCAmelCase__ ... | 713 |
'''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 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> List[str]:
A_ = abs(lowerCAmelCase__ )
A_ = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase__ ( ... | 714 |
'''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 | 0 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__lowerCamelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("ker... | 715 |
'''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 | 0 |
'''simple docstring'''
from math import ceil, sqrt
def UpperCAmelCase__ ( UpperCAmelCase__ = 1_00_00_00 ) -> List[Any]:
A_ = 0
for outer_width in range(3, (limit // 4) + 2 ):
if outer_width**2 > limit:
A_ = max(... | 716 |
'''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 | 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... | 717 |
'''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 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
__lowerCamelCase = {"vocab_file": "vocab.txt", "tokenizer_file": "token... | 718 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> float:
_validate_point(UpperCAmelCase__ )
_validate_point(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError("""B... | 667 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import re... | 719 |
'''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 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wa... | 720 |
'''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 | 0 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCAmelCase__ ( UpperCAmelCase__ ... | 721 |
'''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 | 0 |
'''simple docstring'''
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone ... | 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'''
from typing import List
from .keymap import KEYMAP, get_character
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Any:
def decorator(UpperCAmelCase__ ):
A_ = getattr(__SCREAMING_SNAKE_CASE, """handle_key""", [] )
... | 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 unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transp... | 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 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - usef... | 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'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> List[str]:
A_ = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def UpperCAmelCase__ ( UpperCAmelCase__ = 50_00 ) -> Optional[int]:
A_ = [(i * (3 * i - 1)) ... | 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 inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow... | 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 inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require... | 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'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
__lowerCamelCase = datasets.logging.get_logger(__name__)
__lowerCamelCase = '''\\n@InProce... | 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'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaFor... | 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_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CO... | 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 re
import string
import numpy as np
import datasets
__lowerCamelCase = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
__lowerCamelCase = '''
Ar... | 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 __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Tuple:
A_ , A_ = analyze_text(_snake_case )
A_ = list(""" """ + ... | 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 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_devi... | 712 |
'''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 | 0 |
'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = '''T5Config'''
class A__ ... | 713 |
'''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 | 0 |
'''simple docstring'''
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 BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerO... | 714 |
'''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 | 0 |
'''simple docstring'''
import math
import qiskit
def UpperCAmelCase__ ( UpperCAmelCase__ = 1, UpperCAmelCase__ = 1, UpperCAmelCase__ = 1 ) -> int:
if (
isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
or isinstance(SCREAMING_SNAKE... | 715 |
'''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 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""facebo... | 716 |
'''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 | 0 |
'''simple docstring'''
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__ ( UpperCAmelCase__ ) -> Dict:
... | 717 |
'''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 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArgum... | 718 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> float:
_validate_point(UpperCAmelCase__ )
_validate_point(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError("""B... | 667 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ = 10_00 ) -> Any:
return sum(2 * a * ((a - 1) // 2) for a in range(3, n + 1 ) )
if __name__ == "__main__":
print(solution())
| 719 |
'''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 | 0 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class A__ ( unittest.TestCase ):
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = [10, 20, 30, 40, 50, 60]
... | 720 |
'''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 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from f... | 721 |
'''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 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''facebook/xl... | 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'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# f... | 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'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Dict:
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCAmel... | 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 |
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ = 4 ) -> List[Any]:
A_ = abs(lowerCAmelCase_ ) or 4
return [[1 + x + y * row_size for x in range(lowerCAmelCase_ )] for y in range(lowerCAmelCase_ )]
def UpperCAmelCase__ ( Up... | 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 json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Thr... | 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'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concate... | 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'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-... | 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'''
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():
fr... | 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 manim import *
class A__ ( UpperCAmelCase_ ):
def snake_case_ ( self ) -> str:
'''simple docstring'''
A_ = Rectangle(height=0.5 , width=0.5 )
A_ ... | 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'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Optional[int]:
A_ = current_set.copy()
for row_index, row in enumerate(UpperCAmelCase__ ):
A_ = row[0]
for column_index, column in enumerate(UpperCAmelCase__... | 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'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A__ ( UpperCamelCase__ ):
lowercase = ["image_processor", "tokenizer"]
lowercase = "AutoImageProcessor"
lowercase = "AutoTokenizer"
... | 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'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__lowerCamelCase = object()
# For specifying empty leaf dict `{}`
__lowerCamelCase = object... | 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 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_w... | 712 |
'''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 | 0 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class A__ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None ) -> Tuple:
'''s... | 713 |
'''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 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/mai... | 714 |
'''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 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> str:
for i in range(len(UpperCamelCase__ ) - 1, 0, -1 ):
A_ = False
for j in range(UpperCamelCase__, 0, -1 ):
if unsorted[... | 715 |
'''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 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
Rober... | 716 |
'''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 | 0 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
_... | 717 |
'''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 | 0 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configu... | 718 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> float:
_validate_point(UpperCAmelCase__ )
_validate_point(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError("""B... | 667 | 0 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,... | 719 |
'''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 | 0 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Tuple:
A_ = {}
A_ = job["""started_at"""]
A_ = job["""completed_at"""]... | 720 |
'''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 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_AR... | 721 |
'''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 | 0 |
'''simple docstring'''
from math import factorial
__lowerCamelCase = {str(digit): factorial(digit) for digit in range(10)}
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> List[Any]:
if not isinstance(_lowerCamelCase, _lowerCamelCase ):
raise TypeError... | 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 argparse
import os
import re
import packaging.version
__lowerCamelCase = """examples/"""
__lowerCamelCase = {
"""examples""": (re.compile(r'''^check_min_version\(\"[^\"]+\"\)\s*$''', re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""... | 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 re
import string
import numpy as np
import datasets
__lowerCamelCase = '''\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'''
__lowerCamelCase = ''... | 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 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 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'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def UpperCAmelCase__ ( ) -> Any:
A_ = 9, 14 # noqa: F841
A_ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8]... | 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'''
from __future__ import annotations
from collections import deque
class A__ :
def __init__( self , UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
A_ = []
self.adlist.append(
... | 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 inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common impo... | 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_tf_available, is_torch_available
__lowerCamelCase = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_... | 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 typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 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'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, Upp... | 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 os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
fro... | 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'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class A__ ( unittest.TestCase ):
def snake_case_ ( self ) -> int:
'''... | 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 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> Optional[int]:
... | 712 |
'''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 | 0 |
'''simple docstring'''
import string
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Optional[int]:
for key in range(len(string.ascii_uppercase ) ):
A_ = """"""
for symbol in message:
if symbol in string.ascii_uppercas... | 713 |
'''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 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Dict:
for i in range(len(A__ ) - 1, 0, -1 ):
A_ = False
for j in range(A__, 0, -1 ):
if unsorted[j] <... | 714 |
'''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 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ = 10 ) -> Tuple:
if not isinstance(lowerCamelCase__, lowerCamelCase__ ) or n < 0:
raise ValueError("""Invalid input""" )
A_ = 10**n
A_ = 2_84_33 * (pow(2, ... | 715 |
'''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 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
... | 716 |
'''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 | 0 |
'''simple docstring'''
import heapq
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> set[int]:
A_ = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# ... | 717 |
'''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 | 0 |
'''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
... | 718 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> float:
_validate_point(UpperCAmelCase__ )
_validate_point(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError("""B... | 667 | 0 |
'''simple docstring'''
import os
def UpperCAmelCase__ ( UpperCAmelCase__ = "input.txt" ) -> int:
with open(os.path.join(os.path.dirname(UpperCAmelCase__ ), UpperCAmelCase__ ) ) as input_file:
A_ = [
[int(UpperCAmelCase__ ... | 719 |
'''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 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( __lowerCamelCase ):
lowercase = (IPNDMScheduler,)
lowercase = (("num_inference_steps", 50),)
def snake_case... | 720 |
'''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 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ = 10_00 ) -> List[str]:
A_ = 3
A_ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result... | 721 |
'''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 | 0 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__lowerCamelCase = TypeVar('''T''')
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
return (position - 1) // 2
def UpperCAmelCase__ ( Up... | 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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name_... | 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 functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolv... | 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
def UpperCAmelCase__ ( ) -> Optional[Any]:
A_ = os.path.dirname(os.path.realpath(UpperCamelCase__ ) )
A_ = os.path.join(UpperCamelCase__, """triangle.txt""" )
with open(UpperCamelCase__ ) as f:
A_ = f.readl... | 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'''
def UpperCAmelCase__ ( UpperCAmelCase__ = 60_08_51_47_51_43 ) -> Optional[int]:
try:
A_ = int(__SCREAMING_SNAKE_CASE )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable t... | 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 os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 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 argparse
from collections import defaultdict
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Union[str, Any]:
A_ = F'''{file}_{class_name}_{test_name}... | 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'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> List[str]:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A_ = 1
A_ = 1
while repunit:
A_ = (10 * repunit + 1) % divisor
... | 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 math import factorial
class A__ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
A_ = real
if isinstance(UpperCame... | 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'''
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():... | 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'''
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def UpperCAmelCase__ ( UpperCAmelCase__ = True, *UpperCAmelCase__, **UpperCAmelCase__ ) -> Optional[Any]:
if ... | 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'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowerCamelCase = (720, 1280) # Height, Width
__lowerCamelCase = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowerCa... | 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 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase = {
"configuration_blip": [
"BLIP_PRETRAI... | 712 |
'''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 | 0 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class A__ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None ) -> List[str]:
... | 713 |
'''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 | 0 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_lea... | 714 |
'''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 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class A__ :
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
A_ = []
A_ = 0
A_ ... | 715 |
'''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 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch... | 716 |
'''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 | 0 |
'''simple docstring'''
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class A__ ( __Up... | 717 |
'''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 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelin... | 718 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> float:
_validate_point(UpperCAmelCase__ )
_validate_point(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError("""B... | 667 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelC... | 719 |
'''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 | 0 |
'''simple docstring'''
import re
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Tuple:
A_ = re.compile(
r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$""" )
return bool(re.search(UpperCamelCase__, UpperCamelCase__ ... | 720 |
'''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 | 0 |
'''simple docstring'''
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nest... | 721 |
'''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 | 0 |
'''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 ... | 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'''
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 .... | 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 |
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