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 sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
__a = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n Dorr... | 35 |
'''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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import ... | 35 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class UpperCAmelCa... | 35 |
'''simple docstring'''
import string
from math import logaa
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : List[str] = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" ... | 35 | 1 |
'''simple docstring'''
from typing import Any
import numpy as np
def __snake_case( _lowerCAmelCase ) -> bool:
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Any:
snake_case__ : ... | 35 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from... | 35 | 1 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_uti... | 35 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at https://hugg... | 35 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__a = 5_0000
__a = 5000
__a , __a = os.path.split(__file__)
__a = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENA... | 35 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import Ro... | 35 | 1 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
__a = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
__a = "\nArgs:\n predictions: List of... | 35 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : int = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case__ : Dict ... | 35 | 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
__a = logging.get_logger(__name__)
__a = {
"microsoft/... | 35 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> float:
snake_case__ : str = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def ... | 35 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
imp... | 35 |
'''simple docstring'''
__a = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__a = frozenset(["prompt", "negative_... | 35 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __snake_case( _lowerCAmelCase ) -> str:
return "".join(sorted(_lowerCAmelCase ) )
def __snake_case( _lowerCAmelCase ) -> list[str]:
return word_... | 35 |
'''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 TokenizerTe... | 35 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 35 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = CustomTokenizer
pass
| 35 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onn... | 35 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : Optional[Any] = np.max(_lowerCAmelCase , axis=-1 , keepdims=_lowerCAmelCase )
snake_case__ : List[str]... | 35 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> Union[str, Any]:
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
snake_case__ : Tuple = len(_lowerCAmelCase )
... | 35 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __snake_case( _lowerCAmelCase ) -> Any:
for i in range(0 , _lowerCAmelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
... | 35 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__a = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 35 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase = 1_000 ) -> int:
return sum(e for e in range(3 , _lowerCAmelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"{solution() = }")
| 35 | 1 |
'''simple docstring'''
import numpy as np
def __snake_case( _lowerCAmelCase ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def __snake_case( _lowerCAmelCase ) -> np.ndarray:
return vector * sigmoid(_lowerCAmelCase )
if __name__ == "__main__":
import do... | 35 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not i... | 35 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> str:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
rais... | 35 |
'''simple docstring'''
from PIL import Image
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Image:
def brightness(_lowerCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be ... | 35 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGenera... | 35 |
'''simple docstring'''
import argparse
import os
import re
__a = "src/transformers"
# Pattern that looks at the indentation in a line.
__a = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__a = re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that ... | 35 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():
... | 35 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
... | 35 | 1 |
'''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__a = 2_9979_2458
# Symbols
__a , __a , __a , __a = symbols("ct x y z")
def __snake_case( _lowerCAmelCase ) -> f... | 35 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaV... | 35 | 1 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..contro... | 35 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : Optional[int] = ... | 35 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/confi... | 35 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __snake_case( _lowerCAmelCase ) -> List[Any]:
... | 35 | 1 |
UpperCAmelCase__ = tuple[float, float, float]
UpperCAmelCase__ = tuple[float, float, float]
def _a ( a :Pointad , a :Pointad ) -> Vectorad:
a = end_pointa[0] - end_pointa[0]
a = end_pointa[1] - end_pointa[1]
a = end_pointa[2] - end_... | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__a = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
def __init__( self : List[str] , *s... | 35 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transforme... | 1 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__a = logging... | 35 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 2 |
'''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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import ... | 35 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__snake_case )
class A ( __snake_case ):
# `task` is not a ClassVar since we wan... | 3 |
'''simple docstring'''
import string
from math import logaa
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : List[str] = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" ... | 35 | 0 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def a_ ( lowerCamelCase : str = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(' ' , '' ).lower() ).values() ) < 2
def a_ ( lowerCamelCase : str = "" ):
... | 4 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from... | 35 | 0 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case ) -> Dict:
"""simple docstring"""
_lowercase =0
if start < end:
_lowercase =randint(__sna... | 5 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at https://hugg... | 35 | 0 |
from sklearn.metrics import matthews_corrcoef
import datasets
A : int = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positi... | 6 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import Ro... | 35 | 0 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common imp... | 7 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : int = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case__ : Dict ... | 35 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase_ = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:
if not is_torch_avail... | 8 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> float:
snake_case__ : str = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def ... | 35 | 0 |
from math import ceil
def _UpperCamelCase ( lowercase__ = 1001 ):
__SCREAMING_SNAKE_CASE : Dict = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__SCREAMING_SNAKE_CASE : Optional[Any] = 2 * i + 1
__SCREAMI... | 9 |
'''simple docstring'''
__a = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__a = frozenset(["prompt", "negative_... | 35 | 0 |
# 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 lowerCAmelCase_ ( __a ) -> Union[str, Any]:
"""simple docstring"""
return 1 / (1 + np.exp... | 10 |
'''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 TokenizerTe... | 35 | 0 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowerCAmelCase__ ( a):
'''simple docstring'''
def __init__( ... | 11 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = CustomTokenizer
pass
| 35 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_saf... | 12 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : Optional[Any] = np.max(_lowerCAmelCase , axis=-1 , keepdims=_lowerCAmelCase )
snake_case__ : List[str]... | 35 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regre... | 13 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __snake_case( _lowerCAmelCase ) -> Any:
for i in range(0 , _lowerCAmelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
... | 35 | 0 |
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
_lowerCamelCase : Optional[Any] =... | 14 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase = 1_000 ) -> int:
return sum(e for e in range(3 , _lowerCAmelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"{solution() = }")
| 35 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :str = {
'huggingface/time-series-transformer-tourism-monthly': (
... | 15 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not i... | 35 | 0 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'vocab... | 16 |
'''simple docstring'''
from PIL import Image
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Image:
def brightness(_lowerCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be ... | 35 | 0 |
"""simple docstring"""
def _A ( UpperCamelCase_ : list[int]) -> float:
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("List is empty")
__lowercase = sum(UpperCamelCase_) / len(UpperCamelCase_) # Calculate the average... | 17 |
'''simple docstring'''
import argparse
import os
import re
__a = "src/transformers"
# Pattern that looks at the indentation in a line.
__a = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__a = re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that ... | 35 | 0 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from... | 18 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
... | 35 | 0 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logging
... | 19 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaV... | 35 | 0 |
import os
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> Tuple:
lowercase : Dict = len(grid[0] )
lowercase : Dict = len(SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = 0
lowercase : str ... | 20 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : Optional[int] = ... | 35 | 0 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class _l... | 21 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __snake_case( _lowerCAmelCase ) -> List[Any]:
... | 35 | 0 |
'''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,
loa... | 22 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__a = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
def __init__( self : List[str] , *s... | 35 | 0 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
UpperCamelCase__: ... | 23 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__a = logging... | 35 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : int = 4 ) -> list[list[int]]:
__snake_case = abs(snake_case_ ) or 4
return [[1 + x + y * row_size for x in range(snake_case_ )] for y in range(snake_case_ )]
... | 24 |
'''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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import ... | 35 | 0 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCAmelCase__ : str = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-j... | 25 |
'''simple docstring'''
import string
from math import logaa
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : List[str] = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" ... | 35 | 0 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_snake_case = datasets.utils.logging.get_logge... | 26 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from... | 35 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : Tuple = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Informe... | 27 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at https://hugg... | 35 | 0 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
fro... | 28 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import Ro... | 35 | 0 |
import requests
from bsa import BeautifulSoup
def lowercase__ ( __snake_case : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCAmelCase_ : int = BeautifulSoup(requests.get(__snake_case ).text ... | 29 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : int = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case__ : Dict ... | 35 | 0 |
def a ( snake_case__: int ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise TypeError('''Input value must be an \'int\' type''' )
lowercase_ = 0
while number:
position += 1
number >>= 1
... | 30 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> float:
snake_case__ : str = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def ... | 35 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
... | 31 |
'''simple docstring'''
__a = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__a = frozenset(["prompt", "negative_... | 35 | 0 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCAmelCase_ : Union[str, Any] = datasets.logging.get_logger(__name__)
UpperCAmelCase_ : Dict = '... | 32 |
'''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 TokenizerTe... | 35 | 0 |
"""simple docstring"""
from functools import reduce
__A : int = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050... | 33 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = CustomTokenizer
pass
| 35 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class _a :
def __init__( self : Union[str, Any] , lowercase : int , lowercase : MutableSequence[float] ):
'''simple docstring'''
if len(lo... | 34 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : Optional[Any] = np.max(_lowerCAmelCase , axis=-1 , keepdims=_lowerCAmelCase )
snake_case__ : List[str]... | 35 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
_snake_case = _LazyModule(__name__,... | 36 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __snake_case( _lowerCAmelCase ) -> Any:
for i in range(0 , _lowerCAmelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
... | 35 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''microsof... | 37 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase = 1_000 ) -> int:
return sum(e for e in range(3 , _lowerCAmelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"{solution() = }")
| 35 | 0 |
from __future__ import annotations
UpperCAmelCase_ : List[Any] = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase_ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
... | 38 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not i... | 35 | 0 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_a = '''src/transformers'''
# This is to make sure the transformers module imported ... | 39 |
'''simple docstring'''
from PIL import Image
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Image:
def brightness(_lowerCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be ... | 35 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowercase = logging.get_logger(__name__)
class _A ( ... | 40 |
'''simple docstring'''
import argparse
import os
import re
__a = "src/transformers"
# Pattern that looks at the indentation in a line.
__a = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__a = re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that ... | 35 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_A : int ={
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''Mvp... | 41 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
... | 35 | 0 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
Channe... | 42 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaV... | 35 | 0 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__lowercase = 2048
__lowercase = 4096
__lowercase = 42
__lowercase = os.environ.pop('''PROCESS_TRAIN''', '''false''')
__lowercase = {'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '... | 43 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : Optional[int] = ... | 35 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 1000000 ) -> int:
_lowerCAmelCase : str = 1
_lowerCAmelCase : str = 1
_lowerCAmelCase : List[str] = {1: 1}
for inputa in range(2 ,_lowerCam... | 44 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __snake_case( _lowerCAmelCase ) -> List[Any]:
... | 35 | 0 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if imp... | 45 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__a = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
def __init__( self : List[str] , *s... | 35 | 0 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
fro... | 46 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__a = logging... | 35 | 0 |
'''simple docstring'''
from collections import deque
class A__ :
def __init__( self : List[Any] , _a : str , _a : int , _a : int ) -> None:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =process_name # pro... | 47 |
'''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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import ... | 35 | 0 |
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 import ca... | 48 |
'''simple docstring'''
import string
from math import logaa
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : List[str] = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" ... | 35 | 0 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch model
__a = ... | 49 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from... | 35 | 0 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> None:
create_state_space_tree(_UpperCAmelCase , [] , 0 )
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )... | 50 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at https://hugg... | 35 | 0 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
snake_case_ : Union[str, Any] = logging.get_logger(__name__)
snake_case_ : Optional[int] = {name: getattr... | 51 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import Ro... | 35 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class A__ ... | 52 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : int = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case__ : Dict ... | 35 | 0 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case ( __lowerCamelCase , __lowerCamelCase ):
"""simple docst... | 53 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> float:
snake_case__ : str = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def ... | 35 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase_ ( metaclass=UpperCamelCase):
"""simple docstring"""
snake_case__ : Optional[Any] = ["keras_nlp"]
def __init__( self : Optional[Any] , *UpperCAmelCase__ : List[Any] , ... | 54 |
'''simple docstring'''
__a = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__a = frozenset(["prompt", "negative_... | 35 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
a_ : List[Any] = logging.get_logger(_... | 55 |
'''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 TokenizerTe... | 35 | 0 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable... | 56 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = CustomTokenizer
pass
| 35 | 0 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : Optional[Any] = {
"vocab_file": "vocab.json",
"tokeniz... | 57 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : Optional[Any] = np.max(_lowerCAmelCase , axis=-1 , keepdims=_lowerCAmelCase )
snake_case__ : List[str]... | 35 | 0 |
'''simple docstring'''
from itertools import permutations
def lowerCamelCase ( __lowerCamelCase : tuple ) ->bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_SCREAMING_S... | 58 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __snake_case( _lowerCAmelCase ) -> Any:
for i in range(0 , _lowerCAmelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
... | 35 | 0 |
def UpperCamelCase ( __lowerCamelCase : int = 1000 ):
snake_case : List[Any] = 2**power
snake_case : Any = 0
while n:
snake_case , snake_case : Tuple = r + n % 10, n // 10
return r
if _... | 59 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase = 1_000 ) -> int:
return sum(e for e in range(3 , _lowerCAmelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"{solution() = }")
| 35 | 0 |
"""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'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not i... | 35 | 0 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_loggi... | 61 |
'''simple docstring'''
from PIL import Image
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Image:
def brightness(_lowerCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be ... | 35 | 0 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_A = TypeVar('KEY')
_A = TypeVar('VAL')
@dataclass(frozen=A_ , slots=A_ )
class UpperCAmelCase__ ( Generic[KEY, VAL] ):
"""simple docst... | 62 |
'''simple docstring'''
import argparse
import os
import re
__a = "src/transformers"
# Pattern that looks at the indentation in a line.
__a = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__a = re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that ... | 35 | 0 |
'''simple docstring'''
from functools import reduce
lowerCAmelCase_ : Optional[Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'125406987471585238630507156... | 63 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
... | 35 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase( metaclass=__a ):
'''simple docstring'''
lowercase__ = ["note_seq"]
def __init__( self: Dict, *a_: Union[str, Any], **a_: List[str] )... | 64 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaV... | 35 | 0 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..util... | 65 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : Optional[int] = ... | 35 | 0 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def A_ ( ):
'''simple docstring'''
snake_case_, snake_case_ :Tuple = 9, 14 # noqa: F841
snake_case_ :Optional[Any] = [
[0... | 66 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __snake_case( _lowerCAmelCase ) -> List[Any]:
... | 35 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__... | 67 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__a = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
def __init__( self : List[str] , *s... | 35 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
lowerCAmelCase__ ... | 68 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__a = logging... | 35 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__UpperCamelCase = logging.get_logger(__name__)
class UpperCamelCase ( lowerCAmelCase__ ):
def __init__( self, *lowerCAmelCase__, **l... | 69 |
'''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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import ... | 35 | 0 |
'''simple docstring'''
import sys
from collections import defaultdict
class UpperCAmelCase :
def __init__( self : int ) -> Optional[Any]:
_lowerCAmelCase = []
def lowercase__ ( self : List[str] ... | 70 |
'''simple docstring'''
import string
from math import logaa
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : List[str] = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ).replace("""\n""" ... | 35 | 0 |
from manim import *
class __A ( a ):
"""simple docstring"""
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[Any] =Rectangle(height=0.5 , width=0.5 )
... | 71 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from... | 35 | 0 |
"""simple docstring"""
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengt... | 72 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at https://hugg... | 35 | 0 |
from collections import namedtuple
a =namedtuple("""from_to""", """from_ to""")
a ={
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.0_01, 1000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.0_04_54, 2_64.1_72),
"""cubicyard""": from_to(0.7_64_55, 1.3_07_95),
... | 73 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import Ro... | 35 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def _snake_case ( snake_case__ : Union[str, Any] , snake_case__ : Optional[int] , snake_case__ : List[Any] , snake_case__ : ... | 74 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : int = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case__ : Dict ... | 35 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __UpperCamelCase ( unittest.TestCase , lowerCamelCase__ ):
def lowercase__ ( self ):
"""simple docstring"""
... | 75 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> float:
snake_case__ : str = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def ... | 35 | 0 |
from __future__ import annotations
from collections import deque
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : int , a : list[str] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : list[dict] = ... | 76 |
'''simple docstring'''
__a = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__a = frozenset(["prompt", "negative_... | 35 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech... | 77 |
'''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 TokenizerTe... | 35 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = generate_pascal_triangle(lowercase_ )
for row_idx in range(lowercase_ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
... | 78 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = CustomTokenizer
pass
| 35 | 0 |
'''simple docstring'''
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
lowerCamelCase_ = logging.getLogger(__name__)
lowerCamelC... | 79 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : Optional[Any] = np.max(_lowerCAmelCase , axis=-1 , keepdims=_lowerCAmelCase )
snake_case__ : List[str]... | 35 | 0 |
'''simple docstring'''
from math import factorial
def _UpperCamelCase ( __A = 20 ) -> int:
'''simple docstring'''
UpperCamelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCamelCase__ ... | 80 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __snake_case( _lowerCAmelCase ) -> Any:
for i in range(0 , _lowerCAmelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
... | 35 | 0 |
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