code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, L... | 60 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : Optional[Any] = '''src/transfo... | 60 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case__ : Optional[int] = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''A... | 60 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class snake_case_:
def __init__( self : str , UpperCamelCase_ : int=None , UpperCamelCase_ : List[str]=None ):
# Input as list
lowerCAmelCase : str = li... | 60 | 1 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ):
lowerCAmelCase : float = a
lowerCAmelCase : float = b
if function(_snake_case... | 60 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_f... | 60 | 1 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
snake_case__ : List[Any] = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if not isinstance(_snake_case , _snake_case ):
raise TypeError('''only integers accepted as input''' )
else:
lowerCAmelCase : List[str] = str(abs(_snake_case ) )
lowerCAmelC... | 60 | 1 |
"""simple docstring"""
# 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-... | 60 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_cas... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str , _snake_case : bool = False ):
if not isinstance(_snake_case , _snake_case ):
lowerCAmelCase : Optional[Any] = f'''Expected string as input, found {type(_snake_case )}'''
raise Value... | 60 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str = "The quick brown fox jumps over the lazy dog" , ):
lowerCAmelCase : List[str] = set()
# Replace all the whitespace in our sentence
lowerCAmelCase : List[Any] = input_str.replace(''' ''' ... | 60 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _snake_case ( _snake_case : int ):
for param in module.parameters():
lowerCAmelCase : Optional[int] = False
def _snake_case ( ):
lowerCAmelCase : List[str]... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 100 ):
lowerCAmelCase : Union[str, Any] = (n * (n + 1) // 2) ** 2
lowerCAmelCase : List[str] = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"""{soluti... | 60 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 60 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : str = logging.get_logger(__name__)
snake_case__ : Dict = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/dec... | 60 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformer... | 60 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class snake_case_:
__UpperCamelCase = 42
__UpperCamelCase... | 60 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 | 1 |
"""simple docstring"""
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
snake_case__ : Dict = False
try:
snak... | 60 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 60 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( _snake_case : np.ndarray , _snake_case : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_snake_case , _snak... | 60 | 1 |
"""simple docstring"""
import math
def _snake_case ( _snake_case : list , _snake_case : int ):
lowerCAmelCase : Any = len(_snake_case )
lowerCAmelCase : Dict = int(math.floor(math.sqrt(_snake_case ) ) )
lowerCAmelCase : Union[str, Any] = 0
... | 60 |
"""simple docstring"""
import math
def _snake_case ( ):
lowerCAmelCase : Union[str, Any] = input('''Enter message: ''' )
lowerCAmelCase : Optional[int] = int(input(f'''Enter key [2-{len(_snake_case ) - 1}]: ''' ) )
lowerCAmelCase : str = input('''Encr... | 60 | 1 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 60 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import trans... | 60 | 1 |
"""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... | 60 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 |
"""simple docstring"""
snake_case__ : str = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
snake_case__ ... | 60 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTes... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 | 1 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class snake_case_( unittest.TestCase ):
def lowerCamelCase__ ( self : ... | 60 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 60 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : int = {
'''huggingface/time-series-transformer-tourism-monthly''': (
... | 60 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fla... | 60 | 1 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trus... | 60 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : float ):
if edge <= 0 or not isinstance(_snake_case , _snake_case ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def _snake_case ( _snake_... | 60 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 1000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 60 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 60 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 60 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : Optional[Any] = '''src/transfo... | 60 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fla... | 60 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class snake_case_:
def __init__( self : str , UpperCamelCase_ : int=None , UpperCamelCase_ : List[str]=None ):
# Input as list
lowerCAmelCase : str = li... | 60 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_f... | 60 | 1 |
"""simple docstring"""
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if not isinstance(_snake_case , _snake_case ):
raise TypeError('''only integers accepted as input''' )
else:
lowerCAmelCase : List[str] = str(abs(_snake_case ) )
lowerCAmelC... | 60 | 1 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import trans... | 60 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_cas... | 60 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...... | 60 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_... | 60 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
... | 60 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _snake_case ( _snake_case : int ):
for param in module.parameters():
lowerCAmelCase : Optional[int] = False
def _snake_case ( ):
lowerCAmelCase : List[str]... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 60 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class snake_case_( unittest.TestCase ):
... | 60 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformer... | 60 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Batc... | 60 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 | 1 |
"""simple docstring"""
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuratio... | 60 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
"""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 diffusers.configurati... | 60 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( _snake_case : np.ndarray , _snake_case : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_snake_case , _snak... | 60 | 1 |
"""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,
TestCasePlus,
execu... | 60 |
"""simple docstring"""
import math
def _snake_case ( ):
lowerCAmelCase : Union[str, Any] = input('''Enter message: ''' )
lowerCAmelCase : Optional[int] = int(input(f'''Enter key [2-{len(_snake_case ) - 1}]: ''' ) )
lowerCAmelCase : str = input('''Encr... | 60 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class snake_case_( a__ ):
__UpperCamelCase = '''SpeechT5FeatureExtractor'''
__UpperCamelCase = '''SpeechT5Tokenizer'''
def __init__( self : List[str] , UpperCamelCase_ : Tuple ... | 60 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import trans... | 60 | 1 |
"""simple docstring"""
import math
def _snake_case ( ):
lowerCAmelCase : Union[str, Any] = input('''Enter message: ''' )
lowerCAmelCase : Optional[int] = int(input(f'''Enter key [2-{len(_snake_case ) - 1}]: ''' ) )
lowerCAmelCase : str = input('''Encr... | 60 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 60 | 1 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : i... | 60 |
"""simple docstring"""
snake_case__ : str = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
snake_case__ ... | 60 | 1 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def _snake_case ( _snake_case : Callable ):
@wraps(_snake_case )
def _inner_fn(*_snake_case : Optional[int] , **_snake_case : str ):
warnings.warn(
(f'''\'{fn... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils im... | 60 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : list , _snake_case : int ):
if len(_snake_case ) != len(_snake_case ):
raise ValueError('''The length of profit and weight must be same.''' )
if max_weight <= 0:
raise Value... | 60 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fla... | 60 | 1 |
"""simple docstring"""
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_sin... | 60 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 60 | 1 |
"""simple docstring"""
import os
def _snake_case ( _snake_case : str = "matrix.txt" ):
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as in_file:
lowerCAmelCase : Tuple = in_file.read()
lowerCAmelCase : Dict = [[int(_snak... | 60 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 | 1 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.da... | 60 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str ):
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
lowerCAmelCase : Any = len(_snake_case )
lowerCAmelCase : List[str]... | 60 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : Optional[Any] = '''src/transfo... | 60 | 1 |
"""simple docstring"""
import os
import sys
import unittest
snake_case__ : int = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
ge... | 60 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class snake_case_:
def __init__( self : str , UpperCamelCase_ : int=None , UpperCamelCase_ : List[str]=None ):
# Input as list
lowerCAmelCase : str = li... | 60 | 1 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 60 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_f... | 60 | 1 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case__ : ... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if not isinstance(_snake_case , _snake_case ):
raise TypeError('''only integers accepted as input''' )
else:
lowerCAmelCase : List[str] = str(abs(_snake_case ) )
lowerCAmelC... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if number < 0:
raise ValueError('''number must not be negative''' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_cas... | 60 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 60 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_... | 60 | 1 |
"""simple docstring"""
snake_case__ : Union[str, Any] = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def _snake_case ( _snake_case : str ):
lowerCAmelCase : Any = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
lowerCA... | 60 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _snake_case ( _snake_case : int ):
for param in module.parameters():
lowerCAmelCase : Optional[int] = False
def _snake_case ( ):
lowerCAmelCase : List[str]... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : list[float] , _snake_case : Optional[Any] ):
print(f'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumerate(_snake_case ):
print(f'''{i}\t\t{d}''' )
def _sna... | 60 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 60 | 1 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
... | 60 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformer... | 60 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
snake_case__ : List[Any] = logging.get_logger(__name__)
class snake_case_( a__ ):
def __init__( self : Dict , *UpperCamelCase_ : List[st... | 60 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : str = logging.get_logger(__name__)
snake_case__ : str = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tii... | 60 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case__ : int = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''token... | 60 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( _snake_case : np.ndarray , _snake_case : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_snake_case , _snak... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
for i in range(len(_snake_case ) - 1 , 0 , -1 ):
lowerCAmelCase : int = False
for j in range(_snake_case , 0 , -1 ):
if unsorted[j] < unsor... | 60 |
"""simple docstring"""
import math
def _snake_case ( ):
lowerCAmelCase : Union[str, Any] = input('''Enter message: ''' )
lowerCAmelCase : Optional[int] = int(input(f'''Enter key [2-{len(_snake_case ) - 1}]: ''' ) )
lowerCAmelCase : str = input('''Encr... | 60 | 1 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import trans... | 60 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precis... | 60 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( _snake_case : np.ndarray , _snake_case : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_snake_case , _snak... | 60 |
"""simple docstring"""
snake_case__ : str = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
snake_case__ ... | 60 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case__ : Tuple = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig'... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 | 1 |
"""simple docstring"""
from manim import *
class snake_case_( a__ ):
def lowerCamelCase__ ( self : Tuple ):
lowerCAmelCase : List[str] = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase : Union[str, Any] = Rectangle(height=0.4... | 60 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 60 | 1 |
"""simple docstring"""
import math
import sys
def _snake_case ( _snake_case : int ):
if number != int(_snake_case ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''the value of input must not be a negative number... | 60 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fla... | 60 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : List[str] = logging.get_logger(__name__)
snake_case__ : Tuple = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config... | 60 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 60 | 1 |
"""simple docstring"""
from math import factorial
def _snake_case ( _snake_case : int = 100 ):
return sum(int(_snake_case ) for x in str(factorial(_snake_case ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 60 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : List[str] = {
'''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_A... | 60 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 60 | 1 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
snake_case__ : List[Any] = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Trans... | 60 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : Optional[Any] = '''src/transfo... | 60 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformer... | 60 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class snake_case_:
def __init__( self : str , UpperCamelCase_ : int=None , UpperCamelCase_ : List[str]=None ):
# Input as list
lowerCAmelCase : str = li... | 60 | 1 |
"""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 import Tenso... | 60 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_f... | 60 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ :... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if not isinstance(_snake_case , _snake_case ):
raise TypeError('''only integers accepted as input''' )
else:
lowerCAmelCase : List[str] = str(abs(_snake_case ) )
lowerCAmelC... | 60 | 1 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from acceler... | 60 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_cas... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if not isinstance(_snake_case , _snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
diviso... | 60 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_... | 60 | 1 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from t... | 60 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _snake_case ( _snake_case : int ):
for param in module.parameters():
lowerCAmelCase : Optional[int] = False
def _snake_case ( ):
lowerCAmelCase : List[str]... | 60 | 1 |
"""simple docstring"""
from timeit import timeit
snake_case__ : Any = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a plan a canal panama... | 60 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import ... | 60 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformer... | 60 | 1 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class snake_case_( a__ ):
... | 60 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 | 1 |
"""simple docstring"""
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def _snake_case ( _snake_case : Dict , _snake_case : int ):
lowerCAmelCase : Dict = k_siz... | 60 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, lo... | 60 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( _snake_case : np.ndarray , _snake_case : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_snake_case , _snak... | 60 | 1 |
"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
f... | 60 |
"""simple docstring"""
import math
def _snake_case ( ):
lowerCAmelCase : Union[str, Any] = input('''Enter message: ''' )
lowerCAmelCase : Optional[int] = int(input(f'''Enter key [2-{len(_snake_case ) - 1}]: ''' ) )
lowerCAmelCase : str = input('''Encr... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils... | 60 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import trans... | 60 | 1 |
"""simple docstring"""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
snake_case__ : int = logging.get_logger(__name__)
def _snake_case ( _snake_case : Optional[Any] ):
lowerCAmel... | 60 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 60 | 1 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
snake_case__ : Tuple = datasets.logging.get_logger(__name__)
snake_case__ : Any = '''\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and La... | 60 |
"""simple docstring"""
snake_case__ : str = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
snake_case__ ... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
fro... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : Optional[int] , _snake_case : Optional[Any] , _snake_case : Dict=False ):
if isinstance(_snake_case , _snake_case ) and isinstance(_snake_case , _snake_case ):
lowerCAmelCase : str =... | 60 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 60 | 1 |
"""simple docstring"""
import math
def _snake_case ( _snake_case : List[Any] , _snake_case : Any ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_snake_case )
else:
if x == 0: # 0 raised... | 60 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fla... | 60 | 1 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 60 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 60 | 1 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _snake_case ( _snake_case : int ):
for param in module.parameters():
lowerCAmelCase : Optional[int] = False
def _snake_case ( ):
lowerCAmelCase : List[str]... | 60 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 | 1 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_c... | 60 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 60 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
snake_case__ : Optional[int] = ['''small''', '''medium''', '''large''']
snake_case__ : Dict = '''lm_head.decoder.weight'''
snake_case__ : str = '''lm_head.weight'''
def ... | 60 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__ : Optional[Any] = '''src/transfo... | 60 | 1 |
"""simple docstring"""
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
snake... | 60 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class snake_case_:
def __init__( self : str , UpperCamelCase_ : int=None , UpperCamelCase_ : List[str]=None ):
# Input as list
lowerCAmelCase : str = li... | 60 | 1 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
fr... | 60 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_f... | 60 | 1 |
"""simple docstring"""
import numpy as np
import datasets
snake_case__ : Dict = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
... | 60 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if not isinstance(_snake_case , _snake_case ):
raise TypeError('''only integers accepted as input''' )
else:
lowerCAmelCase : List[str] = str(abs(_snake_case ) )
lowerCAmelC... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def _snake_case ( _snake_case : Sequence[float] , _snake_case : int , _snake_case : int ):
if not arr:
... | 60 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_cas... | 60 | 1 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def _snake_case ( _snake_case : float ):
if num <= 0:
raise ValueError('''math domain error''' )
return quad(_snake_case , 0 , _snake_case , args=(_snake_ca... | 60 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case_... | 60 | 1 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
snake_case__ : List[Any] = '''examples/'''
snake_case__ : int = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init... | 60 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _snake_case ( _snake_case : int ):
for param in module.parameters():
lowerCAmelCase : Optional[int] = False
def _snake_case ( ):
lowerCAmelCase : List[str]... | 60 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case__ : Optional[int] = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig''... | 60 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def _snake_case ( _snake_case : int ):
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, al... | 60 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformer... | 60 | 1 |
"""simple docstring"""
from math import pi, sqrt, tan
def _snake_case ( _snake_case : float ):
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def _snake_case ( _snake_case : float , _sna... | 60 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
if not isinstance(_snake_case , _snake_case ):
raise TypeError('''only integers accepted as input''' )
else:
lowerCAmelCase : List[str] = str(abs(_snake_case ) )
lowerCAmelC... | 60 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
"""simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def _snake_case ( _snake_case : Optional[int] ):
return 1 / (1 + np.exp(-z ))
def _snake... | 60 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( _snake_case : np.ndarray , _snake_case : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_snake_case , _snak... | 60 | 1 |
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