code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase ( unittest.TestCase ):
... | 233 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def snake_case_ ( lowerCAmelCase_ : int = 8 ):
__lowercase : str = ascii_letters + digits + punctuation
return "".join(secrets.choice(l... | 233 | 1 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
lowerCAmelCase_ = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author ... | 302 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase_ = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 302 | 1 |
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
if digit_amount > 0:
return round(number - int(_UpperCAmelCase) , _UpperCAmelCase)
return number - int(_UpperCAmelCase)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
... | 137 |
def lowerCamelCase__ (_UpperCAmelCase):
def merge(_UpperCAmelCase , _UpperCAmelCase) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0)
yield from left
yield from right
return list(_merge())
if ... | 137 | 1 |
import copy
import random
from transformers import CLIPTokenizer
class lowercase_ ( A ):
"""simple docstring"""
def __init__( self : int , *__lowerCamelCase : int , **__lowerCamelCase : int ):
"""simple docstring"""
super().__init__(*__lowerCamelCas... | 369 |
'''simple docstring'''
import random
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : float , __A : bool = False ) -> dict:
_SCREAMING_SNAKE_CASE = {i: [] for i in range(__A )}
# if probability is greater or equal than 1, then generate a complete graph
if probab... | 111 | 0 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class a ( _lowerCamelCase ):
"""simple docstring"""
@require_torch
def ... | 335 |
"""simple docstring"""
import math
class a :
"""simple docstring"""
def __init__( self: List[Any] , UpperCamelCase: List[str]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
A__ = n
A__... | 335 | 1 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
... | 352 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCAmelCase_ ( ) -> List[str]:
UpperCamelCase__ : List[str] = {
'''repo_name''': ['''test_repo1''', '''te... | 247 | 0 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self... | 90 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : str , UpperCamelCa... | 90 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_squeezebert''': [
'''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Squeez... | 253 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 253 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def UpperCAmelCase_ ( __lowerCamelCase : str ):
lowercase_ , lowercase_ :Optional[Any] = analyze_text(__lowerCamelCase ... | 223 |
'''simple docstring'''
lowerCAmelCase : Optional[int] =256
# Modulus to hash a string
lowerCAmelCase : Tuple =1_000_003
def UpperCAmelCase_ ( __lowerCamelCase : str ,__lowerCamelCase : str ):
lowercase_ :Dict = len(__lowe... | 223 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 256 |
from math import factorial
def UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if trials < 0 or successes < 0:
raise ValueError("the function is... | 256 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict ... | 106 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CL... | 70 | 0 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__A : Union[str, Any] = 'src/transformers'
... | 361 |
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
__A : Any = '▁'
__A : Union[s... | 49 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
... | 223 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slo... | 223 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal... | 355 |
import os
import sys
a__: int = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassificat... | 39 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
snake_case : Optional[int] = logging.get_lo... | 240 |
def __lowercase ( __lowerCAmelCase : list[int] ):
a__ = []
if len(__lowerCAmelCase ) == 1:
return [nums.copy()]
for _ in range(len(__lowerCAmelCase ) ):
a__ = nums.pop(0 )
a__ = ... | 240 | 1 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_... | 320 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Tuple = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
'huggingface/informer-tourism-monthly': (
... | 3 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
@property
def ... | 278 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModel... | 350 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _lowerCAmelCase ( ):
print('''Making key files...''' )
make_key_files('''rsa''' , 1... | 217 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from tra... | 256 | """simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
def _UpperCamelCase ( self : List[str] , __UpperCamelCase : Any ) -> Tu... | 256 | 1 |
import math
class lowercase_ :
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ = 0.0
UpperCamelCase_ = 0.0
for i in range(len(lowerCamelCase_ ) ):
... | 361 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_A = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''',
'''G... | 261 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
def lowercase__ ( self : Dict ) -> Tuple:
'''simple docstri... | 134 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : List[str] = ... | 134 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils impor... | 203 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
a__: Any = credit_card_number
a__: Tuple ... | 203 | 1 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoMo... | 159 |
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,
resize,
... | 159 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor... | 55 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__snake_case =logging.getLogger(__name__)
class UpperCAmelCase_ ( __lowercase ):
... | 55 | 1 |
'''simple docstring'''
def __lowerCamelCase ( ) -> Tuple:
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def __lowerCamelCase ( lowerCAmelCase_ ) -> List[Any]:
_a : Any = 1
_a : Tuple = 2
while i * i... | 89 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_r... | 89 | 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 lowerCamelCase_ ( _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : List[... | 211 |
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/xlm-roberta-xl''': '''https://h... | 211 | 1 |
import os
import sys
import transformers
__A : List[Any] = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
p... | 138 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( SCREAMING_SNAKE_CASE_ ):
a_ : int = (KDPMaDiscreteScheduler,)
a_ : List[str] =... | 243 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
... | 260 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCAmelCase: list , lowerCAmelCase: int | None = None , lowerCAmelCase: int | None = None )-> None:
if start is None:
_snake_case : int = 0
if end is None:
_snake_case : Option... | 260 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : List[str] =logging.get_logger(__name__)
lowerCAmelCase__ : str ={
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
c... | 257 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import re... | 257 | 1 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
A_ : Tuple ={
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": operator.ge,... | 80 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> list[int]:
if num <= 0:
raise ValueError('Input must be a positive integer' )
_lowerCamelCase = [True] * (num + 1)
_lowerCamelCase = 2
while p * p <= num:
... | 80 | 1 |
import functools
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = len(_UpperCAmelCase )
__a = len(_UpperCAmelCase )
@functools.cache
def min_distance(_UpperCAmelCase , _UpperCAmelCase ) -> int:
# if first word index is o... | 49 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion impo... | 85 | 0 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class lowerCamelCase ( A_ ):
UpperCAmelCase__ : int = "MCTCTFeatureExtractor"
UpperCAmelCase__ : List[Any] = "AutoTokenizer"
... | 137 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
"configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Op... | 137 | 1 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
__A = "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption th... | 164 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__A = logging.get_logger(_... | 164 | 1 |
'''simple docstring'''
def UpperCamelCase_ ( ):
'''simple docstring'''
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def UpperCamelCase_ ( A__ : Tuple ):
'''simple docstring'''
lowerCAmelCase_ : ... | 89 |
'''simple docstring'''
import requests
def UpperCamelCase_ ( A__ : str , A__ : str ):
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = {"""Content-Type""": """application/json"""}
lowerCAmelCase_ : Uni... | 89 | 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 ...... | 29 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnaly... | 269 | 0 |
import math
def lowerCamelCase__ ( a__ : float , a__ : float ) -> float:
if (
not isinstance(a__ , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("""power_factor must be a valid float... | 365 |
from __future__ import annotations
def lowerCamelCase__ ( a__ : list[list[int]] ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in... | 261 | 0 |
"""simple docstring"""
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
impor... | 260 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-... | 260 | 1 |
'''simple docstring'''
import math
import os
import sys
def a ( __a ) -> str:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = ''''''
try:
with open(__a , '''rb''' ) as binary_file:
UpperCamelCase__ :Opti... | 219 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def a ( __a = "" , ) -> bool:
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def a ( _... | 219 | 1 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_SCREAMING_SNAKE_CASE = False
try:
_SCREAMING_SNAKE_CASE ... | 343 | import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowercase ( snake_case_ , unittest.TestCase ):
low... | 175 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( SCREAMING_SNAKE_CASE__ ... | 291 |
"""simple docstring"""
import numpy as np
import qiskit
def __lowercase ( snake_case_ : int = 8 ,snake_case_ : int | None = None ) ->str:
'''simple docstring'''
__A : str = np.random.default_rng(seed=snake_case_ )
# Roughly 25%... | 291 | 1 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowercase : Dict = 'src/diffusers'
# Matches is_xxx_available()
lowercase : Dict ... | 3 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_... | 3 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/... | 353 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
'configuration_owlvit': [
'OWLVIT_PRETRAINED_CON... | 75 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'configuration_maskformer_swin': ['MaskForm... | 12 |
from functools import lru_cache
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
__a = 2
__a = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(_SCREAMIN... | 302 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class snake_case_( a__ ):
__UpperCamelCase = '''WhisperFeatureExtractor'''
__UpperCamelCase = '''WhisperTokenizer'''
def __init__( self : Union[str, Any] , UpperCamelCase_ : Tu... | 314 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case__ : Tuple = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerCon... | 314 | 1 |
"""simple docstring"""
import math
import sys
def UpperCamelCase ( _lowerCAmelCase : str ) -> str:
_UpperCAmelCase : int = """"""
try:
with open(_lowerCAmelCase, """rb""" ) as binary_file:
_UpperCAmelCase : str = binary_file.read()
fo... | 246 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
fr... | 246 | 1 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections import Counter
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Union[str, Any] = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(... | 353 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator... | 194 | 0 |
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueError(''... | 43 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',... | 43 | 1 |
def _a ( SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
UpperCamelCase__ : str = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of... | 361 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : List[str] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config",
"M... | 51 | 0 |
import math
import unittest
from transformers import BioGptConfig, 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 import Mod... | 178 |
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_acc... | 178 | 1 |
"""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 lowercase__ ( lower... | 353 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCamelCase__ = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\... | 310 | 0 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from ... | 321 | 1 |
import baseaa
def UpperCamelCase_( lowerCamelCase_ ) -> bytes:
return baseaa.aaaencode(string.encode('utf-8' ) )
def UpperCamelCase_( lowerCamelCase_ ) -> str:
return baseaa.aaadecode(lowerCamelCase_ ).decode('utf-8' )
if __name__ == "__main__":
import doctest... | 84 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ = None ) -> list[list[str]]:
_lowercase : Optional[Any] = word_bank or []
# create a table
_lowercase : int = len(lowerCamelCase_ ) + 1
_lowercase : li... | 84 | 1 |
import operator as op
def UpperCAmelCase__ (UpperCamelCase_ ):
"""simple docstring"""
snake_case = []
snake_case = lambda UpperCamelCase_ ,UpperCamelCase_ : int(x / y ) # noqa: E731 integer division operation
snake_case = {
... | 127 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __A , __A , __A ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument m... | 80 | 0 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case : Dict = {
... | 362 |
"""simple docstring"""
__snake_case : Any = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66... | 58 | 0 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def UpperCAmelCase__ ( *lowerCAmelCase__ :List[str] ) -> Optional[Any]:
'''simple docstring'''
if not isinstance(lowerCAme... | 197 | """simple docstring"""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _A ( lowerCAmelCase , ... | 197 | 1 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCamelCase_ = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def UpperCamelCase( lowercase_ ) -> ... | 34 |
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.ut... | 34 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowercase_ = 42
lowercase_ = 42
class _SCREAMING_SNAKE_CASE :
'''si... | 10 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STA... | 257 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__UpperCamelCase = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnn... | 359 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
... | 13 | 0 |
"""simple docstring"""
def a_ ( lowerCamelCase ):
if not nums: # Makes sure that the list is not empty
raise ValueError('List is empty' )
UpperCAmelCase__ = sum(lowerCamelCase ) / len(lowerCamelCase ) # Calculate the average
return sum(ab... | 98 | """simple docstring"""
import math
def a_ ( lowerCamelCase , lowerCamelCase ):
if (
not isinstance(lowerCamelCase , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('power_factor must be a valid float value be... | 98 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availab... | 214 | '''simple docstring'''
class UpperCAmelCase :
'''simple docstring'''
def __init__( self ) -> List[str]:
lowercase__ : Dict = {}
def _lowerCAmelCase( self ) -> None:
print(self.vertex )
for i in self.vertex:
print(__lowe... | 214 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH... | 34 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils imp... | 201 | 0 |
"""simple docstring"""
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
lowercase__ = logging.get_logger(__name__)
lowercase__ = r"""
Args:
input_ids (`torch.Lo... | 161 |
"""simple docstring"""
import os
def __lowerCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
with open(os.path.dirname(__UpperCamelCase ) + "/grid.txt" ) as f:
lowerCAmelCase_ : str = [] # noqa: E741
for _ in range(20 ):
l.append([int(__UpperC... | 161 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : int = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class _SCREAMING_SNAKE_C... | 154 |
def _lowercase ( lowercase__ , lowercase__ ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowercase ( lowercase__ , lowercase__=0 ):
return sorted(lowercase__ , key=lambda lowercase__ : x[column] )
def _lowercase ( lower... | 275 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
_UpperCamelCase = logging.ge... | 16 |
'''simple docstring'''
from collections.abc import Sequence
def lowercase_ ( lowerCAmelCase__ : Sequence[int] | None = None ):
"""simple docstring"""
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
__UpperCAmelCase ... | 16 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
a = (7_2_0, 1_2_8_0) # Height, Width
a = (0.4, 0.6) # if height or width lower than this scale, drop it.
a = ... | 155 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from tran... | 155 | 1 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 282 |
from __future__ import annotations
from typing import Any
def _snake_case( SCREAMING_SNAKE_CASE__ : list ) -> int:
'''simple docstring'''
if not postfix_notation:
return 0
A__ = {'+', '-', '*', '/'}
A__ = [... | 282 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : str = logging.get_logger(__name__)
class snake_case ( lowercase ):
"""simple docstring"""
_lowerCamelCase = "encoder-d... | 55 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
#... | 156 | 0 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( A_ , A_ ) -> int:
while a != 0:
lowerCAmelCase__ : Optional[Any] = b % a, a
return b
def __SCREAMING_SNAKE_CASE ( A_ , A_ ) -> int:
if gcd(_UpperCAmelCase , _UpperCAmelCase ) != ... | 359 |
"""simple docstring"""
from manim import *
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ : List[str] = Rectangle(height=0.5 ,width=0.5 )
lowerCAmelCase__ : ... | 74 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : list[int] , UpperCAmelCase_ : list[int] ) -> None:
'''simple docstring'''
__snake_case : Optional[int] = len(UpperCAmelCase_ )
print('The following activities are selecte... | 172 | """simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class UpperCamelCase ( lowercase ):
... | 172 | 1 |
"""simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Tuple:
_enforce_args(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if n == 0:
return 0
a__: List[Any] = float('-inf' )
for i in range(1 , n + 1 ... | 203 | """simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __a ( _SCREAMING_SNAKE_CASE ) ->Tuple:
a__: Tuple = {}
a__: Tuple = job['started_at']
a__: int = ... | 203 | 1 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def _lowercase ( ... | 176 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case = get_tests_dir("""fixtures/spiece.model""")
... | 176 | 1 |
'''simple docstring'''
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import... | 364 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
lowercase =logging.get_logger(__name__)
lowercase ='T5Config'
def lowerCame... | 242 | 0 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def SCREAMING_SNAKE_CASE( __lowercase ) -> int:
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __n... | 319 |
'''simple docstring'''
from itertools import permutations
def SCREAMING_SNAKE_CASE( __lowercase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
... | 319 | 1 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> str:
UpperCamelCase = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCamelCase = """"""
UpperCamelCase = """"""
# append each character + "|" in new_st... | 183 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CA... | 183 | 1 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
snake_case : int = loggin... | 94 |
def __lowerCamelCase ( UpperCAmelCase_ : list , UpperCAmelCase_ : list , UpperCAmelCase_ : int ):
"""simple docstring"""
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
raise ValueError('''The length of profit and weight must be same.'''... | 94 | 1 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__A = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|", "|"),
... | 2 | """simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class UpperCAmelCase :
"""simple docstring"""
_UpperCAmelCase :s... | 2 | 1 |
'''simple docstring'''
import argparse
import os
import re
lowerCAmelCase_ : Any = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase_ : List[str] = ... | 63 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Dict:
# Initialise PyT... | 48 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
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 Conf... | 363 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase = {
'configuration_efficientformer': [
'EFFICIENTFORMER_PRETRAINED_CONFI... | 270 | 0 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class A_ ( ctypes.Structure ):
"""simple docstring"""
SCREAMING_SNAK... | 126 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
fro... | 159 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A : List[str] = 1
A : int = 1
while repunit:
A : Dict = (10 * repunit... | 350 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table i... | 311 | 0 |
'''simple docstring'''
import math
import random
def __snake_case ( UpperCAmelCase_ : float , UpperCAmelCase_ : bool = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
a_ : Tuple = 0.0_2
... | 55 |
'''simple docstring'''
def lowerCamelCase__ ( _A ):
return 10 - x * x
def lowerCamelCase__ ( _A , _A ):
# Bolzano theory in order to find if there is a root between a and b
if equation(_A ) * equation(_A ) >= 0:
raise ValueError('Wrong space!' )
... | 297 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import ... | 181 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 181 | 1 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class snake_case_ ( __A ):
def __init__( self : Union[str, Any] , *lowercase_ : ... | 87 | 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,
resize,
to_ch... | 87 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig''',
'''ClapTextConfig'... | 342 | import argparse
import struct
import unittest
class _snake_case :
def __init__( self: Tuple , __lowerCamelCase: bytes ) -> None:
__UpperCAmelCase : Tuple = data
# Initialize hash values
__UpperCAmelCase : An... | 342 | 1 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :tuple[int, int] , a_ :int) -> list[tuple[int, int]]:
__a , __a : Any = position
__a : Optional[Any] = [
(y + 1, x + 2),
(y - 1, x +... | 160 |
"""simple docstring"""
from statistics import mean
import numpy as np
def __A ( a_ :list , a_ :list , a_ :list , a_ :int) -> list:
__a : Any = 0
# Number of processes finished
__a : Union[str, Any] = ... | 160 | 1 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 58 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : List[str] = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}... | 58 | 1 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
a__ = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False)... | 317 |
def lowercase ( SCREAMING_SNAKE_CASE__ : str ) -> list:
_snake_case : Optional[Any] = [0] * len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , len(SCREAMING_SNAKE_CASE__ ) ):
# use last results for better performance - dynamic programming
_snake_case : Optiona... | 317 | 1 |
"""simple docstring"""
from collections import namedtuple
SCREAMING_SNAKE_CASE : Any = namedtuple('''from_to''', '''from_ to''')
SCREAMING_SNAKE_CASE : Any = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.0_0_1, 1_0_0_0),
"""kilolitre""": from_to(1, 1),
... | 351 |
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE : list[int] ... | 317 | 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():
from .... | 331 |
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 ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] ):
'''simple do... | 226 | 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_ ( _UpperCAmelCase : str ,_UpperCAmelCase : int ,_UpperCAmelCase : int ... | 0 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, requi... | 0 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not i... | 248 |
def _UpperCAmelCase ( ):
'''simple docstring'''
return [
a * b * (1_0_0_0 - a - b)
for a in range(1 , 9_9_9)
for b in range(a__ , 9_9_9)
if (a * a + b * b == (1_0_0_0 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 248 | 1 |
'''simple docstring'''
import math
def _lowerCAmelCase ( lowercase , lowercase ) -> float:
return math.pow(lowercase , 2 ) - a
def _lowerCAmelCase ( lowercase ) -> float:
return 2 * x
def _lowerCAmelCase ( lowercase... | 46 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a : List[Any] = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Sque... | 46 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co... | 251 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase__( __UpperCamelCase: np.ndarray ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] ... | 251 | 1 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before token... | 355 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils ... | 91 | 0 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simp... | 136 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase : int = {
"facebook/encode... | 136 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available()... | 353 |
"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.s... | 40 | 0 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
_A = logging.get_logger(__name__)
class _lowerCamelCase :
def __init__( sel... | 242 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> int:
def count_of_possible_combinations(__UpperCAmelCase ) -> int:
if target < 0:
return 0
if target == 0:
r... | 242 | 1 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : str ) -> Any:
"""simple docstring"""
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
lowerCAmelCase = (boundary[1] - bou... | 187 |
'''simple docstring'''
from __future__ import annotations
class __snake_case:
'''simple docstring'''
def __init__( self , A_ = 0 ) -> Dict:
lowerCAmelCase = key
def __snake_case ( self , A_ , A_ ) -> list[str]:
... | 187 | 1 |
def _snake_case ( lowerCAmelCase : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = 1
SCREAMING_SNAKE_CASE_ : Optional[Any] = 2
while i * i <= n:
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
while n % i == 0:
n //= i
... | 18 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__snake_case = logging.getLogg... | 310 | 0 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
c... | 353 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
_lowerC... | 130 | 0 |
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