code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __snake_case ( unittest.TestCase ):
_a = JukeboxTokenizer
_a = {
'''artist''': '''Zac Brown Band''',
'''genres''': '''Country... | 103 |
from pathlib import Path
import fire
def UpperCamelCase( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : int ):
lowerCAmelCase_ : List[str] = Path(__UpperCamelCase )
lowerCAmelCase_ : Union[str, Any] = Path(__UpperCamelCase )
d... | 103 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_common import ConfigTester
from ...t... | 103 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, sl... | 103 | 1 |
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.convers... | 221 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A ( nn.Module ):
UpperCamelCase__ : int
UpperCamelCase__ : int
UpperCamelCase__ : fl... | 199 | 0 |
from collections import defaultdict
def __magic_name__ ( __a : str , __a : str ):
'''simple docstring'''
UpperCamelCase__ = first_str.lower().strip()
UpperCamelCase__ = second_str.lower().strip()
# Remove whitespace
UpperCamelCase__ = f... | 178 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFas... | 178 | 1 |
'''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__":
lowerCAmelCase__ = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''S... | 104 |
"""simple docstring"""
import string
def lowerCamelCase__ ( _lowerCamelCase : str ) -> None:
for key in range(len(string.ascii_uppercase ) ):
lowerCamelCase_ = ''
for symbol in message:
if symbol in string.ascii_upp... | 183 | 0 |
"""simple docstring"""
import argparse
import copy
def _snake_case ( lowercase__ ):
_lowerCamelCase : Union[str, Any] = {}
with open(lowerCamelCase_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
... | 355 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
"... | 12 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ : Any = {'configuration_reformer': ['REFORMER_PRETRAIN... | 63 | """simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertE... | 44 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : int = logging.get_logger(__name__)
lowercase__ : Optional[int] = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/reso... | 155 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_uti... | 155 | 1 |
__lowerCamelCase = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
... | 59 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common imp... | 59 | 1 |
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> float:
a = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise... | 347 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __A ( __lowerCamelCase ) -> bool:
a = int(number**0.5 )
return number == sq * sq
def __A ( __lowerCamelCase , __lowerCamelCase , ... | 347 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( ):
UpperCAmelCase = []
UpperCAmelCase = 1
while len(lowercase_ ) < 1e6:
constant.append(str(lowercase_ ) )
i += 1
UpperCAmelCase = ''.join(lowerc... | 78 |
def A__ ( __lowerCamelCase = 10_00 ):
SCREAMING_SNAKE_CASE_ = 2**power
SCREAMING_SNAKE_CASE_ = 0
while n:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 299 | 0 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
__a = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0... | 363 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load... | 43 | 0 |
"""simple docstring"""
from math import factorial
def snake_case_ ( A_ : int, A_ : int ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factor... | 72 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, Hf... | 308 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 76 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : Dict = """"""
try:
with open(UpperCamelCase , """rb""" ) as binary_file:
UpperCAmelCase : str = binary_file.read()
for dat in data:
UpperC... | 76 | 1 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAP... | 215 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self : Tuple , __UpperCAmelCase : Any ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ = data
SCREAMING_SNAKE_CASE__ = None
def __repr__(... | 165 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :list[int] ) -> float:
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
lowercase = sum(lowerCAmelCas... | 32 | """simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__lowerCAmelCase : List[Any] =numpy.array([0, 0])
__lowerCAmelCase : List[str] =numpy.array([0.5, 0.866_0254])
__lowerCAmelCase... | 32 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
"""andre... | 91 |
"""simple docstring"""
from math import factorial
def _A (__a = 20 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
SCREAMING_SNAKE_CASE_ ... | 91 | 1 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Any ) -> int:
_UpperCamelCase : int = ""
_UpperCamelCase... | 310 |
"""simple docstring"""
from typing import Any
def lowercase__ ( lowercase_ ) -> list[Any]:
"""simple docstring"""
if not input_list:
return []
_UpperCamelCase : Dict = [input_list.count(lowercase_ ) for value in input_list]
_UpperCamelCa... | 310 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tr... | 89 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowerCAmelCase = '<<<<<<< This should probably be modified because it mentions: '
lowerCAmelCase = ... | 110 | 0 |
from __future__ import annotations
def __lowerCamelCase (UpperCAmelCase__ : list , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE , SCREAMING... | 206 | import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_lowerCamelCase : Dict = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
_lowerCamelCase : Optional[int] = None
def __lowerCamelCase ():
... | 206 | 1 |
def __lowerCamelCase ( UpperCAmelCase_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
a :Any = set()
# Replace all the whitespace in our sentence
a :str = input_str.replace(''' ''' , '''''' ... | 94 |
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 ShapERenderer
from diffusers.utils impo... | 12 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
lowerCamelCase_ = {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
"albert... | 361 |
"""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,... | 239 | 0 |
"""simple docstring"""
from math import ceil
def lowercase (snake_case__ : List[Any] , snake_case__ : int ) -> Tuple:
'''simple docstring'''
lowerCAmelCase = list(range(0 , snake_case__ ) )
lowerCAme... | 155 |
"""simple docstring"""
def lowercase (snake_case__ : list[int] , snake_case__ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case__ ) == len(snake_case__ ) == 3:
raise ValueError("""Please e... | 155 | 1 |
"""simple docstring"""
import os
import numpy
import onnx
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : Dict = a.name
_lowerCamelCase : Optional[int] = b.name
_lowerCamelCase : Union[st... | 369 |
"""simple docstring"""
def _snake_case ( lowercase__ = 10 ):
if not isinstance(lowercase__ , lowercase__ ) or n < 0:
raise ValueError('Invalid input' )
_lowerCamelCase : str = 10**n
_lowerCamelCase : Union[str, Any] = 28433... | 12 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : Optional[int] = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.c... | 56 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=_lowerCamelCase ):
snake_case_ = ["transformers", "torch", "note_seq"]
def __init__( self : Union[str, Any] , *lowercase_ : Optional[int] , **lowercas... | 56 | 1 |
'''simple docstring'''
from __future__ import annotations
class a__ :
"""simple docstring"""
def __init__(self , __lowercase ):
__lowerCAmelCase = TypeError(
'''Matrices must be formed from a list of zero or more lists cont... | 9 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
fr... | 9 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_config... | 78 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
... | 78 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available... | 351 | '''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pip... | 214 | 0 |
from math import sqrt
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples ... | 273 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester... | 273 | 1 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase = 10**9 ) -> int:
'''simple docstring'''
lowercase : Optional[int] = 1
lowercase : Optional[Any] = 2
lowercase : str = 0
lowercase ... | 53 |
"""simple docstring"""
_UpperCamelCase: Dict = 2_5_6
# Modulus to hash a string
_UpperCamelCase: Union[str, Any] = 1_0_0_0_0_0_3
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
'''simple docstring''... | 53 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( __A : list[int] , __A : int ) -> list[list[int]]:
"""simple docstring"""
a_ : list[list[int]] = []
a_ : list[int] = []
a_ : Dict = 0
... | 32 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE_ ( ) -> Any:
"""simple docstring"""
a_ : Optional[Any] = HfArgumentParser(__A )
a_ : Optional[int] = parser.par... | 32 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise Option... | 334 |
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import Mode... | 334 | 1 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available... | 69 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_com... | 311 | 0 |
import math
import sys
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Tuple = ""
try:
with open(__lowerCamelCase, "rb" ) as binary_file:
UpperCAmelCase_ : Union[str, Any] = binary_file.read()
for dat in data:
UpperCAmelCase_ : List[str] ... | 362 |
"""simple docstring"""
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_para... | 23 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availabl... | 9 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 61 | 0 |
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_tensor, random_attention_mask
f... | 357 |
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,
StableDiffusionPipeline,
UNetaDC... | 201 | 0 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
_UpperCAmelCase : T... | 236 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase : int = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
rai... | 236 | 1 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CAS... | 308 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase : ... | 308 | 1 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A = 1 / sqrt(2 ) ) -> IIRFilter:
_snake_case = tau * frequency / samplerate
_snake_case = sin(__A )
_snake_case... | 42 |
from __future__ import annotations
def lowerCamelCase__ ( A__ : list[int | float] , A__ : int , A__ : int ):
'''simple docstring'''
if len(A__ ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if (... | 12 | 0 |
"""simple docstring"""
from manim import *
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : int = Rectangle(height=0.5 , width=0.5 )
_... | 368 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=__snake_case ):
'''simple docstring'''
lowerCamelCase__ =['transformers', 'torch', 'note_seq']
def __init__(self , *a_ , **a_ ):
'''simple docstring'... | 24 | 0 |
from __future__ import annotations
class _lowercase :
'''simple docstring'''
def __init__( self :Optional[int] , lowerCAmelCase__ :list[list[int]] ) -> str:
__SCREAMING_SNAKE_CASE : Optional[Any] = TypeError(
'''Matrices must be form... | 9 |
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
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeniz... | 9 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase ( SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
def decorator(SCREAMING_SNAKE_CASE : Tuple ):
__UpperCamelCase :Any = getattr(SCREAMING_SNAKE_CASE , '''handle_key... | 359 | import os
import pytest
from transformers.dynamic_module_utils import get_imports
__lowercase = '''
import os
'''
__lowercase = '''
def foo():
import os
return False
'''
__lowercase = '''
def foo():
def bar():
if True:
import os
return False
r... | 105 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Co... | 319 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''Yi... | 319 | 1 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available(... | 323 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[Any] = {
'''configuration_xlm_roberta'''... | 323 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP... | 201 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # ... | 55 | 0 |
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
SCREAMING_SNAKE_CASE_: List[str] =3_00 # TEMPERATURE (unit = K)
def lowerCAmelCase_ ( snake_case_ : float , snake_case_ : float , snake_case_ : float , ) -> float:
'''s... | 106 | '''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_: Any =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: ... | 106 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : Optional[Any] = {
"configuration_re... | 42 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVeca... | 42 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
),
}
... | 348 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self ... | 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
"convert_funnel_original_... | 122 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
UpperCamelCase__: Tuple = numpy.array([0, 0])
UpperCamelCase__: Union[str, Any] = numpy.array([0.5, 0.8660254])
... | 23 | 0 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_t... | 136 |
'''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,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 136 | 1 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
_lowercase : List[Any] = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WE... | 93 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowercase : str = lo... | 93 | 1 |
import argparse
import os
import re
_UpperCAmelCase = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_UpperCAmelCase = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict... | 364 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self: List[str] , *,
_SC... | 328 | 0 |
'''simple docstring'''
_UpperCamelCase : Tuple = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
_Upp... | 304 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class snake_case__ :
a_ = 42 # [batch_size x 3]
a_ = 42 # [batch_size x 3]
a_ = 42 # [batch_size x 3]
a_ = 42 # [batch_siz... | 304 | 1 |
"""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
... | 354 |
"""simple docstring"""
_lowerCAmelCase : dict[tuple[int, int, int], int] = {}
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days lef... | 340 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
ex... | 36 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...te... | 24 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( _UpperCamelCase : list ) -> list:
def merge(_UpperCamelCase : list, _UpperCamelCase : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] ... | 369 | '''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)... | 18 | 0 |
from __future__ import annotations
_a = 1_0
def _a ( SCREAMING_SNAKE_CASE : list[int] ) -> Any:
"""simple docstring"""
__lowerCAmelCase: Any = 1
__lowerCAmelCase: Optional[int] = max(SCREAMING_SNAKE_CASE )
while placement <= max_... | 322 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
fro... | 277 | 0 |
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = len(a__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = []
for i in range(len(a__ ) - pat_len + 1 ):
SCREAMING_SNAKE_CASE : Dict ... | 357 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a__ : Optional[Any] = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''... | 19 | 0 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class _UpperCAmelCase ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCAmelCase_ : List[... | 284 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 185 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
... | 100 |
"""simple docstring"""
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock... | 100 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __a ( A__ ):
_lowerCAmelCase :... | 189 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class __a ( A__ , A__ )... | 189 | 1 |
'''simple docstring'''
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_avai... | 129 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_A : str =WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''... | 129 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCAmelCase ( lo... | 330 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise Opti... | 330 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
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_c... | 13 |
'''simple docstring'''
__UpperCamelCase = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def _a ( ) -> None:
"""simple docstring"""
__snake_case : Dict = input("""Enter message: """ )
__snake_case : Optional[int] = ... | 13 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class A_ :
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[s... | 61 |
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
a_ = '''src/transformers'''
a_ = '''docs/source/en/tasks'''
def ... | 340 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> None:
lowerCamelCase__ : Optional[Any] = len(_UpperCAmelCase )
# If row is eq... | 45 |
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,
)
log... | 45 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDete... | 7 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
... | 161 | 0 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _UpperCamelCase (a__ :str , a__ :str , **a__ :List[str] ):
"""simple docstring"""
UpperCamelCase__ = AutoConfig.from_pretrained(a__ , *... | 87 |
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCamelCase__ = logging.getLogger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Optional[Any] = """masked_bert"""
def __init__( self , __lowerCAmelCase=305... | 87 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] , snake_case_ : int ) -> bool:
if len(snake_case_ ) == 0:
return False
__snake_case = len(snake_case_ ) // 2
if a_list[midpoint] ==... | 24 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data im... | 88 | 0 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> str:
"""simple docstring"""
return "".join(sorted(SCREAMING_SNAKE_CASE ) )
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> list[str]:
... | 267 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSampli... | 267 | 1 |
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_:int = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whis... | 116 | import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():... | 18 | 0 |
# 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 appl... | 29 |
from math import ceil, sqrt
def lowerCamelCase__ ( A__ : int = 1000000 ):
'''simple docstring'''
__lowerCamelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__lowerCamelCase ... | 29 | 1 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _lowerCAmelCase ( lowerCAmelCase_ :Union[str, Any] )... | 159 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils ... | 19 | 0 |
import functools
from typing import Any
def a( A : str , A : List[Any] ) -> Any:
"""simple docstring"""
if not isinstance(A , A ) or len(A ) == 0:
raise ValueError("the string should be not empty string" )
if not isinstance(A , A ) or not all(
... | 371 |
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 a( A : List[s... | 71 | 0 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_d... | 326 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCAmelCase__( lowercase : Option... | 326 | 1 |
def __UpperCamelCase ( lowercase__ : int ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = abs(lowercase__ )
lowerCAmelCase_ : int = 0
while n > 0:
res += n % 10
n //= 10
return res
def __UpperCame... | 354 |
from __future__ import annotations
from typing import Any
class __a :
def __init__( self : Dict , UpperCAmelCase : int = 6 ):
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
self.... | 28 | 0 |
def lowercase_ ( _A : int = 600851475143 ):
"""simple docstring"""
try:
lowerCamelCase__ : Tuple = int(__lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
... | 184 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[Any] = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConf... | 270 | 0 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ... | 363 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils impo... | 189 | 0 |
import inspect
import unittest
from transformers import MobileViTConfig
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_configuration_common import ConfigTester
from ...test_... | 13 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxFo... | 13 | 1 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_C... | 87 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrin... | 87 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replic... | 329 | from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowercase ( ):
UpperCamelCase_ : Optional[Any] = HfArgumentParser(lowerCamelCase )
UpperCamelCase_ : Tuple = parser.parse_args_into_dataclasses()[0]
UpperCamelCase_ : ... | 175 | 0 |
import math
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[str]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase_ )
else:
if x == 0... | 363 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
UpperCamelCase_ = BeautifulSoup(requests.get(UpperCamelCase_ , params=UpperCamelCase_ ).content , "html.parser" )
UpperCam... | 328 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAv... | 48 | 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_available, logging
from .be... | 157 | 0 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def _SCREAMING_SNAKE_CASE (__lowerCAmel... | 313 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
'''simple docstring'''
lowercase_ , lowercase_ = np.shape(__lowerCAmelCase )
if rows !... | 313 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {
... | 204 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowerCamelCase : int = input("Enter image url: ").strip()
print(F"""Downloading image from {url} ...""")
lowerCamelCase : Tuple = BeautifulSoup(requests.get(url).content, "ht... | 204 | 1 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data impo... | 353 |
'''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 Toke... | 37 | 0 |
"""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 Acce... | 25 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_g... | 141 | 0 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
... | 357 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils i... | 4 | 0 |
'''simple docstring'''
import argparse
import os
# New Code #
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_wi... | 251 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
... | 251 | 1 |
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 transformers.utils impo... | 358 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Tra... | 300 | 0 |
"""simple docstring"""
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 ... | 91 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ ... | 91 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {'''configuration_glpn''': ['''GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GLPNConfig''']}
try:... | 368 |
'''simple docstring'''
from __future__ import annotations
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ ) -> None:
lowerCAmelCase__ = order
# a_{0} ... a_{k}
... | 228 | 0 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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... | 184 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from... | 184 | 1 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional im... | 185 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __a ( UpperCAmelCase ):
_a : Union[List[np.ndarray], torch.... | 185 | 1 |
"""simple docstring"""
from math import log
from scipy.constants import Boltzmann, physical_constants
A_ : Optional[int] = 300 # TEMPERATURE (unit = K)
def A ( snake_case__ , snake_case__ , snake_case__ , ):
'''simple docstring'''
if dono... | 165 |
"""simple docstring"""
def A ( snake_case__ ):
'''simple docstring'''
assert isinstance(snake_case__ , snake_case__ ), f"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
SCREAMING_SNAKE_CASE__ ... | 165 | 1 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_snake_case = re.compile(r'\b(a|an|the)\b', re.UNICODE)
_snake_case = None
def lowerCAmelCase__ ( ):
... | 324 |
"""simple docstring"""
import numpy as np
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoi... | 324 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCAmelCas... | 108 | from numpy import exp, pi, sqrt
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Any , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - ... | 219 | 0 |
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
snake_case__ = ["torch", "torchsde"]
def __init__( self : Any , *SCREAMING_SNAKE_CASE__ : Tuple , **SCREAMING_... | 366 |
from statistics import mean, stdev
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : int = 3 ):
"""simple docstring"""
lowerCAmelCase__ = min(lowerCAmelCase_ )
lowerCAmelCase__ = max(lowerCAmelCase_ )
# ... | 221 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCAmelCase_( a__ ):
"""simple docstring"""
re... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCL... | 313 | 1 |
UpperCAmelCase_ : int = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_ba... | 198 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLi... | 198 | 1 |
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