



'''simple docstring'''
import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 				lowercase__      (				lowercase		,   lowercase		,   unittest.TestCase  ):
	lowercase__    							=     StableDiffusionDiffEditPipeline
	lowercase__    							=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
	lowercase__    							=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
	lowercase__    							=     frozenset(
	    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowercase__    							=     frozenset([]  )





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							torch.manual_seed(0					)
							_UpperCamelCase   :						List[Any]												=  UNetaDConditionModel(
							    block_out_channels=(32, 64)					,layers_per_block=2					,sample_size=32					,in_channels=4					,out_channels=4					,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,cross_attention_dim=32					,attention_head_dim=(2, 4)					,use_linear_projection=lowerCamelCase__					,)
							_UpperCamelCase   :						List[Any]												=  DDIMScheduler(
							    beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule='scaled_linear'					,clip_sample=lowerCamelCase__					,set_alpha_to_one=lowerCamelCase__					,)
							_UpperCamelCase   :						str												=  DDIMInverseScheduler(
							    beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule='scaled_linear'					,clip_sample=lowerCamelCase__					,set_alpha_to_zero=lowerCamelCase__					,)
							torch.manual_seed(0					)
							_UpperCamelCase   :						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					,sample_size=128					,)
							torch.manual_seed(0					)
							_UpperCamelCase   :						List[str]												=  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=1000					,hidden_act='gelu'					,projection_dim=512					,)
							_UpperCamelCase   :						List[str]												=  CLIPTextModel(lowerCamelCase__					)
							_UpperCamelCase   :						Dict												=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

							_UpperCamelCase   :						Optional[Any]												=  {
							    'unet': unet,
							    'scheduler': scheduler,
							    'inverse_scheduler': inverse_scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}

							return components





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							str=0					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  floats_tensor((1, 16, 16)					,rng=random.Random(lowerCamelCase__					)					).to(lowerCamelCase__					)
							_UpperCamelCase   :						str												=  floats_tensor((1, 2, 4, 16, 16)					,rng=random.Random(lowerCamelCase__					)					).to(lowerCamelCase__					)
							if str(lowerCamelCase__					).startswith('mps'					):
													_UpperCamelCase   :						Optional[Any]												=  torch.manual_seed(lowerCamelCase__					)
							else:
													_UpperCamelCase   :						str												=  torch.Generator(device=lowerCamelCase__					).manual_seed(lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  {
							    'prompt': 'a dog and a newt',
							    'mask_image': mask,
							    'image_latents': latents,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'inpaint_strength': 1.0,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							}

							return inputs





	def       UpperCamelCase_    (					self   :							Dict					,lowerCamelCase__   :							List[Any]					,lowerCamelCase__   :							List[Any]=0					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  floats_tensor((1, 3, 32, 32)					,rng=random.Random(lowerCamelCase__					)					).to(lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  image.cpu().permute(0					,2					,3					,1					)[0]
							_UpperCamelCase   :						int												=  Image.fromarray(np.uinta(lowerCamelCase__					)					).convert('RGB'					)
							if str(lowerCamelCase__					).startswith('mps'					):
													_UpperCamelCase   :						str												=  torch.manual_seed(lowerCamelCase__					)
							else:
													_UpperCamelCase   :						Any												=  torch.Generator(device=lowerCamelCase__					).manual_seed(lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  {
							    'image': image,
							    'source_prompt': 'a cat and a frog',
							    'target_prompt': 'a dog and a newt',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'num_maps_per_mask': 2,
							    'mask_encode_strength': 1.0,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							}

							return inputs





	def       UpperCamelCase_    (					self   :							Optional[int]					,lowerCamelCase__   :							Any					,lowerCamelCase__   :							Dict=0					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  floats_tensor((1, 3, 32, 32)					,rng=random.Random(lowerCamelCase__					)					).to(lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  image.cpu().permute(0					,2					,3					,1					)[0]
							_UpperCamelCase   :						List[Any]												=  Image.fromarray(np.uinta(lowerCamelCase__					)					).convert('RGB'					)
							if str(lowerCamelCase__					).startswith('mps'					):
													_UpperCamelCase   :						Optional[Any]												=  torch.manual_seed(lowerCamelCase__					)
							else:
													_UpperCamelCase   :						List[Any]												=  torch.Generator(device=lowerCamelCase__					).manual_seed(lowerCamelCase__					)
							_UpperCamelCase   :						Dict												=  {
							    'image': image,
							    'prompt': 'a cat and a frog',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'inpaint_strength': 1.0,
							    'guidance_scale': 6.0,
							    'decode_latents': True,
							    'output_type': 'numpy',
							}
							return inputs





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							if not hasattr(self.pipeline_class					,'_optional_components'					):
													return

							_UpperCamelCase   :						List[Any]												=  self.get_dummy_components()
							_UpperCamelCase   :						Union[str, Any]												=  self.pipeline_class(**lowerCamelCase__					)
							pipe.to(lowerCamelCase__					)
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)

							# set all optional components to None and update pipeline config accordingly
							for optional_component in pipe._optional_components:
													setattr(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)
							pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}					)

							_UpperCamelCase   :						Optional[int]												=  self.get_dummy_inputs(lowerCamelCase__					)
							_UpperCamelCase   :						int												=  pipe(**lowerCamelCase__					)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						List[Any]												=  self.pipeline_class.from_pretrained(lowerCamelCase__					)
													pipe_loaded.to(lowerCamelCase__					)
													pipe_loaded.set_progress_bar_config(disable=lowerCamelCase__					)

							for optional_component in pipe._optional_components:
													self.assertTrue(
													    getattr(lowerCamelCase__					,lowerCamelCase__					) is None					,F'`{optional_component}` did not stay set to None after loading.'					,)

							_UpperCamelCase   :						List[str]												=  self.get_dummy_inputs(lowerCamelCase__					)
							_UpperCamelCase   :						str												=  pipe_loaded(**lowerCamelCase__					)[0]

							_UpperCamelCase   :						Optional[Any]												=  np.abs(output - output_loaded					).max()
							self.assertLess(lowerCamelCase__					,1E-4					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  'cpu'

							_UpperCamelCase   :						List[str]												=  self.get_dummy_components()
							_UpperCamelCase   :						int												=  self.pipeline_class(**lowerCamelCase__					)
							pipe.to(lowerCamelCase__					)
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)

							_UpperCamelCase   :						List[str]												=  self.get_dummy_mask_inputs(lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, Any]												=  pipe.generate_mask(**lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  mask[0, -3:, -3:]

							self.assertEqual(mask.shape					,(1, 16, 16)					)
							_UpperCamelCase   :						Optional[int]												=  np.array([0] * 9					)
							_UpperCamelCase   :						Any												=  np.abs(mask_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(lowerCamelCase__					,1E-3					)
							self.assertEqual(mask[0, -3, -4]					,0					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  'cpu'

							_UpperCamelCase   :						Any												=  self.get_dummy_components()
							_UpperCamelCase   :						Optional[Any]												=  self.pipeline_class(**lowerCamelCase__					)
							pipe.to(lowerCamelCase__					)
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)

							_UpperCamelCase   :						List[Any]												=  self.get_dummy_inversion_inputs(lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  pipe.invert(**lowerCamelCase__					).images
							_UpperCamelCase   :						Tuple												=  image[0, -1, -3:, -3:]

							self.assertEqual(image.shape					,(2, 32, 32, 3)					)
							_UpperCamelCase   :						str												=  np.array(
							    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]					,)
							_UpperCamelCase   :						List[Any]												=  np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(lowerCamelCase__					,1E-3					)





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







							'''simple docstring'''
							super().test_inference_batch_single_identical(expected_max_diff=5E-3					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  'cpu'

							_UpperCamelCase   :						List[str]												=  self.get_dummy_components()

							_UpperCamelCase   :						Any												=  {'beta_start': 0.0_0_0_8_5, 'beta_end': 0.0_1_2, 'beta_schedule': 'scaled_linear'}
							_UpperCamelCase   :						Optional[int]												=  DPMSolverMultistepScheduler(**lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  DPMSolverMultistepInverseScheduler(**lowerCamelCase__					)

							_UpperCamelCase   :						List[Any]												=  self.pipeline_class(**lowerCamelCase__					)
							pipe.to(lowerCamelCase__					)
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)

							_UpperCamelCase   :						Optional[int]												=  self.get_dummy_inversion_inputs(lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  pipe.invert(**lowerCamelCase__					).images
							_UpperCamelCase   :						int												=  image[0, -1, -3:, -3:]

							self.assertEqual(image.shape					,(2, 32, 32, 3)					)
							_UpperCamelCase   :						Any												=  np.array(
							    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]					,)
							_UpperCamelCase   :						Union[str, Any]												=  np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(lowerCamelCase__					,1E-3					)






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





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	@classmethod
	def       UpperCamelCase_    (					cls   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'					)

							_UpperCamelCase   :						Dict												=  raw_image.convert('RGB'					).resize((768, 768)					)

							_UpperCamelCase   :						Optional[Any]												=  raw_image





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  torch.manual_seed(0					)

							_UpperCamelCase   :						Optional[int]												=  StableDiffusionDiffEditPipeline.from_pretrained(
							    'stabilityai/stable-diffusion-2-1'					,safety_checker=lowerCamelCase__					,torch_dtype=torch.floataa					)
							_UpperCamelCase   :						List[str]												=  DDIMScheduler.from_config(pipe.scheduler.config					)
							_UpperCamelCase   :						str												=  DDIMInverseScheduler.from_config(pipe.scheduler.config					)
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)

							_UpperCamelCase   :						str												=  'a bowl of fruit'
							_UpperCamelCase   :						Tuple												=  'a bowl of pears'

							_UpperCamelCase   :						int												=  pipe.generate_mask(
							    image=self.raw_image					,source_prompt=lowerCamelCase__					,target_prompt=lowerCamelCase__					,generator=lowerCamelCase__					,)

							_UpperCamelCase   :						Tuple												=  pipe.invert(
							    prompt=lowerCamelCase__					,image=self.raw_image					,inpaint_strength=0.7					,generator=lowerCamelCase__					).latents

							_UpperCamelCase   :						Optional[int]												=  pipe(
							    prompt=lowerCamelCase__					,mask_image=lowerCamelCase__					,image_latents=lowerCamelCase__					,generator=lowerCamelCase__					,negative_prompt=lowerCamelCase__					,inpaint_strength=0.7					,output_type='numpy'					,).images[0]

							_UpperCamelCase   :						List[Any]												=  (
							    np.array(
							        load_image(
							            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							            '/diffedit/pears.png'					).resize((768, 768)					)					)
							    / 255
							)
							assert np.abs((expected_image - image).max()					) < 5E-1





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  torch.manual_seed(0					)

							_UpperCamelCase   :						Dict												=  StableDiffusionDiffEditPipeline.from_pretrained(
							    'stabilityai/stable-diffusion-2-1'					,safety_checker=lowerCamelCase__					,torch_dtype=torch.floataa					)
							_UpperCamelCase   :						Tuple												=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
							_UpperCamelCase   :						Optional[int]												=  DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config					)
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)

							_UpperCamelCase   :						str												=  'a bowl of fruit'
							_UpperCamelCase   :						Optional[Any]												=  'a bowl of pears'

							_UpperCamelCase   :						List[Any]												=  pipe.generate_mask(
							    image=self.raw_image					,source_prompt=lowerCamelCase__					,target_prompt=lowerCamelCase__					,generator=lowerCamelCase__					,)

							_UpperCamelCase   :						str												=  pipe.invert(
							    prompt=lowerCamelCase__					,image=self.raw_image					,inpaint_strength=0.7					,generator=lowerCamelCase__					,num_inference_steps=25					,).latents

							_UpperCamelCase   :						int												=  pipe(
							    prompt=lowerCamelCase__					,mask_image=lowerCamelCase__					,image_latents=lowerCamelCase__					,generator=lowerCamelCase__					,negative_prompt=lowerCamelCase__					,inpaint_strength=0.7					,num_inference_steps=25					,output_type='numpy'					,).images[0]

							_UpperCamelCase   :						Tuple												=  (
							    np.array(
							        load_image(
							            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							            '/diffedit/pears.png'					).resize((768, 768)					)					)
							    / 255
							)
							assert np.abs((expected_image - image).max()					) < 5E-1







'''simple docstring'''
from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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

snake_case_					:      List[str]    			=    logging.get_logger(__name__)






def A__       (	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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_ = None				):
						_UpperCamelCase   :						Optional[int]												=  tesseract_config if tesseract_config is not None else ''

						# apply OCR
						_UpperCamelCase   :						int												=  to_pil_image(UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  pil_image.size
						_UpperCamelCase   :						Tuple												=  pytesseract.image_to_data(UpperCAmelCase_    ,    lang=UpperCAmelCase_    ,    output_type='dict'    ,    config=UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						int												=  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   :						Any												=  [word for idx, word in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						Any												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						Union[str, Any]												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						Union[str, Any]												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						List[str]												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]

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

						# finally, normalize the bounding boxes
						_UpperCamelCase   :						List[Any]												=  []
						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 				lowercase__      (				lowercase  ):
	lowercase__    							=     ["""pixel_values"""]





	def __init__(					self   :							Dict					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[str] = ""					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  size if size is not None else {'height': 224, 'width': 224}
							_UpperCamelCase   :						Union[str, Any]												=  get_size_dict(lowerCamelCase__					)

							_UpperCamelCase   :						Optional[int]												=  do_resize
							_UpperCamelCase   :						str												=  size
							_UpperCamelCase   :						Any												=  resample
							_UpperCamelCase   :						List[str]												=  apply_ocr
							_UpperCamelCase   :						int												=  ocr_lang
							_UpperCamelCase   :						Union[str, Any]												=  tesseract_config





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Dict[str, int]					,lowerCamelCase__   :							PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							Union[str, Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  get_size_dict(lowerCamelCase__					)
							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   :						List[str]												=  (size['height'], size['width'])
							return resize(lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							ImageInput					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							PILImageResampling = None					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[Union[str, TensorType]] = None					,lowerCamelCase__   :							ChannelDimension = ChannelDimension.FIRST					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase   :						Tuple												=  size if size is not None else self.size
							_UpperCamelCase   :						Optional[int]												=  get_size_dict(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  resample if resample is not None else self.resample
							_UpperCamelCase   :						Union[str, Any]												=  apply_ocr if apply_ocr is not None else self.apply_ocr
							_UpperCamelCase   :						Union[str, Any]												=  ocr_lang if ocr_lang is not None else self.ocr_lang
							_UpperCamelCase   :						int												=  tesseract_config if tesseract_config is not None else self.tesseract_config

							_UpperCamelCase   :						Dict												=  make_list_of_images(lowerCamelCase__					)

							if not valid_images(lowerCamelCase__					):
													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.'					)

							# All transformations expect numpy arrays.
							_UpperCamelCase   :						Union[str, Any]												=  [to_numpy_array(lowerCamelCase__					) for image in images]

							if apply_ocr:
													requires_backends(self					,'pytesseract'					)
													_UpperCamelCase   :						Tuple												=  []
													_UpperCamelCase   :						Tuple												=  []
													for image in images:
																			_UpperCamelCase							,     _UpperCamelCase   :						Optional[Any]												=  apply_tesseract(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)
																			words_batch.append(lowerCamelCase__					)
																			boxes_batch.append(lowerCamelCase__					)

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

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							_UpperCamelCase   :						Optional[int]												=  [flip_channel_order(lowerCamelCase__					) for image in images]
							_UpperCamelCase   :						Dict												=  [to_channel_dimension_format(lowerCamelCase__					,lowerCamelCase__					) for image in images]

							_UpperCamelCase   :						Union[str, Any]												=  BatchFeature(data={'pixel_values': images}					,tensor_type=lowerCamelCase__					)

							if apply_ocr:
													_UpperCamelCase   :						Dict												=  words_batch
													_UpperCamelCase   :						str												=  boxes_batch
							return data


