




'''simple docstring'''







from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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


if is_tf_available():
      import tensorflow as tf

      from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor







class 					_a :





     '''simple docstring'''



     def __init__(				self							,__a							,__a=13							,__a=30							,__a=2							,__a=3							,__a=True							,__a=True							,__a=32							,__a=2							,__a=4							,__a=37							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=10							,__a=0.02							,__a=3							,__a=0.6							,__a=None							,)      ->				Union[str, Any]:
          snake_case			:      List[str]        =			parent
          snake_case			:      Any        =			batch_size
          snake_case			:      List[str]        =			image_size
          snake_case			:      Any        =			patch_size
          snake_case			:      Optional[Any]        =			num_channels
          snake_case			:      Optional[Any]        =			is_training
          snake_case			:      int        =			use_labels
          snake_case			:      Any        =			hidden_size
          snake_case			:      Dict        =			num_hidden_layers
          snake_case			:      str        =			num_attention_heads
          snake_case			:      List[Any]        =			intermediate_size
          snake_case			:      Union[str, Any]        =			hidden_act
          snake_case			:      int        =			hidden_dropout_prob
          snake_case			:      Optional[int]        =			attention_probs_dropout_prob
          snake_case			:      Optional[Any]        =			type_sequence_label_size
          snake_case			:      int        =			initializer_range
          snake_case			:      List[str]        =			mask_ratio
          snake_case			:      Union[str, Any]        =			scope

          # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
          # (we add 1 for the [CLS] token)
          snake_case			:      Dict        =			(image_size // patch_size) ** 2
          snake_case			:      Any        =			int(math.ceil((1 - mask_ratio) * (num_patches + 1)							)							)







     def 			snake_case_      (				self							)      ->				Any:
          snake_case			:      Union[str, Any]        =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          snake_case			:      Any        =			None
          if self.use_labels:
               snake_case			:      Optional[int]        =			ids_tensor([self.batch_size]							,self.type_sequence_label_size							)

          snake_case			:      Union[str, Any]        =			self.get_config()

          return config, pixel_values, labels







     def 			snake_case_      (				self							)      ->				List[str]:
          return ViTMAEConfig(
              image_size=self.image_size							,patch_size=self.patch_size							,num_channels=self.num_channels							,hidden_size=self.hidden_size							,num_hidden_layers=self.num_hidden_layers							,num_attention_heads=self.num_attention_heads							,intermediate_size=self.intermediate_size							,decoder_hidden_size=self.hidden_size							,decoder_num_hidden_layers=self.num_hidden_layers							,decoder_num_attention_heads=self.num_attention_heads							,decoder_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							,is_decoder=__a							,initializer_range=self.initializer_range							,mask_ratio=self.mask_ratio							,)







     def 			snake_case_      (				self							,__a							,__a							,__a							)      ->				List[Any]:
          snake_case			:      str        =			TFViTMAEModel(config=__a							)
          snake_case			:      int        =			model(__a							,training=__a							)
          self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.seq_length, self.hidden_size)							)







     def 			snake_case_      (				self							,__a							,__a							,__a							)      ->				Optional[int]:
          snake_case			:      Any        =			TFViTMAEForPreTraining(__a							)
          snake_case			:      Optional[Any]        =			model(__a							,training=__a							)
          # expected sequence length = num_patches
          snake_case			:      Optional[int]        =			(self.image_size // self.patch_size) ** 2
          snake_case			:      str        =			self.patch_size**2 * self.num_channels
          self.parent.assertEqual(result.logits.shape							,(self.batch_size, num_patches, expected_num_channels)							)

          # test greyscale images
          snake_case			:      Any        =			1
          snake_case			:      int        =			TFViTMAEForPreTraining(__a							)

          snake_case			:      List[str]        =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
          snake_case			:      List[Any]        =			model(__a							,training=__a							)
          snake_case			:      List[Any]        =			self.patch_size**2
          self.parent.assertEqual(result.logits.shape							,(self.batch_size, num_patches, expected_num_channels)							)







     def 			snake_case_      (				self							)      ->				Tuple:
          snake_case			:      Union[str, Any]        =			self.prepare_config_and_inputs()
          ((snake_case) ,		(snake_case) ,		(snake_case))			:      str        =			config_and_inputs
          snake_case			:      Optional[Any]        =			{"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_tf
class 					_a (a__,		a__,		unittest.TestCase	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
     lowerCAmelCase_  :     List[str]          =					{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

     lowerCAmelCase_  :     str          =					False
     lowerCAmelCase_  :     Optional[Any]          =					False
     lowerCAmelCase_  :     Optional[Any]          =					False
     lowerCAmelCase_  :     Tuple          =					False







     def 			snake_case_      (				self							)      ->				Union[str, Any]:
          snake_case			:      str        =			TFViTMAEModelTester(self							)
          snake_case			:      List[Any]        =			ConfigTester(self							,config_class=__a							,has_text_modality=__a							,hidden_size=37							)







     def 			snake_case_      (				self							)      ->				str:
          self.config_tester.run_common_tests()







     @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""							)
     def 			snake_case_      (				self							)      ->				Optional[int]:
          pass







     def 			snake_case_      (				self							)      ->				Any:
          snake_case ,		snake_case			:      Tuple        =			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               snake_case			:      str        =			model_class(__a							)
               self.assertIsInstance(model.get_input_embeddings()							,(tf.keras.layers.Layer)							)
               snake_case			:      Optional[Any]        =			model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(__a							,tf.keras.layers.Layer							)							)







     def 			snake_case_      (				self							)      ->				Tuple:
          snake_case ,		snake_case			:      Any        =			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               snake_case			:      str        =			model_class(__a							)
               snake_case			:      Union[str, Any]        =			inspect.signature(model.call							)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               snake_case			:      str        =			[*signature.parameters.keys()]

               snake_case			:      Union[str, Any]        =			["""pixel_values"""]
               self.assertListEqual(arg_names[:1]							,__a							)







     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      Dict        =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__a							)







     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      Union[str, Any]        =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*__a							)







     def 			snake_case_      (				self							)      ->				int:
          # make the mask reproducible
          np.random.seed(2							)

          snake_case ,		snake_case			:      Union[str, Any]        =			self.model_tester.prepare_config_and_inputs_for_common()

          snake_case			:      Optional[int]        =			int((config.image_size // config.patch_size) ** 2							)
          snake_case			:      int        =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

          for model_class in self.all_model_classes:
               snake_case			:      Dict        =			model_class(__a							)
               snake_case			:      int        =			self._prepare_for_class(__a							,__a							)

               snake_case			:      Optional[int]        =			model(__a							,noise=__a							)

               snake_case			:      List[str]        =			copy.deepcopy(self._prepare_for_class(__a							,__a							)							)
               snake_case			:      List[str]        =			model(**__a							,noise=__a							)
               snake_case			:      Optional[Any]        =			outputs_dict[0].numpy()
               snake_case			:      Tuple        =			outputs_keywords[0].numpy()

               self.assertLess(np.sum(np.abs(output_dict - output_keywords							)							)							,1E-6							)







     def 			snake_case_      (				self							)      ->				Optional[int]:
          # make the mask reproducible
          np.random.seed(2							)

          snake_case ,		snake_case			:      Tuple        =			self.model_tester.prepare_config_and_inputs_for_common()

          snake_case			:      Optional[int]        =			int((config.image_size // config.patch_size) ** 2							)
          snake_case			:      Optional[int]        =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

          def prepare_numpy_arrays(__a							):
               snake_case			:      List[Any]        =			{}
               for k, v in inputs_dict.items():
                    if tf.is_tensor(__a							):
                         snake_case			:      List[str]        =			v.numpy()
                    else:
                         snake_case			:      str        =			np.array(__a							)

               return inputs_np_dict

          for model_class in self.all_model_classes:
               snake_case			:      Optional[Any]        =			model_class(__a							)

               snake_case			:      Union[str, Any]        =			self._prepare_for_class(__a							,__a							)
               snake_case			:      str        =			prepare_numpy_arrays(__a							)

               snake_case			:      Dict        =			model(__a							,noise=__a							)
               snake_case			:      Any        =			model(**__a							,noise=__a							)
               self.assert_outputs_same(__a							,__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a							)      ->				Union[str, Any]:
          # make masks reproducible
          np.random.seed(2							)

          snake_case			:      Tuple        =			int((tf_model.config.image_size // tf_model.config.patch_size) ** 2							)
          snake_case			:      Any        =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
          snake_case			:      int        =			tf.constant(__a							)

          # Add `noise` argument.
          # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
          snake_case			:      int        =			tf_noise

          super().check_pt_tf_models(__a							,__a							,__a							)







     def 			snake_case_      (				self							)      ->				Optional[Any]:
          # make mask reproducible
          np.random.seed(2							)

          snake_case ,		snake_case			:      Union[str, Any]        =			self.model_tester.prepare_config_and_inputs_for_common()

          snake_case			:      Union[str, Any]        =			{
              module_member
              for model_class in self.all_model_classes
              for module in (import_module(model_class.__module__							),)
              for module_member_name in dir(__a							)
              if module_member_name.endswith("""MainLayer"""							)
              # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
              and module_member_name[: -len("""MainLayer"""							)] == model_class.__name__[: -len("""Model"""							)]
              for module_member in (getattr(__a							,__a							),)
              if isinstance(__a							,__a							)
              and tf.keras.layers.Layer in module_member.__bases__
              and getattr(__a							,"""_keras_serializable"""							,__a							)
          }

          snake_case			:      Any        =			int((config.image_size // config.patch_size) ** 2							)
          snake_case			:      int        =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
          snake_case			:      List[Any]        =			tf.convert_to_tensor(__a							)
          inputs_dict.update({"""noise""": noise}							)

          for main_layer_class in tf_main_layer_classes:
               snake_case			:      Optional[int]        =			main_layer_class(__a							)

               snake_case			:      int        =			{
                   name: tf.keras.Input(tensor.shape[1:]							,dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
               }

               snake_case			:      Dict        =			tf.keras.Model(__a							,outputs=main_layer(__a							)							)
               snake_case			:      str        =			model(__a							)

               with tempfile.TemporaryDirectory() as tmpdirname:
                    snake_case			:      Tuple        =			os.path.join(__a							,"""keras_model.h5"""							)
                    model.save(__a							)
                    snake_case			:      Union[str, Any]        =			tf.keras.models.load_model(
                        __a							,custom_objects={main_layer_class.__name__: main_layer_class}							)
                    assert isinstance(__a							,tf.keras.Model							)
                    snake_case			:      List[Any]        =			model(__a							)
                    self.assert_outputs_same(__a							,__a							)







     @slow
     def 			snake_case_      (				self							)      ->				Dict:
          # make mask reproducible
          np.random.seed(2							)

          snake_case ,		snake_case			:      str        =			self.model_tester.prepare_config_and_inputs_for_common()

          snake_case			:      str        =			int((config.image_size // config.patch_size) ** 2							)
          snake_case			:      Any        =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

          for model_class in self.all_model_classes:
               snake_case			:      List[str]        =			model_class(__a							)
               snake_case			:      str        =			self._prepare_for_class(__a							,__a							)
               snake_case			:      List[Any]        =			model(__a							,noise=__a							)

               if model_class.__name__ == "TFViTMAEModel":
                    snake_case			:      Dict        =			outputs.last_hidden_state.numpy()
                    snake_case			:      List[Any]        =			0
               else:
                    snake_case			:      Any        =			outputs.logits.numpy()
                    snake_case			:      List[Any]        =			0

               with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(__a							,saved_model=__a							)
                    snake_case			:      List[Any]        =			model_class.from_pretrained(__a							)
                    snake_case			:      Optional[Any]        =			model(__a							,noise=__a							)

                    if model_class.__name__ == "TFViTMAEModel":
                         snake_case			:      int        =			after_outputs["""last_hidden_state"""].numpy()
                         snake_case			:      List[str]        =			0
                    else:
                         snake_case			:      str        =			after_outputs["""logits"""].numpy()
                         snake_case			:      Optional[int]        =			0

                    snake_case			:      List[Any]        =			np.amax(np.abs(out_a - out_a							)							)
                    self.assertLessEqual(__a							,1E-5							)







     def 			snake_case_      (				self							)      ->				str:
          # make mask reproducible
          np.random.seed(2							)

          snake_case ,		snake_case			:      Tuple        =			self.model_tester.prepare_config_and_inputs_for_common()

          snake_case			:      Dict        =			int((config.image_size // config.patch_size) ** 2							)
          snake_case			:      Any        =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

          for model_class in self.all_model_classes:
               snake_case			:      Optional[Any]        =			model_class(__a							)
               snake_case			:      str        =			self._prepare_for_class(__a							,__a							)

               snake_case			:      str        =			model(__a							,noise=__a							)
               snake_case			:      int        =			model.get_config()
               # make sure that returned config is jsonifiable, which is required by keras
               json.dumps(__a							)
               snake_case			:      List[Any]        =			model_class.from_config(model.get_config()							)
               # make sure it also accepts a normal config
               snake_case			:      Any        =			model_class.from_config(model.config							)
               snake_case			:      Dict        =			new_model(__a							)  # Build model
               new_model.set_weights(model.get_weights()							)
               snake_case			:      List[Any]        =			new_model(__a							,noise=__a							)

               self.assert_outputs_same(__a							,__a							)







     @unittest.skip(
         reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""							)
     def 			snake_case_      (				self							)      ->				Dict:
          pass







     @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""							)
     def 			snake_case_      (				self							)      ->				int:
          pass







     @slow
     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      Dict        =			TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""							)
          self.assertIsNotNone(__a							)



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[int]        =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
     return image







@require_tf
@require_vision
class 					_a (unittest.TestCase	):





     '''simple docstring'''



     @cached_property
     def 			snake_case_      (				self							)      ->				Dict:
          return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""							) if is_vision_available() else None







     @slow
     def 			snake_case_      (				self							)      ->				Dict:
          # make random mask reproducible across the PT and TF model
          np.random.seed(2							)

          snake_case			:      Optional[Any]        =			TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""							)

          snake_case			:      Optional[Any]        =			self.default_image_processor
          snake_case			:      str        =			prepare_img()
          snake_case			:      List[Any]        =			image_processor(images=__a							,return_tensors="""tf"""							)

          # prepare a noise vector that will be also used for testing the TF model
          # (this way we can ensure that the PT and TF models operate on the same inputs)
          snake_case			:      List[Any]        =			ViTMAEConfig()
          snake_case			:      Optional[int]        =			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
          snake_case			:      Dict        =			np.random.uniform(size=(1, num_patches)							)

          # forward pass
          snake_case			:      Union[str, Any]        =			model(**__a							,noise=__a							)

          # verify the logits
          snake_case			:      List[Any]        =			tf.convert_to_tensor([1, 196, 768]							)
          self.assertEqual(outputs.logits.shape							,__a							)

          snake_case			:      Optional[int]        =			tf.convert_to_tensor(
              [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]							)

          tf.debugging.assert_near(outputs.logits[0, :3, :3]							,__a							,atol=1E-4							)









'''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

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



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     return [
         int(1_000 * (box[0] / width)				),
         int(1_000 * (box[1] / height)				),
         int(1_000 * (box[2] / width)				),
         int(1_000 * (box[3] / height)				),
     ]



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      List[Any]        =			to_pil_image(__lowercase				)
     snake_case ,		snake_case			:      Union[str, Any]        =			pil_image.size
     snake_case			:      Optional[Any]        =			pytesseract.image_to_data(__lowercase     ,   lang=__lowercase     ,   output_type="""dict"""     ,   config=__lowercase				)
     snake_case ,		snake_case ,		snake_case ,		snake_case ,		snake_case			:      Dict        =			data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

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

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

     # finally, normalize the bounding boxes
     snake_case			:      str        =			[]
     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 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					["""pixel_values"""]







     def __init__(				self							,__a = True							,__a = None							,__a = PILImageResampling.BILINEAR							,__a = True							,__a = 1 / 255							,__a = True							,__a = None							,__a = None							,__a = True							,__a = None							,__a = ""							,**__a							,)      ->				None:
          super().__init__(**__a							)
          snake_case			:      int        =			size if size is not None else {"""height""": 224, """width""": 224}
          snake_case			:      Union[str, Any]        =			get_size_dict(__a							)

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







     def 			snake_case_      (				self							,__a							,__a							,__a = PILImageResampling.BILINEAR							,__a = None							,**__a							,)      ->				np.ndarray:
          snake_case			:      List[str]        =			get_size_dict(__a							)
          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			:      Tuple        =			(size["""height"""], size["""width"""])
          return resize(__a							,size=__a							,resample=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          return rescale(__a							,scale=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          return normalize(__a							,mean=__a							,std=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a = None							,__a = None							,__a=None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = ChannelDimension.FIRST							,**__a							,)      ->				PIL.Image.Image:
          snake_case			:      int        =			do_resize if do_resize is not None else self.do_resize
          snake_case			:      List[Any]        =			size if size is not None else self.size
          snake_case			:      Any        =			get_size_dict(__a							)
          snake_case			:      Any        =			resample if resample is not None else self.resample
          snake_case			:      str        =			do_rescale if do_rescale is not None else self.do_rescale
          snake_case			:      List[str]        =			rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case			:      Dict        =			do_normalize if do_normalize is not None else self.do_normalize
          snake_case			:      Optional[int]        =			image_mean if image_mean is not None else self.image_mean
          snake_case			:      Tuple        =			image_std if image_std is not None else self.image_std
          snake_case			:      List[str]        =			apply_ocr if apply_ocr is not None else self.apply_ocr
          snake_case			:      str        =			ocr_lang if ocr_lang is not None else self.ocr_lang
          snake_case			:      Dict        =			tesseract_config if tesseract_config is not None else self.tesseract_config

          snake_case			:      Any        =			make_list_of_images(__a							)

          if not valid_images(__a							):
               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			:      Union[str, Any]        =			[to_numpy_array(__a							) for image in images]

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

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

          if do_rescale:
               snake_case			:      Tuple        =			[self.rescale(image=__a							,scale=__a							) for image in images]

          if do_normalize:
               snake_case			:      Optional[Any]        =			[self.normalize(image=__a							,mean=__a							,std=__a							) for image in images]

          snake_case			:      Dict        =			[to_channel_dimension_format(__a							,__a							) for image in images]

          snake_case			:      List[Any]        =			BatchFeature(data={"""pixel_values""": images}							,tensor_type=__a							)

          if apply_ocr:
               snake_case			:      Optional[Any]        =			words_batch
               snake_case			:      Optional[int]        =			boxes_batch
          return data



