


'''simple docstring'''
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class      lowerCamelCase_  (      __a	,			__a	,			unittest.TestCase ):
      lowerCAmelCase__							=	StableDiffusionPanoramaPipeline
      lowerCAmelCase__							=	TEXT_TO_IMAGE_PARAMS
      lowerCAmelCase__							=	TEXT_TO_IMAGE_BATCH_PARAMS
      lowerCAmelCase__							=	TEXT_TO_IMAGE_IMAGE_PARAMS
      lowerCAmelCase__							=	TEXT_TO_IMAGE_IMAGE_PARAMS






      def lowercase_				(   self							:       List[str]					):


          '''simple docstring'''


          torch.manual_seed(0					)
          UpperCAmelCase__     :						List[Any]						=							UNetaDConditionModel(
              block_out_channels=(32, 64)		,		layers_per_block=1		,		sample_size=32		,		in_channels=4		,		out_channels=4		,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,		cross_attention_dim=32		,		)
          UpperCAmelCase__     :						Optional[Any]						=							DDIMScheduler()
          torch.manual_seed(0					)
          UpperCAmelCase__     :						Optional[int]						=							AutoencoderKL(
              block_out_channels=[32, 64]		,		in_channels=3		,		out_channels=3		,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,		latent_channels=4		,		)
          torch.manual_seed(0					)
          UpperCAmelCase__     :						List[Any]						=							CLIPTextConfig(
              bos_token_id=0		,		eos_token_id=2		,		hidden_size=32		,		intermediate_size=37		,		layer_norm_eps=1e-05		,		num_attention_heads=4		,		num_hidden_layers=5		,		pad_token_id=1		,		vocab_size=1_000		,		)
          UpperCAmelCase__     :						Any						=							CLIPTextModel(_A					)
          UpperCAmelCase__     :						List[str]						=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

          UpperCAmelCase__     :						Any						=							{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''safety_checker''': None,
              '''feature_extractor''': None,
          }
          return components






      def lowercase_				(   self							:       Any		,		_A							:       Optional[Any]		,		_A							:       Dict=0					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							torch.manual_seed(_A					)
          UpperCAmelCase__     :						Optional[Any]						=							{
              '''prompt''': '''a photo of the dolomites''',
              '''generator''': generator,
              # Setting height and width to None to prevent OOMs on CPU.
              '''height''': None,
              '''width''': None,
              '''num_inference_steps''': 1,
              '''guidance_scale''': 6.0,
              '''output_type''': '''numpy''',
          }
          return inputs






      def lowercase_				(   self							:       Optional[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[Any]						=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase__     :						Tuple						=							self.get_dummy_components()
          UpperCAmelCase__     :						str						=							StableDiffusionPanoramaPipeline(**_A					)
          UpperCAmelCase__     :						int						=							sd_pipe.to(_A					)
          sd_pipe.set_progress_bar_config(disable=_A					)

          UpperCAmelCase__     :						List[Any]						=							self.get_dummy_inputs(_A					)
          UpperCAmelCase__     :						int						=							sd_pipe(**_A					).images
          UpperCAmelCase__     :						Any						=							image[0, -3:, -3:, -1]
          assert image.shape == (1, 64, 64, 3)

          UpperCAmelCase__     :						Dict						=							np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7]					)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          super().test_inference_batch_consistent(batch_sizes=[1, 2]					)






      def lowercase_				(   self							:       int					):


          '''simple docstring'''


          super().test_inference_batch_single_identical(batch_size=2		,		expected_max_diff=3.25e-3					)






      def lowercase_				(   self							:       Union[str, Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase__     :						Optional[int]						=							self.get_dummy_components()
          UpperCAmelCase__     :						Dict						=							StableDiffusionPanoramaPipeline(**_A					)
          UpperCAmelCase__     :						Optional[Any]						=							sd_pipe.to(_A					)
          sd_pipe.set_progress_bar_config(disable=_A					)

          UpperCAmelCase__     :						List[str]						=							self.get_dummy_inputs(_A					)
          UpperCAmelCase__     :						List[Any]						=							'''french fries'''
          UpperCAmelCase__     :						Tuple						=							sd_pipe(**_A		,		negative_prompt=_A					)
          UpperCAmelCase__     :						Tuple						=							output.images
          UpperCAmelCase__     :						Union[str, Any]						=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          UpperCAmelCase__     :						List[Any]						=							np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7]					)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase__     :						Union[str, Any]						=							self.get_dummy_components()
          UpperCAmelCase__     :						str						=							StableDiffusionPanoramaPipeline(**_A					)
          UpperCAmelCase__     :						Optional[int]						=							sd_pipe.to(_A					)
          sd_pipe.set_progress_bar_config(disable=_A					)

          UpperCAmelCase__     :						str						=							self.get_dummy_inputs(_A					)
          UpperCAmelCase__     :						int						=							sd_pipe(**_A		,		view_batch_size=2					)
          UpperCAmelCase__     :						Optional[int]						=							output.images
          UpperCAmelCase__     :						List[Any]						=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          UpperCAmelCase__     :						int						=							np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7]					)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase__     :						Optional[Any]						=							self.get_dummy_components()
          UpperCAmelCase__     :						List[Any]						=							EulerAncestralDiscreteScheduler(
              beta_start=0.0_0_0_8_5		,		beta_end=0.0_1_2		,		beta_schedule='''scaled_linear'''					)
          UpperCAmelCase__     :						List[str]						=							StableDiffusionPanoramaPipeline(**_A					)
          UpperCAmelCase__     :						Union[str, Any]						=							sd_pipe.to(_A					)
          sd_pipe.set_progress_bar_config(disable=_A					)

          UpperCAmelCase__     :						List[Any]						=							self.get_dummy_inputs(_A					)
          UpperCAmelCase__     :						Optional[int]						=							sd_pipe(**_A					).images
          UpperCAmelCase__     :						Tuple						=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          UpperCAmelCase__     :						List[Any]						=							np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2]					)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


          UpperCAmelCase__     :						str						=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase__     :						Tuple						=							self.get_dummy_components()
          UpperCAmelCase__     :						List[str]						=							PNDMScheduler(
              beta_start=0.0_0_0_8_5		,		beta_end=0.0_1_2		,		beta_schedule='''scaled_linear'''		,		skip_prk_steps=_A					)
          UpperCAmelCase__     :						Optional[int]						=							StableDiffusionPanoramaPipeline(**_A					)
          UpperCAmelCase__     :						str						=							sd_pipe.to(_A					)
          sd_pipe.set_progress_bar_config(disable=_A					)

          UpperCAmelCase__     :						Tuple						=							self.get_dummy_inputs(_A					)
          UpperCAmelCase__     :						Optional[int]						=							sd_pipe(**_A					).images
          UpperCAmelCase__     :						Optional[Any]						=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          UpperCAmelCase__     :						int						=							np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9]					)

          assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2




@slow
@require_torch_gpu
class      lowerCamelCase_  (      unittest.TestCase ):






      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






      def lowercase_				(   self							:       int		,		_A							:       Tuple=0					):


          '''simple docstring'''


          UpperCAmelCase__     :						str						=							torch.manual_seed(_A					)
          UpperCAmelCase__     :						str						=							{
              '''prompt''': '''a photo of the dolomites''',
              '''generator''': generator,
              '''num_inference_steps''': 3,
              '''guidance_scale''': 7.5,
              '''output_type''': '''numpy''',
          }
          return inputs






      def lowercase_				(   self							:       Optional[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							'''stabilityai/stable-diffusion-2-base'''
          UpperCAmelCase__     :						Optional[Any]						=							DDIMScheduler.from_pretrained(_A		,		subfolder='''scheduler'''					)
          UpperCAmelCase__     :						Dict						=							StableDiffusionPanoramaPipeline.from_pretrained(_A		,		scheduler=_A		,		safety_checker=_A					)
          pipe.to(_A					)
          pipe.set_progress_bar_config(disable=_A					)
          pipe.enable_attention_slicing()

          UpperCAmelCase__     :						Tuple						=							self.get_inputs()
          UpperCAmelCase__     :						Dict						=							pipe(**_A					).images
          UpperCAmelCase__     :						List[Any]						=							image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 2_048, 3)

          UpperCAmelCase__     :						Optional[Any]						=							np.array(
              [
                  0.3_6_9_6_8_3_9_2,
                  0.2_7_0_2_5_3_7_2,
                  0.3_2_4_4_6_7_6_6,
                  0.2_8_3_7_9_3_8_7,
                  0.3_6_3_6_3_2_7_4,
                  0.3_0_7_3_3_3_4_7,
                  0.2_7_1_0_0_0_2_7,
                  0.2_7_0_5_4_1_2_5,
                  0.2_5_5_3_6_0_9_6,
              ]					)

          assert np.abs(expected_slice - image_slice					).max() < 1e-2






      def lowercase_				(   self							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							StableDiffusionPanoramaPipeline.from_pretrained(
              '''stabilityai/stable-diffusion-2-base'''		,		safety_checker=_A					)
          UpperCAmelCase__     :						Tuple						=							LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
          pipe.to(_A					)
          pipe.set_progress_bar_config(disable=_A					)
          pipe.enable_attention_slicing()

          UpperCAmelCase__     :						Union[str, Any]						=							self.get_inputs()
          UpperCAmelCase__     :						Any						=							pipe(**_A					).images
          UpperCAmelCase__     :						Dict						=							image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 2_048, 3)

          UpperCAmelCase__     :						int						=							np.array(
              [
                  [
                      0.0,
                      0.0,
                      0.0,
                      0.0,
                      0.0,
                      0.0,
                      0.0,
                      0.0,
                      0.0,
                  ]
              ]					)

          assert np.abs(expected_slice - image_slice					).max() < 1e-3






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							0

          def callback_fn(_A							:       int		,		_A							:       int		,		_A							:       torch.FloatTensor					) -> None:
              UpperCAmelCase__     :						str						=							True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 1:
                  UpperCAmelCase__     :						List[str]						=							latents.detach().cpu().numpy()
                  assert latents.shape == (1, 4, 64, 256)
                  UpperCAmelCase__     :						str						=							latents[0, -3:, -3:, -1]

                  UpperCAmelCase__     :						Tuple						=							np.array(
                      [
                          0.1_8_6_8_1_8_6_9,
                          0.3_3_9_0_7_8_1_6,
                          0.5_3_6_1_2_7_6,
                          0.1_4_4_3_2_8_6_5,
                          -0.0_2_8_5_6_6_1_1,
                          -0.7_3_9_4_1_1_2_3,
                          0.2_3_3_9_7_9_8_7,
                          0.4_7_3_2_2_6_8_2,
                          -0.3_7_8_2_3_1_6_4,
                      ]					)
                  assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5e-2
              elif step == 2:
                  UpperCAmelCase__     :						Tuple						=							latents.detach().cpu().numpy()
                  assert latents.shape == (1, 4, 64, 256)
                  UpperCAmelCase__     :						int						=							latents[0, -3:, -3:, -1]

                  UpperCAmelCase__     :						Union[str, Any]						=							np.array(
                      [
                          0.1_8_5_3_9_6_4_5,
                          0.3_3_9_8_7_2_4_8,
                          0.5_3_7_8_5_5_9,
                          0.1_4_4_3_7_1_4_2,
                          -0.0_2_4_5_5_2_6_1,
                          -0.7_3_3_8_3_1_7,
                          0.2_3_9_9_0_7_5_5,
                          0.4_7_3_5_6_2_7_2,
                          -0.3_7_8_6_5_0_5,
                      ]					)

                  assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5e-2

          UpperCAmelCase__     :						Union[str, Any]						=							False

          UpperCAmelCase__     :						Dict						=							'''stabilityai/stable-diffusion-2-base'''
          UpperCAmelCase__     :						List[Any]						=							DDIMScheduler.from_pretrained(_A		,		subfolder='''scheduler'''					)
          UpperCAmelCase__     :						List[str]						=							StableDiffusionPanoramaPipeline.from_pretrained(_A		,		scheduler=_A		,		safety_checker=_A					)
          UpperCAmelCase__     :						Tuple						=							pipe.to(_A					)
          pipe.set_progress_bar_config(disable=_A					)
          pipe.enable_attention_slicing()

          UpperCAmelCase__     :						Optional[Any]						=							self.get_inputs()
          pipe(**_A		,		callback=_A		,		callback_steps=1					)
          assert callback_fn.has_been_called
          assert number_of_steps == 3






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          UpperCAmelCase__     :						Tuple						=							'''stabilityai/stable-diffusion-2-base'''
          UpperCAmelCase__     :						List[Any]						=							DDIMScheduler.from_pretrained(_A		,		subfolder='''scheduler'''					)
          UpperCAmelCase__     :						Optional[int]						=							StableDiffusionPanoramaPipeline.from_pretrained(_A		,		scheduler=_A		,		safety_checker=_A					)
          UpperCAmelCase__     :						Tuple						=							pipe.to(_A					)
          pipe.set_progress_bar_config(disable=_A					)
          pipe.enable_attention_slicing(1					)
          pipe.enable_sequential_cpu_offload()

          UpperCAmelCase__     :						Dict						=							self.get_inputs()
          UpperCAmelCase__     :						Optional[Any]						=							pipe(**_A					)

          UpperCAmelCase__     :						Optional[int]						=							torch.cuda.max_memory_allocated()
          # make sure that less than 5.2 GB is allocated
          assert mem_bytes < 5.5 * 10**9






'''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



