
from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      for param, grad_param in zip(model_a.parameters() ,		model_b.parameters()		):
            if not param.requires_grad:
                  continue
            if not did_step:
                  # Grads should not be in sync
                  assert (
                      torch.allclose(param.grad ,		grad_param.grad		) is False
                  ), f'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
            else:
                  # Grads should be in sync
                  assert (
                      torch.allclose(param.grad ,		grad_param.grad		) is True
                  ), f'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__=True		):
      model.train()
      UpperCamelCase__    :   Optional[int]					=				model(_lowerCamelCase		)
      UpperCamelCase__    :   Union[str, Any]					=				F.mse_loss(_lowerCamelCase ,		target.to(output.device		)		)
      if not do_backward:
            loss /= accelerator.gradient_accumulation_steps
            loss.backward()
      else:
            accelerator.backward(_lowerCamelCase		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__=False		):
      set_seed(4_2		)
      UpperCamelCase__    :   Dict					=				RegressionModel()
      UpperCamelCase__    :   Optional[Any]					=				deepcopy(_lowerCamelCase		)
      UpperCamelCase__    :   str					=				RegressionDataset(length=8_0		)
      UpperCamelCase__    :   int					=				DataLoader(_lowerCamelCase ,		batch_size=1_6		)
      model.to(accelerator.device		)
      if sched:
            UpperCamelCase__    :   List[str]					=				AdamW(params=model.parameters() ,		lr=1e-3		)
            UpperCamelCase__    :   Dict					=				AdamW(params=ddp_model.parameters() ,		lr=1e-3		)
            UpperCamelCase__    :   Tuple					=				LambdaLR(_lowerCamelCase ,		lr_lambda=lambda UpperCamelCase__		: epoch**0.65		)
            UpperCamelCase__    :   Union[str, Any]					=				LambdaLR(_lowerCamelCase ,		lr_lambda=lambda UpperCamelCase__		: epoch**0.65		)
      # Make a copy of `model`
      if sched:
            UpperCamelCase__    :   List[Any]					=				accelerator.prepare(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)
      else:
            UpperCamelCase__    :   Any					=				accelerator.prepare(_lowerCamelCase ,		_lowerCamelCase		)
      if sched:
            return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
      return model, ddp_model, dataloader


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # Test when on a single CPU or GPU that the context manager does nothing
      UpperCamelCase__    :   str					=				get_training_setup(_lowerCamelCase		)
      # Use a single batch
      UpperCamelCase__    :   List[str]					=				next(iter(_lowerCamelCase		)		).values()
      for iteration in range(3		):
            # Gather the distributed inputs and targs for the base model
            UpperCamelCase__    :   int					=				accelerator.gather((ddp_input, ddp_target)		)
            UpperCamelCase__    :   str					=				input.to(accelerator.device		), target.to(accelerator.device		)
            # Perform our initial ground truth step in non "DDP"
            step_model(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)
            # Do "gradient accumulation" (noop)
            if iteration % 2 == 0:
                  # Accumulate grads locally
                  with accelerator.no_sync(_lowerCamelCase		):
                        step_model(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)
            else:
                  # Sync grads
                  step_model(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)

            # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
            check_model_parameters(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)
            for param, ddp_param in zip(model.parameters() ,		ddp_model.parameters()		):
                  if not param.requires_grad:
                        continue
                  assert torch.allclose(
                      param.grad ,		ddp_param.grad		), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

            # Shuffle ddp_input on each iteration
            torch.manual_seed(1_3_3_7 + iteration		)
            UpperCamelCase__    :   List[Any]					=				ddp_input[torch.randperm(len(_lowerCamelCase		)		)]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # Test on distributed setup that context manager behaves properly
      UpperCamelCase__    :   List[Any]					=				get_training_setup(_lowerCamelCase		)
      # Use a single batch
      UpperCamelCase__    :   List[Any]					=				next(iter(_lowerCamelCase		)		).values()
      for iteration in range(3		):
            # Gather the distributed inputs and targs for the base model
            UpperCamelCase__    :   List[str]					=				accelerator.gather((ddp_input, ddp_target)		)
            UpperCamelCase__    :   List[Any]					=				input.to(accelerator.device		), target.to(accelerator.device		)
            # Perform our initial ground truth step in non "DDP"
            step_model(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)
            # Do "gradient accumulation" (noop)
            if iteration % 2 == 0:
                  # Accumulate grads locally
                  with accelerator.no_sync(_lowerCamelCase		):
                        step_model(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)
            else:
                  # Sync grads
                  step_model(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)

            # DDP model and model should only be in sync when not (iteration % 2 == 0)
            for param, ddp_param in zip(model.parameters() ,		ddp_model.parameters()		):
                  if not param.requires_grad:
                        continue
                  if iteration % 2 == 0:
                        # Grads should not be in sync
                        assert (
                            torch.allclose(param.grad ,		ddp_param.grad		) is False
                        ), f'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
                  else:
                        # Grads should be in sync
                        assert (
                            torch.allclose(param.grad ,		ddp_param.grad		) is True
                        ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
            torch.manual_seed(1_3_3_7 + iteration		)
            UpperCamelCase__    :   Optional[Any]					=				ddp_input[torch.randperm(len(_lowerCamelCase		)		)]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__=False ,		UpperCamelCase__=False		):
      UpperCamelCase__    :   Tuple					=				Accelerator(
          split_batches=_lowerCamelCase ,		dispatch_batches=_lowerCamelCase ,		gradient_accumulation_steps=2		)
      # Test that context manager behaves properly
      UpperCamelCase__    :   List[Any]					=				get_training_setup(_lowerCamelCase		)
      for iteration, batch in enumerate(_lowerCamelCase		):
            UpperCamelCase__    :   Optional[Any]					=				batch.values()
            # Gather the distributed inputs and targs for the base model
            UpperCamelCase__    :   Dict					=				accelerator.gather((ddp_input, ddp_target)		)
            UpperCamelCase__    :   Any					=				input.to(accelerator.device		), target.to(accelerator.device		)
            # Perform our initial ground truth step in non "DDP"
            step_model(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)
            # Do "gradient accumulation" (noop)
            with accelerator.accumulate(_lowerCamelCase		):
                  step_model(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)

            # DDP model and model should only be in sync when not (iteration % 2 == 0)
            for param, ddp_param in zip(model.parameters() ,		ddp_model.parameters()		):
                  if not param.requires_grad:
                        continue
                  if ((iteration + 1) % 2 == 0) or (iteration == len(_lowerCamelCase		) - 1):
                        # Grads should be in sync
                        assert (
                            torch.allclose(param.grad ,		ddp_param.grad		) is True
                        ), f'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
                  else:
                        # Grads should not be in sync
                        assert (
                            torch.allclose(param.grad ,		ddp_param.grad		) is False
                        ), f'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
            torch.manual_seed(1_3_3_7 + iteration		)
            UpperCamelCase__    :   int					=				ddp_input[torch.randperm(len(_lowerCamelCase		)		)]
      GradientState._reset_state()


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__=False ,		UpperCamelCase__=False		):
      UpperCamelCase__    :   Any					=				Accelerator(
          split_batches=_lowerCamelCase ,		dispatch_batches=_lowerCamelCase ,		gradient_accumulation_steps=2		)
      # Test that context manager behaves properly
      UpperCamelCase__    :   Any					=				get_training_setup(_lowerCamelCase ,		_lowerCamelCase		)
      for iteration, batch in enumerate(_lowerCamelCase		):
            UpperCamelCase__    :   Optional[int]					=				batch.values()
            # Gather the distributed inputs and targs for the base model
            UpperCamelCase__    :   Optional[Any]					=				accelerator.gather((ddp_input, ddp_target)		)
            UpperCamelCase__    :   Optional[int]					=				input.to(accelerator.device		), target.to(accelerator.device		)
            # Perform our initial ground truth step in non "DDP"
            model.train()
            ddp_model.train()
            step_model(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)
            opt.step()

            if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_lowerCamelCase		)):
                  if split_batches:
                        sched.step()
                  else:
                        for _ in range(accelerator.num_processes		):
                              sched.step()
            opt.zero_grad()
            # Perform gradient accumulation under wrapper
            with accelerator.accumulate(_lowerCamelCase		):
                  step_model(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)
                  ddp_opt.step()
                  ddp_sched.step()
                  ddp_opt.zero_grad()

            # Learning rates should be the same
            assert (
                opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
            ), f'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'''
            UpperCamelCase__    :   Optional[int]					=				(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_lowerCamelCase		))
            if accelerator.num_processes > 1:
                  check_model_parameters(_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase ,		_lowerCamelCase		)
            # Shuffle ddp_input on each iteration
            torch.manual_seed(1_3_3_7 + iteration		)
      GradientState._reset_state()


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   List[Any]					=				Accelerator()

      UpperCamelCase__    :   str					=				RegressionDataset(length=8_0		)
      UpperCamelCase__    :   Dict					=				DataLoader(_lowerCamelCase ,		batch_size=1_6		)
      UpperCamelCase__    :   int					=				RegressionDataset(length=9_6		)
      UpperCamelCase__    :   List[Any]					=				DataLoader(_lowerCamelCase ,		batch_size=1_6		)
      UpperCamelCase__    :   List[str]					=				accelerator.prepare(_lowerCamelCase ,		_lowerCamelCase		)
      assert accelerator.gradient_state.active_dataloader is None
      for iteration, _ in enumerate(_lowerCamelCase		):
            assert id(accelerator.gradient_state.active_dataloader		) == id(_lowerCamelCase		)
            if iteration < len(_lowerCamelCase		) - 1:
                  assert not accelerator.gradient_state.end_of_dataloader
                  if iteration == 1:
                        for batch_num, _ in enumerate(_lowerCamelCase		):
                              assert id(accelerator.gradient_state.active_dataloader		) == id(_lowerCamelCase		)
                              if batch_num < len(_lowerCamelCase		) - 1:
                                    assert not accelerator.gradient_state.end_of_dataloader
                              else:
                                    assert accelerator.gradient_state.end_of_dataloader
            else:
                  assert accelerator.gradient_state.end_of_dataloader
      assert accelerator.gradient_state.active_dataloader is None


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   int					=				Accelerator()
      UpperCamelCase__    :   str					=				accelerator.state
      if state.local_process_index == 0:
            print('''**Test `accumulate` gradient accumulation with dataloader break**'''		)
      test_dataloader_break()
      if state.distributed_type == DistributedType.NO:
            if state.local_process_index == 0:
                  print('''**Test NOOP `no_sync` context manager**'''		)
            test_noop_sync(_lowerCamelCase		)
      if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
            if state.local_process_index == 0:
                  print('''**Test Distributed `no_sync` context manager**'''		)
            test_distributed_sync(_lowerCamelCase		)
      if state.distributed_type == DistributedType.MULTI_GPU:
            for split_batch in [True, False]:
                  for dispatch_batches in [True, False]:
                        if state.local_process_index == 0:
                              print(
                                  '''**Test `accumulate` gradient accumulation, ''' ,		f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' ,		)
                        test_gradient_accumulation(_lowerCamelCase ,		_lowerCamelCase		)

    # Currently will break on torch 2.0 +, need to investigate why
      if is_torch_version('''<''' ,		'''2.0'''		) or state.distributed_type == DistributedType.NO:
            if state.local_process_index == 0:
                  print(
                      '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' ,		'''`split_batches=False`, `dispatch_batches=False`**''' ,		)
            test_gradient_accumulation_with_opt_and_scheduler()
            if state.distributed_type == DistributedType.MULTI_GPU:
                  for split_batch in [True, False]:
                        for dispatch_batches in [True, False]:
                              if not split_batch and not dispatch_batches:
                                    continue
                              if state.local_process_index == 0:
                                    print(
                                        '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' ,		f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' ,		)
                              test_gradient_accumulation_with_opt_and_scheduler(_lowerCamelCase ,		_lowerCamelCase		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
    main()




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


