
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import numpy
    import tensorflow as tf

    from transformers import (
        TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
        BertConfig,
        DPRConfig,
        TFDPRContextEncoder,
        TFDPRQuestionEncoder,
        TFDPRReader,
    )




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1_3						,						__SCREAMING_SNAKE_CASE=7						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=9_9						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=4						,						__SCREAMING_SNAKE_CASE=3_7						,						__SCREAMING_SNAKE_CASE="gelu"						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=5_1_2						,						__SCREAMING_SNAKE_CASE=1_6						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=4						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=0						,						)  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				parent
           UpperCamelCase__    :   Dict					=				batch_size
           UpperCamelCase__    :   Optional[Any]					=				seq_length
           UpperCamelCase__    :   Union[str, Any]					=				is_training
           UpperCamelCase__    :   Optional[Any]					=				use_input_mask
           UpperCamelCase__    :   List[str]					=				use_token_type_ids
           UpperCamelCase__    :   Tuple					=				use_labels
           UpperCamelCase__    :   int					=				vocab_size
           UpperCamelCase__    :   List[Any]					=				hidden_size
           UpperCamelCase__    :   Tuple					=				num_hidden_layers
           UpperCamelCase__    :   Any					=				num_attention_heads
           UpperCamelCase__    :   int					=				intermediate_size
           UpperCamelCase__    :   int					=				hidden_act
           UpperCamelCase__    :   Optional[int]					=				hidden_dropout_prob
           UpperCamelCase__    :   Optional[int]					=				attention_probs_dropout_prob
           UpperCamelCase__    :   List[Any]					=				max_position_embeddings
           UpperCamelCase__    :   Tuple					=				type_vocab_size
           UpperCamelCase__    :   List[Any]					=				type_sequence_label_size
           UpperCamelCase__    :   List[str]					=				initializer_range
           UpperCamelCase__    :   Optional[Any]					=				num_labels
           UpperCamelCase__    :   Any					=				num_choices
           UpperCamelCase__    :   Optional[int]					=				scope
           UpperCamelCase__    :   Any					=				projection_dim






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size  )

           UpperCamelCase__    :   Optional[Any]					=				None
           if self.use_input_mask:
                 # follow test_modeling_tf_ctrl.py
                 UpperCamelCase__    :   Union[str, Any]					=				random_attention_mask([self.batch_size, self.seq_length]  )

           UpperCamelCase__    :   Dict					=				None
           if self.use_token_type_ids:
                 UpperCamelCase__    :   Optional[int]					=				ids_tensor([self.batch_size, self.seq_length]						,						self.type_vocab_size  )

           UpperCamelCase__    :   Union[str, Any]					=				None
           UpperCamelCase__    :   Tuple					=				None
           UpperCamelCase__    :   Any					=				None
           if self.use_labels:
                 UpperCamelCase__    :   Dict					=				ids_tensor([self.batch_size]						,						self.type_sequence_label_size  )
                 UpperCamelCase__    :   Any					=				ids_tensor([self.batch_size, self.seq_length]						,						self.num_labels  )
                 UpperCamelCase__    :   Dict					=				ids_tensor([self.batch_size]						,						self.num_choices  )

           UpperCamelCase__    :   List[Any]					=				BertConfig(
               vocab_size=self.vocab_size						,						hidden_size=self.hidden_size						,						num_hidden_layers=self.num_hidden_layers						,						num_attention_heads=self.num_attention_heads						,						intermediate_size=self.intermediate_size						,						hidden_act=self.hidden_act						,						hidden_dropout_prob=self.hidden_dropout_prob						,						attention_probs_dropout_prob=self.attention_probs_dropout_prob						,						max_position_embeddings=self.max_position_embeddings						,						type_vocab_size=self.type_vocab_size						,						is_decoder=__SCREAMING_SNAKE_CASE						,						initializer_range=self.initializer_range						,						)
           UpperCamelCase__    :   int					=				DPRConfig(projection_dim=self.projection_dim						,						**config.to_dict()  )

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				TFDPRContextEncoder(config=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   str					=				model(__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						token_type_ids=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				model(__SCREAMING_SNAKE_CASE						,						token_type_ids=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[int]					=				model(__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.pooler_output.shape						,						(self.batch_size, self.projection_dim or self.hidden_size)  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   int					=				TFDPRQuestionEncoder(config=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[Any]					=				model(__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						token_type_ids=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   str					=				model(__SCREAMING_SNAKE_CASE						,						token_type_ids=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				model(__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.pooler_output.shape						,						(self.batch_size, self.projection_dim or self.hidden_size)  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				TFDPRReader(config=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				model(__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE  )

           self.parent.assertEqual(result.start_logits.shape						,						(self.batch_size, self.seq_length)  )
           self.parent.assertEqual(result.end_logits.shape						,						(self.batch_size, self.seq_length)  )
           self.parent.assertEqual(result.relevance_logits.shape						,						(self.batch_size,)  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				self.prepare_config_and_inputs()
           (
               UpperCamelCase__	
           )    :   Tuple					=				config_and_inputs
           UpperCamelCase__    :   Optional[Any]					=				{'''input_ids''': input_ids}
           return config, inputs_dict




@require_tf
class 				_lowerCamelCase (      UpperCamelCase_	,							UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (
         (
             TFDPRContextEncoder,
             TFDPRQuestionEncoder,
             TFDPRReader,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_		   =  {'''feature-extraction''': TFDPRQuestionEncoder} if is_tf_available() else {}

     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				TFDPRModelTester(self  )
           UpperCamelCase__    :   List[str]					=				ConfigTester(self						,						config_class=__SCREAMING_SNAKE_CASE						,						hidden_size=3_7  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           self.config_tester.run_common_tests()






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_dpr_context_encoder(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   int					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_dpr_question_encoder(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_dpr_reader(*__SCREAMING_SNAKE_CASE  )






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCamelCase__    :   List[str]					=				TFDPRContextEncoder.from_pretrained(__SCREAMING_SNAKE_CASE  )
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )

           for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCamelCase__    :   Dict					=				TFDPRContextEncoder.from_pretrained(__SCREAMING_SNAKE_CASE  )
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )

           for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCamelCase__    :   List[str]					=				TFDPRQuestionEncoder.from_pretrained(__SCREAMING_SNAKE_CASE  )
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )

           for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCamelCase__    :   Union[str, Any]					=				TFDPRReader.from_pretrained(__SCREAMING_SNAKE_CASE  )
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )




@require_tf
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''  )

           UpperCamelCase__    :   List[Any]					=				tf.constant(
               [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]  )  # [CLS] hello, is my dog cute? [SEP]
           UpperCamelCase__    :   Tuple					=				model(__SCREAMING_SNAKE_CASE  )[0]  # embedding shape = (1, 768)
           # compare the actual values for a slice.
           UpperCamelCase__    :   Any					=				tf.constant(
               [
                   [
                       0.03236253,
                       0.12753335,
                       0.16818509,
                       0.00279786,
                       0.3896933,
                       0.24264945,
                       0.2178971,
                       -0.02335227,
                       -0.08481959,
                       -0.14324117,
                   ]
               ]  )
           self.assertTrue(numpy.allclose(output[:, :1_0].numpy()						,						expected_slice.numpy()						,						atol=1e-4  )  )




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

lowerCamelCase      =logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      return [
          int(1_0_0_0 * (box[0] / width)		),
          int(1_0_0_0 * (box[1] / height)		),
          int(1_0_0_0 * (box[2] / width)		),
          int(1_0_0_0 * (box[3] / height)		),
      ]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   List[Any]					=				to_pil_image(UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__    :   Optional[int]					=				pil_image.size
      UpperCamelCase__    :   str					=				pytesseract.image_to_data(UpperCamelCase__ ,		lang=UpperCamelCase__ ,		output_type='''dict''' ,		config=UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

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

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

      return words, normalized_boxes




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  ['''pixel_values''']






     def __init__(							self						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = 1 / 2_5_5						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ""						,						**__SCREAMING_SNAKE_CASE						,						)  ->   None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           UpperCamelCase__    :   Optional[int]					=				get_size_dict(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				do_resize
           UpperCamelCase__    :   Optional[int]					=				size
           UpperCamelCase__    :   Optional[Any]					=				resample
           UpperCamelCase__    :   Optional[Any]					=				do_rescale
           UpperCamelCase__    :   str					=				rescale_value
           UpperCamelCase__    :   Any					=				do_normalize
           UpperCamelCase__    :   List[Any]					=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           UpperCamelCase__    :   Optional[int]					=				image_std if image_std is not None else IMAGENET_STANDARD_STD
           UpperCamelCase__    :   Dict					=				apply_ocr
           UpperCamelCase__    :   List[Any]					=				ocr_lang
           UpperCamelCase__    :   str					=				tesseract_config






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				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()}'''  )
           UpperCamelCase__    :   int					=				(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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           return rescale(__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   np.ndarray:




           """simple docstring"""







           return normalize(__SCREAMING_SNAKE_CASE						,						mean=__SCREAMING_SNAKE_CASE						,						std=__SCREAMING_SNAKE_CASE						,						data_format=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,						**__SCREAMING_SNAKE_CASE						,						)  ->   PIL.Image.Image:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__    :   List[str]					=				size if size is not None else self.size
           UpperCamelCase__    :   Dict					=				get_size_dict(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				resample if resample is not None else self.resample
           UpperCamelCase__    :   int					=				do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__    :   List[str]					=				rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase__    :   str					=				do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase__    :   Union[str, Any]					=				image_mean if image_mean is not None else self.image_mean
           UpperCamelCase__    :   Tuple					=				image_std if image_std is not None else self.image_std
           UpperCamelCase__    :   Optional[Any]					=				apply_ocr if apply_ocr is not None else self.apply_ocr
           UpperCamelCase__    :   List[str]					=				ocr_lang if ocr_lang is not None else self.ocr_lang
           UpperCamelCase__    :   List[Any]					=				tesseract_config if tesseract_config is not None else self.tesseract_config

           UpperCamelCase__    :   List[Any]					=				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.
           UpperCamelCase__    :   List[Any]					=				[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'''  )
                 UpperCamelCase__    :   Optional[int]					=				[]
                 UpperCamelCase__    :   List[Any]					=				[]
                 for image in images:
                       UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				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:
                 UpperCamelCase__    :   str					=				[self.resize(image=__SCREAMING_SNAKE_CASE						,						size=__SCREAMING_SNAKE_CASE						,						resample=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_rescale:
                 UpperCamelCase__    :   List[Any]					=				[self.rescale(image=__SCREAMING_SNAKE_CASE						,						scale=__SCREAMING_SNAKE_CASE  ) for image in images]

           if do_normalize:
                 UpperCamelCase__    :   str					=				[self.normalize(image=__SCREAMING_SNAKE_CASE						,						mean=__SCREAMING_SNAKE_CASE						,						std=__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   int					=				[to_channel_dimension_format(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for image in images]

           UpperCamelCase__    :   List[Any]					=				BatchFeature(data={'''pixel_values''': images}						,						tensor_type=__SCREAMING_SNAKE_CASE  )

           if apply_ocr:
                 UpperCamelCase__    :   str					=				words_batch
                 UpperCamelCase__    :   Union[str, Any]					=				boxes_batch
           return data


