





'''simple docstring'''







import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any],      SCREAMING_SNAKE_CASE__   :      Optional[int]=256 )			->						str:
						UpperCAmelCase_      :   str      =	min(img.size )

						if s < target_image_size:
												raise ValueError(F"""min dim for image {s} < {target_image_size}""" )

						UpperCAmelCase_      :   Any      =	target_image_size / s
						UpperCAmelCase_      :   Union[str, Any]      =	(round(r * img.size[1] ), round(r * img.size[0] ))
						UpperCAmelCase_      :   Any      =	TF.resize(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      interpolation=PIL.Image.LANCZOS )
						UpperCAmelCase_      :   Optional[Any]      =	TF.center_crop(SCREAMING_SNAKE_CASE__,      output_size=2 * [target_image_size] )
						UpperCAmelCase_      :   int      =	torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE__ ),      0 )
						return img





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int )			->						Dict:
						UpperCAmelCase_      :   str      =	2.0 * x - 1.0
						return x





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      Optional[int]=True,      SCREAMING_SNAKE_CASE__   :      int="RGB" )			->						Optional[Any]:
						UpperCAmelCase_      :   Optional[Any]      =	x.detach().cpu()
						if process:
												UpperCAmelCase_      :   Tuple      =	post_process_tensor(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   str      =	x.numpy()
						if process:
												UpperCAmelCase_      :   int      =	(255 * x).astype(np.uinta )
						UpperCAmelCase_      :   Optional[int]      =	Image.fromarray(SCREAMING_SNAKE_CASE__ )
						if not x.mode == mode:
												UpperCAmelCase_      :   Any      =	x.convert(SCREAMING_SNAKE_CASE__ )
						return x





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int )			->						Optional[Any]:
						UpperCAmelCase_      :   List[str]      =	torch.clamp(SCREAMING_SNAKE_CASE__,      -1.0,      1.0 )
						UpperCAmelCase_      :   List[Any]      =	(x + 1.0) / 2.0
						UpperCAmelCase_      :   List[Any]      =	x.permute(1,      2,      0 )
						return x





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						Optional[Any]:
						UpperCAmelCase_      :   str      =	post_process_tensor(x.squeeze() )
						return x.permute(2,      0,      1 ).unsqueeze(0 )












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_  :							Any            =	{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							str            =	[
					    "DPRContextEncoderTokenizerFast",
					    "DPRQuestionEncoderTokenizerFast",
					    "DPRReaderTokenizerFast",
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Optional[int]            =	[
					    "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPRContextEncoder",
					    "DPRPretrainedContextEncoder",
					    "DPRPreTrainedModel",
					    "DPRPretrainedQuestionEncoder",
					    "DPRPretrainedReader",
					    "DPRQuestionEncoder",
					    "DPRReader",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Dict            =	[
					    "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFDPRContextEncoder",
					    "TFDPRPretrainedContextEncoder",
					    "TFDPRPretrainedQuestionEncoder",
					    "TFDPRPretrainedReader",
					    "TFDPRQuestionEncoder",
					    "TFDPRReader",
					]


if TYPE_CHECKING:
					from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
					from .tokenization_dpr import (
					    DPRContextEncoderTokenizer,
					    DPRQuestionEncoderTokenizer,
					    DPRReaderOutput,
					    DPRReaderTokenizer,
					)

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_dpr_fast import (
										    DPRContextEncoderTokenizerFast,
										    DPRQuestionEncoderTokenizerFast,
										    DPRReaderTokenizerFast,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpr import (
										    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPRContextEncoder,
										    DPRPretrainedContextEncoder,
										    DPRPreTrainedModel,
										    DPRPretrainedQuestionEncoder,
										    DPRPretrainedReader,
										    DPRQuestionEncoder,
										    DPRReader,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_dpr import (
										    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDPRContextEncoder,
										    TFDPRPretrainedContextEncoder,
										    TFDPRPretrainedQuestionEncoder,
										    TFDPRPretrainedReader,
										    TFDPRQuestionEncoder,
										    TFDPRReader,
										)

else:
					import sys

					snake_case_  :							List[str]            =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





