





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Optional[int]  )   ->    int:
  _lowercase						    =			384
  _lowercase						    =			7
  if "tiny" in model_name:
    _lowercase						    =			96
    _lowercase						    =			(2, 2, 6, 2)
    _lowercase						    =			(3, 6, 12, 24)
  elif "small" in model_name:
    _lowercase						    =			96
    _lowercase						    =			(2, 2, 18, 2)
    _lowercase						    =			(3, 6, 12, 24)
  elif "base" in model_name:
    _lowercase						    =			128
    _lowercase						    =			(2, 2, 18, 2)
    _lowercase						    =			(4, 8, 16, 32)
    _lowercase						    =			12
    _lowercase						    =			512
  elif "large" in model_name:
    _lowercase						    =			192
    _lowercase						    =			(2, 2, 18, 2)
    _lowercase						    =			(6, 12, 24, 48)
    _lowercase						    =			12
    _lowercase						    =			768

  # set label information
  _lowercase						    =			150
  _lowercase						    =			'huggingface/label-files'
  _lowercase						    =			'ade20k-id2label.json'
  _lowercase						    =			json.load(open(hf_hub_download(UpperCAmelCase_      ,				UpperCAmelCase_      ,				repo_type='dataset'  )      ,				'r'  )  )
  _lowercase						    =			{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
  _lowercase						    =			{v: k for k, v in idalabel.items()}

  _lowercase						    =			SwinConfig(
      embed_dim=UpperCAmelCase_      ,				depths=UpperCAmelCase_      ,				num_heads=UpperCAmelCase_      ,				window_size=UpperCAmelCase_      ,				out_features=['stage1', 'stage2', 'stage3', 'stage4']      ,				)
  _lowercase						    =			UperNetConfig(
      backbone_config=UpperCAmelCase_      ,				auxiliary_in_channels=UpperCAmelCase_      ,				num_labels=UpperCAmelCase_      ,				idalabel=UpperCAmelCase_      ,				labelaid=UpperCAmelCase_      ,				)

  return config

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Union[str, Any]  )   ->    List[Any]:
  _lowercase						    =			[]

  # fmt: off
  # stem
  rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')  )
  rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')  )
  rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')  )
  rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')  )
  # stages
  for i in range(len(config.backbone_config.depths  )  ):
    for j in range(config.backbone_config.depths[i]  ):
      rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")  )
      rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")  )
      rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")  )
      rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")  )
      rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")  )
      rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")  )
      rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")  )
      rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")  )
      rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")  )
      rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")  )
      rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")  )
      rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")  )

    if i < 3:
      rename_keys.append((F"""backbone.stages.{i}.downsample.reduction.weight""", F"""backbone.encoder.layers.{i}.downsample.reduction.weight""")  )
      rename_keys.append((F"""backbone.stages.{i}.downsample.norm.weight""", F"""backbone.encoder.layers.{i}.downsample.norm.weight""")  )
      rename_keys.append((F"""backbone.stages.{i}.downsample.norm.bias""", F"""backbone.encoder.layers.{i}.downsample.norm.bias""")  )
    rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""")  )
    rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""")  )

  # decode head
  rename_keys.extend(
      [
          ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
          ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
          ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
          ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
      ]  )
  # fmt: on

  return rename_keys

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Dict      ,				snake_case__	:str      ,				snake_case__	:Optional[Any]  )   ->    Any:
  _lowercase						    =			dct.pop(UpperCAmelCase_  )
  _lowercase						    =			val

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:Union[str, Any]  )   ->    Optional[Any]:
  _lowercase						    =			[int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
  for i in range(len(backbone_config.depths  )  ):
    _lowercase						    =			num_features[i]
    for j in range(backbone_config.depths[i]  ):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      _lowercase						    =			state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""  )
      _lowercase						    =			state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""  )
      # next, add query, keys and values (in that order) to the state dict
      _lowercase						    =			in_proj_weight[:dim, :]
      _lowercase						    =			in_proj_bias[: dim]
      _lowercase						    =			in_proj_weight[
          dim : dim * 2, :
      ]
      _lowercase						    =			in_proj_bias[
          dim : dim * 2
      ]
      _lowercase						    =			in_proj_weight[
          -dim :, :
      ]
      _lowercase						    =			in_proj_bias[-dim :]
      # fmt: on

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[Any]  )   ->    int:
  _lowercase						    =			x.shape
  _lowercase						    =			x.reshape(UpperCAmelCase_      ,				4      ,				in_channel // 4  )
  _lowercase						    =			x[:, [0, 2, 1, 3], :].transpose(1      ,				2  ).reshape(UpperCAmelCase_      ,				UpperCAmelCase_  )
  return x

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Dict  )   ->    Optional[int]:
  _lowercase						    =			x.shape
  _lowercase						    =			x.reshape(UpperCAmelCase_      ,				in_channel // 4      ,				4  )
  _lowercase						    =			x[:, :, [0, 2, 1, 3]].transpose(1      ,				2  ).reshape(UpperCAmelCase_      ,				UpperCAmelCase_  )

  return x

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[Any]  )   ->    Union[str, Any]:
  _lowercase						    =			x.shape[0]
  _lowercase						    =			x.reshape(4      ,				in_channel // 4  )
  _lowercase						    =			x[[0, 2, 1, 3], :].transpose(0      ,				1  ).reshape(UpperCAmelCase_  )
  return x

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Any  )   ->    Union[str, Any]:
  _lowercase						    =			x.shape[0]
  _lowercase						    =			x.reshape(in_channel // 4      ,				4  )
  _lowercase						    =			x[:, [0, 2, 1, 3]].transpose(0      ,				1  ).reshape(UpperCAmelCase_  )
  return x

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:str      ,				snake_case__	:str      ,				snake_case__	:Optional[int]  )   ->    Optional[int]:
  _lowercase						    =			{
      'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
      'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
      'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
      'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
  }
  _lowercase						    =			model_name_to_url[model_name]
  _lowercase						    =			torch.hub.load_state_dict_from_url(UpperCAmelCase_      ,				map_location='cpu'      ,				file_name=UpperCAmelCase_  )[
      'state_dict'
  ]

  for name, param in state_dict.items():
    print(UpperCAmelCase_      ,				param.shape  )

  _lowercase						    =			get_upernet_config(UpperCAmelCase_  )
  _lowercase						    =			UperNetForSemanticSegmentation(UpperCAmelCase_  )
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    _lowercase						    =			state_dict.pop(UpperCAmelCase_  )
    if "bn" in key:
      _lowercase						    =			key.replace('bn'      ,				'batch_norm'  )
    _lowercase						    =			val

  # rename keys
  _lowercase						    =			create_rename_keys(UpperCAmelCase_  )
  for src, dest in rename_keys:
    rename_key(UpperCAmelCase_      ,				UpperCAmelCase_      ,				UpperCAmelCase_  )
  read_in_q_k_v(UpperCAmelCase_      ,				config.backbone_config  )

  # fix downsample parameters
  for key, value in state_dict.items():
    if "downsample" in key:
      if "reduction" in key:
        _lowercase						    =			reverse_correct_unfold_reduction_order(UpperCAmelCase_  )
      if "norm" in key:
        _lowercase						    =			reverse_correct_unfold_norm_order(UpperCAmelCase_  )

  model.load_state_dict(UpperCAmelCase_  )

  # verify on image
  _lowercase						    =			'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
  _lowercase						    =			Image.open(requests.get(UpperCAmelCase_      ,				stream=UpperCAmelCase_  ).raw  ).convert('RGB'  )

  _lowercase						    =			SegformerImageProcessor()
  _lowercase						    =			processor(UpperCAmelCase_      ,				return_tensors='pt'  ).pixel_values

  with torch.no_grad():
    _lowercase						    =			model(UpperCAmelCase_  )
    _lowercase						    =			outputs.logits

  print(logits.shape  )
  print('First values of logits:'      ,				logits[0, 0, :3, :3]  )
  # assert values
  if model_name == "upernet-swin-tiny":
    _lowercase						    =			torch.tensor(
        [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]  )
  elif model_name == "upernet-swin-small":
    _lowercase						    =			torch.tensor(
        [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]  )
  elif model_name == "upernet-swin-base":
    _lowercase						    =			torch.tensor(
        [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]  )
  elif model_name == "upernet-swin-large":
    _lowercase						    =			torch.tensor(
        [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]  )
  print('Logits:'      ,				outputs.logits[0, 0, :3, :3]  )
  assert torch.allclose(outputs.logits[0, 0, :3, :3]      ,				UpperCAmelCase_      ,				atol=1E-4  )
  print('Looks ok!'  )

  if pytorch_dump_folder_path is not None:
    print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""  )
    model.save_pretrained(UpperCAmelCase_  )
    print(F"""Saving processor to {pytorch_dump_folder_path}"""  )
    processor.save_pretrained(UpperCAmelCase_  )

  if push_to_hub:
    print(F"""Pushing model and processor for {model_name} to hub"""  )
    model.push_to_hub(F"""openmmlab/{model_name}"""  )
    processor.push_to_hub(F"""openmmlab/{model_name}"""  )


if __name__ == "__main__":
      snake_case								=							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""upernet-swin-tiny""",
          type=str,
          choices=[F"""upernet-swin-{size}""" for size in ["""tiny""", """small""", """base""", """large"""]],
          help="""Name of the Swin + UperNet model you\'d like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
      )

      snake_case								=							parser.parse_args()
      convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''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


