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 |
|---|---|---|---|---|
from collections.abc import Generator
def lowerCAmelCase_ ():
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_: Optional[int] = 0, 1
while True:
UpperCAmelCase_ , UpperCAmelCase_: Union[str, Any] = b... | 556 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
a : Optional[int] = logging.getLogger(__name__)
@dataclass
class _a ( _lowerCAmelCase ... | 556 | 1 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_lowerCamelCase =loggin... | 252 |
def snake_case__ ( lowerCAmelCase_ = 1000000 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =limit + 1
SCREAMING_SNAKE_CASE =[0] * limit
for first_term in range(1, lowerCAmelCase_ ):
for n in range(lowerCAmelCase_, lowerCAmelCase_, lowe... | 252 | 1 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE :... | 532 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolv... | 532 | 1 |
from math import pi, sqrt, tan
def _lowerCAmelCase ( __magic_name__ :Optional[int] ):
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def _lowerCAmelCase ( __magic_name__ :Tup... | 710 |
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.st... | 407 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig... | 90 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _snake_case ( a_ ):
SCREAMING_SNAKE_CASE : Dict = (DDPMScheduler,)
def _SCREAMING_SNAKE_CASE ( self , **_SCREAMING_SNAKE_CASE ):
... | 284 | 0 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def __A(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = int(np.ceil((x_end - xa) ... | 721 |
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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_... | 202 | 0 |
def _a ( lowercase__ : str ):
'''simple docstring'''
return "".join(chr(ord(lowercase__ ) - 32 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 85 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCAmelCase = {
"""configuration_clip""": [
"""CLIP_PR... | 569 | 0 |
"""simple docstring"""
def UpperCAmelCase ( UpperCamelCase__=28_123 ):
"""simple docstring"""
A__ = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i... | 718 | """simple docstring"""
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
__lowerCamelCase = yaml.safe_load(
"\\nname: \"\"\nallow_empty: false\nallow_empty_tex... | 536 | 0 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class lowerCamelCase_ ( unittest.TestCase ):
def lowerCAmelCase_ ( self : Optional[int] ):
__A : str = get_activation("""swish""" )
self.... | 17 |
'''simple docstring'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_... | 5 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowercase__ ( __lowercase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return [ord(lowerCAmelCase__ ) - 96 for elem in plain]
def lowercase__ ( __lowercase : Any ) ... | 714 |
'''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 lowercase__ ( __lowercase : List[str] ) -> Tuple:
"""simple docstri... | 434 | 0 |
"""simple docstring"""
_lowercase = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609_344,
"knot": 1.852,
}
_lowercase = {
"km/h": 1.0,
"m/s": 0.277_777_778,
"mph": 0.621_371_192,
"knot": 0.539_956_803,
}
def _snake_case ( snake_case__ : float , snake_case... | 91 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureE... | 695 | 0 |
'''simple docstring'''
def a_ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
"""simple docstring"""
snake_case: Optional[int] =''
for word_or_phrase in separated:
if not isinstance(__UpperCAmelCase , __Upper... | 347 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 347 | 1 |
'''simple docstring'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_sin... | 41 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from tra... | 176 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
"tokenizati... | 715 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__a = ""
__a = ""
__a = ""
__a = 1 # (0 is vertical, 1 is horizontal)
def A_ ( ):
'''simple docstring'''
... | 310 | 0 |
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = [1]
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__: Optional[Any] = 0, 0, 0
UpperCAmelCase__: Tuple = ugly_nums[ia] * 2
UpperCAmelCase__: Tuple = ugly_nums[ia] * 3
UpperCAmelCase... | 113 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoT... | 113 | 1 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.Wav... | 180 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (... | 180 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 211 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
lowercase = datasets.logging.get_logger(__name__)
lowercase = '''\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
... | 211 | 1 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 139 |
lowercase__ : Optional[int] = range(2, 20 + 1)
lowercase__ : List[str] = [10**k for k in range(ks[-1] + 1)]
lowercase__ : dict[int, dict[int, list[list[int]]]] = {}
def lowerCamelCase__ ( _A , _A , _A , _A ):
'''simple docstring''... | 139 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase ( lowercase_ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''image_processor''', '''tokenizer''']
__SCREAMING_SNAKE_CASE : Tuple = '''... | 362 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCAm... | 362 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''tokenization_roc_bert''': [... | 679 | def UpperCAmelCase__( __UpperCAmelCase : list ):
__snake_case : List[Any] = len(__UpperCAmelCase )
for _ in range(__UpperCAmelCase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
__snake_case , __snake_... | 679 | 1 |
'''simple docstring'''
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 Tok... | 18 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqCo... | 18 | 1 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowerCAmelCase_ : Any = numpy.array([0, 0])
lowerCAmelCase_ : Optional[int] = numpy.array([0.5, 0.8660254])
lowerCAmelCase_ : List[str] ... | 716 | '''simple docstring'''
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 461 | 0 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = 'E... | 293 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'transformers', 'onnx']
def __init__( self: Union[str, Any]... | 293 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers... | 709 |
"""simple docstring"""
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_tor... | 213 | 0 |
import argparse
import gc
import json
import os
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 Accelera... | 171 |
from __future__ import annotations
def UpperCamelCase( __UpperCamelCase : int ):
lowerCAmelCase_ : str = 2
lowerCAmelCase_ : Union[str, Any] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__UpperCamelCase ... | 171 | 1 |
import numpy as np
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Any ) -> np.array:
"""simple docstring"""
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 712 |
import random
from typing import Any
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : list ) -> list[Any]:
"""simple docstring"""
for _ in range(len(__magic_name__ ) ):
UpperCamelCase :Dict = random.randint(0 , len(__magic_name__ ) - 1 )... | 590 | 0 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 1_00 * 2**20, 9_00 * 2**20] )
def ... | 329 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbo... | 329 | 1 |
import torch
from diffusers import StableDiffusionPipeline
A__ = """path-to-your-trained-model"""
A__ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
A__ = """A photo of sks dog in a bucket"""
A__ = ... | 49 | 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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
... | 49 | 1 |
"""simple docstring"""
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... | 470 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase_ (lowerCamelCase_ ... | 470 | 1 |
'''simple docstring'''
import os
from distutils.util import strtobool
def _UpperCamelCase ( __A , __A ) -> Optional[int]:
'''simple docstring'''
for e in env_keys:
UpperCamelCase__ = int(os.environ.get(__A , -1 ) )
i... | 223 |
'''simple docstring'''
def _UpperCamelCase ( __A , __A ) -> int:
'''simple docstring'''
while b:
UpperCamelCase__ , UpperCamelCase__ = b, a % b
return a
def _UpperCamelCase ( __A , __A ) -> int:
'''simple... | 223 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE__: Dict, SCREAMING_SNAKE_CASE__: Optional[Any] ) -> List[Any]:
"""simple docstring"""
__a = [0 for i in range(r + 1 )]
# nc0 = 1
__a = 1
for i in range... | 448 |
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/focalnet-tiny""": """https://hugg... | 658 | 0 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
a_ = namedtuple("""covid_data""", """cases deaths recovered""")
def a__ ( _UpperCamelCase : str = "https://www.worldometers.info/coronavirus/" ):
__lowerCamelCase = '''//div[@class... | 706 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a_ = """src/transformers"""
# This is to make sure the transformers module... | 622 | 0 |
'''simple docstring'''
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_SCREAMING_SNAKE_CASE = {
'tiny.en': 'https://openaipublic.azureedge.net/main/... | 366 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tunin... | 246 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable(... | 710 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 597 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __snake_case (__UpperCAmelCase , __UpperCAmelCase ):
"""simple docstring"""
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(__UpperCAmelCa... | 501 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from acc... | 501 | 1 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
... | 414 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __magic_name__ ( lowercase_ = "isbn/0140328726" ) -> dict:
'''simple docstring'''
UpperCamelCase = olid.strip().strip("/" ) # Remove le... | 414 | 1 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : str = [
['attention', 'attn'... | 89 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Dict = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig',
'BigBirdPegasusOnnxCon... | 16 | 0 |
"""simple docstring"""
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
A__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def UpperCAmelCase ( UpperCa... | 713 | """simple docstring"""
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__lowerCamelCase = "."
if __name__ == "__main__":
__lowerCamelCase = os.path.join(REPO_PATH, "ut... | 536 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = {
"SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/c... | 325 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
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_configuration_common import... | 325 | 1 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __snake_case ( _UpperCAmelCase ):
__a = []
embed.append(
... | 60 |
from collections.abc import Generator
from math import sin
def __snake_case ( _UpperCAmelCase ):
if len(_UpperCAmelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
__a = b''''''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * ... | 60 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCamelCase( a__ ,a__ = "cpu" ,a__ = None):
_SCREAMING_SNAKE_CASE =torch.load(snake_case__ ,map_location=snake_case__)
for k, v in tqdm(state_dict.items()):
if not isinstance(snake_case__ ... | 691 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_imag... | 67 | 0 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {"""vocab_file... | 258 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class SCREAMING_SNAKE_CAS... | 258 | 1 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
... | 258 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
_A = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}... | 258 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils im... | 109 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
A = TypeVar('T')
class UpperCAmelCase__ ( Generic[T] ):
lowerCAmelCase_ : deque[T] # Cache store of keys
lowerCAmelCase_ : set[T]... | 109 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase__ : Any = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvai... | 387 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAM... | 27 | 0 |
'''simple docstring'''
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
_lowercase = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input(... | 713 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_lowercase = logg... | 427 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
... | 15 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
A : Optional[int] = int(_lowerCAmelCase )
# Initialize Result
A : int = []
# Traverse through all denomination
for denomination in reversed(... | 662 | 0 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformer... | 554 |
"""simple docstring"""
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_c... | 554 | 1 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__lowerCAmelCase = re.compile(r"\b(a|an|the)\b", re.UNICODE)
__lowerCAmelCase = None
def __lowerCamelCase ( ) -> int:
_UpperCAmelCase = ... | 684 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot... | 684 | 1 |
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __UpperCAmelCase ( lower... | 275 | """simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a ( lowerCAmelCase_ ):
@staticmethod
@abstractmethod
def lowerCAmelCase_ ( __lowerCAmelCase : ArgumentParser ):
raise NotImplementedError()
@abstractmethod
d... | 275 | 1 |
# Lint as: python3
import itertools
import os
import re
UpperCAmelCase__ = re.compile(r'''([A-Z]+)([A-Z][a-z])''')
UpperCAmelCase__ = re.compile(r'''([a-z\d])([A-Z])''')
UpperCAmelCase__ = re.compile(r'''(?<!_)_(?!_)''')
UpperCAmelCase__ = re.compile(r'''(_{... | 351 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLPN models ... | 351 | 1 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets ... | 701 | """simple docstring"""
# flake8: noqa
# Lint as: python3
__UpperCamelCase : Optional[Any] = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging im... | 227 | 0 |
import sys
import turtle
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,) ... | 311 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 311 | 1 |
'''simple docstring'''
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowerCAmelCase : List[str] ... | 39 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CO... | 39 | 1 |
def __lowercase ( _UpperCAmelCase = 600_851_475_143 ) -> List[Any]:
'''simple docstring'''
try:
__lowercase = int(__lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Pa... | 321 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
lowercase_ : Dict ... | 304 | 0 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__lowerCAmelCase = "\\n\n"
__lowerCAmelCase = "\nPerplexity (PPL) is one of the most common metrics for... | 129 |
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=lowercase):
__SCREAMING_SNAKE_CASE : List[str] = ["""torch""", """torchsde"""]
def __init__( self : Optional[Any] , *__UpperCamelCase : int , **__UpperCamel... | 129 | 1 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
f... | 637 |
'''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 = {
"""sail/poolformer_s12"... | 158 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available... | 714 | '''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
#
# ... | 415 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if i... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 122 | 1 |
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 __lowercase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
... | 721 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs... | 423 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transfor... | 413 |
"""simple docstring"""
import argparse
import copy
def a_ ( __a ):
A__ = {}
with open(__a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
A__ = []
_... | 571 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.js... | 440 |
from collections.abc import Generator
from math import sin
def _lowerCAmelCase ( _a : bytes ) -> bytes:
if len(_a ) != 32:
raise ValueError("""Input must be of length 32""" )
lowerCAmelCase_ : Any = B""""""
for i in [3, 2, 1, 0]:
little... | 440 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__lowercase = False
class a__( unittest.TestCase ):
... | 370 | '''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import Heun... | 370 | 1 |
from __future__ import annotations
def A ( __UpperCamelCase ) -> list[int]:
return [ord(lowerCamelCase__ ) - 96 for elem in plain]
def A ( __UpperCamelCase ) -> str:
return "".join(chr(elem + 96 ) for elem in encoded )
def A ( ) ... | 710 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'''roberta-base''': '''https:/... | 52 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE :Optional[Any] = {
'''configuration_blip''': [
'''BLIP_PRE... | 236 |
'''simple docstring'''
import math
import sys
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
_UpperCAmelCase = ""
try:
with open(__lowercase , "rb" ) as binary_file:
_UpperCAmelCase = binary_file.read... | 236 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generatio... | 150 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
lowercase = """"""
lowercase = """"""
lowercase = """"""
lowercase = """"""
def A__ ( _UpperCAmelCase : str ) -> None:
'''simple docstring'''
snake_case__ : Any = tweepy.OAuthH... | 150 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import ... | 101 |
# Copyright 2021 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
#
# Unless required by appli... | 148 | 0 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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
#
... | 570 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a__ : Tuple = {
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
'tokeni... | 570 | 1 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase = '''src/transformers'''
UpperCAmelCas... | 677 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}... | 677 | 1 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
_UpperCamelCase : Optional[int] = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, ... | 706 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_... | 134 | 0 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
a_ :Tuple = l... | 35 | """simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _A ( lowerCAmelCase ):
# `task` is not a Class... | 359 | 0 |
import numpy as np
from transformers import Pipeline
def _lowercase ( UpperCamelCase_ ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = np.max(UpperCamelCase_ , axis=-1 , keepdims=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = np.exp(outputs - max... | 400 |
import math
class lowercase__ :
def A_ ( self : int , UpperCAmelCase_ : list[list[float]] , UpperCAmelCase_ : list[int] ):
SCREAMING_SNAKE_CASE__ = 0.0
SCREAMING_SNAKE_CASE__ = 0.0
for i in range(len(UpperCAmelCa... | 400 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()... | 37 |
"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_mo... | 169 | 0 |
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.dataclass
class ... | 704 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_uti... | 148 | 0 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBe... | 430 |
'''simple docstring'''
from math import factorial
def __lowerCamelCase ( A__ , A__ , A__ ) -> float:
"""simple docstring"""
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if ... | 430 | 1 |
'''simple docstring'''
from __future__ import annotations
import bisect
def a_ ( _UpperCAmelCase : list[int] ,_UpperCAmelCase : int ,_UpperCAmelCase : int = 0 ,_UpperCAmelCase : int = -1 ) -> int:
if hi < 0:
__snake_case : ... | 709 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[int] = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not... | 124 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
A_ = TypeVar("""T""")
class __lowerCamelCase ( Generic[T] ):
def __init__( self , UpperCAmelCase ):
lowerCamelCase_ = data
lowerCamelCase_ = self
... | 29 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__lowerCamelCase : Dict = pytest.mark.integration
@pytest.mar... | 310 | 0 |
"""simple docstring"""
import unittest
import numpy as np
def _snake_case ( UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : np.ndarray | None = None , ):
A__ = np.s... | 500 |
"""simple docstring"""
import warnings
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_ : Any = log... | 500 | 1 |
'''simple docstring'''
from __future__ import annotations
lowercase : Optional[int] = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __a ( A__ , A__ , A__ , A__ , A__ , ) ->... | 649 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
fro... | 649 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaMod... | 700 |
'''simple docstring'''
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> list:
if len(__A ) != 2 or len(a[0] ) != 2 or len(__A ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
_snake_case = [
[... | 542 | 0 |
import cva
import numpy as np
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Tuple , UpperCamelCase__ : float , UpperCamelCase__ : int):
'''simple docstring'''
if k in (0.04, 0.06):
... | 654 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Optional[int] = ''''''
_lowercase : str = ... | 654 | 1 |
import pytest
import datasets
# Import fixture modules as plugins
_SCREAMING_SNAKE_CASE : Dict = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
for item in items:
... | 472 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : List[Any] = {
'''configuration_roberta''': ['''ROBERTA_PRETRAI... | 472 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : Optional[Any] = {
"configuration_roberta_prelayernorm": [
... | 275 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__A : str = {
"configuration_audio_spectrogram_transformer": [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAIN... | 275 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_can... | 280 |
'''simple docstring'''
import numpy as np
def a ( __a , __a , __a , __a , __a ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Tuple = int(np.ceil((x_end - xa) / h ) )
UpperCamelCase__ :Optional... | 280 | 1 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : int , SCREAMING_SNAKE_CASE__ : list ) -> None:
lowerCAmelCase__ = set_counts
lowerCAmelCase__ = max(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ ... | 61 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
... | 298 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 707 |
'''simple docstring'''
import functools
def _A ( A ,A ) -> int:
lowercase : Union[str, Any] = len(A )
lowercase : Dict = len(A )
@functools.cache
def min_distance(A ,A ) -> int:
# if first word index is overflow - delete all fro... | 425 | 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
from .tokenization_gpta import GPTa... | 499 |
"""simple docstring"""
import math
import random
def A_ ( snake_case_ : float ,snake_case_ : bool = False ):
'''simple docstring'''
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
__A : int = ... | 499 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case = {
"""configuration_conditional_detr""": [
"""CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ConditionalDetrConfig""",
... | 488 |
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = [0] * len(lowercase )
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : ... | 488 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vis... | 507 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
f... | 507 | 1 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lowercase=sys.maxsize ... | 313 |
from math import factorial
__A ={str(digit): factorial(digit) for digit in range(1_0)}
def lowerCamelCase_ ( lowerCamelCase__ ):
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise TypeError("Parameter number must be int" )
if number < 0:
ra... | 313 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
... | 419 |
from __future__ import annotations
from cmath import sqrt
def _lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if a == 0:
raise ValueError("""C... | 419 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 472 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNe... | 472 | 1 |
class lowerCAmelCase_ :
def __init__( self, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCamelCase : Tuple = n
UpperCamelCase : List[Any] = [None] * self.n
UpperCamelCase : str = 0 # ind... | 40 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.... | 89 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learne... | 382 |
def UpperCamelCase_( __magic_name__ : str ):
"""simple docstring"""
_lowerCAmelCase :Optional[Any] = [0 for i in range(len(__magic_name__ ) )]
# initialize interval's left pointer and right pointer
_lowerCAmelCase , _lowerCAmelCase :List[An... | 382 | 1 |
class a :
"""simple docstring"""
def __init__( self : List[str] ) -> None:
__UpperCAmelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCAmelCase : List[str] = False
def UpperCAmel... | 63 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchF... | 65 | 0 |
"""simple docstring"""
def UpperCAmelCase ( a_, a_, a_ ):
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def UpperCAmelCase ( a_, a_, a_ ):
'''simple docstring'''
return round(float((moles * 0.0_8_2_1 * temperature) / (vol... | 133 |
"""simple docstring"""
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def UpperCAmelCase ( a_, a_ ):
'''simple docstring'''
lowerCamelCase : int = Mock()
lowerCamelCase ... | 133 | 1 |
"""simple docstring"""
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transforme... | 82 |
import math
from numpy import inf
from scipy.integrate import quad
def SCREAMING_SNAKE_CASE_ ( __A : float ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError('math domain error' )
return quad(__A ... | 570 | 0 |
'''simple docstring'''
def A__ ( __lowerCamelCase, __lowerCamelCase ):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"""{price_plus_tax(1_00, 0.25) = }""")
print(F"""{price_plus_tax(125.50, 0.05) = }""")
| 714 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase__ ( __SCREAMING_SNAKE_CASE ):
... | 597 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = ""
for word_or_phrase in separated:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise Except... | 636 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common imp... | 397 | 0 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class lowerCAmelCase_ :
def __init__( self ,snake_case__ = None ):
SCREAMING_SNAKE_CASE_ : int = value
SCREAMING_SNAKE_CASE_ : int = None # Added ... | 720 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@req... | 685 | 0 |
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