code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import to... | 30 |
from __future__ import annotations
import math
__a = '2020.9.26'
__a = 'xcodz-dot, cclaus, dhruvmanila'
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if not all(isinstance(... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
snake_case__ : Tuple = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFI... | 702 |
snake_case__ : List[Any] = """Tobias Carryer"""
from time import time
class _a :
"""simple docstring"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=int(time() ) ) -> Tuple: # noqa... | 618 | 0 |
"""simple docstring"""
import numpy
class lowercase__ :
"""simple docstring"""
def __init__( self , _A , _A ):
'''simple docstring'''
UpperCamelCase : Dict = input_array
... | 102 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
f... | 149 | 0 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self : str ):
... | 602 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
__magic_name__ : Optional[Any] = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
__magic_name__ ... | 602 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 560 |
"""simple docstring"""
lowerCAmelCase_ = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kil... | 560 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : List[Any] = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConfig"... | 705 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def Uppe... | 441 | 0 |
"""simple docstring"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _lowerCamelCase ( UpperCAmelCase_ : Optional[int], UpperCAmelCase_ : Tuple, UpperCAmelCase_ : str, UpperCAmelC... | 104 |
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
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_... | 311 | 0 |
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
lowerCamelCase =logging.get_logger(__name__)
lowerCamelCase ={
"sail/poolformer_s12": "https://... | 462 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase ={
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileBertCon... | 462 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
log... | 88 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging... | 187 | 0 |
"""simple docstring"""
from math import factorial, radians
def lowercase (snake_case__ : float , snake_case__ : int = 18 , snake_case__ : int = 10 ) -> List[str]:
'''simple docstring'''
lowerCAmelCase = angle_in_degrees - ((angle_in_degree... | 709 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_... | 529 | 0 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion impo... | 130 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __a ( ) -> int:
'''simple docstring'''
UpperCAmelCase_, UpperCAmelCase_= 9, 14 # noqa: F841
UpperCAmelCase_= [
[0, 1, 4],
[0, 7, 8],
... | 593 | 0 |
import torch
def UpperCAmelCase_ ( ) -> Optional[Any]:
if torch.cuda.is_available():
__lowercase : Dict = torch.cuda.device_count()
else:
__lowercase : Optional[Any] = 0
print(F'Successfully ran on {num_gpus} GPUs' )
if __... | 718 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from ... | 284 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase ={
"configuration_vision_encoder_decoder": ["VisionEncoderDecod... | 617 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
... | 41 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.t... | 535 |
def SCREAMING_SNAKE_CASE__ ( snake_case__ :List[str] ) -> Dict:
_lowercase = len(snake_case__ )
while cur > 1:
# Find the maximum number in arr
_lowercase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
_lowercase = a... | 535 | 1 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 0 ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = end or len(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ... | 591 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.model... | 591 | 1 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCamelCase_ :
def _snake_case ( self :Any , __A :Tuple ) -> Optional[Any]:
"""simple docstring"""
r... | 59 |
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: list[list[float]] ):
SCREAMING_SNAKE_CASE__ = []
for data in source_data:
for i, el in enumerate(UpperCamelCase__ ):
if len(UpperCamelCase__ ) < i + 1:
data_lists.append([] )
data_lists[i].append(fl... | 59 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in ran... | 46 |
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers impo... | 588 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_torch_available():
... | 220 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, req... | 220 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = (DDPMScheduler,)
def SCREAMING_SNAK... | 41 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def lowerCamelCase_( ) -> None:
'''simple docstring'''
print("Making key files..." )
make_key_... | 46 | 0 |
from __future__ import annotations
__UpperCAmelCase : Any = []
def lowercase_ ( __snake_case : list[list[int]] , __snake_case : int , __snake_case : int ) -> bool:
'''simple docstring'''
for i in range(len(__... | 712 |
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
from... | 57 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from... | 23 |
import argparse
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
from accelerate i... | 23 | 1 |
'''simple docstring'''
import re
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
if len(re.findall('[ATCG]' , snake_case_ ) ) != len(snake_case_ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC... | 718 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokenizer"""],
}
try:
i... | 69 | 0 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testi... | 519 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__UpperCamelCase : Optional[Any] = 6_378_137.0
__UpperCamelCase : Any = 6_356_752.314_245
__UpperCamelCase : Optional[int] = 6378137
def _UpperCAmelCase ( Up... | 519 | 1 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lo... | 15 | import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
'''simple docstring'''
_snake_case = (EulerDiscreteScheduler,)
... | 15 | 1 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_SCREAMING_SNAKE_CASE = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import w... | 18 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = "\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"
_SCREAMING_SNAKE_CASE = ... | 18 | 1 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
return getitem, k
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return setitem, k, v... | 706 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase = [ord(letter) for letter in string.ascii_lowercase]
Upp... | 152 | 0 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = (CMStochasticIterativeScheduler,)
SCREAMING_SNAKE_CASE : Lis... | 699 |
from __future__ import annotations
import time
lowerCamelCase_ : Union[str, Any] = list[tuple[int, int]]
lowerCamelCase_ : str = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0,... | 548 | 0 |
def __lowerCamelCase (UpperCAmelCase__ : int ):
assert isinstance(UpperCAmelCase__ , UpperCAmelCase__ ), F"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
SCREAMING_SNAKE_CASE = F"The input value o... | 647 | from __future__ import annotations
import math
def __lowerCamelCase (UpperCAmelCase__ : 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... | 647 | 1 |
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 BatchFeature
from ...utils impor... | 170 |
# Copyright (c) 2021-, NVIDIA CORPORATION. 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
#
# Unless required by a... | 170 | 1 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__UpperCAmelCase = loggin... | 713 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Tuple = "bert-generation"
def __init__( self , __A=5_0358 , __A=1024 , __A=24 , __A=16 , ... | 256 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowercase_( SCREAMING_SNAKE_CASE_ = "" ):
'''simple docstring'''
lowerCamelCase : Any = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
lowerCa... | 340 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dat... | 340 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json'... | 712 |
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_case ... | 170 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImga... | 123 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_... | 123 | 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 tensorflow as tf
from transf... | 704 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCamelCase__ = 3
def _SCREAMING_SNAKE_CASE( snake_case_ : int ) ->int:
'''simple docstring'''
... | 411 | 0 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCamelCase__ = HfApi()
UpperCamelCase__ = {}
# fmt: off
UpperCamelCase__ = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
... | 322 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class UpperCAmelCase__ ( A_ ):
'''simple docstring'''
def __init__( self : int , *UpperCamelCase : ... | 322 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/config.json''',
'''google/fnet-large''': ... | 702 | '''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
UpperCamelCase_ = loggin... | 320 | 0 |
"""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 ...im... | 630 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffuse... | 630 | 1 |
def snake_case_ ( lowercase__ : int ):
'''simple docstring'''
_lowerCAmelCase =[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =0, 0, 0
_lowerCAmelCase =ugly_nums[ia] * 2
_lowerCAmelCase =ugly_nums[ia] * 3
_lowerCAme... | 712 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
# TODO Update this
__SCREAMING_SNAKE_CASE : int = {
''... | 149 | 0 |
import argparse
import struct
import unittest
class snake_case_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _UpperCamelCase : bytes ) ->None:
snake_case_ = data
# Initialize hash values
s... | 39 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin... | 538 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : str ... | 719 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A_ : Un... | 616 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCAmelCase : int ={
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Chin... | 440 | import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenC... | 537 | 0 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( A__ ):
'''simple docstring'''
def __init__( self :List[Any] , lowerCAmelCase__ :Dict , lowerCAmelCase__ ... | 260 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = (EulerDiscreteScheduler,)
SCREAMI... | 260 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_a: Dict = get_logger(__name__)
_a: int = r"""\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length... | 162 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, Te... | 208 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .va... | 700 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def __UpperCAmelCase ( lowerCamelCase_ , lowerCamelCase_ = "cpu" , lowerCamelCase_ = None) -> None:
UpperCamelCase__ : List[Any] ... | 6 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING... | 40 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUE... | 468 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''distilbert-b... | 702 |
"""simple docstring"""
from math import ceil, sqrt
def lowercase ( __snake_case : int = 1_0_0_0_0_0_0 ):
lowercase_ : Tuple = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowercase_ : in... | 141 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from... | 633 |
"""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 A_ ( snake_case__ ) -> str:
return 1 / (1 + np.exp(-z ))
def A_ ... | 355 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...te... | 122 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available... | 122 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_distilbert''': [
... | 467 |
'''simple docstring'''
class UpperCAmelCase :
def __init__( self : Union[str, Any] ):
UpperCAmelCase__ :dict[str, TrieNode] = {} # Mapping from char to TrieNode
UpperCAmelCase__ :Union[str, Any] = False
def __SCREAMING_SNAKE_CASE ( self :... | 467 | 1 |
from __future__ import annotations
def _lowerCamelCase ( _a ):
"""simple docstring"""
_lowerCamelCase = 0.00
_lowerCamelCase = 0
for resistor in resistors:
if resistor <= 0:
_lowerCamelCase = F'''Resistor at index {index} has a negative or zero value... | 297 |
from maths.prime_factors import prime_factors
def _lowerCamelCase ( _a ):
"""simple docstring"""
if not isinstance(_a , _a ):
_lowerCamelCase = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_a )
if number < 1:
raise ValueError('... | 297 | 1 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a__ = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'''c... | 14 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowercase__( A ):
if "model" in orig_key:
snake_case__ : Any = orig_key.replace('model.' , '' )
if "norm1" in orig_key:
snake_case__ : Optional... | 170 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case =logging.get_logger(__name__)
__snake_case ={
"""microsoft/fo... | 513 |
'''simple docstring'''
def a_ ( lowerCamelCase : Tuple , lowerCamelCase : Tuple ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
lowerCAmelCase = (boundary[1] - boundary[0]) / steps
lowerCAmelCase = boundary[0]
lowerCAmelCase... | 513 | 1 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCa... | 105 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class _a ( A__ ):
"""simple docstring"""
snake_case ="""EncodecFeatureExtractor"""
snake_case =("""T5Tokenizer""", """T5TokenizerF... | 408 | 0 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a :Union[str, Any] = 10
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase... | 709 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/r... | 12 | 0 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class snake_case_ :
'''simple docstring'''
__UpperCamelCase = None
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase ... | 375 |
def SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , snake_case ) -> int:
# Return True if there is node that has not iterated.
__lowercase = [False] * len(snake_case )
__lowercase = []
queue.append(snake_case )
... | 375 | 1 |
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
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""fac... | 45 |
from PIL import Image
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Image:
def brightness(_SCREAMING_SNAKE_CASE ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError('level must be between ... | 45 | 1 |
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 ..utils.dummy_pt_objects import ... | 339 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization... | 339 | 1 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is j... | 113 |
'''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)
__A =logging.getLogger()
... | 113 | 1 |
def lowerCAmelCase_ ( _lowercase : Tuple , _lowercase : Union[str, Any]) -> float:
"""simple docstring"""
_validate_point(a__)
_validate_point(a__)
if len(a__) != len(a__):
raise ValueError("""Both points must be in the same n-dimensional space"... | 136 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class __A( unittest.TestCas... | 219 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase = {}
UpperCAmelCase = 2
while True:
UpperCAmelCase = factor_map.pop(A ... | 711 |
'''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
#
#... | 50 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def a (lowerCAmelCase__ = "isbn/0140328726" ):
__a = olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("""/""" ) != 1:
__a ... | 99 |
import warnings
from ..trainer import Trainer
from ..utils import logging
_SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class A ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self : List[str] , _UpperCamelCase : ... | 226 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _A ( snake_case ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ... | 700 |
def lowercase ( __A : Union[str, Any] ) -> int:
'''simple docstring'''
snake_case : Dict = [0] * len(__A )
snake_case : int = []
snake_case : Optional[Any] = [1] * len(__A )
for values in graph.values():
for i in v... | 315 | 0 |
'''simple docstring'''
from math import pi, sqrt
def _lowercase ( lowerCamelCase__ : float ):
if num <= 0:
raise ValueError("math domain error" )
if num > 1_71.5:
raise OverflowError("math range error" )
elif num - int(lowerCamelCase__ ) not i... | 131 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from... | 131 | 1 |
'''simple docstring'''
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
__... | 61 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a_ :
def __init__(self , __a = None) -> None:
"""simple docstring"""
... | 61 | 1 |
def _lowerCamelCase ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
"""simple docstring"""
a_ : int = ... | 419 |
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=__A ):
UpperCAmelCase : Optional[int] = ["""sentencepiece"""]
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> List[Any]:
"""simple docstring"""
... | 419 | 1 |
"""simple docstring"""
from ... import PretrainedConfig
__A = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class a ( A_ ):
A_ : int = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
A_ : ... | 173 | """simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : int = 1000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 173 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase (SCREAMING_SNAKE_CASE_ ):
__A = (DDPMScheduler,)
def _a ( self , **_lowerCAmelCase ) -> Any:
'''simpl... | 588 | from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
lowerCAmelCase_ = 42
lowerCAmelCase_ = 42
class ... | 544 | 0 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_lowercase : Any = _symbol_d... | 546 |
import argparse
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
from accelerate import Accelerator, Di... | 546 | 1 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
A__ = models.Sequential()
# Step 1 - Convolution
# ... | 166 | import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase ( a_ : Optional[int] , a_ : Optional[Any] , a_ : List[st... | 166 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorT... | 607 |
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():
import jax.numpy as jnp
from tra... | 607 | 1 |
from ...configuration_utils import PretrainedConfig
A : Optional[int] = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-wtq': (
'https://huggingface.co/goog... | 15 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 120 | 0 |
'''simple docstring'''
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'kakaobrain/align-base': 'https://hugg... | 709 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
a_ = logging.get... | 193 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : Dict , lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
def get_matched_characters(lowerCAmelCase : Optional[int] , lowerCAmelCase : int ) -> str:
__... | 561 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase__ ( A__ , unittest.TestCase ):
"""simple docstring"""
a = TransfoXLTo... | 493 | 0 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
neste... | 400 |
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 BatchFeature
from ...utils import ... | 400 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , UpperCAmelCase__ : Union[str, Any]=2 , UpperCAmelCa... | 92 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_lowerCAmelCase = '''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = "mumbai" ):
""... | 565 | 0 |
'''simple docstring'''
def A ( _UpperCAmelCase : int ,_UpperCAmelCase : Any ,_UpperCAmelCase : Any=False ) -> List[str]:
'''simple docstring'''
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and isinstance(_UpperCAmelCase ,_UpperCAmelCase ... | 123 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def A ( ) -> Tuple:
'''simple docstring'''
__lowerCAmelCase : int = {
'repo_name': ['test_repo1... | 123 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTra... | 523 | '''simple docstring'''
import numpy as np
def UpperCamelCase__ ( _lowercase : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 523 | 1 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config_file""... | 716 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_A = logging.getLo... | 507 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryTransformerConfig""",
... | 687 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self: int ):
_lowerC... | 687 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf... | 705 |
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=_lowerCAmelCase ):
a_ : Dict = ['''torch''', '''transformers''', '''onnx''']
def __init__(self , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
... | 142 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def A ( _A, _A = 2, _A = 1, _A = 3, ):
"""simple docstring"""
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError("The input value cannot be les... | 584 |
"""simple docstring"""
def A_ ( snake_case_ : int = 1_0_0_0_0_0_0 ):
'''simple docstring'''
UpperCamelCase : List[Any] = [i - 1 for i in range(limit + 1 )]
for i in range(2 ,limit + 1 ):
if phi[i] == i - 1:
for j in range(... | 499 | 0 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a_ ( __UpperCamelCase ):
def __init__( self ... | 707 | """simple docstring"""
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
f... | 674 | 0 |
'''simple docstring'''
import argparse
import datetime
def _UpperCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
__magic_name__ : Union[str, Any] = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday... | 436 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_SCREAMING_SNAKE_CASE : List[Any] = Lock()
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCa... | 436 | 1 |
'''simple docstring'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__lowerCamelCase : Tuple = re.compile(r"""^(?P<major>\d+)""" r"""\.(?P<minor>\d+)""" r"""\.(?P<patch>\d+)$""")... | 418 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Tuple = {"""configuration_fnet""": ["""FNET_PRET... | 418 | 1 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 645 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
lowe... | 645 | 1 |
'''simple docstring'''
__UpperCAmelCase = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
_... | 692 |
'''simple docstring'''
import os
import sys
import unittest
__UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, crea... | 692 | 1 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
lowercase__ = str(bin(SCREAMING_SNAKE_CASE ) )[2:] # remove the leading "0b"
lowercase__ = str(... | 43 |
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return "".join([hex(SCREAMING_SNAKE_CASE )[2:].zfill(2 ).upper() for byte in list(SCREAMING_SNAKE_CASE )] )
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if (len(SCREAMING_SNAKE_CASE ) % 2) != ... | 43 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERende... | 349 |
"""simple docstring"""
import numpy as np
def UpperCAmelCase_ ( __a : np.array ):
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
... | 458 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __magic_name__ ( ) -> Union[str, Any]:
"""simple docstring"""
wit... | 458 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def _A ( A__ ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not pri... | 702 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_con... | 624 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''image_processor''', '''tokenizer''']
lowerCAmelCase_... | 74 |
def lowerCAmelCase ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: int = [0 for i in range(r + 1 )]
# nc0 = 1
__SCREAMING_SNAKE_CASE: Dict = 1
... | 202 | 0 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets impo... | 701 |
'''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_to... | 271 | 0 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_... | 68 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class lowercase__ ( uni... | 475 | 0 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def UpperCAmelCase__ ( lowerCamelCase_ : Union[str, Any] ):
__a : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only... | 720 |
def UpperCAmelCase__ ( lowerCamelCase_ : list[int] , lowerCamelCase_ : list[int] ):
# Check if the input is valid
if not len(lowerCamelCase_ ) == len(lowerCamelCase_ ) == 3:
raise ValueError('Please enter a valid equation.' )
if e... | 577 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :str) -> list[int]:
return [ord(a_) - 96 for elem in plain]
def __A ( a_ :list[int]) -> str:
return "".join(chr(elem + 96) for elem in encoded)
def __A ( ) ->... | 52 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@req... | 24 | 0 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --... | 700 |
'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
lowercase = logging.get_logger(__name__)
lowercase = '''T5Config'''
class __lowerCamelCase ... | 564 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 0 , _lowerCAmelCase = 0 )-> int:
__UpperCAmelCase = right or len(__lowercase ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif... | 126 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | 0 |
def lowercase ( _a ) -> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def lowercase ( _a ) -> bool:
UpperCAmelCase_: Any = 0
UpperCAmelCase_: List[str] = number
while duplicate > 0:
UpperCAmelCase_ , UpperCAm... | 306 |
from __future__ import annotations
_lowerCAmelCase = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCAmelCase = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowercase ( _a ) -> list[float]:
UpperCAmelCase_: Dict = []
U... | 306 | 1 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 521 |
from __future__ import annotations
def __UpperCamelCase ( lowerCAmelCase__ : list[float] , lowerCAmelCase__ : int ):
print(f"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(lowerCAmelCase__ ):
print(f"{i}\t\t{d}" )
def __UpperCamelCase ( ... | 521 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
__lowerCAmelCase = tuple[int, int]
class SCREAMING_SNAKE_CASE :
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE ... | 714 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet i... | 666 | 0 |
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