code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import functools
def SCREAMING_SNAKE_CASE ( snake_case_ : str , snake_case_ : str ):
snake_case__ : Any = len(lowerCAmelCase__ )
snake_case__ : Optional[int] = len(lowerCAmelCase__ )
@functools.cache
def min_distance(snake_case_ : int , snake_case_ ... | 297 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCAmelCase ( _UpperCamelCase ):
# to overwrite a... | 642 | 0 |
'''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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 676 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'... | 642 | 0 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCamelCase : Tuple = "\\n\n"
lowerCamelCase : int = "\nPerplexity (PP... | 70 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__UpperCAmelCase = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def lowercase__ ( lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : str... | 642 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 359 |
"""simple docstring"""
import functools
def lowercase__ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str ) -> int:
'''simple docstring'''
a__ : Any = len(lowerCAmelCase__ )
a__ : Optional[int] = len(lowerCAmelCase__ )
@functools.cache
d... | 642 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> np.array:
lowercase__ : Dict = F"""{sampling_rate}"""
lowercase__ ... | 397 |
"""simple docstring"""
def lowercase__ ( lowerCAmelCase__ : str ) -> list[int]:
'''simple docstring'''
a__ : List[str] = [0 for i in range(len(lowerCAmelCase__ ) )]
# initialize interval's left pointer and right pointer
a__ , a__ : int = 0, 0
for i in ra... | 642 | 0 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] , _lowerCAmelCase : list[list[str]] , _lowerCAmelCase : int , ):
... | 44 |
"""simple docstring"""
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
__UpperCAmelCase = loggi... | 642 | 0 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configu... | 354 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase__ ( lowerCAmelCase__ : Union[str... | 642 | 0 |
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _snake_case ( _UpperCamelCase ):
_A : str = (KDPMaDiscreteScheduler,)
_A ... | 143 |
"""simple docstring"""
import os
def lowercase__ ( ) -> Optional[Any]:
'''simple docstring'''
with open(os.path.dirname(lowerCAmelCase__ ) + "/p022_names.txt" ) as file:
a__ : Optional[int] = str(file.readlines()[0] )
a__ : Optional[int] = names.repla... | 642 | 0 |
def _UpperCamelCase (a__ :str ):
"""simple docstring"""
UpperCamelCase__ = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
UpperCamelCase__ = hex_num[0] == "-"
if is_ne... | 619 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
def lowercase__ ( lowerCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
'... | 642 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
'''simple docstring'''
_UpperCAmelCase : Dict = ["image_processor", "tokenizer"]
_UpperCAmelCase : Dict ... | 686 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffus... | 642 | 0 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.te... | 454 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( lowerCAmelCase__ : int ) -> list[int]:
'''simple docstring'''
a__ : Tuple = [True] * limit
a__ : Tuple = False
a__ : int = False
a__ : Tuple = True
for i in range(3... | 642 | 0 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__lowerCamelCase : Any = {
"""User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
""" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582... | 297 |
"""simple docstring"""
__UpperCAmelCase = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowercase__ ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[in... | 642 | 0 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
a_ : Tuple = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def a_ ( __snake_case : Optional[Any] , __snake_case : str ) -> str:
"... | 676 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __UpperCAmelCase ( unittest.TestCase , _UpperCamelCase ):
def UpperCAmelCase ( self : Dict ) -> List[Any]:
'''simple do... | 642 | 0 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
lowerCamelCase : List[str] = "scheduler_config.json"
... | 70 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
__UpperCAmelCase = False
class __UpperCAmelCase ( unittest.TestCase ... | 642 | 0 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def UpperCAmelCase__ ( lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
lowercase = prime_factors(lowerCAmelCase__ ... | 359 |
"""simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__UpperCAmelCase = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '... | 642 | 0 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version... | 397 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> tuple[int, int]:
'''simple docstring'''
if b == 0:
return (1, 0)
((a__) , (a__)) : List[Any] = extended_euclid(lowerCA... | 642 | 0 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowe... | 44 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
__UpperCAmelCase = logging.get_logger(__name__)
clas... | 642 | 0 |
from __future__ import annotations
import math
def __lowerCAmelCase ( __lowerCamelCase : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numb... | 354 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowercase__ ( lowerCAmelCase__ : int ) -> int:
'''simple docstring'''
a__ : Tuple = prime_factors(lowerCAmelCase__ )
if is_square_free(lowerCA... | 642 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base... | 143 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWater... | 642 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import... | 619 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversat... | 642 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MA... | 643 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 1 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : float ):
def brightness(snake_case__ : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) a... | 643 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 1 |
import requests
from bsa import BeautifulSoup
def a__ ( snake_case__ : str = "AAPL" ):
_UpperCAmelCase : Optional[int] = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
_UpperCAmelCase : Union[str, Any] = BeautifulSoup(requests.get(snake_ca... | 643 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 1 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.u... | 643 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 1 |
from math import factorial
class _SCREAMING_SNAKE_CASE :
def __init__( self , A_ , A_ ):
_UpperCAmelCase : int = real
if isinstance(A_ , A_ ):
_UpperCAmelCase : List[str] = [1] * rank
else:
_U... | 643 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[int] = {
'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
'tokenization_ctrl': ['CTRLTokeniz... | 643 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 1 |
import pytest
SCREAMING_SNAKE_CASE__ : Any = '__dummy_dataset1__'
SCREAMING_SNAKE_CASE__ : Optional[int] = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "... | 643 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 1 |
from heapq import heappop, heappush
import numpy as np
def a__ ( snake_case__ : np.ndarray , snake_case__ : tuple[int, int] , snake_case__ : tuple[int, int] , snake_case__ : bool , ):
_UpperCAmelCase,_UpperCAmelCase : List[Any] ... | 643 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 643 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 1 |
from math import factorial
def a__ ( snake_case__ : int , snake_case__ : int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("""Please... | 643 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def a__ ( snake_case__ : Tuple , snake_case__ : str ):
_UpperCAmelCase : int = int(snake_case__ )
assert noofclusters < len(snake_case__ )
# Find out the dimensionalit... | 643 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 1 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a__ ( snake_case__ : np.ndarray , snake_case__ : np.ndarray , snake_case__ : np.ndarray , snake_case__ : int , snake_case__ : int ... | 643 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 1 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 643 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 1 |
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, slow
from .test_pipelines_... | 643 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Tuple = {
'configuration_electra': ['ELECTRA_PRETRAINED_CON... | 643 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import... | 643 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 643 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 1 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
P... | 643 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 1 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
SCREAMING_SNAKE_CASE__ : Optional[Any] = '\\n@misc{chen2021evaluating,\n title=... | 643 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 | 1 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Optional[int] = TypeVar('T')
SCREAMING_SNAKE_CASE__ : List[Any] = TypeVar('U')
class _SCREAMING_SNAKE_CASE ( Generic[T, U] ):
... | 643 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVP... | 643 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
SCREAMING_SNAKE_CASE__ : Union[str, Any] = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translati... | 643 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 | 1 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE__ : int = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.195... | 643 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 1 |
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 a__ ( snake_case__ : List[Any] , snake_case__ : List[Any] ):
_UpperCAmelCase : int = k_s... | 643 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_util... | 643 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( A , unittest.TestCase ):
... | 643 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_... | 643 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 1 |
import unittest
from knapsack import greedy_knapsack as kp
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __snake_case( self ):
_UpperCAmelCase : List[Any] = [10, 20, 30, 40, 50, 60]
_UpperCAmelCase : Optional[int] = [2, 4, 6... | 643 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 1 |
def a__ ( snake_case__ : int = 1000 ):
_UpperCAmelCase : Optional[int] = -1
_UpperCAmelCase : List[str] = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_UpperCAmelCase : Tuple ... | 643 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 1 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class _SCREAMING_SNAKE_CASE :
__SCREAMING_SNAKE_CASE = field(
metadata={'''help'''... | 643 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 643 | 1 |
def a__ ( snake_case__ : int = 400_0000 ):
_UpperCAmelCase : List[Any] = [0, 1]
_UpperCAmelCase : Any = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
_UpperCAmelCase : Optional[int] ... | 643 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acceler... | 643 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
__SCREAMING_SNAKE_CASE = JukeboxTokenizer
__SCREAMING_SNAKE_CASE = {
'''artist''': '''Zac Brow... | 643 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 1 |
import math
SCREAMING_SNAKE_CASE__ : Optional[int] = 10
SCREAMING_SNAKE_CASE__ : str = 7
SCREAMING_SNAKE_CASE__ : str = BALLS_PER_COLOUR * NUM_COLOURS
def a__ ( snake_case__ : int = 20 ):
_UpperCAmelCase : Any = math.c... | 643 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 1 |
class _SCREAMING_SNAKE_CASE :
def __init__( self ):
_UpperCAmelCase : Any = {}
def __snake_case( self ):
print(self.vertex )
for i in self.vertex:
print(A_ , """ -> """ , """ -> """.join([str(A_ ) for j in sel... | 643 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 1 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def a__ ( snake_case__ : str , snake_case__ : str = "cpu" , snake_case__ : Union[str, None] = None ):
_UpperCAmelCase : Optional[Any] = torch.load(snake_case__ ,... | 643 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import... | 643 | 1 |
def a__ ( snake_case__ : list[int] , snake_case__ : list[int] , snake_case__ : int ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(snake_case__ ) )
def a__ ( snake_case__ : ... | 643 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Tuple = {
'configuration_speech_to_text': ['SPEECH_TO_... | 643 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
SCREAMING_SNAKE_CASE__ : Any = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|')... | 643 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType,... | 643 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 | 1 |
SCREAMING_SNAKE_CASE__ : Any = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
SCREAMING_SNA... | 643 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 1 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ..... | 643 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVP... | 643 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModel... | 643 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/conf... | 643 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
SCREAMING_SNAKE_CASE__ : Tuple = {
'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}
try:
if not ... | 643 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 1 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor,... | 643 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(A ) , '''Tatoeba directory does... | 643 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 1 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class _SCREAMING_SNAKE_CASE ( A , unit... | 643 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 1 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 1 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10 ... | 643 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Any = {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/con... | 643 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 643 | 1 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import Back... | 643 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 1 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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... | 643 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 1 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
SCREAMING_SNAKE_CASE__ : int = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metr... | 643 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 1 |
from math import pow, sqrt
def a__ ( *snake_case__ : float ):
_UpperCAmelCase : Any = len(snake_case__ ) > 0 and all(value > 0.0 for value in values )
return result
def a__ ( snake_case__ : float , snake_case__ : float ):... | 643 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 1 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... | 643 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 1 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A ):
def __init__( self , *A_ , **A_ ):
... | 643 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 |
import collections
import importlib.util
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = 'src/transformers'
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import... | 643 | 1 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def a__ ( snake_case__ : Any , snake_case__ : Union[str, Any] , snake_case__ : Tuple ):
_UpperCAmelCase : List[Any] = AutoConfig.f... | 643 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 643 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def _... | 643 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pytor... | 643 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_... | 643 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return 1 if input_a == input_a else 0
def a__ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ... | 643 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[int] ... | 643 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVP... | 643 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self , A_ ):
_UpperCAmelCase : Any = data
_UpperCAmelCase : Node | None = None
class _SCREAMING_... | 643 |
from PIL import Image
def a__ ( snake_case__ : Image , snake_case__ : int ):
_UpperCAmelCase : Optional[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ : int ) -> int:
return int(128 + factor * (c - 128) ... | 643 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 643 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] =... | 643 | 1 |
def a__ ( snake_case__ : str , snake_case__ : str ):
_UpperCAmelCase : Union[str, Any] = len(snake_case__ ) + 1
_UpperCAmelCase : Optional[Any] = len(snake_case__ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix str... | 643 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 643 | 1 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTes... | 643 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A )
class _SCREAMING_SNAKE_CASE ( A ):
__SCREAMING_SNAKE_CASE = field(default='''image-clas... | 643 | 1 |
def a__ ( snake_case__ : str , snake_case__ : str ):
_UpperCAmelCase : List[Any] = len(snake_case__ )
_UpperCAmelCase : Dict = []
for i in range(len(snake_case__ ) - pat_len + 1 ):
_UpperCAmelCase : List[str] =... | 643 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 1 |
def a__ ( snake_case__ : int ):
if number > 0:
raise ValueError("""input must be a negative integer""" )
_UpperCAmelCase : Dict = len(bin(snake_case__ )[3:] )
_UpperCAmelCase : Tuple = bin(abs(snake_case__ ) - (1 << binary_number_length... | 643 |
from random import randint, random
def a__ ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : int = 5 , ):
_Upper... | 643 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : ... | 643 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__... | 643 | 1 |
import os
import sys
import unittest
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_mo... | 643 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[i... | 643 | 1 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Any = R'\n Args:... | 643 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 643 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.util... | 643 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ : List[str] = 1 << 8
SCREAMING_SNAKE_CASE__ : str = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'... | 643 | 1 |
import string
from math import logaa
def a__ ( snake_case__ : str , snake_case__ : str ):
_UpperCAmelCase : Tuple = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" , """""" ... | 643 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[int] = log... | 643 | 1 |
def a__ ( snake_case__ : Tuple ): # noqa: E741
_UpperCAmelCase : Dict = len(snake_case__ )
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Union[str, Any] = [0] * n
_UpperCAmelCase : Union[str, Any] = [False] * n
_U... | 643 |
SCREAMING_SNAKE_CASE__ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr... | 643 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 643 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 643 | 1 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Union[str,... | 643 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 643 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_config... | 643 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 643 | 1 |
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