
import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import BertGenerationDecoder, BertGenerationEncoder




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=9_9						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=5						,						__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_0						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=None						,						)  ->   Optional[Any]:




           """simple docstring"""







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






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




           """simple docstring"""







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

           UpperCamelCase__    :   Tuple					=				None
           if self.use_input_mask:
                 UpperCamelCase__    :   Optional[int]					=				random_attention_mask([self.batch_size, self.seq_length]  )

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

           UpperCamelCase__    :   int					=				self.get_config()

           return config, input_ids, input_mask, token_labels






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




           """simple docstring"""







           return BertGenerationConfig(
               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						,						is_decoder=__SCREAMING_SNAKE_CASE						,						initializer_range=self.initializer_range						,						)






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           (
               (
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,
           )    :   Union[str, Any]					=				self.prepare_config_and_inputs()

           UpperCamelCase__    :   Dict					=				True
           UpperCamelCase__    :   Dict					=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
           UpperCamelCase__    :   Dict					=				ids_tensor([self.batch_size, self.seq_length]						,						vocab_size=2  )

           return (
               config,
               input_ids,
               input_mask,
               token_labels,
               encoder_hidden_states,
               encoder_attention_mask,
           )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE						,						)  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				BertGenerationEncoder(config=__SCREAMING_SNAKE_CASE  )
           model.to(__SCREAMING_SNAKE_CASE  )
           model.eval()
           UpperCamelCase__    :   List[Any]					=				model(__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				model(__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, 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__    :   Dict					=				True
           UpperCamelCase__    :   List[Any]					=				BertGenerationEncoder(config=__SCREAMING_SNAKE_CASE  )
           model.to(__SCREAMING_SNAKE_CASE  )
           model.eval()
           UpperCamelCase__    :   int					=				model(
               __SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						encoder_hidden_states=__SCREAMING_SNAKE_CASE						,						encoder_attention_mask=__SCREAMING_SNAKE_CASE						,						)
           UpperCamelCase__    :   Dict					=				model(
               __SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						encoder_hidden_states=__SCREAMING_SNAKE_CASE						,						)
           self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, 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						,						)  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				True
           UpperCamelCase__    :   List[str]					=				True
           UpperCamelCase__    :   List[Any]					=				BertGenerationDecoder(config=__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE  ).eval()

           # first forward pass
           UpperCamelCase__    :   int					=				model(
               __SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						encoder_hidden_states=__SCREAMING_SNAKE_CASE						,						encoder_attention_mask=__SCREAMING_SNAKE_CASE						,						use_cache=__SCREAMING_SNAKE_CASE						,						)
           UpperCamelCase__    :   Tuple					=				outputs.past_key_values

           # create hypothetical multiple next token and extent to next_input_ids
           UpperCamelCase__    :   Dict					=				ids_tensor((self.batch_size, 3)						,						config.vocab_size  )
           UpperCamelCase__    :   List[Any]					=				ids_tensor((self.batch_size, 3)						,						vocab_size=2  )

           # append to next input_ids and
           UpperCamelCase__    :   int					=				torch.cat([input_ids, next_tokens]						,						dim=-1  )
           UpperCamelCase__    :   Dict					=				torch.cat([input_mask, next_mask]						,						dim=-1  )

           UpperCamelCase__    :   Tuple					=				model(
               __SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						encoder_hidden_states=__SCREAMING_SNAKE_CASE						,						encoder_attention_mask=__SCREAMING_SNAKE_CASE						,						output_hidden_states=__SCREAMING_SNAKE_CASE						,						)['''hidden_states'''][0]
           UpperCamelCase__    :   List[Any]					=				model(
               __SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						encoder_hidden_states=__SCREAMING_SNAKE_CASE						,						encoder_attention_mask=__SCREAMING_SNAKE_CASE						,						past_key_values=__SCREAMING_SNAKE_CASE						,						output_hidden_states=__SCREAMING_SNAKE_CASE						,						)['''hidden_states'''][0]

           # select random slice
           UpperCamelCase__    :   List[Any]					=				ids_tensor((1,)						,						output_from_past.shape[-1]  ).item()
           UpperCamelCase__    :   Union[str, Any]					=				output_from_no_past[:, -3:, random_slice_idx].detach()
           UpperCamelCase__    :   Optional[int]					=				output_from_past[:, :, random_slice_idx].detach()

           self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						atol=1e-3  )  )






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




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				BertGenerationDecoder(__SCREAMING_SNAKE_CASE  )
           model.to(__SCREAMING_SNAKE_CASE  )
           model.eval()
           UpperCamelCase__    :   Any					=				model(__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						labels=__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.vocab_size)  )






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




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   str					=				self.prepare_config_and_inputs()
           UpperCamelCase__    :   Optional[int]					=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict




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





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_		   =  (BertGenerationDecoder,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_		   =  (
         {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
         if is_torch_available()
         else {}
     )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				BertGenerationEncoderTester(self  )
           UpperCamelCase__    :   Tuple					=				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  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				self.model_tester.prepare_config_and_inputs()
           UpperCamelCase__    :   List[str]					=				'''bert'''
           self.model_tester.create_and_check_model(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







           UpperCamelCase__    :   Any					=				self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           (
               (
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,(
               UpperCamelCase__
           )				,
           )    :   Optional[Any]					=				self.model_tester.prepare_config_and_inputs_for_decoder()

           UpperCamelCase__    :   Tuple					=				None

           self.model_tester.create_and_check_model_as_decoder(
               __SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						)






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   int					=				self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_for_causal_lm(*__SCREAMING_SNAKE_CASE  )






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''  )
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )




@require_torch
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







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




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''  )
           UpperCamelCase__    :   List[Any]					=				torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]  )
           with torch.no_grad():
                 UpperCamelCase__    :   int					=				model(__SCREAMING_SNAKE_CASE  )[0]
           UpperCamelCase__    :   Union[str, Any]					=				torch.Size([1, 8, 1_0_2_4]  )
           self.assertEqual(output.shape						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				torch.tensor(
               [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]  )
           self.assertTrue(torch.allclose(output[:, :3, :3]						,						__SCREAMING_SNAKE_CASE						,						atol=1e-4  )  )




@require_torch
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







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




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''  )
           UpperCamelCase__    :   Optional[int]					=				torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]  )
           with torch.no_grad():
                 UpperCamelCase__    :   str					=				model(__SCREAMING_SNAKE_CASE  )[0]
           UpperCamelCase__    :   Tuple					=				torch.Size([1, 8, 5_0_3_5_8]  )
           self.assertEqual(output.shape						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				torch.tensor(
               [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]  )
           self.assertTrue(torch.allclose(output[:, :3, :3]						,						__SCREAMING_SNAKE_CASE						,						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


