
'''simple docstring'''


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()


class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							StableDiffusionLDMaDPipeline
   _snake_case		   =							TEXT_TO_IMAGE_PARAMS
   _snake_case		   =							TEXT_TO_IMAGE_BATCH_PARAMS
   _snake_case		   =							TEXT_TO_IMAGE_IMAGE_PARAMS







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      torch.manual_seed(0   )
      UpperCamelCase       =  UNetaDConditionModel(
          block_out_channels=(3_2, 6_4) ,   layers_per_block=2 ,   sample_size=3_2 ,   in_channels=4 ,   out_channels=4 ,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,   cross_attention_dim=3_2 ,   )
      UpperCamelCase       =  DDIMScheduler(
          beta_start=0.00085 ,   beta_end=0.012 ,   beta_schedule='''scaled_linear''' ,   clip_sample=lowerCamelCase__ ,   set_alpha_to_one=lowerCamelCase__ ,   )
      torch.manual_seed(0   )
      UpperCamelCase       =  AutoencoderKL(
          block_out_channels=[3_2, 6_4] ,   in_channels=6 ,   out_channels=6 ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,   latent_channels=4 ,   )
      torch.manual_seed(0   )
      UpperCamelCase       =  CLIPTextConfig(
          bos_token_id=0 ,   eos_token_id=2 ,   hidden_size=3_2 ,   intermediate_size=3_7 ,   layer_norm_eps=1e-0_5 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=1_0_0_0 ,   )
      UpperCamelCase       =  CLIPTextModel(lowerCamelCase__   )
      UpperCamelCase       =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__=0   ):

      '''simple docstring'''







      if str(lowerCamelCase__   ).startswith('''mps'''   ):
         UpperCamelCase       =  torch.manual_seed(lowerCamelCase__   )
      else:
         UpperCamelCase       =  torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
      UpperCamelCase       =  {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
      }
      return inputs







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator

      UpperCamelCase       =  self.get_dummy_components()
      UpperCamelCase       =  StableDiffusionLDMaDPipeline(**lowerCamelCase__   )
      UpperCamelCase       =  ldmad_pipe.to(lowerCamelCase__   )
      ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

      UpperCamelCase       =  self.get_dummy_inputs(lowerCamelCase__   )
      UpperCamelCase       =  ldmad_pipe(**lowerCamelCase__   )
      UpperCamelCase     , UpperCamelCase       =  output.rgb, output.depth

      UpperCamelCase       =  rgb[0, -3:, -3:, -1]
      UpperCamelCase       =  depth[0, -3:, -1]

      assert rgb.shape == (1, 6_4, 6_4, 3)
      assert depth.shape == (1, 6_4, 6_4)

      UpperCamelCase       =  np.array(
          [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]   )
      UpperCamelCase       =  np.array([103.46727, 85.812004, 87.849236]   )

      assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb   ).max() < 1e-2
      assert np.abs(image_slice_depth.flatten() - expected_slice_depth   ).max() < 1e-2







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.get_dummy_components()
      UpperCamelCase       =  StableDiffusionLDMaDPipeline(**lowerCamelCase__   )
      UpperCamelCase       =  ldmad_pipe.to(lowerCamelCase__   )
      ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

      UpperCamelCase       =  self.get_dummy_inputs(lowerCamelCase__   )
      UpperCamelCase       =  3 * [inputs['''prompt''']]

      # forward
      UpperCamelCase       =  ldmad_pipe(**lowerCamelCase__   )
      UpperCamelCase     , UpperCamelCase       =  output.rgb, output.depth
      UpperCamelCase       =  rgb_slice_a[0, -3:, -3:, -1]
      UpperCamelCase       =  depth_slice_a[0, -3:, -1]

      UpperCamelCase       =  self.get_dummy_inputs(lowerCamelCase__   )
      UpperCamelCase       =  3 * [inputs.pop('''prompt'''   )]

      UpperCamelCase       =  ldmad_pipe.tokenizer(
          lowerCamelCase__ ,   padding='''max_length''' ,   max_length=ldmad_pipe.tokenizer.model_max_length ,   truncation=lowerCamelCase__ ,   return_tensors='''pt''' ,   )
      UpperCamelCase       =  text_inputs['''input_ids'''].to(lowerCamelCase__   )

      UpperCamelCase       =  ldmad_pipe.text_encoder(lowerCamelCase__   )[0]

      UpperCamelCase       =  prompt_embeds

      # forward
      UpperCamelCase       =  ldmad_pipe(**lowerCamelCase__   )
      UpperCamelCase     , UpperCamelCase       =  output.rgb, output.depth
      UpperCamelCase       =  rgb_slice_a[0, -3:, -3:, -1]
      UpperCamelCase       =  depth_slice_a[0, -3:, -1]

      assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()   ).max() < 1e-4
      assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()   ).max() < 1e-4







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
      UpperCamelCase       =  self.get_dummy_components()
      UpperCamelCase       =  PNDMScheduler(skip_prk_steps=lowerCamelCase__   )
      UpperCamelCase       =  StableDiffusionLDMaDPipeline(**lowerCamelCase__   )
      UpperCamelCase       =  ldmad_pipe.to(lowerCamelCase__   )
      ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

      UpperCamelCase       =  self.get_dummy_inputs(lowerCamelCase__   )
      UpperCamelCase       =  '''french fries'''
      UpperCamelCase       =  ldmad_pipe(**lowerCamelCase__ ,   negative_prompt=lowerCamelCase__   )

      UpperCamelCase     , UpperCamelCase       =  output.rgb, output.depth
      UpperCamelCase       =  rgb[0, -3:, -3:, -1]
      UpperCamelCase       =  depth[0, -3:, -1]

      assert rgb.shape == (1, 6_4, 6_4, 3)
      assert depth.shape == (1, 6_4, 6_4)

      UpperCamelCase       =  np.array(
          [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]   )
      UpperCamelCase       =  np.array([107.84738, 84.62802, 89.962135]   )
      assert np.abs(rgb_slice.flatten() - expected_slice_rgb   ).max() < 1e-2
      assert np.abs(depth_slice.flatten() - expected_slice_depth   ).max() < 1e-2




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






   '''simple docstring'''







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__="cpu" ,   lowerCamelCase__=torch.floataa ,   lowerCamelCase__=0   ):

      '''simple docstring'''







      UpperCamelCase       =  torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
      UpperCamelCase       =  np.random.RandomState(lowerCamelCase__   ).standard_normal((1, 4, 6_4, 6_4)   )
      UpperCamelCase       =  torch.from_numpy(lowerCamelCase__   ).to(device=lowerCamelCase__ ,   dtype=lowerCamelCase__   )
      UpperCamelCase       =  {
          '''prompt''': '''a photograph of an astronaut riding a horse''',
          '''latents''': latents,
          '''generator''': generator,
          '''num_inference_steps''': 3,
          '''guidance_scale''': 7.5,
          '''output_type''': '''numpy''',
      }
      return inputs







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''   )
      UpperCamelCase       =  ldmad_pipe.to(lowerCamelCase__   )
      ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

      UpperCamelCase       =  self.get_inputs(lowerCamelCase__   )
      UpperCamelCase       =  ldmad_pipe(**lowerCamelCase__   )
      UpperCamelCase     , UpperCamelCase       =  output.rgb, output.depth
      UpperCamelCase       =  rgb[0, -3:, -3:, -1].flatten()
      UpperCamelCase       =  rgb[0, -3:, -1].flatten()

      assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
      assert depth.shape == (1, 5_1_2, 5_1_2)

      UpperCamelCase       =  np.array(
          [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]   )
      UpperCamelCase       =  np.array(
          [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]   )
      assert np.abs(rgb_slice - expected_slice_rgb   ).max() < 3e-3
      assert np.abs(depth_slice - expected_slice_depth   ).max() < 3e-3




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






   '''simple docstring'''







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__="cpu" ,   lowerCamelCase__=torch.floataa ,   lowerCamelCase__=0   ):

      '''simple docstring'''







      UpperCamelCase       =  torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
      UpperCamelCase       =  np.random.RandomState(lowerCamelCase__   ).standard_normal((1, 4, 6_4, 6_4)   )
      UpperCamelCase       =  torch.from_numpy(lowerCamelCase__   ).to(device=lowerCamelCase__ ,   dtype=lowerCamelCase__   )
      UpperCamelCase       =  {
          '''prompt''': '''a photograph of an astronaut riding a horse''',
          '''latents''': latents,
          '''generator''': generator,
          '''num_inference_steps''': 5_0,
          '''guidance_scale''': 7.5,
          '''output_type''': '''numpy''',
      }
      return inputs







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''   ).to(lowerCamelCase__   )
      ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

      UpperCamelCase       =  self.get_inputs(lowerCamelCase__   )
      UpperCamelCase       =  ldmad_pipe(**lowerCamelCase__   )
      UpperCamelCase     , UpperCamelCase       =  output.rgb, output.depth

      UpperCamelCase       =  0.495586
      UpperCamelCase       =  0.33795515
      UpperCamelCase       =  112.48518
      UpperCamelCase       =  98.489746
      assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1e-3
      assert np.abs(expected_rgb_std - rgb.std()   ) < 1e-3
      assert np.abs(expected_depth_mean - depth.mean()   ) < 1e-3
      assert np.abs(expected_depth_std - depth.std()   ) < 1e-3







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''   ).to(lowerCamelCase__   )
      ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

      UpperCamelCase       =  self.get_inputs(lowerCamelCase__   )
      UpperCamelCase       =  ldmad_pipe(**lowerCamelCase__   )
      UpperCamelCase     , UpperCamelCase       =  output.rgb, output.depth

      UpperCamelCase       =  0.4194127
      UpperCamelCase       =  0.35375586
      UpperCamelCase       =  0.5638502
      UpperCamelCase       =  0.34686103

      assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
      assert depth.shape == (1, 5_1_2, 5_1_2, 1)
      assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1e-3
      assert np.abs(expected_rgb_std - rgb.std()   ) < 1e-3
      assert np.abs(expected_depth_mean - depth.mean()   ) < 1e-3
      assert np.abs(expected_depth_std - depth.std()   ) < 1e-3







'''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

snake_case_ :  Tuple     =					logging.get_logger(__name__)



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Optional[int]):
   return [
       int(1000 * (box[0] / width)),
       int(1000 * (box[1] / height)),
       int(1000 * (box[2] / width)),
       int(1000 * (box[3] / height)),
   ]



def  __snake_case     (    _UpperCAmelCase					:       np.ndarray,      _UpperCAmelCase					:       Optional[str],      _UpperCAmelCase					:       Optional[str]):
   UpperCamelCase       =  to_pil_image(_UpperCAmelCase)
   UpperCamelCase     , UpperCamelCase       =  pil_image.size
   UpperCamelCase       =  pytesseract.image_to_data(_UpperCAmelCase,      lang=_UpperCAmelCase,      output_type='''dict''',      config=_UpperCAmelCase)
   UpperCamelCase     , UpperCamelCase     , UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

   # filter empty words and corresponding coordinates
   UpperCamelCase       =  [idx for idx, word in enumerate(_UpperCAmelCase) if not word.strip()]
   UpperCamelCase       =  [word for idx, word in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
   UpperCamelCase       =  [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
   UpperCamelCase       =  [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
   UpperCamelCase       =  [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
   UpperCamelCase       =  [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]

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

   # finally, normalize the bounding boxes
   UpperCamelCase       =  []
   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__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							['''pixel_values''']







   def __init__(			self ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   lowerCamelCase__ = PILImageResampling.BILINEAR ,   lowerCamelCase__ = True ,   lowerCamelCase__ = 1 / 2_5_5 ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   lowerCamelCase__ = "" ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      super().__init__(**lowerCamelCase__   )
      UpperCamelCase       =  size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
      UpperCamelCase       =  get_size_dict(lowerCamelCase__   )

      UpperCamelCase       =  do_resize
      UpperCamelCase       =  size
      UpperCamelCase       =  resample
      UpperCamelCase       =  do_rescale
      UpperCamelCase       =  rescale_value
      UpperCamelCase       =  do_normalize
      UpperCamelCase       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      UpperCamelCase       =  image_std if image_std is not None else IMAGENET_STANDARD_STD
      UpperCamelCase       =  apply_ocr
      UpperCamelCase       =  ocr_lang
      UpperCamelCase       =  tesseract_config







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = PILImageResampling.BILINEAR ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  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       =  (size['''height'''], size['''width'''])
      return resize(lowerCamelCase__ ,   size=lowerCamelCase__ ,   resample=lowerCamelCase__ ,   data_format=lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      return rescale(lowerCamelCase__ ,   scale=lowerCamelCase__ ,   data_format=lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      return normalize(lowerCamelCase__ ,   mean=lowerCamelCase__ ,   std=lowerCamelCase__ ,   data_format=lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__=None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = ChannelDimension.FIRST ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  do_resize if do_resize is not None else self.do_resize
      UpperCamelCase       =  size if size is not None else self.size
      UpperCamelCase       =  get_size_dict(lowerCamelCase__   )
      UpperCamelCase       =  resample if resample is not None else self.resample
      UpperCamelCase       =  do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase       =  rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase       =  do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase       =  image_mean if image_mean is not None else self.image_mean
      UpperCamelCase       =  image_std if image_std is not None else self.image_std
      UpperCamelCase       =  apply_ocr if apply_ocr is not None else self.apply_ocr
      UpperCamelCase       =  ocr_lang if ocr_lang is not None else self.ocr_lang
      UpperCamelCase       =  tesseract_config if tesseract_config is not None else self.tesseract_config

      UpperCamelCase       =  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.'''   )

      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       =  [to_numpy_array(lowerCamelCase__   ) for image in images]

      # Tesseract OCR to get words + normalized bounding boxes
      if apply_ocr:
         requires_backends(self ,   '''pytesseract'''   )
         UpperCamelCase       =  []
         UpperCamelCase       =  []
         for image in images:
            UpperCamelCase     , UpperCamelCase       =  apply_tesseract(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )
            words_batch.append(lowerCamelCase__   )
            boxes_batch.append(lowerCamelCase__   )

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

      if do_rescale:
         UpperCamelCase       =  [self.rescale(image=lowerCamelCase__ ,   scale=lowerCamelCase__   ) for image in images]

      if do_normalize:
         UpperCamelCase       =  [self.normalize(image=lowerCamelCase__ ,   mean=lowerCamelCase__ ,   std=lowerCamelCase__   ) for image in images]

      UpperCamelCase       =  [to_channel_dimension_format(lowerCamelCase__ ,   lowerCamelCase__   ) for image in images]

      UpperCamelCase       =  BatchFeature(data={'''pixel_values''': images} ,   tensor_type=lowerCamelCase__   )

      if apply_ocr:
         UpperCamelCase       =  words_batch
         UpperCamelCase       =  boxes_batch
      return data





