



'''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

_A				:    Optional[Any]     						=logging.get_logger(__name__)







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Optional[Any]:
	return [
	    int(1000 * (box[0] / width)					),
	    int(1000 * (box[1] / height)					),
	    int(1000 * (box[2] / width)					),
	    int(1000 * (box[3] / height)					),
	]







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase = None					) ->			str:
	_lowercase					:       Any       =		tesseract_config if tesseract_config is not None else ''

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

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

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

	# finally, normalize the bounding boxes
	_lowercase					:       Union[str, Any]       =		[]
	for box in actual_boxes:
		normalized_boxes.append(normalize_box(_lowercase,				_lowercase,				_lowercase					)					)

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

	return words, normalized_boxes






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						["""pixel_values"""]




					def __init__(  self				:	Optional[int]     ,  UpperCamelCase_				:	bool = True     ,  UpperCamelCase_				:	Dict[str, int] = None     ,  UpperCamelCase_				:	PILImageResampling = PILImageResampling.BILINEAR     ,  UpperCamelCase_				:	bool = True     ,  UpperCamelCase_				:	Optional[str] = None     ,  UpperCamelCase_				:	Optional[str] = ""     ,  **UpperCamelCase_				:	Union[str, Any]     ,  )  ->       None:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)
						_lowercase					:       Tuple       =		size if size is not None else {'height': 224, 'width': 224}
						_lowercase					:       str       =		get_size_dict(UpperCamelCase_					)

						_lowercase					:       Union[str, Any]       =		do_resize
						_lowercase					:       int       =		size
						_lowercase					:       List[Any]       =		resample
						_lowercase					:       List[str]       =		apply_ocr
						_lowercase					:       Dict       =		ocr_lang
						_lowercase					:       Any       =		tesseract_config




					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	np.ndarray     ,  UpperCamelCase_				:	Dict[str, int]     ,  UpperCamelCase_				:	PILImageResampling = PILImageResampling.BILINEAR     ,  UpperCamelCase_				:	Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase_				:	Dict     ,  )  ->       np.ndarray:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		get_size_dict(UpperCamelCase_					)
						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()}'''					)
						_lowercase					:       Tuple       =		(size['height'], size['width'])
						return resize(UpperCamelCase_     ,  size=UpperCamelCase_     ,  resample=UpperCamelCase_     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	ImageInput     ,  UpperCamelCase_				:	bool = None     ,  UpperCamelCase_				:	Dict[str, int] = None     ,  UpperCamelCase_				:	PILImageResampling = None     ,  UpperCamelCase_				:	bool = None     ,  UpperCamelCase_				:	Optional[str] = None     ,  UpperCamelCase_				:	Optional[str] = None     ,  UpperCamelCase_				:	Optional[Union[str, TensorType]] = None     ,  UpperCamelCase_				:	ChannelDimension = ChannelDimension.FIRST     ,  **UpperCamelCase_				:	Any     ,  )  ->       PIL.Image.Image:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		do_resize if do_resize is not None else self.do_resize
						_lowercase					:       List[str]       =		size if size is not None else self.size
						_lowercase					:       Dict       =		get_size_dict(UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		resample if resample is not None else self.resample
						_lowercase					:       int       =		apply_ocr if apply_ocr is not None else self.apply_ocr
						_lowercase					:       List[str]       =		ocr_lang if ocr_lang is not None else self.ocr_lang
						_lowercase					:       int       =		tesseract_config if tesseract_config is not None else self.tesseract_config

						_lowercase					:       Union[str, Any]       =		make_list_of_images(UpperCamelCase_					)

						if not valid_images(UpperCamelCase_					):
							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.
						_lowercase					:       str       =		[to_numpy_array(UpperCamelCase_					) for image in images]

						if apply_ocr:
							requires_backends(self     ,  'pytesseract'					)
							_lowercase					:       Tuple       =		[]
							_lowercase					:       Optional[Any]       =		[]
							for image in images:
								_lowercase  ,    _lowercase					:       List[str]       =		apply_tesseract(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
								words_batch.append(UpperCamelCase_					)
								boxes_batch.append(UpperCamelCase_					)

						if do_resize:
							_lowercase					:       Any       =		[self.resize(image=UpperCamelCase_     ,  size=UpperCamelCase_     ,  resample=UpperCamelCase_					) for image in images]

						# flip color channels from RGB to BGR (as Detectron2 requires this)
						_lowercase					:       Tuple       =		[flip_channel_order(UpperCamelCase_					) for image in images]
						_lowercase					:       int       =		[to_channel_dimension_format(UpperCamelCase_     ,  UpperCamelCase_					) for image in images]

						_lowercase					:       int       =		BatchFeature(data={'pixel_values': images}     ,  tensor_type=UpperCamelCase_					)

						if apply_ocr:
							_lowercase					:       Dict       =		words_batch
							_lowercase					:       int       =		boxes_batch
						return data





'''simple docstring'''

_A				:    Dict     						='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

_A				:    Dict     						=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_A				:    Dict     						={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}
