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 random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 29 |
from string import ascii_lowercase, ascii_uppercase
def a__ ( _UpperCamelCase : str ):
if not sentence:
return ""
__lowerCamelCase = dict(zip(_UpperCamelCase ,_UpperCamelCase ) )
return lower_to_upper.get(sentence[0] ,sentence[0] ) +... | 330 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils ... | 365 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licens... | 181 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
__A : int = logging.get_logger(__name__)
__A : Optional[Any] ... | 138 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
UpperCAmelCase : int = False
class __lowerCA... | 95 | 0 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowercase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_to... | 280 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase__ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm"... | 280 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__lowerCamelCase : Union[str, Any] = TypeVar('''T''')
class __snake_case ( Generic[T] ):
def __init__( self : Dict , _lowercase : lis... | 219 | import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType,... | 219 | 1 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate im... | 351 |
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 import ... | 110 | 0 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils im... | 75 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixi... | 75 | 1 |
def __lowerCamelCase ( __snake_case : float, __snake_case : list[float] ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError("""Cash flows... | 353 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
def lowercase__ ( self : List[str] , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Dict=None , ... | 136 | 0 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as ... | 115 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaV... | 115 | 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 lowercase_ ( A__ ) -> int:
"""simple docstring""... | 137 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class lowerCamelCase ( unittest.TestCase ):
def UpperCAmelCase(self : Tuple ) -> ... | 137 | 1 |
"""simple docstring"""
SCREAMING_SNAKE_CASE = {
"meter": "m",
"kilometer": "km",
"megametre": "Mm",
"gigametre": "Gm",
"terametre": "Tm",
"petametre": "Pm",
"exametre": "Em",
"zettametre": "Zm",
"yottametre": "Ym",
}
# Exponent of the factor(meter)
SCREAMING_SNAKE_CA... | 247 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : list ) -> list:
if any(not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in ... | 239 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowercase =logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase ):
def __init__( self , *snake_case , **sna... | 357 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowercase =logging.getLogger(__name__)
class __magic_name__ ( lowerCAmelCase ):
... | 242 | 0 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A = logging.get_logger(__name__)
_A ... | 62 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixi... | 107 | 0 |
"""simple docstring"""
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
... | 365 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from fl... | 23 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is... | 10 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 10 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ : Optional[Any] ={'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise ... | 351 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ : Optional[Any] =logging.get_logger(__name__)
lowerCAmelCase__ ... | 162 | 0 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def a ( snake_case__: Tuple ):
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowercase_... | 30 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__snake_case :Optional[int] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generati... | 49 | 0 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CL... | 351 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> Union[... | 1 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__... | 141 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.... | 100 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : List[str] = {
'configuration_distilbert': [
'DISTILBERT_... | 151 |
import qiskit
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 2) -> qiskit.result.counts.Counts:
'''simple docstring'''
__UpperCamelCase : List[str] = qubits
# Using Aer's simulator
__UpperCamelCase : i... | 151 | 1 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Any = os.path.join(args.tf_model_dir , '''parameters.json''' )
lowerCAmelCa... | 106 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class lowerCAmelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase__ = None ) -> ... | 113 | 0 |
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 = {
'roberta-base': 'https://hug... | 232 |
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.conversational import Conversation
_UpperC... | 232 | 1 |
lowercase : Optional[int] = [
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def ... | 20 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __snake_case :
_a : int
_a : TreeNode | None= None
_a : TreeNode | None= None
lowercase : Dict = namedtuple("""CoinsDistribResult""",... | 20 | 1 |
a__ = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def __Upp... | 15 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
a__ = ['''small''', '''medium''', '''large''']
a__ = '''lm_head.decoder.weight'''
a__ = '''lm_head.weight'''
def __UpperCAmelCase ( __a : str ,__a : str ) -> ... | 15 | 1 |
def UpperCAmelCase_ ( __snake_case ) -> int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - ... | 5 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> Matrix:
"""simple docstring"""
_lowercase =len(__snake_case )
_lowercase ... | 5 | 1 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _A (lowerCAmelCase__ :str = "laptop" ) -> DataFrame:
'''simple docstring'''
_a = f'https:/... | 104 |
'''simple docstring'''
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_com... | 104 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ..... | 152 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A ={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Optional... | 19 | 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_with_warmup, set_seed
... | 2 | """simple docstring"""
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A = get_tests_dir("fixtu... | 2 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( lowerCAmelCase : list , lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : int ):
"""simple docstring"""
__magic_name__ : List[Any] = []
__magic_name__ , __magic... | 331 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():... | 331 | 1 |
import argparse
import copy
def __snake_case ( _UpperCAmelCase ):
__a = {}
with open(lowerCamelCase_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
__a = []
_list.append([line.split()[1], line.s... | 356 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe... | 131 | 0 |
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 import f... | 6 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A( a ):
snake_case_ = ['''image_processor''', '''tokenizer''']
snake_case_ = '''ChineseCLIPImageProcessor'''
snake_case_ = ('''BertTokeni... | 6 | 1 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str ) -> int:
"""simple docstring"""
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise ValueError('String lengths must match... | 289 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mod... | 289 | 1 |
from __future__ import annotations
_UpperCAmelCase : Optional[int] = tuple[int, int, int]
_UpperCAmelCase : int = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
_UpperCAmelCase : Optional[int] = """ABCDEFGHIJKLMNOPQRSTU... | 50 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : List[str] = len(_UpperCAmelCase )
lowerCamelCase__ : str = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zer... | 50 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
de... | 34 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 34 | 1 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ( _lowerCamelCase : Union[str, Any] ... | 237 |
import math
import sys
def lowerCAmelCase_ ( _lowercase : str) -> str:
"""simple docstring"""
a__ : str = """"""
try:
with open(_lowercase , """rb""") as binary_file:
a__ : Any = binary_file.read... | 170 | 0 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_imag... | 364 | import math
import flax.linen as nn
import jax.numpy as jnp
def __lowercase ( lowerCamelCase : jnp.ndarray , lowerCamelCase : int , lowerCamelCase : float = 1 , lowerCamelCase : float = 1 , lowerCamelCase : float = 1.0e4 , lowerCamelCase : bool = False , lowerCa... | 50 | 0 |
from string import ascii_uppercase
__snake_case :str = {str(ord(c) - 55): c for c in ascii_uppercase}
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('''int() can\'t convert non-string with ... | 49 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__snake_case :str = logging.get_logger(__name__)
__snake_case ... | 49 | 1 |
from __future__ import annotations
class __lowercase :
'''simple docstring'''
def __init__( self : List[Any] , _a : int ):
UpperCamelCase__ = data
UpperCamelCase__ = None
UpperCamelCase__ = ... | 35 | import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowercase = logging.get_logger(__name__)
class __lowercase ( A ):
'''simple docstring'''
def __init__( self : Any , *_a : Optional[A... | 35 | 1 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_prop... | 31 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassific... | 165 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MA... | 356 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Layo... | 17 | 0 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils impo... | 123 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case : Optional[int] = {
"configuration_distilbert": [
"DISTILBERT_PRETRAI... | 123 | 1 |
import argparse
import json
import subprocess
def _lowerCAmelCase ( A__: Any , A__: Optional[int] ):
'''simple docstring'''
UpperCAmelCase = []
UpperCAmelCase = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"A... | 152 |
def _lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
UpperCAmelCase = 6
UpperCAmelCase = 1
UpperCAmelCase = 1901
UpperCAmelCase = 0
while yea... | 152 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__A : Any = TypeVar('''T''')
class __A ( Generic[T] ):
def __init__( self : Dict , UpperCAmelCase_ : list[T] , UpperCAmelCase_ : Callable[[T, ... | 138 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
lowerCAmelCase : D... | 138 | 1 |
'''simple docstring'''
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__lowerCAmelCase = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < vers... | 5 |
'''simple docstring'''
import qiskit
def UpperCAmelCase_ (__a : int , __a : int ):
"""simple docstring"""
_a : Any = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
_a : List[Any] = qi... | 5 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
@staticmethod
@abstractmethod
def SCREAMING_SNAKE_CASE ( __A ) ... | 81 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _A ( ):
"""simple docstring"""
a =ArgumentParser(
description=(
... | 81 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase_ : List[str] = {
"configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE... | 359 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Tuple = logging.get_logger(__name__)
lowerCamelCase_ : List[Any] = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""google/fnet-la... | 197 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
_lowerCamelCase =[int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def _a ( ):
lowerCamelCase : Any = os.path.dirname(os.path.realpath(lowerCamelCase ) )
lowerCamelCase : str = os.path.joi... | 287 |
import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_... | 287 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A_ ( _UpperCAmelCase ):
lowerCAmelCase__ = ["image_processor", "tokenizer"]
lowerCAmelCase__ = "ViTImageProcessor"
lowerCAme... | 352 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCAmelCase : Dict ... | 340 | 0 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def a_ ( _lowerCAmelCase : jnp.ndarray , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 , _lowerCAmelCase : float = 1 , _lowerCAmelCase : float = 1.0E4 , _lowerCAmelCase : bool = False , _lo... | 77 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase_ ( _snake_case : str = "laptop" ) -> DataFrame:
'''simple docstring'''
__magic_name__ : Tuple = F'''https://www.amazon.in/laptop/s?k={product... | 281 | 0 |
from collections import defaultdict
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ):
"""simple docstring"""
A_ = first_str.lower().strip()
A_ = second_str.lower().strip()
# Remove whitespace
... | 329 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a :Union[str, Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}... | 329 | 1 |
def __UpperCamelCase ( _A = 1000000 ):
lowerCAmelCase_ = 1
lowerCAmelCase_ = 1
lowerCAmelCase_ = {1: 1}
for inputa in range(2 , _A ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = inputa
while True:
... | 278 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
f... | 278 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformer... | 362 | from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 197 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Any... | 323 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/gi... | 323 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> str:
if isinstance(_lowercase , _lowercase ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(_lowercase , _lowercase ):
raise TypeErro... | 338 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
... | 338 | 1 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel... | 263 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as or... | 225 | 0 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__lowerCAmelCase = 0B1011_0011_1110_1100_1001_0000_011... | 5 |
'''simple docstring'''
def UpperCAmelCase_ (__a : str , __a : str ):
"""simple docstring"""
_a : int = len(__a ) + 1
_a : List[str] = len(__a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of... | 5 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sage... | 106 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__UpperCAmelCase = [[] for _ in range(SCREAMING_SNAKE_CASE )]
__UpperCAmelCase = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t... | 333 | 0 |
"""simple docstring"""
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def snake_case ( ... | 253 |
"""simple docstring"""
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class UpperCamelCase_ (__A ):
__magic_name__ = '''M-CLIP'''
def __init__( self : Any , lowerCAmelCase_ : str=1_024 , lowerCAmelCase_ : str=76... | 253 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 9 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__lowerCAmelCase : List[Any] =datasets.load_iris()
__lowerCAmelCase : Tuple =np.array(data['data'])
__lowerCAmelCase : Dict =np.array(data['target'])
__lowerCAmelCase... | 9 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}... | 364 |
def lowercase__ ( __snake_case : str , __snake_case : int , __snake_case : List[str] ):
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__snake_case , n - 1 , __... | 145 | 0 |
'''simple docstring'''
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
a : Dict = models.Sequential()
... | 311 |
'''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 | 1 |
from manim import *
class a ( _A ):
'''simple docstring'''
def lowerCamelCase_ ( self : Optional[int] ):
UpperCAmelCase_ = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase_ = Rectangle... | 364 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available()... | 177 | 0 |
'''simple docstring'''
import os
def lowerCamelCase ( UpperCAmelCase__ : str = "input.txt" ) -> int:
with open(os.path.join(os.path.dirname(UpperCAmelCase__ ) , UpperCAmelCase__ ) ) as input_file:
lowercase_ : str = [
... | 239 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 239 | 1 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import Tokenizer... | 349 |
'''simple docstring'''
import argparse
import copy
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict = {}
with open(lowerCAmelCase_ ) as f:
for line in f:
if line.split()[0] ... | 349 | 1 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_... | 231 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Optional... | 231 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Ef... | 319 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
snake_case = get_logger(__name__)
snake_case = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_le... | 319 | 1 |
'''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowerCamelCase : Dict = [
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw ... | 258 |
'''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_... | 258 | 1 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
UpperCAmelCase__ = 'examples/'
UpperCAmelCase__ = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__v... | 366 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _Upper... | 40 | 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 app... | 50 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 78 | 0 |
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
@require... | 358 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Optional[int] = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MA... | 33 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
class a__ ( __A ):
"""simple docstring"""
def __init__(sel... | 174 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = {
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''... | 174 | 1 |
"""simple docstring"""
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'''stable diffusion controlnet''',
'''0.22.0''',
'''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.sta... | 363 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase_( _lowerCamelCase ) -> Tuple:
'''simple docstring'''
_lowerC... | 340 | 0 |
from manim import *
class lowercase ( UpperCamelCase__ ):
def a__ ( self ) -> Dict:
_A : List[Any] = Rectangle(height=0.5 , width=0.5 )
_A : Optional[int] = Rectangle(height=0.46 , width=0.46 ).s... | 26 |
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 ModelTesterMixin, ids_tenso... | 38 | 0 |
def lowerCAmelCase__ ( a__ ) ->list:
'''simple docstring'''
if any(not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(lowerCAmelCase_ ) ):
for... | 368 | 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 ( lowerCAmelCase ):
'''simple docst... | 63 | 0 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
a__ : int =int(number**0.5 )
return number == sq * sq
def _A ( SCREAM... | 95 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apach... | 183 | 0 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transfor... | 3 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from tr... | 3 | 1 |
import math
from datetime import datetime, timedelta
def __UpperCamelCase ( lowerCAmelCase__ : int ):
__a : str = year % 1_9
__a : Any = year % 4
__a : List[str] = year % 7
__a : int = math.floor(year / 1_0_0 )
__a : Union[str, Any] = math.floor((1_3 + 8... | 216 |
import re
from filelock import FileLock
try:
import nltk
_snake_case : Any = True
except (ImportError, ModuleNotFoundError):
_snake_case : Union[str, Any] = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
d... | 284 | 0 |
"""simple docstring"""
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
fr... | 356 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
),
}... | 144 | 0 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VAR... | 61 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = 1 / sqrt(2 ) ):
UpperCAmelCase_ : int = tau * frequency / samplerate
UpperCAmelCase_ : List[... | 61 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowerCAmelCase : Optional[int] = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
... | 127 |
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... | 127 | 1 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jn... | 333 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 60 | 0 |
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 SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ , ... | 286 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Union[str, Any] = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TimeS... | 286 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase_ ):
def UpperCamelCase ( self,__lowerCamelCase ):
with open(__lowercase,encoding='''utf-8''' ) as ... | 193 | from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size... | 43 | 0 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging ... | 16 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
_UpperCamelCase = logging.get_logger(__name__)
class _A ( __SCREAMING_SNAKE_CASE ):
def __init__( self , *__Uppe... | 16 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
A_ :Dict = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''toke... | 71 |
from __future__ import annotations
import math
lowerCamelCase : List[Any] = '''2020.9.26'''
lowerCamelCase : str = '''xcodz-dot, cclaus, dhruvmanila'''
def snake_case_ ( lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCas... | 233 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_t... | 361 | def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [0] * len(__UpperCamelCase )
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = [1] * len(__UpperCa... | 204 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A ( metaclass=SCREAMING_SNAKE_CASE__ ):
lowerCamelCase : str = ["""transformers""", """torch""", """note_seq"""]
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ... | 164 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
a_ = 50000
a_ = 5000
a_ , a_ = os.path.split(__file__)
a_ = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
... | 179 | 0 |
'''simple docstring'''
# 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 .s... | 46 |
'''simple docstring'''
import warnings
from .generation import TFGenerationMixin
class _UpperCAmelCase ( lowerCAmelCase_ ):
# warning at import time
warnings.warn(
"""Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
... | 46 | 1 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDi... | 79 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'ClapFeatureExtractor'
SCREAMING_SNAKE_CASE__ = ('RobertaTokenizer', 'RobertaTokenizerFast')
def __init__( ... | 94 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase : s... | 45 |
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... | 45 | 1 |
"""simple docstring"""
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 = pipe(prompt, num_inference... | 315 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class snak... | 243 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
try:
if not is_torch_avail... | 368 |
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 lowerCamelCase :
"""simple docstring"""
... | 191 | 0 |
'''simple docstring'''
def lowerCamelCase ( __lowerCamelCase : int ) ->int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def lowerCamelCase ( __lowerCamelCase : int ) ->bool:
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE ... | 58 |
'''simple docstring'''
def _A ( lowercase__ = 1000000 ):
lowercase__ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowercase__ ... | 164 | 0 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__A ='sshleifer/bart-tiny-random'
__A ='patrickvonplaten/t5-tiny-r... | 355 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A =pd.read_csv('''sample_data.csv''', header=None)
__A =df.shape[:1][0]
# If you're using som... | 47 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: Dict = {
'configuration_mobilebert': [
'MOBILEBERT... | 297 |
'''simple docstring'''
def lowerCamelCase__ ( _A ):
return 10 - x * x
def lowerCamelCase__ ( _A , _A ):
# Bolzano theory in order to find if there is a root between a and b
if equation(_A ) * equation(_A ) >= 0:
raise ValueError('Wrong space!' )
... | 297 | 1 |
"""simple docstring"""
import numpy as np
def UpperCAmelCase__ (snake_case__ : np.ndarray ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def UpperCAmelCase__ (snake_case__ : np.ndarray ):
"""simple docstring"""
return vector * sigmoid(snake_case_... | 364 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
A_ = '''<<<<<<< This should probably be modified because it mentions: '''
A_ ... | 132 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCamelCase__ : List[str] = logging.get_logger(__name__)
class lowerCamelCase_ :
'''simple do... | 225 |
import re
from filelock import FileLock
try:
import nltk
lowerCamelCase__ : str = True
except (ImportError, ModuleNotFoundError):
lowerCamelCase__ : Union[str, Any] = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def ... | 225 | 1 |
A : Dict = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = True
... | 358 | 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, BlipaProcessor, BlipImageProces... | 305 | 0 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
f... | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
i... | 154 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDep... | 273 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils imp... | 273 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.