code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , *... | 114 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( lowerCamelCase__ )... | 114 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',... | 114 |
import copy
import random
from transformers import CLIPTokenizer
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
"""simple docstring"""
super().__init__(*a__ , **a__)
... | 114 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 114 |
import unittest
import numpy as np
import requests
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... | 114 | 1 |
def __UpperCAmelCase( lowercase_ , lowercase_ ):
_lowerCamelCase : List[Any] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
_lowerCamelCase : Any = n - k
# Calculate C(n,k)
for i in range(lowercase_ ... | 114 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Le... | 114 | 1 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase = '\\n\n'
_lowerCamelCase = '\nPerplexity (PPL) is one of the most common metrics for eval... | 114 |
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
_lowerCamelCase = transfo... | 114 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = '▁'
_lowerCam... | 114 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionCon... | 114 | 1 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common impor... | 114 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 114 | 1 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformer... | 114 |
def __UpperCAmelCase( lowercase_ , lowercase_ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : int = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Any = boundary[0]
_lowerCamelCase : Tuple ... | 114 | 1 |
import sys
_lowerCamelCase = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098... | 114 |
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... | 114 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils im... | 114 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __UpperCAmelCase( lowercase_ ):
# vision encoder
if "img_encoder.pos_embed" in name:
_lowerCamelCase : Tuple = name.replace(... | 114 | 1 |
import os
def __UpperCAmelCase( ):
with open(os.path.dirname(lowercase_ ) + '''/p022_names.txt''' ) as file:
_lowerCamelCase : Optional[int] = str(file.readlines()[0] )
_lowerCamelCase : List[Any] = names.replace('''"''' , '''''' ).split(... | 114 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCame... | 114 | 1 |
from __future__ import annotations
def __UpperCAmelCase( lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = False , ):
_lowerCamelCase : Tuple = cipher_alphabet or [chr(lowercase_ ) for i in range(97 , 1_23 )]
# If the argument is No... | 114 |
import os
def __UpperCAmelCase( ):
with open(os.path.dirname(lowercase_ ) + '''/p022_names.txt''' ) as file:
_lowerCamelCase : Optional[int] = str(file.readlines()[0] )
_lowerCamelCase : List[Any] = names.replace('''"''' , '''''' ).split(... | 114 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase ... | 114 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 114 | 1 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __UpperCAmelCase( lowercase_ ):
_lowerCamelCase : Any = [
'''decoder.version''',
'''decoder.output_projection.weight''',
... | 114 |
import mpmath # for roots of unity
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , a__=None , a__=None):
"""simple docstring"""
_lowerCamelCase : List[str] = list(poly_a or [0])[:]
... | 114 | 1 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 114 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase = '\\n\n'
_lowerCamelCase = '\nPerplexity (PPL) is one of the most common metrics for eval... | 114 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acce... | 114 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __A :
"""simple docstring"""
def __init__( self , a__):
"""simple docstring"""
_lowerCamelCase : str = list_of_points
# ... | 114 | 1 |
def __UpperCAmelCase( lowercase_ ):
_lowerCamelCase : str = len(lowercase_ )
for _ in range(lowercase_ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
_lowerCamelCase, _lowerCamelCas... | 114 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
... | 114 | 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
_lowerCamelCase = transfo... | 114 |
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 TensorTy... | 114 | 1 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __UpperCAmelCase( lowercase_ , lowercase_=False ):
_lowerCamelCase : Union[str, Any] = OmegaConf.load(lowercase_ )
if display:
print(yaml.du... | 114 |
import doctest
from collections import deque
import numpy as np
class __A :
"""simple docstring"""
def __init__( self):
"""simple docstring"""
_lowerCamelCase : Any = [2, 1, 2, -1]
_lowerCamelCase : Optional[i... | 114 | 1 |
def __UpperCAmelCase( lowercase_ ):
if edge <= 0 or not isinstance(lowercase_ , lowercase_ ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def __UpperCAmelCase( lowercase_ ):
if edge ... | 114 |
from __future__ import annotations
def __UpperCAmelCase( lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = False , ):
_lowerCamelCase : Tuple = cipher_alphabet or [chr(lowercase_ ) for i in range(97 , 1_23 )]
# If the argument is No... | 114 | 1 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
_lowerCamelCase = TypeVar('KT')
_lowerCamelCase = TypeVar('VT')
class __A ( Generic[KT, VT] ):
"""simple docstring"""
def __init__( self , ... | 114 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at... | 114 | 1 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __A ( lowerCamelCase__ ):
"""simple docstring"""
UpperCAmelCase__ = """MCTCTFeatureExtractor"""
UpperCAmelCase__ = """AutoTokenizer"""
... | 114 |
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,
... | 114 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDepende... | 114 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( lowerCamelCase__ )... | 114 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': '... | 114 |
import copy
import random
from transformers import CLIPTokenizer
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
"""simple docstring"""
super().__init__(*a__ , **a__)
... | 114 | 1 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowerCamelCase = '.'
# Internal TensorFlow ops that can ... | 114 |
import unittest
import numpy as np
import requests
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... | 114 | 1 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
_lowerCamelCase = 1.054_571_817E-34 # unit of ℏ : J * s
_lowerCamelCase = 3E8 # unit of c : m * s^-1
def __UpperCAmelCase( ... | 114 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Le... | 114 | 1 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Le... | 114 |
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
_lowerCamelCase = transfo... | 114 | 1 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
... | 114 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionCon... | 114 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'YituTech/conv-bert-base': 'https://... | 114 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 114 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
'uclanlp/visualbert-vqa-pre... | 114 |
def __UpperCAmelCase( lowercase_ , lowercase_ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : int = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Any = boundary[0]
_lowerCamelCase : Tuple ... | 114 | 1 |
import mpmath # for roots of unity
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , a__=None , a__=None):
"""simple docstring"""
_lowerCamelCase : List[str] = list(poly_a or [0])[:]
... | 114 |
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... | 114 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase ... | 114 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __UpperCAmelCase( lowercase_ ):
# vision encoder
if "img_encoder.pos_embed" in name:
_lowerCamelCase : Tuple = name.replace(... | 114 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class __A :
"""simple docstring"""
def __init__( self):
"""simple docstring"""
_lowerCamelCase : int = {}
def __snake_ca... | 114 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCame... | 114 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 114 |
import os
def __UpperCAmelCase( ):
with open(os.path.dirname(lowercase_ ) + '''/p022_names.txt''' ) as file:
_lowerCamelCase : Optional[int] = str(file.readlines()[0] )
_lowerCamelCase : List[Any] = names.replace('''"''' , '''''' ).split(... | 114 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase = {
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingface.co/facebook/mask2former-sw... | 114 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 114 | 1 |
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
_lowerCamelCase = False
class __A ( unittest.TestCase ):
... | 114 |
import mpmath # for roots of unity
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , a__=None , a__=None):
"""simple docstring"""
_lowerCamelCase : List[str] = list(poly_a or [0])[:]
... | 114 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __A ( unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ = JukeboxTokenizer
UpperCAmelCase__ = {
"""artist""":... | 114 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase = '\\n\n'
_lowerCamelCase = '\nPerplexity (PPL) is one of the most common metrics for eval... | 114 | 1 |
import argparse
import json
import subprocess
def __UpperCAmelCase( lowercase_ , lowercase_ ):
_lowerCamelCase : int = []
_lowerCamelCase : int = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
''... | 114 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __A :
"""simple docstring"""
def __init__( self , a__):
"""simple docstring"""
_lowerCamelCase : str = list_of_points
# ... | 114 | 1 |
import cva
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , a__ , a__):
"""simple docstring"""
if k in (0.04, 0.06):
_lowerCamelCase : Optional[Any] = k
... | 114 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
... | 114 | 1 |
import os
def __UpperCAmelCase( ):
_lowerCamelCase : Union[str, Any] = os.path.dirname(os.path.realpath(lowercase_ ) )
_lowerCamelCase : Tuple = os.path.join(lowercase_ , '''triangle.txt''' )
with open(lowercase_ ) as f:
_lowerCamelCase ... | 114 |
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 TensorTy... | 114 | 1 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( lowerCamelCase__ ,unittest.TestCase ):
"""simple docstring"""
UpperCAm... | 114 |
import doctest
from collections import deque
import numpy as np
class __A :
"""simple docstring"""
def __init__( self):
"""simple docstring"""
_lowerCamelCase : Any = [2, 1, 2, -1]
_lowerCamelCase : Optional[i... | 114 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCame... | 114 |
from __future__ import annotations
def __UpperCAmelCase( lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = False , ):
_lowerCamelCase : Tuple = cipher_alphabet or [chr(lowercase_ ) for i in range(97 , 1_23 )]
# If the argument is No... | 114 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __UpperCAmelCase( lowercase_ , lowercase_ , **lowercase_ ):
_lowerCamelCase : Optional[Any] = AutoConfig.from_pretrained(lowercase_ , **lowercase_ )
_lowerCamelCase : ... | 114 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at... | 114 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __UpperCAmelCase( lowercase_ ):
... | 114 |
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,
... | 114 | 1 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRo... | 114 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( lowerCamelCase__ )... | 114 | 1 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
... | 114 |
import copy
import random
from transformers import CLIPTokenizer
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
"""simple docstring"""
super().__init__(*a__ , **a__)
... | 114 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
try:
if not is_torch_available():
... | 114 |
import unittest
import numpy as np
import requests
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... | 114 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vis... | 114 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Le... | 114 | 1 |
def __UpperCAmelCase( lowercase_ ):
if not isinstance(lowercase_ , lowercase_ ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(lowercase_ ) == 0:
raise ValueError('''Input list must be a non empty list''' )
i... | 114 |
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
_lowerCamelCase = transfo... | 114 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, t... | 114 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionCon... | 114 | 1 |
from __future__ import annotations
_lowerCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __UpperCAmelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , ):
_lowerCamelCase : Optio... | 114 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 114 | 1 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_config... | 114 |
def __UpperCAmelCase( lowercase_ , lowercase_ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : int = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Any = boundary[0]
_lowerCamelCase : Tuple ... | 114 | 1 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, requi... | 114 |
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... | 114 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
_lowerCamelCase = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTCo... | 114 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __UpperCAmelCase( lowercase_ ):
# vision encoder
if "img_encoder.pos_embed" in name:
_lowerCamelCase : Tuple = name.replace(... | 114 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
_lowerCamelCase = datasets.logging.get_logger(__name__)
_lowerCamelCase = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title ... | 114 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCame... | 114 | 1 |
from __future__ import annotations
def __UpperCAmelCase( lowercase_ , lowercase_ = None , lowercase_ = None ):
if start is None:
_lowerCamelCase : Union[str, Any] = 0
if end is None:
_lowerCamelCase : Union[str, Any] = len(lowerca... | 114 |
import os
def __UpperCAmelCase( ):
with open(os.path.dirname(lowercase_ ) + '''/p022_names.txt''' ) as file:
_lowerCamelCase : Optional[int] = str(file.readlines()[0] )
_lowerCamelCase : List[Any] = names.replace('''"''' , '''''' ).split(... | 114 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'v... | 114 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 114 | 1 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = r'\n Args:\n input_ids (`torch.LongTenso... | 114 |
import mpmath # for roots of unity
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , a__=None , a__=None):
"""simple docstring"""
_lowerCamelCase : List[str] = list(poly_a or [0])[:]
... | 114 | 1 |
import math
from numpy import inf
from scipy.integrate import quad
def __UpperCAmelCase( lowercase_ ):
if num <= 0:
raise ValueError('''math domain error''' )
return quad(lowercase_ , 0 , lowercase_ , args=(lowercase_) )[0]
def __UpperCAmelCase( ... | 114 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase = '\\n\n'
_lowerCamelCase = '\nPerplexity (PPL) is one of the most common metrics for eval... | 114 | 1 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__... | 114 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __A :
"""simple docstring"""
def __init__( self , a__):
"""simple docstring"""
_lowerCamelCase : str = list_of_points
# ... | 114 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_u... | 114 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
... | 114 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __A ( datasets.BeamBasedBuilder ):
"""simple docstring"""
def __snak... | 114 |
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 TensorTy... | 114 | 1 |
from ..utils import DummyObject, requires_backends
class __A ( metaclass=lowerCamelCase__ ):
"""simple docstring"""
UpperCAmelCase__ = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *a__ , **a__):
"""simple ... | 114 |
import doctest
from collections import deque
import numpy as np
class __A :
"""simple docstring"""
def __init__( self):
"""simple docstring"""
_lowerCamelCase : Any = [2, 1, 2, -1]
_lowerCamelCase : Optional[i... | 114 | 1 |
def __UpperCAmelCase( lowercase_ ):
if num < 0:
return False
_lowerCamelCase : int = num
_lowerCamelCase : int = 0
while num > 0:
_lowerCamelCase : Dict = rev_num * 10 + (num % 10)
num //= 10
return n... | 114 |
from __future__ import annotations
def __UpperCAmelCase( lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = False , ):
_lowerCamelCase : Tuple = cipher_alphabet or [chr(lowercase_ ) for i in range(97 , 1_23 )]
# If the argument is No... | 114 | 1 |
import math
def __UpperCAmelCase( lowercase_ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return F... | 114 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at... | 114 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_lowerCamelCase = {
'sample_size': 32,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 2,
'num_class_embeds': 1000,
... | 114 |
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,
... | 114 | 1 |
def __UpperCAmelCase( lowercase_ ):
_lowerCamelCase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
_lowerCamelCase : set[int] = set()
return any(
node not in visited and depth_first_search(lowercas... | 114 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( lowerCamelCase__ )... | 114 | 1 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __UpperCAmelCase( lowercase_ , lowercase_ , lowercase_=10_24 , lowercase_=10_24 , lowercase_=False , **lowercase_... | 114 |
import copy
import random
from transformers import CLIPTokenizer
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
"""simple docstring"""
super().__init__(*a__ , **a__)
... | 114 | 1 |
import qiskit
def __UpperCAmelCase( lowercase_ , lowercase_ ):
_lowerCamelCase : Union[str, Any] = qiskit.Aer.get_backend('''aer_simulator''' )
_lowerCamelCase : List[str] = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if... | 114 |
import unittest
import numpy as np
import requests
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... | 114 | 1 |
from math import factorial
_lowerCamelCase = {str(digit): factorial(digit) for digit in range(10)}
def __UpperCAmelCase( lowercase_ ):
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError('''Parameter number must be int''' )
if number < 0:
... | 114 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Le... | 114 | 1 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __A :
"""simple docstring"""
def __init__( self , a__ , a__ , a__):
"""simple docstring"""
if dst_width < 0 or dst_height < 0:
... | 114 |
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
_lowerCamelCase = transfo... | 114 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
... | 114 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionCon... | 114 | 1 |
def __UpperCAmelCase( lowercase_ ):
_lowerCamelCase : str = [0] * len(lowercase_ )
_lowerCamelCase : Optional[int] = []
_lowerCamelCase : Tuple = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 114 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 114 | 1 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArgume... | 114 |
def __UpperCAmelCase( lowercase_ , lowercase_ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : int = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Any = boundary[0]
_lowerCamelCase : Tuple ... | 114 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_commo... | 114 |
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... | 114 | 1 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common... | 114 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __UpperCAmelCase( lowercase_ ):
# vision encoder
if "img_encoder.pos_embed" in name:
_lowerCamelCase : Tuple = name.replace(... | 114 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __UpperCAmelCase( ):
_lowerCamelCase : int = HfArgumentParser(lowercase_ )
_lowerCamelCase : int = parser.parse_args_into_dataclasses()[0]
_lowerCamelCase : Tuple ... | 114 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCame... | 114 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowerCamelCase = {'tokenization_byt5': ['ByT5Tokenizer']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['__f... | 114 |
import os
def __UpperCAmelCase( ):
with open(os.path.dirname(lowercase_ ) + '''/p022_names.txt''' ) as file:
_lowerCamelCase : Optional[int] = str(file.readlines()[0] )
_lowerCamelCase : List[Any] = names.replace('''"''' , '''''' ).split(... | 114 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 114 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 114 | 1 |
import math
def __UpperCAmelCase( lowercase_ , lowercase_ ):
return math.pow(lowercase_ , 2 ) - a
def __UpperCAmelCase( lowercase_ ):
return 2 * x
def __UpperCAmelCase( lowercase_ ):
_lowerCamelCase : Optional[int] = 2.0
while start <... | 114 |
import mpmath # for roots of unity
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , a__=None , a__=None):
"""simple docstring"""
_lowerCamelCase : List[str] = list(poly_a or [0])[:]
... | 114 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from trans... | 114 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase = '\\n\n'
_lowerCamelCase = '\nPerplexity (PPL) is one of the most common metrics for eval... | 114 | 1 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutp... | 114 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __A :
"""simple docstring"""
def __init__( self , a__):
"""simple docstring"""
_lowerCamelCase : str = list_of_points
# ... | 114 | 1 |
import logging
from transformers import PretrainedConfig
_lowerCamelCase = logging.getLogger(__name__)
_lowerCamelCase = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}
c... | 114 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
... | 114 | 1 |
def __UpperCAmelCase( lowercase_ ):
_lowerCamelCase : Optional[Any] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __UpperCAmelCase( lowercase_ = 1_00 ):
_lowerCamelCase : Any = 1
_lowerCamel... | 114 |
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 TensorTy... | 114 | 1 |
from __future__ import annotations
_lowerCamelCase = tuple[int, int, int]
_lowerCamelCase = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
_lowerCamelCase = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# -------------------------- defaul... | 114 |
import doctest
from collections import deque
import numpy as np
class __A :
"""simple docstring"""
def __init__( self):
"""simple docstring"""
_lowerCamelCase : Any = [2, 1, 2, -1]
_lowerCamelCase : Optional[i... | 114 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
]
}
try:
if not is_torch_available():
... | 114 |
from __future__ import annotations
def __UpperCAmelCase( lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = False , ):
_lowerCamelCase : Tuple = cipher_alphabet or [chr(lowercase_ ) for i in range(97 , 1_23 )]
# If the argument is No... | 114 | 1 |
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_VARS_TRUE_VALUES,
FEAT... | 114 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at... | 114 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'kssteven/ibert-roberta-base': 'http... | 114 |
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,
... | 114 | 1 |
def __UpperCAmelCase( lowercase_ ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
... | 114 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( lowerCamelCase__ )... | 114 | 1 |
def __UpperCAmelCase( lowercase_ , lowercase_ ):
while b:
_lowerCamelCase, _lowerCamelCase : Union[str, Any] = b, a % b
return a
def __UpperCAmelCase( lowercase_ , lowercase_ ):
return a if b == 0 else euclidean_gcd_recursive(lowercase_ , ... | 114 |
import copy
import random
from transformers import CLIPTokenizer
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
"""simple docstring"""
super().__init__(*a__ , **a__)
... | 114 | 1 |
import warnings
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 __A ( lowerCamelCase__ ):
... | 114 |
import unittest
import numpy as np
import requests
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... | 114 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase = {'configuration_xglm': ['XGLM_PRETRAINE... | 114 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Le... | 114 | 1 |
# Copyright 2022 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... | 114 |
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
_lowerCamelCase = transfo... | 114 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKI... | 114 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionCon... | 114 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DU... | 114 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 114 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioGPT models at https://hugg... | 114 |
def __UpperCAmelCase( lowercase_ , lowercase_ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : int = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Any = boundary[0]
_lowerCamelCase : Tuple ... | 114 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 114 |
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... | 114 | 1 |
import os
# Precomputes a list of the 100 first triangular numbers
_lowerCamelCase = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def __UpperCAmelCase( ):
_lowerCamelCase : List[str] = os.path.dirname(os.path.realpath(lowercase_ ) )
_lowerCamelCase : Option... | 114 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __UpperCAmelCase( lowercase_ ):
# vision encoder
if "img_encoder.pos_embed" in name:
_lowerCamelCase : Tuple = name.replace(... | 114 | 1 |
from __future__ import annotations
from collections import namedtuple
def __UpperCAmelCase( lowercase_ , lowercase_ , lowercase_ ):
_lowerCamelCase : str = namedtuple('''result''' , '''name value''' )
if (voltage, current, power).count(0 ) != 1:
... | 114 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCame... | 114 | 1 |
import torch
from diffusers import StableDiffusionPipeline
_lowerCamelCase = 'path-to-your-trained-model'
_lowerCamelCase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
_lowerCamelCase = 'A photo of sks dog in a bucket'
_lowe... | 114 |
import os
def __UpperCAmelCase( ):
with open(os.path.dirname(lowercase_ ) + '''/p022_names.txt''' ) as file:
_lowerCamelCase : Optional[int] = str(file.readlines()[0] )
_lowerCamelCase : List[Any] = names.replace('''"''' , '''''' ).split(... | 114 | 1 |
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