
import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
    import torch

    from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE="resnet50"						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						)  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				parent
           UpperCamelCase__    :   Optional[int]					=				out_indices if out_indices is not None else [4]
           UpperCamelCase__    :   Optional[int]					=				stage_names
           UpperCamelCase__    :   Any					=				out_features
           UpperCamelCase__    :   str					=				backbone
           UpperCamelCase__    :   str					=				batch_size
           UpperCamelCase__    :   str					=				image_size
           UpperCamelCase__    :   List[Any]					=				num_channels
           UpperCamelCase__    :   Any					=				use_pretrained_backbone
           UpperCamelCase__    :   Optional[Any]					=				is_training






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
           UpperCamelCase__    :   Optional[Any]					=				self.get_config()

           return config, pixel_values






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




           """simple docstring"""







           return TimmBackboneConfig(
               image_size=self.image_size						,						num_channels=self.num_channels						,						out_features=self.out_features						,						out_indices=self.out_indices						,						stage_names=self.stage_names						,						use_pretrained_backbone=self.use_pretrained_backbone						,						backbone=self.backbone						,						)






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




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				TimmBackbone(config=__SCREAMING_SNAKE_CASE  )
           model.to(__SCREAMING_SNAKE_CASE  )
           model.eval()
           with torch.no_grad():
                 UpperCamelCase__    :   Tuple					=				model(__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(
               result.feature_map[-1].shape						,						(self.batch_size, model.channels[-1], 1_4, 1_4)						,						)






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







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




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





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (TimmBackbone,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_		   =  {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False






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




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				TimmBackboneModelTester(self  )
           UpperCamelCase__    :   List[str]					=				ConfigTester(self						,						config_class=__SCREAMING_SNAKE_CASE						,						has_text_modality=__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				'''resnet18'''
           UpperCamelCase__    :   Tuple					=				'''microsoft/resnet-18'''

           UpperCamelCase__    :   str					=				AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE						,						use_timm_backbone=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE  )

           self.assertEqual(len(timm_model.out_features  )						,						len(transformers_model.out_features  )  )
           self.assertEqual(len(timm_model.stage_names  )						,						len(transformers_model.stage_names  )  )
           self.assertEqual(timm_model.channels						,						transformers_model.channels  )
           # Out indices are set to the last layer by default. For timm models, we don't know
           # the number of layers in advance, so we set it to (-1,), whereas for transformers
           # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
           self.assertEqual(timm_model.out_indices						,						(-1,)  )
           self.assertEqual(transformers_model.out_indices						,						[len(timm_model.stage_names  ) - 1]  )

           UpperCamelCase__    :   Optional[int]					=				AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE						,						use_timm_backbone=__SCREAMING_SNAKE_CASE						,						out_indices=[1, 2, 3]  )
           UpperCamelCase__    :   str					=				AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE						,						out_indices=[1, 2, 3]  )

           self.assertEqual(timm_model.out_indices						,						transformers_model.out_indices  )
           self.assertEqual(len(timm_model.out_features  )						,						len(transformers_model.out_features  )  )
           self.assertEqual(timm_model.channels						,						transformers_model.channels  )






     @unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           pass






     @unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           pass






     @unittest.skip('''TimmBackbone initialization is managed on the timm side'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           pass






     @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           pass






     @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           pass






     @unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           pass






     @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           pass






     @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           pass






     @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           pass






     @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           pass






     @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           pass






     @unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           pass






     @unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           pass






     @unittest.skip('''Safetensors is not supported by timm.'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           pass






     @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           pass






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




           """simple docstring"""







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

           for model_class in self.all_model_classes:
                 UpperCamelCase__    :   Optional[int]					=				model_class(__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   Optional[Any]					=				inspect.signature(model.forward  )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 UpperCamelCase__    :   Union[str, Any]					=				[*signature.parameters.keys()]

                 UpperCamelCase__    :   Tuple					=				['''pixel_values''']
                 self.assertListEqual(arg_names[:1]						,						__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   str					=				self.model_tester.prepare_config_and_inputs_for_common()
           UpperCamelCase__    :   int					=				True
           UpperCamelCase__    :   List[Any]					=				self.has_attentions

           # no need to test all models as different heads yield the same functionality
           UpperCamelCase__    :   List[str]					=				self.all_model_classes[0]
           UpperCamelCase__    :   Any					=				model_class(__SCREAMING_SNAKE_CASE  )
           model.to(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[Any]					=				self._prepare_for_class(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				model(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				outputs[0][-1]

           # Encoder-/Decoder-only models
           UpperCamelCase__    :   Dict					=				outputs.hidden_states[0]
           hidden_states.retain_grad()

           if self.has_attentions:
                 UpperCamelCase__    :   Optional[int]					=				outputs.attentions[0]
                 attentions.retain_grad()

           output.flatten()[0].backward(retain_graph=__SCREAMING_SNAKE_CASE  )

           self.assertIsNotNone(hidden_states.grad  )

           if self.has_attentions:
                 self.assertIsNotNone(attentions.grad  )






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




           """simple docstring"""







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

           for model_class in self.all_model_classes:
                 UpperCamelCase__    :   List[str]					=				model_class(__SCREAMING_SNAKE_CASE  )
                 model.to(__SCREAMING_SNAKE_CASE  )
                 model.eval()
                 UpperCamelCase__    :   Tuple					=				model(**__SCREAMING_SNAKE_CASE  )

                 self.assertEqual(len(result.feature_maps  )						,						len(config.out_indices  )  )
                 self.assertEqual(len(model.channels  )						,						len(config.out_indices  )  )

                 # Check output of last stage is taken if out_features=None, out_indices=None
                 UpperCamelCase__    :   Tuple					=				copy.deepcopy(__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   str					=				None
                 UpperCamelCase__    :   Any					=				model_class(__SCREAMING_SNAKE_CASE  )
                 model.to(__SCREAMING_SNAKE_CASE  )
                 model.eval()
                 UpperCamelCase__    :   Tuple					=				model(**__SCREAMING_SNAKE_CASE  )

                 self.assertEqual(len(result.feature_maps  )						,						1  )
                 self.assertEqual(len(model.channels  )						,						1  )

                 # Check backbone can be initialized with fresh weights
                 UpperCamelCase__    :   str					=				copy.deepcopy(__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   int					=				False
                 UpperCamelCase__    :   List[Any]					=				model_class(__SCREAMING_SNAKE_CASE  )
                 model.to(__SCREAMING_SNAKE_CASE  )
                 model.eval()
                 UpperCamelCase__    :   List[Any]					=				model(**__SCREAMING_SNAKE_CASE  )




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


