



'''simple docstring'''






from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
						import PIL

# soft dependency
if is_pytesseract_available():
						import pytesseract

UpperCamelCase__:   str								=			logging.get_logger(__name__)


def  snake_case_     (					_lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						List[Any]  )      ->   List[str]:
							return [
							    int(1000 * (box[0] / width)  ),
							    int(1000 * (box[1] / height)  ),
							    int(1000 * (box[2] / width)  ),
							    int(1000 * (box[3] / height)  ),
							]


def  snake_case_     (					_lowerCAmelCase  :						np.ndarray   ,      _lowerCAmelCase  :						Optional[str]   ,      _lowerCAmelCase  :						Optional[str] = None  )      ->   Optional[Any]:
							UpperCAmelCase :       int 							=		tesseract_config if tesseract_config is not None else ''''''

							# apply OCR
							UpperCAmelCase :       Any 							=		to_pil_image(_lowerCAmelCase  )
							UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		pil_image.size
							UpperCAmelCase :       List[Any] 							=		pytesseract.image_to_data(_lowerCAmelCase   ,      lang=_lowerCAmelCase   ,      output_type='''dict'''   ,      config=_lowerCAmelCase  )
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       int 							=		data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

							# filter empty words and corresponding coordinates
							UpperCAmelCase :       str 							=		[idx for idx, word in enumerate(_lowerCAmelCase  ) if not word.strip()]
							UpperCAmelCase :       Any 							=		[word for idx, word in enumerate(_lowerCAmelCase  ) if idx not in irrelevant_indices]
							UpperCAmelCase :       Tuple 							=		[coord for idx, coord in enumerate(_lowerCAmelCase  ) if idx not in irrelevant_indices]
							UpperCAmelCase :       Dict 							=		[coord for idx, coord in enumerate(_lowerCAmelCase  ) if idx not in irrelevant_indices]
							UpperCAmelCase :       int 							=		[coord for idx, coord in enumerate(_lowerCAmelCase  ) if idx not in irrelevant_indices]
							UpperCAmelCase :       int 							=		[coord for idx, coord in enumerate(_lowerCAmelCase  ) if idx not in irrelevant_indices]

							# turn coordinates into (left, top, left+width, top+height) format
							UpperCAmelCase :       int 							=		[]
							for x, y, w, h in zip(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  ):
														UpperCAmelCase :       List[Any] 							=		[x, y, x + w, y + h]
														actual_boxes.append(_lowerCAmelCase  )

							# finally, normalize the bounding boxes
							UpperCAmelCase :       int 							=		[]
							for box in actual_boxes:
														normalized_boxes.append(normalize_box(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )  )

							assert len(_lowerCAmelCase  ) == len(_lowerCAmelCase  ), "Not as many words as there are bounding boxes"

							return words, normalized_boxes




class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       ["""pixel_values"""]







	def __init__(						self			:     Optional[int]		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = PILImageResampling.BILINEAR		,			__snake_case			:     bool = True		,			__snake_case			:     Optional[str] = None		,			__snake_case			:     Optional[str] = ""		,			**__snake_case			:     str		,			)			->		None:
								super().__init__(**__snake_case		)
								UpperCAmelCase :       List[str] 							=		size if size is not None else {'''height''': 224, '''width''': 224}
								UpperCAmelCase :       List[str] 							=		get_size_dict(__snake_case		)

								UpperCAmelCase :       List[Any] 							=		do_resize
								UpperCAmelCase :       Any 							=		size
								UpperCAmelCase :       List[str] 							=		resample
								UpperCAmelCase :       int 							=		apply_ocr
								UpperCAmelCase :       str 							=		ocr_lang
								UpperCAmelCase :       Optional[Any] 							=		tesseract_config







	def      A	(						self			:     List[str]		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     PILImageResampling = PILImageResampling.BILINEAR		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     List[str]		,			)			->		np.ndarray:
								UpperCAmelCase :       int 							=		get_size_dict(__snake_case		)
								if "height" not in size or "width" not in size:
															raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""		)
								UpperCAmelCase :       Any 							=		(size['''height'''], size['''width'''])
								return resize(__snake_case		,			size=__snake_case		,			resample=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Tuple		,			__snake_case			:     ImageInput		,			__snake_case			:     bool = None		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = None		,			__snake_case			:     bool = None		,			__snake_case			:     Optional[str] = None		,			__snake_case			:     Optional[str] = None		,			__snake_case			:     Optional[Union[str, TensorType]] = None		,			__snake_case			:     ChannelDimension = ChannelDimension.FIRST		,			**__snake_case			:     List[Any]		,			)			->		PIL.Image.Image:
								UpperCAmelCase :       int 							=		do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase :       List[Any] 							=		size if size is not None else self.size
								UpperCAmelCase :       int 							=		get_size_dict(__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		resample if resample is not None else self.resample
								UpperCAmelCase :       List[str] 							=		apply_ocr if apply_ocr is not None else self.apply_ocr
								UpperCAmelCase :       Optional[Any] 							=		ocr_lang if ocr_lang is not None else self.ocr_lang
								UpperCAmelCase :       Optional[int] 							=		tesseract_config if tesseract_config is not None else self.tesseract_config

								UpperCAmelCase :       Union[str, Any] 							=		make_list_of_images(__snake_case		)

								if not valid_images(__snake_case		):
															raise ValueError(
															    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
															    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

								if do_resize and size is None:
															raise ValueError('''Size must be specified if do_resize is True.'''		)

								# All transformations expect numpy arrays.
								UpperCAmelCase :       List[Any] 							=		[to_numpy_array(__snake_case		) for image in images]

								if apply_ocr:
															requires_backends(self		,			'''pytesseract'''		)
															UpperCAmelCase :       Dict 							=		[]
															UpperCAmelCase :       Union[str, Any] 							=		[]
															for image in images:
																						UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		apply_tesseract(__snake_case		,			__snake_case		,			__snake_case		)
																						words_batch.append(__snake_case		)
																						boxes_batch.append(__snake_case		)

								if do_resize:
															UpperCAmelCase :       List[Any] 							=		[self.resize(image=__snake_case		,			size=__snake_case		,			resample=__snake_case		) for image in images]

								# flip color channels from RGB to BGR (as Detectron2 requires this)
								UpperCAmelCase :       List[Any] 							=		[flip_channel_order(__snake_case		) for image in images]
								UpperCAmelCase :       Dict 							=		[to_channel_dimension_format(__snake_case		,			__snake_case		) for image in images]

								UpperCAmelCase :       List[str] 							=		BatchFeature(data={'''pixel_values''': images}		,			tensor_type=__snake_case		)

								if apply_ocr:
															UpperCAmelCase :       Dict 							=		words_batch
															UpperCAmelCase :       List[str] 							=		boxes_batch
								return data






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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}



class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Any    			=		'''resnet'''
 lowerCamelCase	:Any    			=		['''basic''', '''bottleneck''']



 def __init__(					self		,				lowerCAmelCase_=3		,				lowerCAmelCase_=64		,				lowerCAmelCase_=[2_56, 5_12, 10_24, 20_48]		,				lowerCAmelCase_=[3, 4, 6, 3]		,				lowerCAmelCase_="bottleneck"		,				lowerCAmelCase_="relu"		,				lowerCAmelCase_=False		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				**lowerCAmelCase_		,				)  ->					Union[str, Any]:
        super().__init__(**lowerCAmelCase_							)
        if layer_type not in self.layer_types:
               raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types							)}'''							)
        _A        =  num_channels
        _A        =  embedding_size
        _A        =  hidden_sizes
        _A        =  depths
        _A        =  layer_type
        _A        =  hidden_act
        _A        =  downsample_in_first_stage
        _A        =  ["""stem"""] + [F'''stage{idx}''' for idx in range(1		,				len(lowerCAmelCase_							) + 1							)]
        _A   ,  _A        =  get_aligned_output_features_output_indices(
            out_features=lowerCAmelCase_		,				out_indices=lowerCAmelCase_		,				stage_names=self.stage_names							)



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Optional[Any]    			=		version.parse('''1.11'''       )



 @property
 def      UpperCAmelCase							(					self							)  ->					Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ]							)



 @property
 def      UpperCAmelCase							(					self							)  ->					float:
        return 1E-3




