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 |
|---|---|---|---|---|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
de... | 357 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCamelCase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxCon... | 38 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import ... | 358 |
"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils i... | 38 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pi
def lowercase (SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ) -> dict[str, float]:
if (inductance, frequency, react... | 359 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCamelCase = models.Se... | 38 | 0 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowerCAmelCase ( ctypes.Structure ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] = [("""size""... | 360 |
"""simple docstring"""
class lowerCAmelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase__ ) -> None:
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = [0] * size
SCREAMING_SNAKE_CAS... | 38 | 0 |
"""simple docstring"""
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa:... | 361 |
"""simple docstring"""
from collections import deque
from .hash_table import HashTable
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Dict:
super()... | 38 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase (SCREAMING_SNAKE_CASE_ : list[int] ) -> bool:
return len(set(SCREAMING_SNAKE_CASE_ ) ) == len(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod... | 362 |
"""simple docstring"""
from __future__ import annotations
def lowercase (SCREAMING_SNAKE_CASE_ : list[int] ) -> bool:
return len(set(SCREAMING_SNAKE_CASE_ ) ) == len(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 38 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_devi... | 363 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e impo... | 38 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaX... | 364 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligne... | 38 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__UpperCamelCase = 6378137.0
__UpperCamelCase = 6356752.314245
__UpperCamelCase = 6378137
def lowercase (SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ :... | 365 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> bool:
SCREAMING_SNAKE_CASE = int(number**0.5 )
return number == sq * sq
def ... | 38 | 0 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowerCAme... | 366 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {'''vocab_file''': '''vocab.json'''}
__Upp... | 38 | 0 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> bool:
SCREAMING_SNAKE_CASE = int(number**0.5 )
return number == sq * sq
def ... | 367 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class lowerCAmelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> None:
if len(lowerCAme... | 38 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
... | 368 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_ )
class lowerCAmelCase ( lowerCamelCase_ ):
'''simp... | 38 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import requ... | 369 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbos... | 38 | 0 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
impo... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {'''configuration_opt''': ['''OP... | 38 | 0 |
"""simple docstring"""
from itertools import permutations
def lowercase (SCREAMING_SNAKE_CASE_ : tuple ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
... | 371 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils... | 38 | 0 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {'''vocab_file''': '''vocab.json'''}
__Upp... | 350 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from a... | 38 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines... | 351 |
"""simple docstring"""
from math import sqrt
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> int:
SCREAMING_SNAKE_CASE = 0
for i in range(1 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) ):
if n % i == 0 and i != sqrt(SCREAMING_SNAKE... | 38 | 0 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink... | 352 |
"""simple docstring"""
import operator as op
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> int:
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = lambda SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int(x / y ) # noqa: E731 i... | 38 | 0 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : int = 10_00 ) -> int:
SCREAMING_SNAKE_CASE = 3
SCREAMING_SNAKE_CASE = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
eli... | 353 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lower... | 38 | 0 |
from __future__ import annotations
import bisect
def lowercase (SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int = 0 , SCREAMING_SNAKE_CASE_ : int = -1 ) -> int:
if hi < 0:
SCREAMING_S... | 354 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
def lowercase (SCREAMING_SNAKE_C... | 38 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def lowercase (SCREAMING_SNAKE_CASE_ : Sequence[float] , SCREAMING_SNAKE_CASE_ : bool = False ) -> float:
if not arr:
return 0
SCREAMING_SNAKE_CASE = 0 if allow_emp... | 355 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> List[str]:
# A local function to see if a dot lands in the circle.
d... | 38 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration... | 356 |
"""simple docstring"""
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 TFM... | 38 | 0 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_... | 357 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCamelCase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxCon... | 38 | 0 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 358 |
"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils i... | 38 | 0 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : int = 10 ) -> str:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or n < 0:
raise ValueError('Invalid input' )
SCREAMING_SNAKE_CASE = 10**n
SCREA... | 359 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCamelCase = models.Se... | 38 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__UpperCamelCase = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
('''... | 360 |
"""simple docstring"""
class lowerCAmelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase__ ) -> None:
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = [0] * size
SCREAMING_SNAKE_CAS... | 38 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase (SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str ... | 361 |
"""simple docstring"""
from collections import deque
from .hash_table import HashTable
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Dict:
super()... | 38 | 0 |
"""simple docstring"""
from __future__ import annotations
__UpperCamelCase = list[list[int]]
# assigning initial values to the grid
__UpperCamelCase = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, ... | 362 |
"""simple docstring"""
from __future__ import annotations
def lowercase (SCREAMING_SNAKE_CASE_ : list[int] ) -> bool:
return len(set(SCREAMING_SNAKE_CASE_ ) ) == len(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 38 | 0 |
"""simple docstring"""
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import ... | 363 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e impo... | 38 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''... | 364 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligne... | 38 | 0 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
... | 365 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> bool:
SCREAMING_SNAKE_CASE = int(number**0.5 )
return number == sq * sq
def ... | 38 | 0 |
"""simple docstring"""
from __future__ import annotations
import queue
class lowerCAmelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase__ ) -> Optional[Any]:
SCREAMING_SNAKE_CASE = data
SCREAMING_SNA... | 366 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {'''vocab_file''': '''vocab.json'''}
__Upp... | 38 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCamelCase = loggi... | 367 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class lowerCAmelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> None:
if len(lowerCAme... | 38 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCamelCase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'... | 368 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_ )
class lowerCAmelCase ( lowerCamelCase_ ):
'''simp... | 38 | 0 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
SCREAMING_SNAKE_CASE = _modexp... | 369 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbos... | 38 | 0 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
def lowercase (SCREAMING_SNAKE_CASE_ : List[str] ) ->... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {'''configuration_opt''': ['''OP... | 38 | 0 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils... | 371 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils... | 38 | 0 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowerCAmelCase ( unittest.TestCase ):
'''si... | 350 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from a... | 38 | 0 |
"""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
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''T5Config... | 351 |
"""simple docstring"""
from math import sqrt
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> int:
SCREAMING_SNAKE_CASE = 0
for i in range(1 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) ):
if n % i == 0 and i != sqrt(SCREAMING_SNAKE... | 38 | 0 |
"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils i... | 352 |
"""simple docstring"""
import operator as op
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> int:
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = lambda SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int(x / y ) # noqa: E731 i... | 38 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_ima... | 353 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lower... | 38 | 0 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase (SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] , SCREA... | 354 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
def lowercase (SCREAMING_SNAKE_C... | 38 | 0 |
"""simple docstring"""
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] = """openai/whis... | 355 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> List[str]:
# A local function to see if a dot lands in the circle.
d... | 38 | 0 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : str ) -> bool:
SCREAMING_SNAKE_CASE = [int(SCREAMING_SNAKE_CASE_ ) for i in ip_va_address.split('.' ) if i.isdigit()]
return len(SCREAMING_SNAKE_CASE_ ) == 4 and all(0 <= int(SCREAMING_SNAKE... | 356 |
"""simple docstring"""
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 TFM... | 38 | 0 |
"""simple docstring"""
from sklearn.metrics import mean_squared_error
import datasets
__UpperCamelCase = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and G... | 357 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCamelCase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxCon... | 38 | 0 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = (DDIMParallelScheduler,)
SCREAMING_SNAKE_CA... | 358 |
"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils i... | 38 | 0 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
fro... | 359 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCamelCase = models.Se... | 38 | 0 |
import copy
import re
class lowerCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] = """hp"""
SCREAMING_SNAKE_CASE_ : List[str] = {}
SCREAMING_SNAKE_CASE_ : Tuple = None
@classm... | 360 |
"""simple docstring"""
class lowerCAmelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase__ ) -> None:
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = [0] * size
SCREAMING_SNAKE_CAS... | 38 | 0 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
__UpperCamelCase = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BL... | 361 |
"""simple docstring"""
from collections import deque
from .hash_table import HashTable
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Dict:
super()... | 38 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {'''configuration_opt''': ['''OP... | 362 |
"""simple docstring"""
from __future__ import annotations
def lowercase (SCREAMING_SNAKE_CASE_ : list[int] ) -> bool:
return len(set(SCREAMING_SNAKE_CASE_ ) ) == len(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 38 | 0 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCamelCase = ''''''
__UpperCamelCase = ''''''
__UpperCamelCase = ''''''
__UpperCamelCase = 1 # (0 is vertical, 1 is horizontal)
def low... | 363 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e impo... | 38 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_con... | 364 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligne... | 38 | 0 |
def lowercase (SCREAMING_SNAKE_CASE_ : int = 50 ) -> int:
SCREAMING_SNAKE_CASE = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range... | 365 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> bool:
SCREAMING_SNAKE_CASE = int(number**0.5 )
return number == sq * sq
def ... | 38 | 0 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from d... | 366 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {'''vocab_file''': '''vocab.json'''}
__Upp... | 38 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase = {
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 367 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class lowerCAmelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> None:
if len(lowerCAme... | 38 | 0 |
"""simple docstring"""
from math import isqrt
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) )
def lowercase (SCREAMING_SNAKE_CASE_ : int = 10**6 ) -... | 368 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_ )
class lowerCAmelCase ( lowerCamelCase_ ):
'''simp... | 38 | 0 |
"""simple docstring"""
from __future__ import annotations
__UpperCamelCase = tuple[int, int, int]
__UpperCamelCase = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
__UpperCamelCase = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
# --------------------... | 369 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbos... | 38 | 0 |
def lowercase (SCREAMING_SNAKE_CASE_ : int = 10_00 ) -> int:
return sum(e for e in range(3 , SCREAMING_SNAKE_CASE_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {'''configuration_opt''': ['''OP... | 38 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
def __... | 371 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils... | 38 | 0 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_av... | 39 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a__: Union[str, Any] = False
class SCREAMING_SNAKE_CASE_... | 39 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, requir... | 39 |
# Algorithm for the pigeonhole sorting
def UpperCamelCase__( UpperCamelCase__ : int )->str:
A__ = min(UpperCamelCase__ ) # min() finds the minimum value
A__ = max(UpperCamelCase__ ) # max() finds the maximum value
A__ = max_v... | 39 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
a__: List[Any] = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def UpperCamelCase__( UpperCamelCase__ : str = "mumbai" )->Generator[tuple[str... | 39 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
a__: str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a__: list[int] = [ord(letter) for letter in string.ascii_lowercase]
a__:... | 39 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def UpperCamelCase__( UpperCamelCase__ : Dict )->An... | 39 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self,__lowerCamelCase ):
A__ = data
A__ = None
class SCREAMING_SNAKE_CASE__ :
... | 39 | 1 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/LICEN... | 39 |
def UpperCamelCase__( UpperCamelCase__ : int , UpperCamelCase__ : int )->float:
return base * power(UpperCamelCase__ , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...')
a__:... | 39 | 1 |
from collections.abc import Generator
from math import sin
def UpperCamelCase__( UpperCamelCase__ : bytes )->bytes:
if len(UpperCamelCase__ ) != 32:
raise ValueError('''Input must be of length 32''' )
A__ = b''''''
for i in [3, 2, 1, 0]:... | 39 |
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 | 1 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...... | 39 |
a__: dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_93_44,
"knot": 1.8_52,
}
a__: dict[str, float] = {
"km/h": 1.0,
"m/s": 0.2_77_77_77_78,
"mph": 0.6_21_37_11_92,
"knot": 0.5_39_95_68_03,
}
def UpperCamelCase__( UpperCamelCas... | 39 | 1 |
import numpy as np
a__: Dict = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class SCREAMING_SNAKE_CASE__ :
def __init__( self ):
A__ ... | 39 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def UpperCamelCase ( self ):
A__ = [
'''safety_checker/pytorch_model.bin''',
... | 39 | 1 |
from ... import PretrainedConfig
a__: Optional[int] = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = NEZHA_PRETRAINED_CONFI... | 39 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_... | 39 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE__ :
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
a__: Dict ... | 39 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = '''MCTCTFeatureExtractor'''
__SCREAMING_SNAKE_CASE = '''AutoTokenizer'''
... | 39 | 1 |
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... | 39 |
def UpperCamelCase__( )->Dict:
A__ = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
A__ = 6
A__ = 1
A__ = 19_01
A__ = 0
while year < 20_01:
day += 7
if (year % ... | 39 | 1 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_v... | 39 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
)
| 39 | 1 |
from __future__ import annotations
def UpperCamelCase__( UpperCamelCase__ : tuple[int, int] , UpperCamelCase__ : int )->list[tuple[int, int]]:
A__ , A__ = position
A__ = [
(y + 1, x + 2),
(y - 1, x + 2),
... | 39 |
# Copyright 2023 The HuggingFace Inc. 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
#
# Unl... | 39 | 1 |
def UpperCamelCase__( UpperCamelCase__ : int = 10**12 )->int:
A__ = 1
A__ = 0
A__ = 1
A__ = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 ... | 39 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE__ ( UpperCamelCas... | 39 | 1 |
from functools import reduce
a__: Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896... | 39 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@requ... | 39 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__: int = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVision... | 39 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class SCREAMING_SNAKE_CASE__ ( tf.keras.optimizers.sched... | 39 | 1 |
import numpy as np
def UpperCamelCase__( UpperCamelCase__ : np.array )->np.array:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 39 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__: List[Any] = logging.get_logger(__name__)
a__: Optional[Any] = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeec... | 39 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def UpperCamelCase__( )->tuple[list[int], int]:
A__ = [randint(-10_00 , 10_00 ) for i in range(10 )]
A__ = randint(-50_00 ... | 39 |
def UpperCamelCase__( UpperCamelCase__ : str )->str:
A__ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def UpperCamelCase__( UpperCamelCase__ ... | 39 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a__: Any = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_... | 39 |
def UpperCamelCase__( UpperCamelCase__ : int = 1_00 )->int:
A__ = (n * (n + 1) // 2) ** 2
A__ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F"{solution() = }")
| 39 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def UpperCamelCase ( self ):
A__ = [
'''safety_checker/pytorch_model.bin''',
... | 39 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, Deco... | 39 | 1 |
from ...configuration_utils import PretrainedConfig
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = '''bert-generation'''
def __init__( self,__lowerCamelCase=5_0358,__lowerCamelCase=1024,__lowerCamelCase=24,__lowerCamelCase=16,__lo... | 39 |
from __future__ import annotations
import time
import numpy as np
a__: Optional[Any] = [8, 5, 9, 7]
a__: Dict = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a__: List[Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
... | 39 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.mo... | 39 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
a__: int = {
'gwf-44... | 39 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def UpperCamelCase__( )->Union[str, Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_... | 39 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a__: Union[str, Any] = False
class SCREAMING_SNAKE_CASE_... | 39 | 1 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE__ ( UpperCamelCas... | 39 |
# Algorithm for the pigeonhole sorting
def UpperCamelCase__( UpperCamelCase__ : int )->str:
A__ = min(UpperCamelCase__ ) # min() finds the minimum value
A__ = max(UpperCamelCase__ ) # max() finds the maximum value
A__ = max_v... | 39 | 1 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_config_docstri... | 39 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
a__: str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a__: list[int] = [ord(letter) for letter in string.ascii_lowercase]
a__:... | 39 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__: Optional[Any] = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CON... | 39 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self,__lowerCamelCase ):
A__ = data
A__ = None
class SCREAMING_SNAKE_CASE__ :
... | 39 | 1 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
... | 39 |
def UpperCamelCase__( UpperCamelCase__ : int , UpperCamelCase__ : int )->float:
return base * power(UpperCamelCase__ , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...')
a__:... | 39 | 1 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require... | 39 |
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 | 1 |
from string import ascii_uppercase
a__: List[Any] = {str(ord(c) - 55): c for c in ascii_uppercase}
def UpperCamelCase__( UpperCamelCase__ : int , UpperCamelCase__ : int )->str:
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise Type... | 39 |
a__: dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_93_44,
"knot": 1.8_52,
}
a__: dict[str, float] = {
"km/h": 1.0,
"m/s": 0.2_77_77_77_78,
"mph": 0.6_21_37_11_92,
"knot": 0.5_39_95_68_03,
}
def UpperCamelCase__( UpperCamelCas... | 39 | 1 |
def UpperCamelCase__( UpperCamelCase__ : str )->str:
A__ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def UpperCamelCase__( UpperCamelCase__ ... | 39 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def UpperCamelCase ( self ):
A__ = [
'''safety_checker/pytorch_model.bin''',
... | 39 | 1 |
def UpperCamelCase__( UpperCamelCase__ : Any )->Tuple:
A__ = [0] * len(UpperCamelCase__ )
A__ = []
A__ = []
A__ = 0
for values in graph.values():
for i in values:
indegree[i... | 39 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_... | 39 | 1 |
import math
import sys
def UpperCamelCase__( UpperCamelCase__ : str )->str:
A__ = ''''''
try:
with open(UpperCamelCase__ , '''rb''' ) as binary_file:
A__ = binary_file.read()
for dat in data:
... | 39 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = '''MCTCTFeatureExtractor'''
__SCREAMING_SNAKE_CASE = '''AutoTokenizer'''
... | 39 | 1 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
a__: List[Any] ... | 39 |
def UpperCamelCase__( )->Dict:
A__ = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
A__ = 6
A__ = 1
A__ = 19_01
A__ = 0
while year < 20_01:
day += 7
if (year % ... | 39 | 1 |
a__: dict[tuple[int, int, int], int] = {}
def UpperCamelCase__( UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int )->int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
... | 39 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
)
| 39 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, ... | 39 |
# Copyright 2023 The HuggingFace Inc. 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
#
# Unl... | 39 | 1 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
)
| 39 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE__ ( UpperCamelCas... | 39 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a__: List[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViT... | 39 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@requ... | 39 | 1 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
pass
class SCREAMING_SNAKE_CASE__ :
def __init__( self,__lowerCamelCase ):
A__ = data
A__ = None
... | 39 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class SCREAMING_SNAKE_CASE__ ( tf.keras.optimizers.sched... | 39 | 1 |
from __future__ import annotations
def UpperCamelCase__( UpperCamelCase__ : list[float] , UpperCamelCase__ : Optional[Any] )->List[Any]:
print(f"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(UpperCamelCase__ ):
print(f"{i}\t\... | 39 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__: List[Any] = logging.get_logger(__name__)
a__: Optional[Any] = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeec... | 39 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
a__: Union[str, Any] = datasets.logging.get_logger(__name__)
a__: List[str] = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n tit... | 39 |
def UpperCamelCase__( UpperCamelCase__ : str )->str:
A__ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def UpperCamelCase__( UpperCamelCase__ ... | 39 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common ... | 39 |
def UpperCamelCase__( UpperCamelCase__ : int = 1_00 )->int:
A__ = (n * (n + 1) // 2) ** 2
A__ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F"{solution() = }")
| 39 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.