





'''simple docstring'''



from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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

__magic_name__ :    List[str]			 =      logging.get_logger(__name__)




def 					snake_case_   (		SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__							):







  '''simple docstring'''



  return [
      int(10_00 * (box[0] / width)							),
      int(10_00 * (box[1] / height)							),
      int(10_00 * (box[2] / width)							),
      int(10_00 * (box[3] / height)							),
  ]




def 					snake_case_   (		SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__							):







  '''simple docstring'''



  _snake_case  		=					to_pil_image(snake_case__							)
  _snake_case  		=					pil_image.size
  _snake_case  		=					pytesseract.image_to_data(snake_case__       ,						lang=snake_case__       ,						output_type="dict"       ,						config=snake_case__							)
  _snake_case  		=					data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

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

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

  return words, normalized_boxes




class    __SCREAMING_SNAKE_CASE						(    lowerCamelCase__      ):



     '''simple docstring'''






     UpperCAmelCase__							:    Tuple     	= ['''pixel_values''']

     def __init__(       self    , lowerCamelCase = True    , lowerCamelCase = None    , lowerCamelCase = PILImageResampling.BILINEAR    , lowerCamelCase = True    , lowerCamelCase = 1 / 255    , lowerCamelCase = True    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase = True    , lowerCamelCase = None    , lowerCamelCase = ""    , **lowerCamelCase    , ):
       super().__init__(**SCREAMING_SNAKE_CASE__						)
       _snake_case  		=					size if size is not None else {'''height''': 224, '''width''': 224}
       _snake_case  		=					get_size_dict(SCREAMING_SNAKE_CASE__						)

       _snake_case  		=					do_resize
       _snake_case  		=					size
       _snake_case  		=					resample
       _snake_case  		=					do_rescale
       _snake_case  		=					rescale_value
       _snake_case  		=					do_normalize
       _snake_case  		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       _snake_case  		=					image_std if image_std is not None else IMAGENET_STANDARD_STD
       _snake_case  		=					apply_ocr
       _snake_case  		=					ocr_lang
       _snake_case  		=					tesseract_config

     def 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase = PILImageResampling.BILINEAR    , lowerCamelCase = None    , **lowerCamelCase    , ):
       _snake_case  		=					get_size_dict(SCREAMING_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()}'''						)
       _snake_case  		=					(size['''height'''], size['''width'''])
       return resize(SCREAMING_SNAKE_CASE__    , size=SCREAMING_SNAKE_CASE__    , resample=SCREAMING_SNAKE_CASE__    , data_format=SCREAMING_SNAKE_CASE__    , **SCREAMING_SNAKE_CASE__						)

     def 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase = None    , **lowerCamelCase    , ):
       return rescale(SCREAMING_SNAKE_CASE__    , scale=SCREAMING_SNAKE_CASE__    , data_format=SCREAMING_SNAKE_CASE__    , **SCREAMING_SNAKE_CASE__						)

     def 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase = None    , **lowerCamelCase    , ):
       return normalize(SCREAMING_SNAKE_CASE__    , mean=SCREAMING_SNAKE_CASE__    , std=SCREAMING_SNAKE_CASE__    , data_format=SCREAMING_SNAKE_CASE__    , **SCREAMING_SNAKE_CASE__						)






     def 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase=None    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase = None    , lowerCamelCase = ChannelDimension.FIRST    , **lowerCamelCase    , ):
       _snake_case  		=					do_resize if do_resize is not None else self.do_resize
       _snake_case  		=					size if size is not None else self.size
       _snake_case  		=					get_size_dict(SCREAMING_SNAKE_CASE__						)
       _snake_case  		=					resample if resample is not None else self.resample
       _snake_case  		=					do_rescale if do_rescale is not None else self.do_rescale
       _snake_case  		=					rescale_factor if rescale_factor is not None else self.rescale_factor
       _snake_case  		=					do_normalize if do_normalize is not None else self.do_normalize
       _snake_case  		=					image_mean if image_mean is not None else self.image_mean
       _snake_case  		=					image_std if image_std is not None else self.image_std
       _snake_case  		=					apply_ocr if apply_ocr is not None else self.apply_ocr
       _snake_case  		=					ocr_lang if ocr_lang is not None else self.ocr_lang
       _snake_case  		=					tesseract_config if tesseract_config is not None else self.tesseract_config

       _snake_case  		=					make_list_of_images(SCREAMING_SNAKE_CASE__						)

       if not valid_images(SCREAMING_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."						)

       if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."						)

       if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("If do_normalize is True, image_mean and image_std must be specified."						)

       # All transformations expect numpy arrays.
       _snake_case  		=					[to_numpy_array(SCREAMING_SNAKE_CASE__						) for image in images]

       # Tesseract OCR to get words + normalized bounding boxes
       if apply_ocr:
         requires_backends(self    , "pytesseract"						)
         _snake_case  		=					[]
         _snake_case  		=					[]
         for image in images:
           _snake_case  		=					apply_tesseract(SCREAMING_SNAKE_CASE__    , SCREAMING_SNAKE_CASE__    , SCREAMING_SNAKE_CASE__						)
           words_batch.append(SCREAMING_SNAKE_CASE__						)
           boxes_batch.append(SCREAMING_SNAKE_CASE__						)

       if do_resize:
         _snake_case  		=					[self.resize(image=SCREAMING_SNAKE_CASE__    , size=SCREAMING_SNAKE_CASE__    , resample=SCREAMING_SNAKE_CASE__						) for image in images]

       if do_rescale:
         _snake_case  		=					[self.rescale(image=SCREAMING_SNAKE_CASE__    , scale=SCREAMING_SNAKE_CASE__						) for image in images]

       if do_normalize:
         _snake_case  		=					[self.normalize(image=SCREAMING_SNAKE_CASE__    , mean=SCREAMING_SNAKE_CASE__    , std=SCREAMING_SNAKE_CASE__						) for image in images]

       _snake_case  		=					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__    , SCREAMING_SNAKE_CASE__						) for image in images]

       _snake_case  		=					BatchFeature(data={"pixel_values": images}    , tensor_type=SCREAMING_SNAKE_CASE__						)

       if apply_ocr:
         _snake_case  		=					words_batch
         _snake_case  		=					boxes_batch
       return data






"""simple docstring"""






import unittest

import numpy as np

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():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor






class      A(  unittest.TestCase		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=7					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=18					,				SCREAMING_SNAKE_CASE__=30					,				SCREAMING_SNAKE_CASE__=4_00					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=None					,				)      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	size if size is not None else {'''shortest_edge''': 20}
					_UpperCamelCase					:Union[str, Any]					     =	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
					_UpperCamelCase					:str					     =	parent
					_UpperCamelCase					:str					     =	batch_size
					_UpperCamelCase					:str					     =	num_channels
					_UpperCamelCase					:Optional[Any]					     =	image_size
					_UpperCamelCase					:List[str]					     =	min_resolution
					_UpperCamelCase					:Tuple					     =	max_resolution
					_UpperCamelCase					:List[Any]					     =	do_resize
					_UpperCamelCase					:Tuple					     =	size
					_UpperCamelCase					:int					     =	do_center_crop
					_UpperCamelCase					:Optional[int]					     =	crop_size





		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					}






@require_torch
@require_vision
class      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							MobileNetVaImageProcessor if is_vision_available() else None
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	MobileNetVaImageProcessingTester(self   )
		@property
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					return self.image_processor_tester.prepare_image_processor_dict()
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	self.image_processing_class(**self.image_processor_dict   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''do_resize'''   )   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''size'''   )   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''do_center_crop'''   )   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''crop_size'''   )   )
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.image_processing_class.from_dict(self.image_processor_dict   )
					self.assertEqual(image_processor.size					,				{'''shortest_edge''': 20}   )
					self.assertEqual(image_processor.crop_size					,				{'''height''': 18, '''width''': 18}   )

					_UpperCamelCase					:List[Any]					     =	self.image_processing_class.from_dict(self.image_processor_dict					,				size=42					,				crop_size=84   )
					self.assertEqual(image_processor.size					,				{'''shortest_edge''': 42}   )
					self.assertEqual(image_processor.crop_size					,				{'''height''': 84, '''width''': 84}   )
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					pass
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	self.image_processing_class(**self.image_processor_dict   )
					# create random PIL images
					_UpperCamelCase					:Union[str, Any]					     =	prepare_image_inputs(self.image_processor_tester					,				equal_resolution=SCREAMING_SNAKE_CASE__   )
					for image in image_inputs:
								self.assertIsInstance(SCREAMING_SNAKE_CASE__					,				Image.Image   )

					# Test not batched input
					_UpperCamelCase					:Any					     =	image_processing(image_inputs[0]					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )					,				)

					# Test batched
					_UpperCamelCase					:Any					     =	image_processing(SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )					,				)
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.image_processing_class(**self.image_processor_dict   )
					# create random numpy tensors
					_UpperCamelCase					:Any					     =	prepare_image_inputs(self.image_processor_tester					,				equal_resolution=SCREAMING_SNAKE_CASE__					,				numpify=SCREAMING_SNAKE_CASE__   )
					for image in image_inputs:
								self.assertIsInstance(SCREAMING_SNAKE_CASE__					,				np.ndarray   )

					# Test not batched input
					_UpperCamelCase					:Any					     =	image_processing(image_inputs[0]					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )					,				)

					# Test batched
					_UpperCamelCase					:List[str]					     =	image_processing(SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )					,				)





		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	self.image_processing_class(**self.image_processor_dict   )
					# create random PyTorch tensors
					_UpperCamelCase					:Union[str, Any]					     =	prepare_image_inputs(self.image_processor_tester					,				equal_resolution=SCREAMING_SNAKE_CASE__					,				torchify=SCREAMING_SNAKE_CASE__   )
					for image in image_inputs:
								self.assertIsInstance(SCREAMING_SNAKE_CASE__					,				torch.Tensor   )

					# Test not batched input
					_UpperCamelCase					:Union[str, Any]					     =	image_processing(image_inputs[0]					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )					,				)

					# Test batched
					_UpperCamelCase					:Optional[int]					     =	image_processing(SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )					,				)

