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 gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import M... | 290 |
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__ = {
"facebook/xmod-base": "https://huggingface.co/facebo... | 290 | 1 |
import gc
import unittest
from transformers import CTRLConfig, 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 ModelTeste... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_ful... | 290 | 1 |
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
while a != 0:
_UpperCAmelCase , _UpperCAmelCase = b % a, a
return b
def A ( _UpperCAmelCase : int , _U... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CONFIG_ARCHIV... | 290 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCAmelCase__ = r"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the do... | 290 | 1 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
UpperCAmelCase__ = transforms.Compo... | 290 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__... | 290 | 1 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
UpperCAmelCase__ = models.Sequential()
# Step 1... | 290 |
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
@requ... | 290 | 1 |
class __lowerCAmelCase ( A ):
pass
class __lowerCAmelCase ( A ):
pass
class __lowerCAmelCase :
def __init__( self : List[Any]) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = [
... | 290 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeni... | 290 | 1 |
UpperCAmelCase__ = "Input must be a string of 8 numbers plus letter"
UpperCAmelCase__ = "TRWAGMYFPDXBNJZSQVHLCKE"
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxC... | 290 | 1 |
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 ...tes... | 290 |
def A ( _UpperCAmelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCAmelCase =... | 290 | 1 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 290 |
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 ...tes... | 290 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set... | 290 |
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 impor... | 290 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 290 |
import string
import numpy
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
class __lowerCAmelCase :
... | 290 | 1 |
import baseaa
def A ( _UpperCAmelCase : str ) -> bytes:
'''simple docstring'''
return baseaa.aaaencode(string.encode('utf-8' ) )
def A ( _UpperCAmelCase : bytes ) -> str:
'''simple docstring'''
return baseaa.aaadec... | 290 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__ = "src/transformers"
UpperCAmelCase__ = "docs/source/en/ta... | 290 | 1 |
from ...processing_utils import ProcessorMixin
class __lowerCAmelCase ( A ):
UpperCamelCase = '''SpeechT5FeatureExtractor'''
UpperCamelCase = '''SpeechT5Tokenizer'''
def __init__( self : Optional[int] , A : Any , A : List[Any... | 290 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_Up... | 290 | 1 |
def A ( _UpperCAmelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCAmelCase =... | 290 |
UpperCAmelCase__ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : Optional[int] , _Upper... | 290 | 1 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from... | 290 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase__ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFF... | 290 | 1 |
class __lowerCAmelCase :
def __init__( self : Dict , A : int , A : List[str]=None , A : Dict=None) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = data
_UpperCAmelCase = previous
_UpperCAmelCa... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenizati... | 290 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractio... | 290 |
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 sagemaker.huggingface import H... | 290 | 1 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pi... | 290 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/ma... | 290 | 1 |
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 imp... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
"configuration_clip": [
"CLIP_PRETRAINED_... | 290 | 1 |
import math
def A ( _UpperCAmelCase : int ) -> 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 of 3 are no... | 290 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from... | 290 | 1 |
def A ( _UpperCAmelCase : dict ) -> bool:
'''simple docstring'''
_UpperCAmelCase = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
_UpperCAmelCase = set()
return any(
node not in visited and depth_firs... | 290 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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_tensor, random_attention_mask
f... | 290 | 1 |
def A ( _UpperCAmelCase : int = 4_000_000 ) -> int:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase , _UpperCAmelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_UpperCAmelCase )
_UpperCAmelCase , ... | 290 |
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__ = {
"facebook/xmod-base": "https://huggingface.co/facebo... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_ful... | 290 | 1 |
from math import sqrt
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
for i in range(1 , int(sqrt(_UpperCAmelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(_UpperCAmelCase ):
total += i + n... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
... | 290 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 290 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCAmelCase__ = r"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the do... | 290 | 1 |
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_ddp... | 290 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__... | 290 | 1 |
UpperCAmelCase__ = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
UpperCAmelCase__ = ["a", "b", "c", "d", "e"]
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int] ) ... | 290 |
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
@requ... | 290 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
... | 290 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeni... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_blenderbot_small": [
"BLENDERBOT_SMALL_PRETRAINED_C... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxC... | 290 | 1 |
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__ = {
"andreasmadsen/efficient_mlm_m0.40": (
"http... | 290 |
def A ( _UpperCAmelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCAmelCase =... | 290 | 1 |
from statistics import mean
import numpy as np
def A ( _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : int ) -> list:
'''simple docstring'''
_UpperCAmelCase ... | 290 |
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 ...tes... | 290 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from trans... | 290 |
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 impor... | 290 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType... | 290 |
import string
import numpy
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
class __lowerCAmelCase :
... | 290 | 1 |
UpperCAmelCase__ = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] , _Up... | 290 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__ = "src/transformers"
UpperCAmelCase__ = "docs/source/en/ta... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
"configuration_clip": [
"CLIP_PRETRAINED_... | 290 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_Up... | 290 | 1 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( A ):
UpperCamelCase = (CMStochasticIterativeScheduler,)
UpperCamelCase = 1_0
def _lowerCamelCase ( self ... | 290 |
UpperCAmelCase__ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : Optional[int] , _Upper... | 290 | 1 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class __lowerCAmelCase ( A ):
def __init__( self : int , *A : List[Any] , **A : int) ->... | 290 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase__ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFF... | 290 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=A ):
UpperCamelCase = ['''onnx''']
def __init__( self : List[Any] , *A : Tuple , **A : int) -> Union[str, Any]:
"""simple docstring"""
... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenizati... | 290 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
... | 290 |
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 sagemaker.huggingface import H... | 290 | 1 |
UpperCAmelCase__ = 256
# Modulus to hash a string
UpperCAmelCase__ = 100_0003
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase... | 290 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/ma... | 290 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase__ = ""
UpperCAmelCase__ = ""
UpperCAmelCase__ = ""
UpperCAmelCase__ = 1 # (0 is vertical, 1 is horizontal)
def A ( ) -> None:
'''simple docstring'''
... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
"configuration_clip": [
"CLIP_PRETRAINED_... | 290 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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_tensor, random_attention_mask
f... | 290 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from... | 290 | 1 |
import string
import numpy
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
class __lowerCAmelCase :
... | 290 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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_tensor, random_attention_mask
f... | 290 | 1 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowerCAmelCase ( A ):
UpperCamelCase = DistilBertTo... | 290 |
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__ = {
"facebook/xmod-base": "https://huggingface.co/facebo... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_ful... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
"tokenization_mvp": ["MvpTokenizer"],
}
try:... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
... | 290 | 1 |
def A ( _UpperCAmelCase : list ) -> list:
'''simple docstring'''
if len(_UpperCAmelCase ) < 2:
return collection
def circle_sort_util(_UpperCAmelCase : list , _UpperCAmelCase : int , _UpperCAmelCase : int )... | 290 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCAmelCase__ = r"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the do... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenizati... | 290 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__... | 290 | 1 |
def A ( _UpperCAmelCase : Union[str, Any] ) -> Any:
'''simple docstring'''
_UpperCAmelCase = [0] * len(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = 0
for values in graph.values():
for i ... | 290 |
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
@requ... | 290 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"facebook/deit-base-d... | 290 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeni... | 290 | 1 |
import numpy as np
def A ( _UpperCAmelCase : np.array ) -> np.array:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def A ( _UpperCAmelCase : np.array ) -> np.array:
'''simple docstring'''
return vector * s... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxC... | 290 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,... | 290 |
def A ( _UpperCAmelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCAmelCase =... | 290 | 1 |
from __future__ import annotations
from cmath import sqrt
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> tuple[complex, complex]:
'''simple docstring'''
if a == 0:
raise ValueError(... | 290 |
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 ...tes... | 290 | 1 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def A ( _UpperCAmelCase : Dict ) -> Any:
'''simple docstring'''
if not is_accelerate_available():
return method
_UpperCA... | 290 |
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 impor... | 290 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class __lowerCAmelCase ( A ):
def __init__( self : Dict , *A : List[str] , **A : Optional[Any])... | 290 |
import string
import numpy
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
class __lowerCAmelCase :
... | 290 | 1 |
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('Input value must be a \'int\' type' ... | 290 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__ = "src/transformers"
UpperCAmelCase__ = "docs/source/en/ta... | 290 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get... | 290 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_Up... | 290 | 1 |
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
from diffusers.utils.testing_utils import e... | 290 |
UpperCAmelCase__ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : Optional[int] , _Upper... | 290 | 1 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
UpperCAmelCase__ = TypeVar("T")
UpperCAmelCase__ = Union[List[T], Tuple[T, ...]]
UpperCAmelCase__ = Union[T, List[T], Dict[str, T]]
UpperCAmelCase__ = Union[str, bytes, os.PathLike]
| 290 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase__ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFF... | 290 | 1 |
from maths.prime_check import is_prime
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = F"Input value of [number={number}] must be an integer"
rai... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenizati... | 290 | 1 |
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
while b:
_UpperCAmelCase , _UpperCAmelCase = b, a % b
return a
def A ( _UpperCAmelCase : int , _UpperC... | 290 |
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 sagemaker.huggingface import H... | 290 | 1 |
import collections
import importlib.util
import os
import re
from pathlib import Path
UpperCAmelCase__ = "src/transformers"
# Matches is_xxx_available()
UpperCAmelCase__ = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase__ = re.compile(r"^_imp... | 290 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/ma... | 290 | 1 |
from __future__ import annotations
import os
from collections.abc import Mapping
UpperCAmelCase__ = tuple[int, int]
class __lowerCAmelCase :
def __init__( self : List[Any] , A : set[int] , A : Mapping[EdgeT, int]) -> None:
"""simple docst... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
"configuration_clip": [
"CLIP_PRETRAINED_... | 290 | 1 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def A ( *_UpperCAmelCase : Any , _UpperCAmelCase : Optional[Union[Dict, Any]] = None , _UpperCAmelCase : Optional[int]=True , ... | 290 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from... | 290 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}
class __lowerCAmelCase (... | 290 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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_tensor, random_attention_mask
f... | 290 | 1 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __lowerCAmelCase ( nn.Module ):
def __init__( self : Tuple , A : int = 16 , A : int = 88 , A : Optional[int] = No... | 290 |
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__ = {
"facebook/xmod-base": "https://huggingface.co/facebo... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPegasusConfig",
"BigBirdPegasusOnnxConfig",
... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_ful... | 290 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"hustvl/yolos-small":... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
... | 290 | 1 |
UpperCAmelCase__ = {
"meter": "m",
"kilometer": "km",
"megametre": "Mm",
"gigametre": "Gm",
"terametre": "Tm",
"petametre": "Pm",
"exametre": "Em",
"zettametre": "Zm",
"yottametre": "Ym",
}
# Exponent of the factor(meter)
UpperCAmelCase__ = {
"m": 0,
"km": 3,
... | 290 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCAmelCase__ = r"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the do... | 290 | 1 |
UpperCAmelCase__ = {str(digit): digit**5 for digit in range(10)}
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_UpperCAmelCase ) )
def A ( ) -> int:
''... | 290 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__... | 290 | 1 |
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Any ) -> str:
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , ... | 290 |
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
@requ... | 290 | 1 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __lowerCAmelCase ( A ... | 290 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeni... | 290 | 1 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxC... | 290 | 1 |
from math import pi, sqrt, tan
def A ( _UpperCAmelCase : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def A ( _UpperCAme... | 290 |
def A ( _UpperCAmelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCAmelCase =... | 290 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 290 |
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 ...tes... | 290 | 1 |
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 impor... | 290 |
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 impor... | 290 | 1 |
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__ = {
"kssteven/ibert-roberta-base": "https://huggingface.... | 290 |
import string
import numpy
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
class __lowerCAmelCase :
... | 290 | 1 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()... | 290 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__ = "src/transformers"
UpperCAmelCase__ = "docs/source/en/ta... | 290 | 1 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as t... | 290 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_Up... | 290 | 1 |
from math import pow, sqrt
def A ( *_UpperCAmelCase : float ) -> bool:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase ) > 0 and all(value > 0.0 for value in values )
return result
def A ( _UpperCAmelCase : flo... | 290 |
UpperCAmelCase__ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : Optional[int] , _Upper... | 290 | 1 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_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_modeling_comm... | 290 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase__ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFF... | 290 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCAmelCase__ = {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.js... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenizati... | 290 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"google/mobilenet_v1_... | 290 |
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 sagemaker.huggingface import H... | 290 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
UpperCAmelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ ... | 290 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/ma... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxC... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
"configuration_clip": [
"CLIP_PRETRAINED_... | 290 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_Up... | 290 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from... | 290 | 1 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __lowerCAmelCase ( unitt... | 290 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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_tensor, random_attention_mask
f... | 290 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
t... | 290 |
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__ = {
"facebook/xmod-base": "https://huggingface.co/facebo... | 290 | 1 |
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : Tuple ) -> Tuple:
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if... | 290 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_ful... | 290 | 1 |
def A ( _UpperCAmelCase : str , _UpperCAmelCase : list[str] ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
for word_or_phrase in separated:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise Exc... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
... | 290 | 1 |
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCAmelCase__ = r"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the do... | 290 | 1 |
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 A ... | 290 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__... | 290 | 1 |
from __future__ import annotations
from random import choice
def A ( _UpperCAmelCase : List[Any] ) -> Optional[Any]:
'''simple docstring'''
return choice(_UpperCAmelCase )
def A ( _UpperCAmelCase : list[int] , _UpperCAmelCa... | 290 |
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
@requ... | 290 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=A ):
UpperCamelCase = ['''sentencepiece''']
def __init__( self : Union[str, Any] , *A : Any , **A : int) -> List[str]:
"""simple docstri... | 290 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeni... | 290 | 1 |
def A ( _UpperCAmelCase : str , _UpperCAmelCase : bool = False ) -> str:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = F"Expected string as input, found {type(_UpperCAmelCa... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxC... | 290 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils ... | 290 |
def A ( _UpperCAmelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCAmelCase =... | 290 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/config.json",
"funnel-transformer/small-base": "https... | 290 |
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 ...tes... | 290 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Inte... | 290 |
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 impor... | 290 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
UpperCAmelCase__ = _LazyModule(... | 290 |
import string
import numpy
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
class __lowerCAmelCase :
... | 290 | 1 |
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