
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       =					logging.get_logger(__name__)






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	return [
	    int(1000 * (box[0] / width)				),
	    int(1000 * (box[1] / height)				),
	    int(1000 * (box[2] / width)				),
	    int(1000 * (box[3] / height)				),
	]






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE = None				):
	lowercase	    =       tesseract_config if tesseract_config is not None else ''

	# apply OCR
	lowercase	    =       to_pil_image(__SCREAMING_SNAKE_CASE				)
	lowercase    ,   lowercase	    =       pil_image.size
	lowercase	    =       pytesseract.image_to_data(__SCREAMING_SNAKE_CASE					,				lang=__SCREAMING_SNAKE_CASE					,				output_type='dict'					,				config=__SCREAMING_SNAKE_CASE				)
	lowercase    ,   lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       data['text'], data['left'], data['top'], data['width'], data['height']

	# filter empty words and corresponding coordinates
	lowercase	    =       [idx for idx, word in enumerate(__SCREAMING_SNAKE_CASE				) if not word.strip()]
	lowercase	    =       [word for idx, word in enumerate(__SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
	lowercase	    =       [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
	lowercase	    =       [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
	lowercase	    =       [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
	lowercase	    =       [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]

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

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

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

	return words, normalized_boxes







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	List[str]									=			["""pixel_values"""]





	def __init__(			self						,       snake_case = True						,       snake_case = None						,       snake_case = PILImageResampling.BILINEAR						,       snake_case = True						,       snake_case = None						,       snake_case = ""						,       **snake_case						,       ):
		super().__init__(**snake_case			)
		lowercase	    =       size if size is not None else {'height': 224, 'width': 224}
		lowercase	    =       get_size_dict(snake_case			)

		lowercase	    =       do_resize
		lowercase	    =       size
		lowercase	    =       resample
		lowercase	    =       apply_ocr
		lowercase	    =       ocr_lang
		lowercase	    =       tesseract_config





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case = PILImageResampling.BILINEAR						,       snake_case = None						,       **snake_case						,       ):
		lowercase	    =       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()}'''			)
		lowercase	    =       (size['height'], size['width'])
		return resize(snake_case						,       size=snake_case						,       resample=snake_case						,       data_format=snake_case						,       **snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None						,       snake_case = None						,       snake_case = None						,       snake_case = None						,       snake_case = None						,       snake_case = None						,       snake_case = None						,       snake_case = ChannelDimension.FIRST						,       **snake_case						,       ):
		lowercase	    =       do_resize if do_resize is not None else self.do_resize
		lowercase	    =       size if size is not None else self.size
		lowercase	    =       get_size_dict(snake_case			)
		lowercase	    =       resample if resample is not None else self.resample
		lowercase	    =       apply_ocr if apply_ocr is not None else self.apply_ocr
		lowercase	    =       ocr_lang if ocr_lang is not None else self.ocr_lang
		lowercase	    =       tesseract_config if tesseract_config is not None else self.tesseract_config

		lowercase	    =       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.
		lowercase	    =       [to_numpy_array(snake_case			) for image in images]

		if apply_ocr:
			requires_backends(self						,       'pytesseract'			)
			lowercase	    =       []
			lowercase	    =       []
			for image in images:
				lowercase    ,   lowercase	    =       apply_tesseract(snake_case						,       snake_case						,       snake_case			)
				words_batch.append(snake_case			)
				boxes_batch.append(snake_case			)

		if do_resize:
			lowercase	    =       [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)
		lowercase	    =       [flip_channel_order(snake_case			) for image in images]
		lowercase	    =       [to_channel_dimension_format(snake_case						,       snake_case			) for image in images]

		lowercase	    =       BatchFeature(data={'pixel_values': images}						,       tensor_type=snake_case			)

		if apply_ocr:
			lowercase	    =       words_batch
			lowercase	    =       boxes_batch
		return data









import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
					import jax.numpy as jnp

if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch
class     lowerCamelCase_					(   unittest.TestCase			):







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   {
								    '''task_specific_params''': {
								        '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_2_8, '''min_length''': 1_2, '''num_beams''': 4},
								        '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_4_2, '''min_length''': 5_6, '''num_beams''': 4},
								        '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 6_2, '''min_length''': 1_1, '''num_beams''': 6},
								    }
								}
								__magic_name__		:List[str]   =   {
								    '''task_specific_params.summarization.length_penalty''': 1.0,
								    '''task_specific_params.summarization.max_length''': 1_2_8,
								    '''task_specific_params.summarization.min_length''': 1_2,
								    '''task_specific_params.summarization.num_beams''': 4,
								    '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
								    '''task_specific_params.summarization_cnn.max_length''': 1_4_2,
								    '''task_specific_params.summarization_cnn.min_length''': 5_6,
								    '''task_specific_params.summarization_cnn.num_beams''': 4,
								    '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
								    '''task_specific_params.summarization_xsum.max_length''': 6_2,
								    '''task_specific_params.summarization_xsum.min_length''': 1_1,
								    '''task_specific_params.summarization_xsum.num_beams''': 6,
								}

								self.assertEqual(flatten_dict(__lowerCAmelCase  )							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   np.random.randn(3							,       4  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase  )							,       x.transpose()  )  )

								__magic_name__		:List[Any]   =   np.random.randn(3							,       4							,       5  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  )							,       x.transpose((1, 2, 0)  )  )  )







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   np.random.randn(3							,       4  )
								__magic_name__		:Tuple   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase  )							,       transpose(__lowerCAmelCase  ).numpy()  )  )

								__magic_name__		:int   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:Union[str, Any]   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  )							,       transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  ).numpy()  )  )







				@require_tf
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   np.random.randn(3							,       4  )
								__magic_name__		:Optional[Any]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase  )							,       transpose(__lowerCAmelCase  ).numpy()  )  )

								__magic_name__		:List[str]   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:Optional[Any]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  )							,       transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  ).numpy()  )  )







				@require_flax
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   np.random.randn(3							,       4  )
								__magic_name__		:Dict   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase  )							,       np.asarray(transpose(__lowerCAmelCase  )  )  )  )

								__magic_name__		:Dict   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:Dict   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  )							,       np.asarray(transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  )  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   np.random.randn(3							,       4  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (4, 3)  )							,       np.reshape(__lowerCAmelCase							,       (4, 3)  )  )  )

								__magic_name__		:Union[str, Any]   =   np.random.randn(3							,       4							,       5  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (1_2, 5)  )							,       np.reshape(__lowerCAmelCase							,       (1_2, 5)  )  )  )







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   np.random.randn(3							,       4  )
								__magic_name__		:Tuple   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (4, 3)  )							,       reshape(__lowerCAmelCase							,       (4, 3)  ).numpy()  )  )

								__magic_name__		:Union[str, Any]   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:List[str]   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (1_2, 5)  )							,       reshape(__lowerCAmelCase							,       (1_2, 5)  ).numpy()  )  )







				@require_tf
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   np.random.randn(3							,       4  )
								__magic_name__		:Union[str, Any]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (4, 3)  )							,       reshape(__lowerCAmelCase							,       (4, 3)  ).numpy()  )  )

								__magic_name__		:List[Any]   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:Optional[int]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (1_2, 5)  )							,       reshape(__lowerCAmelCase							,       (1_2, 5)  ).numpy()  )  )







				@require_flax
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   np.random.randn(3							,       4  )
								__magic_name__		:Any   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (4, 3)  )							,       np.asarray(reshape(__lowerCAmelCase							,       (4, 3)  )  )  )  )

								__magic_name__		:List[Any]   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:List[str]   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (1_2, 5)  )							,       np.asarray(reshape(__lowerCAmelCase							,       (1_2, 5)  )  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   np.random.randn(1							,       3							,       4  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase  )							,       np.squeeze(__lowerCAmelCase  )  )  )

								__magic_name__		:Optional[Any]   =   np.random.randn(1							,       4							,       1							,       5  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase							,       axis=2  )							,       np.squeeze(__lowerCAmelCase							,       axis=2  )  )  )







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   np.random.randn(1							,       3							,       4  )
								__magic_name__		:List[Any]   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase  )							,       squeeze(__lowerCAmelCase  ).numpy()  )  )

								__magic_name__		:List[str]   =   np.random.randn(1							,       4							,       1							,       5  )
								__magic_name__		:str   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase							,       axis=2  )							,       squeeze(__lowerCAmelCase							,       axis=2  ).numpy()  )  )







				@require_tf
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   np.random.randn(1							,       3							,       4  )
								__magic_name__		:Tuple   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase  )							,       squeeze(__lowerCAmelCase  ).numpy()  )  )

								__magic_name__		:Tuple   =   np.random.randn(1							,       4							,       1							,       5  )
								__magic_name__		:Optional[int]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase							,       axis=2  )							,       squeeze(__lowerCAmelCase							,       axis=2  ).numpy()  )  )







				@require_flax
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   np.random.randn(1							,       3							,       4  )
								__magic_name__		:Optional[Any]   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase  )							,       np.asarray(squeeze(__lowerCAmelCase  )  )  )  )

								__magic_name__		:List[Any]   =   np.random.randn(1							,       4							,       1							,       5  )
								__magic_name__		:Optional[Any]   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase							,       axis=2  )							,       np.asarray(squeeze(__lowerCAmelCase							,       axis=2  )  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   np.random.randn(3							,       4  )
								self.assertTrue(np.allclose(expand_dims(__lowerCAmelCase							,       axis=1  )							,       np.expand_dims(__lowerCAmelCase							,       axis=1  )  )  )







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   np.random.randn(3							,       4  )
								__magic_name__		:Any   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(expand_dims(__lowerCAmelCase							,       axis=1  )							,       expand_dims(__lowerCAmelCase							,       axis=1  ).numpy()  )  )







				@require_tf
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   np.random.randn(3							,       4  )
								__magic_name__		:Union[str, Any]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(expand_dims(__lowerCAmelCase							,       axis=1  )							,       expand_dims(__lowerCAmelCase							,       axis=1  ).numpy()  )  )







				@require_flax
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   np.random.randn(3							,       4  )
								__magic_name__		:Tuple   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(expand_dims(__lowerCAmelCase							,       axis=1  )							,       np.asarray(expand_dims(__lowerCAmelCase							,       axis=1  )  )  )  )






