code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
_lowerCAmelCase: List[str] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def _lowercase( ):
a__ =input('Enter message: ' )
a__ =input('Enter key [alphanumeric]: ' )
a__ =input('Encrypt/Decrypt [e/d]: ' )
if mode.lower().startswith('e' ):
... | 20 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
# Initialise ... | 89 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
UpperCamelCase = ... | 21 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if n == 1 or not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
return 0
elif n == 2:
return 1
else:
_lowercase : List[str] = [0, 1]
for i in range(2 , n + 1 ):
... | 89 | 0 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , ):
'''simple docstring'''
_a = [reds... | 22 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : Optional[Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetr... | 89 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common impor... | 23 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
SCREAMING_SNAKE_CASE : Union[str, Any] = 1.0_5457_1817E-34 # unit of ℏ : J * s
SCREAMING_SNAKE_CASE : int = 3E8 # unit of c : m * s^-1
... | 89 | 0 |
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCAmelCase_ : str = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCamelCase (_lowerCamelCase : dict[int, list[int]] , _lowerCamelCase :... | 24 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multiplicative_persistence() does not accept neg... | 89 | 0 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def lowerCamelCase__ ( _a):
random.seed(_a)
np.random.see... | 25 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set... | 89 | 0 |
'''simple docstring'''
import os
import sys
__UpperCamelCase = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAns... | 26 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
SCREAMING_SNAKE_CASE : str = "scheduler_config.json"
class _lowerCamelCase( _a ... | 89 | 0 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils... | 27 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> float:
if not nums:
raise ValueError('List is empty' )
return sum(lowerCamelCase_ ) / len(lowerCamelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 89 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
res... | 28 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from... | 89 | 0 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
A_ = """\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation ... | 29 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import i... | 89 | 0 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name... | 30 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : List[Any] = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"Poo... | 89 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["image_processor", "tokenizer"]
lowercase_ = "CLI... | 31 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
SCREAMING_SNAKE_CASE : int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
SCREAMING_SNAKE_CASE : Dict = [file for fil... | 89 | 0 |
def A__ ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int ) -> list[str]:
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(SCREAMING_SNAKE_CASE_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doc... | 32 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def UpperCamelCase_( ) -> Any:
_lowercase... | 89 | 0 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPi... | 33 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE : str = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available()... | 89 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'... | 34 |
import unittest
from transformers import XLMConfig, 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 ModelTesterMixin, ids_... | 89 | 0 |
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_STAN... | 35 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataC... | 89 | 0 |
from __future__ import annotations
def lowercase ( __A : str , __A : list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
snake_case : Optional[int] = word_bank or []
# create a table
snake_case : int = len(__A )... | 36 |
from maths.prime_factors import prime_factors
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
_lowercase : str = F'''Input value of [number={number}] must be an integer'''
raise TypeError(lowerCa... | 89 | 0 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : str = {
"""kakaobrai... | 37 |
from __future__ import annotations
from typing import Any
class _lowerCamelCase:
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase = 0) -> None:
"""simple docstring"""
_lowercase , _lowercase : str = row, column
_low... | 89 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( __magic_name__ : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
snake_case__ : Tuple = set()
# Replace all the whitespace in our sentence
snake_case__ : List[Any]... | 38 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def UpperCamelCase_( lowerCamelCase_ ) -> int:
_lowercase : List[str] = int(lowerCamelCas... | 89 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): # noqa: E741
while r - l > 1:
snake_case_ = (l + r) // 2
if v[m] >= key:
... | 39 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGEN... | 89 | 0 |
class lowerCAmelCase_ :
def __init__( self, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCamelCase : Tuple = n
UpperCamelCase : List[Any] = [None] * self.n
UpperCamelCase : str = 0 # ind... | 40 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.... | 89 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Ima... | 41 |
from __future__ import annotations
import numpy as np
def UpperCamelCase_( lowerCamelCase_ ) -> Optional[int]:
return np.maximum(0 , lowerCamelCase_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 89 | 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_warmup, set_seed
... | 42 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
# Initialise ... | 89 | 0 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tun... | 43 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if n == 1 or not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
return 0
elif n == 2:
return 1
else:
_lowercase : List[str] = [0, 1]
for i in range(2 , n + 1 ):
... | 89 | 0 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def A_ ( _lowerCAmelCase : dict , _lowerCAmelCase : str , _lowerCAmelCase : set , _lowerCAmelCase : set , _lowerCAmelCase : dict , ... | 44 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : Optional[Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetr... | 89 | 0 |
from math import sqrt
def A ( lowercase__ : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format... | 45 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
SCREAMING_SNAKE_CASE : Union[str, Any] = 1.0_5457_1817E-34 # unit of ℏ : J * s
SCREAMING_SNAKE_CASE : int = 3E8 # unit of c : m * s^-1
... | 89 | 0 |
"""simple docstring"""
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowerCamelCase_( _lowe... | 46 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multiplicative_persistence() does not accept neg... | 89 | 0 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
SCREAMING_SNAKE_C... | 47 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set... | 89 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, D... | 48 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
SCREAMING_SNAKE_CASE : str = "scheduler_config.json"
class _lowerCamelCase( _a ... | 89 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import gcd
def lowercase__ ( snake_case_ :int = 1_500_000 ):
__UpperCAmelCase = defaultdict(snake_case_ )
__UpperCAmelCase = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for e... | 49 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> float:
if not nums:
raise ValueError('List is empty' )
return sum(lowerCamelCase_ ) / len(lowerCamelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 89 | 0 |
'''simple docstring'''
from collections.abc import Generator
def A__ ( ):
lowerCamelCase__ , lowerCamelCase__ = 0, 1
while True:
lowerCamelCase__ , lowerCamelCase__ = b, a + b
yield b
def A__ ( __lowerCAmelCa... | 50 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from... | 89 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils... | 51 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import i... | 89 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {
'''configuration_squeezebert''': [
'''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 52 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : List[Any] = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"Poo... | 89 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ... | 53 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
SCREAMING_SNAKE_CASE : int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
SCREAMING_SNAKE_CASE : Dict = [file for fil... | 89 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class A ( ... | 54 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def UpperCamelCase_( ) -> Any:
_lowercase... | 89 | 0 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
SCREAMING_SNAKE_CASE :List[Any] = {
# 1536-bit
5: {
'prime': int(
... | 55 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE : str = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available()... | 89 | 0 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def _a (lowercase__ : Optional[Any] , lowercase__ : int=7 ) -> Tuple:
"""simple docstring"""
__snake_case = N... | 56 |
import unittest
from transformers import XLMConfig, 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 ModelTesterMixin, ids_... | 89 | 0 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVeca... | 57 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataC... | 89 | 0 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCAmelCase : ... | 58 |
from maths.prime_factors import prime_factors
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
_lowercase : str = F'''Input value of [number={number}] must be an integer'''
raise TypeError(lowerCa... | 89 | 0 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_... | 59 |
from __future__ import annotations
from typing import Any
class _lowerCamelCase:
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase = 0) -> None:
"""simple docstring"""
_lowercase , _lowercase : str = row, column
_low... | 89 | 0 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowerCamelCase_ ( _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
snake_case_ : List[Any] = {}
snake_case_ : Optional[int] ... | 60 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def UpperCamelCase_( lowerCamelCase_ ) -> int:
_lowercase : List[str] = int(lowerCamelCas... | 89 | 0 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase ... | 61 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGEN... | 89 | 0 |
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowerCamelCase__ ( ):
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ... | 62 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.... | 89 | 0 |
from math import asin, atan, cos, radians, sin, sqrt, tan
a : Tuple = 6_37_81_37.0
a : Any = 6_35_67_52.31_42_45
a : Tuple = 6_378_137
def lowerCamelCase__ ( __lowerCamelCase : float , __lowerCamelCase : float ... | 63 |
from __future__ import annotations
import numpy as np
def UpperCamelCase_( lowerCamelCase_ ) -> Optional[int]:
return np.maximum(0 , lowerCamelCase_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 89 | 0 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class _lowerCamelCase :
@property
def UpperCamelCase_ ( self ) -> Tup... | 64 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
# Initialise ... | 89 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(__UpperCamelCase ) )
def lowerCAmelCase ( __UpperCamelCase , __U... | 65 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if n == 1 or not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
return 0
elif n == 2:
return 1
else:
_lowercase : List[str] = [0, 1]
for i in range(2 , n + 1 ):
... | 89 | 0 |
import requests
from bsa import BeautifulSoup
def __magic_name__ ( SCREAMING_SNAKE_CASE = "AAPL" ) -> str:
_lowercase : str = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
_lowercase : int = BeautifulSoup(requests.get(SCREAMING... | 66 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : Optional[Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetr... | 89 | 0 |
import os
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
with open(os.path.dirname(snake_case__ ) + '/grid.txt' ) as f:
_lowercase = [] # noqa: E741
for _ in range(20 ):
l.append([int(snake_case__ ) for x in f.readline().split()] )
_lowercase ... | 67 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
SCREAMING_SNAKE_CASE : Union[str, Any] = 1.0_5457_1817E-34 # unit of ℏ : J * s
SCREAMING_SNAKE_CASE : int = 3E8 # unit of c : m * s^-1
... | 89 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint t... | 68 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multiplicative_persistence() does not accept neg... | 89 | 0 |
'''simple docstring'''
import math
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> list:
__snake_case = [True] * n
__snake_case = False
__snake_case = False
__snake_case = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
_... | 69 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set... | 89 | 0 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversati... | 70 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
SCREAMING_SNAKE_CASE : str = "scheduler_config.json"
class _lowerCamelCase( _a ... | 89 | 0 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int | None = None , _SCREAMING_SNAKE_CASE : int | None = None ) -> None:
"""simple docstring"""
if start is None:
U... | 71 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> float:
if not nums:
raise ValueError('List is empty' )
return sum(lowerCamelCase_ ) / len(lowerCamelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 89 | 0 |
'''simple docstring'''
_UpperCAmelCase : dict[str, float] = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_... | 72 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from... | 89 | 0 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
... | 73 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import i... | 89 | 0 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
fr... | 74 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : List[Any] = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"Poo... | 89 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
def __init__( self : Dict , _A : str=2 , _A : Tuple=3 ,... | 75 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
SCREAMING_SNAKE_CASE : int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
SCREAMING_SNAKE_CASE : Dict = [file for fil... | 89 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/mai... | 76 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def UpperCamelCase_( ) -> Any:
_lowercase... | 89 | 0 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: ... | 77 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE : str = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available()... | 89 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import ... | 78 |
import unittest
from transformers import XLMConfig, 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 ModelTesterMixin, ids_... | 89 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ : Any = {
"""configuration_perceiver""": ["""PERCEIVER_PRETRA... | 79 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataC... | 89 | 0 |
from math import factorial
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(lowerCamelCase ) // (factorial(lowerCamelCase... | 80 |
from maths.prime_factors import prime_factors
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
_lowercase : str = F'''Input value of [number={number}] must be an integer'''
raise TypeError(lowerCa... | 89 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
_snake_case : List[str] = logging.get_logger(__name__)
_... | 81 |
from __future__ import annotations
from typing import Any
class _lowerCamelCase:
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase = 0) -> None:
"""simple docstring"""
_lowercase , _lowercase : str = row, column
_low... | 89 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ ):
if number > 0:
raise ValueError("input must be a negative integer" )
UpperCAmelCase_ = len(bin(lowerCAmelCase__ )[3:] )
UpperCAmelCase_ = bin(abs(lowerCAmelCase__ ) - (1 << binary_num... | 82 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def UpperCamelCase_( lowerCamelCase_ ) -> int:
_lowercase : List[str] = int(lowerCamelCas... | 89 | 0 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase__ = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def snake_case_ ( A_ : list[float] )... | 83 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGEN... | 89 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configur... | 84 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.... | 89 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_ba... | 85 |
from __future__ import annotations
import numpy as np
def UpperCamelCase_( lowerCamelCase_ ) -> Optional[int]:
return np.maximum(0 , lowerCamelCase_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 89 | 0 |
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6") )
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
A_ = credit_card_nu... | 86 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
# Initialise ... | 89 | 0 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table impo... | 87 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if n == 1 or not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
return 0
elif n == 2:
return 1
else:
_lowercase : List[str] = [0, 1]
for i in range(2 , n + 1 ):
... | 89 | 0 |
"""simple docstring"""
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... | 88 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : Optional[Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetr... | 89 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 90 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
SCREAMING_SNAKE_CASE : Union[str, Any] = 1.0_5457_1817E-34 # unit of ℏ : J * s
SCREAMING_SNAKE_CASE : int = 3E8 # unit of c : m * s^-1
... | 89 | 0 |
"""simple docstring"""
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Optional[int] ) -> List[str]:
A = 0
A = 0
A = {}
def _SCREAMING_SNAKE_CASE ( self : int ,A_ : Optional[Any] ) -> int:
if vertex... | 91 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multiplicative_persistence() does not accept neg... | 89 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int ) -> int:
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase : List[str] =f'''Input value of [number={number}] must be an integer'''
raise TypeError(__magic_nam... | 92 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set... | 89 | 0 |
"""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 __A (_SCREAMING_SNAK... | 93 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
SCREAMING_SNAKE_CASE : str = "scheduler_config.json"
class _lowerCamelCase( _a ... | 89 | 0 |
'''simple docstring'''
import cmath
import math
def lowercase_ ( __A : float , __A : float , __A : float , __A : float ) -> complex:
"""simple docstring"""
lowercase : Any =math.radians(__A )
lowercase... | 94 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> float:
if not nums:
raise ValueError('List is empty' )
return sum(lowerCamelCase_ ) / len(lowerCamelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 89 | 0 |
"""simple docstring"""
class UpperCamelCase_ :
def __init__( self : List[Any] , lowerCAmelCase_ : list[int] ) -> None:
UpperCAmelCase_ : str = len(lowerCAmelCase_ )
UpperCAmelCase_ : Dict = [0] * len_array
if len_array > 0:
Upper... | 95 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from... | 89 | 0 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTe... | 96 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import i... | 89 | 0 |
import string
def a ( snake_case__: str ):
'''simple docstring'''
lowercase_ = ''''''
for i in sequence:
lowercase_ = ord(snake_case__ )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= 122:
... | 97 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : List[Any] = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"Poo... | 89 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_... | 98 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
SCREAMING_SNAKE_CASE : int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
SCREAMING_SNAKE_CASE : Dict = [file for fil... | 89 | 0 |
from string import ascii_uppercase
SCREAMING_SNAKE_CASE = {char: i for i, char in enumerate(ascii_uppercase)}
SCREAMING_SNAKE_CASE = dict(enumerate(ascii_uppercase))
def a (lowerCAmelCase__ , lowerCAmelCase__ ):
__a = len(lowerCAmelCase__ )
__a ... | 99 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def UpperCamelCase_( ) -> Any:
_lowercase... | 89 | 0 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logg... | 100 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE : str = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available()... | 89 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT... | 101 |
import unittest
from transformers import XLMConfig, 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 ModelTesterMixin, ids_... | 89 | 0 |
"""simple docstring"""
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
__mag... | 102 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataC... | 89 | 0 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class ... | 103 |
from maths.prime_factors import prime_factors
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
_lowercase : str = F'''Input value of [number={number}] must be an integer'''
raise TypeError(lowerCa... | 89 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase = {
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 104 |
from __future__ import annotations
from typing import Any
class _lowerCamelCase:
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase = 0) -> None:
"""simple docstring"""
_lowercase , _lowercase : str = row, column
_low... | 89 | 0 |
from __future__ import annotations
import math
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negat... | 105 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def UpperCamelCase_( lowerCamelCase_ ) -> int:
_lowercase : List[str] = int(lowerCamelCas... | 89 | 0 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import Padd... | 106 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGEN... | 89 | 0 |
'''simple docstring'''
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 lowercase_ ( unit... | 107 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.... | 89 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ..... | 108 |
from __future__ import annotations
import numpy as np
def UpperCamelCase_( lowerCamelCase_ ) -> Optional[int]:
return np.maximum(0 , lowerCamelCase_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 89 | 0 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import ... | 109 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
# Initialise ... | 89 | 0 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : List[str] = [0] * len(_snake_case )
for i in range(1 ,len(_snake_case ) ):
# use last results for better performance - dynamic programming
UpperCAmelCase__ : Optional[int] ... | 110 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if n == 1 or not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
return 0
elif n == 2:
return 1
else:
_lowercase : List[str] = [0, 1]
for i in range(2 , n + 1 ):
... | 89 | 0 |
def A__ ( lowercase: Optional[int] ) -> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def A__ ( lowercase: List[str] ) -> bool:
A : int =0
A : Optional[int] =number
while duplicate > 0:
... | 305 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : Optional[Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetr... | 89 | 0 |
def lowerCamelCase_(lowerCamelCase_ ) -> int:
assert column_title.isupper()
UpperCAmelCase = 0
UpperCAmelCase = len(lowerCamelCase_ ) - 1
UpperCAmelCase = 0
while index >= 0:
UpperCAmelCase = (ord(column_title[index] ) -... | 323 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
SCREAMING_SNAKE_CASE : Union[str, Any] = 1.0_5457_1817E-34 # unit of ℏ : J * s
SCREAMING_SNAKE_CASE : int = 3E8 # unit of c : m * s^-1
... | 89 | 0 |
import torch
from diffusers import DiffusionPipeline
class SCREAMING_SNAKE_CASE ( _a ):
'''simple docstring'''
def __init__( self : Tuple , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Any ):
super().__init__()... | 62 |
def UpperCamelCase_( lowerCamelCase_ ) -> int:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multiplicative_persistence() does not accept neg... | 89 | 0 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
a =logging.get_logger("""transformers.models.speecht5""")
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCas... | 652 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set... | 89 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : int = logging.get_logger(__name__)
_lowerCAmelCase : Tuple = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.jso... | 454 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
SCREAMING_SNAKE_CASE : str = "scheduler_config.json"
class _lowerCamelCase( _a ... | 89 | 0 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
... | 28 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> float:
if not nums:
raise ValueError('List is empty' )
return sum(lowerCamelCase_ ) / len(lowerCamelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 89 | 0 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common i... | 55 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from... | 89 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase : List[str] =logging.get_logger(__name__)
_UpperCamelCase : Optional[int] ={
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encod... | 206 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import i... | 89 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if not... | 580 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : List[Any] = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"Poo... | 89 | 0 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __lowerCAmelCase ( _UpperCamelCase , _UpperCamelCase=None ) -> Optional[Any]:
'''simple docstring'''
l... | 306 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
SCREAMING_SNAKE_CASE : int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
SCREAMING_SNAKE_CASE : Dict = [file for fil... | 89 | 0 |
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