

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase    				=     logging.get_logger(__name__)







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    DPTConfig(embedding_type="hybrid"				)

     if "large" in checkpoint_url:
          _lowerCAmelCase   :					Optional[int]        =    1024
          _lowerCAmelCase   :					Optional[Any]        =    4096
          _lowerCAmelCase   :					Union[str, Any]        =    24
          _lowerCAmelCase   :					List[str]        =    16
          _lowerCAmelCase   :					Dict        =    [5, 11, 17, 23]
          _lowerCAmelCase   :					Optional[int]        =    [256, 512, 1024, 1024]
          _lowerCAmelCase   :					Tuple        =    (1, 384, 384)

     if "nyu" or "midas" in checkpoint_url:
          _lowerCAmelCase   :					Any        =    768
          _lowerCAmelCase   :					List[str]        =    [1, 1, 1, 0.5]
          _lowerCAmelCase   :					int        =    [256, 512, 768, 768]
          _lowerCAmelCase   :					Any        =    150
          _lowerCAmelCase   :					int        =    16
          _lowerCAmelCase   :					Dict        =    (1, 384, 384)
          _lowerCAmelCase   :					List[Any]        =    False
          _lowerCAmelCase   :					Optional[Any]        =    "project"

     if "ade" in checkpoint_url:
          _lowerCAmelCase   :					Union[str, Any]        =    True
          _lowerCAmelCase   :					Any        =    768
          _lowerCAmelCase   :					int        =    [1, 1, 1, 0.5]
          _lowerCAmelCase   :					Any        =    150
          _lowerCAmelCase   :					str        =    16
          _lowerCAmelCase   :					Optional[int]        =    "huggingface/label-files"
          _lowerCAmelCase   :					List[Any]        =    "ade20k-id2label.json"
          _lowerCAmelCase   :					Dict        =    json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							repo_type="dataset"				)				)		,							"r"				)				)
          _lowerCAmelCase   :					List[Any]        =    {int(SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
          _lowerCAmelCase   :					Any        =    idalabel
          _lowerCAmelCase   :					Dict        =    {v: k for k, v in idalabel.items()}
          _lowerCAmelCase   :					Tuple        =    [1, 150, 480, 480]

     return config, expected_shape







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Dict        =    ["pretrained.model.head.weight", "pretrained.model.head.bias"]
     for k in ignore_keys:
          state_dict.pop(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      int:

     """simple docstring"""







     if (
         "pretrained.model" in name
         and "cls_token" not in name
         and "pos_embed" not in name
         and "patch_embed" not in name
     ):
          _lowerCAmelCase   :					Optional[Any]        =    name.replace("pretrained.model"		,							"dpt.encoder"				)
     if "pretrained.model" in name:
          _lowerCAmelCase   :					Optional[Any]        =    name.replace("pretrained.model"		,							"dpt.embeddings"				)
     if "patch_embed" in name:
          _lowerCAmelCase   :					Dict        =    name.replace("patch_embed"		,							""				)
     if "pos_embed" in name:
          _lowerCAmelCase   :					Dict        =    name.replace("pos_embed"		,							"position_embeddings"				)
     if "attn.proj" in name:
          _lowerCAmelCase   :					Optional[int]        =    name.replace("attn.proj"		,							"attention.output.dense"				)
     if "proj" in name and "project" not in name:
          _lowerCAmelCase   :					List[str]        =    name.replace("proj"		,							"projection"				)
     if "blocks" in name:
          _lowerCAmelCase   :					int        =    name.replace("blocks"		,							"layer"				)
     if "mlp.fc1" in name:
          _lowerCAmelCase   :					Tuple        =    name.replace("mlp.fc1"		,							"intermediate.dense"				)
     if "mlp.fc2" in name:
          _lowerCAmelCase   :					Tuple        =    name.replace("mlp.fc2"		,							"output.dense"				)
     if "norm1" in name and "backbone" not in name:
          _lowerCAmelCase   :					List[Any]        =    name.replace("norm1"		,							"layernorm_before"				)
     if "norm2" in name and "backbone" not in name:
          _lowerCAmelCase   :					Union[str, Any]        =    name.replace("norm2"		,							"layernorm_after"				)
     if "scratch.output_conv" in name:
          _lowerCAmelCase   :					int        =    name.replace("scratch.output_conv"		,							"head"				)
     if "scratch" in name:
          _lowerCAmelCase   :					str        =    name.replace("scratch"		,							"neck"				)
     if "layer1_rn" in name:
          _lowerCAmelCase   :					Any        =    name.replace("layer1_rn"		,							"convs.0"				)
     if "layer2_rn" in name:
          _lowerCAmelCase   :					Union[str, Any]        =    name.replace("layer2_rn"		,							"convs.1"				)
     if "layer3_rn" in name:
          _lowerCAmelCase   :					List[str]        =    name.replace("layer3_rn"		,							"convs.2"				)
     if "layer4_rn" in name:
          _lowerCAmelCase   :					Any        =    name.replace("layer4_rn"		,							"convs.3"				)
     if "refinenet" in name:
          _lowerCAmelCase   :					Dict        =    int(name[len("neck.refinenet"				) : len("neck.refinenet"				) + 1]				)
          # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
          _lowerCAmelCase   :					Any        =    name.replace(f'''refinenet{layer_idx}'''		,							f'''fusion_stage.layers.{abs(layer_idx-4				)}'''				)
     if "out_conv" in name:
          _lowerCAmelCase   :					Any        =    name.replace("out_conv"		,							"projection"				)
     if "resConfUnit1" in name:
          _lowerCAmelCase   :					Optional[int]        =    name.replace("resConfUnit1"		,							"residual_layer1"				)
     if "resConfUnit2" in name:
          _lowerCAmelCase   :					int        =    name.replace("resConfUnit2"		,							"residual_layer2"				)
     if "conv1" in name:
          _lowerCAmelCase   :					Union[str, Any]        =    name.replace("conv1"		,							"convolution1"				)
     if "conv2" in name:
          _lowerCAmelCase   :					Optional[Any]        =    name.replace("conv2"		,							"convolution2"				)
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          _lowerCAmelCase   :					Union[str, Any]        =    name.replace("pretrained.act_postprocess1.0.project.0"		,							"neck.reassemble_stage.readout_projects.0.0"				)
     if "pretrained.act_postprocess2.0.project.0" in name:
          _lowerCAmelCase   :					Dict        =    name.replace("pretrained.act_postprocess2.0.project.0"		,							"neck.reassemble_stage.readout_projects.1.0"				)
     if "pretrained.act_postprocess3.0.project.0" in name:
          _lowerCAmelCase   :					Optional[Any]        =    name.replace("pretrained.act_postprocess3.0.project.0"		,							"neck.reassemble_stage.readout_projects.2.0"				)
     if "pretrained.act_postprocess4.0.project.0" in name:
          _lowerCAmelCase   :					Union[str, Any]        =    name.replace("pretrained.act_postprocess4.0.project.0"		,							"neck.reassemble_stage.readout_projects.3.0"				)

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          _lowerCAmelCase   :					Tuple        =    name.replace("pretrained.act_postprocess1.3"		,							"neck.reassemble_stage.layers.0.projection"				)
     if "pretrained.act_postprocess1.4" in name:
          _lowerCAmelCase   :					int        =    name.replace("pretrained.act_postprocess1.4"		,							"neck.reassemble_stage.layers.0.resize"				)
     if "pretrained.act_postprocess2.3" in name:
          _lowerCAmelCase   :					List[str]        =    name.replace("pretrained.act_postprocess2.3"		,							"neck.reassemble_stage.layers.1.projection"				)
     if "pretrained.act_postprocess2.4" in name:
          _lowerCAmelCase   :					Union[str, Any]        =    name.replace("pretrained.act_postprocess2.4"		,							"neck.reassemble_stage.layers.1.resize"				)
     if "pretrained.act_postprocess3.3" in name:
          _lowerCAmelCase   :					Dict        =    name.replace("pretrained.act_postprocess3.3"		,							"neck.reassemble_stage.layers.2.projection"				)
     if "pretrained.act_postprocess4.3" in name:
          _lowerCAmelCase   :					Optional[Any]        =    name.replace("pretrained.act_postprocess4.3"		,							"neck.reassemble_stage.layers.3.projection"				)
     if "pretrained.act_postprocess4.4" in name:
          _lowerCAmelCase   :					Tuple        =    name.replace("pretrained.act_postprocess4.4"		,							"neck.reassemble_stage.layers.3.resize"				)
     if "pretrained" in name:
          _lowerCAmelCase   :					List[str]        =    name.replace("pretrained"		,							"dpt"				)
     if "bn" in name:
          _lowerCAmelCase   :					Optional[int]        =    name.replace("bn"		,							"batch_norm"				)
     if "head" in name:
          _lowerCAmelCase   :					List[str]        =    name.replace("head"		,							"head.head"				)
     if "encoder.norm" in name:
          _lowerCAmelCase   :					Tuple        =    name.replace("encoder.norm"		,							"layernorm"				)
     if "auxlayer" in name:
          _lowerCAmelCase   :					Any        =    name.replace("auxlayer"		,							"auxiliary_head.head"				)
     if "backbone" in name:
          _lowerCAmelCase   :					int        =    name.replace("backbone"		,							"backbone.bit.encoder"				)

     if ".." in name:
          _lowerCAmelCase   :					Tuple        =    name.replace(".."		,							"."				)

     if "stem.conv" in name:
          _lowerCAmelCase   :					List[Any]        =    name.replace("stem.conv"		,							"bit.embedder.convolution"				)
     if "blocks" in name:
          _lowerCAmelCase   :					Any        =    name.replace("blocks"		,							"layers"				)
     if "convolution" in name and "backbone" in name:
          _lowerCAmelCase   :					List[str]        =    name.replace("convolution"		,							"conv"				)
     if "layer" in name and "backbone" in name:
          _lowerCAmelCase   :					Dict        =    name.replace("layer"		,							"layers"				)
     if "backbone.bit.encoder.bit" in name:
          _lowerCAmelCase   :					str        =    name.replace("backbone.bit.encoder.bit"		,							"backbone.bit"				)
     if "embedder.conv" in name:
          _lowerCAmelCase   :					Optional[int]        =    name.replace("embedder.conv"		,							"embedder.convolution"				)
     if "backbone.bit.encoder.stem.norm" in name:
          _lowerCAmelCase   :					List[Any]        =    name.replace("backbone.bit.encoder.stem.norm"		,							"backbone.bit.embedder.norm"				)
     return name







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     for i in range(config.num_hidden_layers				):
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          _lowerCAmelCase   :					int        =    state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''				)
          _lowerCAmelCase   :					List[Any]        =    state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''				)
          # next, add query, keys and values (in that order) to the state dict
          _lowerCAmelCase   :					Dict        =    in_proj_weight[: config.hidden_size, :]
          _lowerCAmelCase   :					Optional[int]        =    in_proj_bias[: config.hidden_size]
          _lowerCAmelCase   :					Any        =    in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _lowerCAmelCase   :					Dict        =    in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          _lowerCAmelCase   :					Dict        =    in_proj_weight[
              -config.hidden_size :, :
          ]
          _lowerCAmelCase   :					List[Any]        =    in_proj_bias[-config.hidden_size :]







def 	_snake_case		(    )       ->      Optional[int]:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    "http://images.cocodataset.org/val2017/000000039769.jpg"
     _lowerCAmelCase   :					Optional[Any]        =    Image.open(requests.get(SCREAMING_SNAKE_CASE		,							stream=SCREAMING_SNAKE_CASE				).raw				)
     return im







@torch.no_grad()
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Dict:

     """simple docstring"""







     _lowerCAmelCase      ,			_lowerCAmelCase   :					List[Any]        =    get_dpt_config(SCREAMING_SNAKE_CASE				)
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     _lowerCAmelCase   :					int        =    torch.load(SCREAMING_SNAKE_CASE		,							map_location="cpu"				)
     # remove certain keys
     remove_ignore_keys_(SCREAMING_SNAKE_CASE				)
     # rename keys
     for key in state_dict.copy().keys():
          _lowerCAmelCase   :					Tuple        =    state_dict.pop(SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					Optional[Any]        =    val
     # read in qkv matrices
     read_in_q_k_v(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

     # load HuggingFace model
     _lowerCAmelCase   :					List[str]        =    DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE				) if "ade" in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE				)
     model.load_state_dict(SCREAMING_SNAKE_CASE				)
     model.eval()

     # Check outputs on an image
     _lowerCAmelCase   :					Any        =    480 if "ade" in checkpoint_url else 384
     _lowerCAmelCase   :					Dict        =    DPTImageProcessor(size=SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					Optional[int]        =    prepare_img()
     _lowerCAmelCase   :					Dict        =    image_processor(SCREAMING_SNAKE_CASE		,							return_tensors="pt"				)

     # forward pass
     _lowerCAmelCase   :					str        =    model(**SCREAMING_SNAKE_CASE				).logits if "ade" in checkpoint_url else model(**SCREAMING_SNAKE_CASE				).predicted_depth

     if show_prediction:
          _lowerCAmelCase   :					Optional[int]        =    (
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1				)		,							size=(image.size[1], image.size[0])		,							mode="bicubic"		,							align_corners=SCREAMING_SNAKE_CASE		,							)
              .squeeze()
              .cpu()
              .numpy()
          )

          Image.fromarray((prediction / prediction.max()) * 255				).show()

     if pytorch_dump_folder_path is not None:
          Path(SCREAMING_SNAKE_CASE				).mkdir(exist_ok=SCREAMING_SNAKE_CASE				)
          print(f'''Saving model to {pytorch_dump_folder_path}'''				)
          model.save_pretrained(SCREAMING_SNAKE_CASE				)
          print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
          image_processor.save_pretrained(SCREAMING_SNAKE_CASE				)

     if push_to_hub:
          model.push_to_hub("ybelkada/dpt-hybrid-midas"				)
          image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"				)


if __name__ == "__main__":
  __UpperCAmelCase    				=     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--checkpoint_url',
      default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
      type=str,
      help='URL of the original DPT checkpoint you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default=None,
      type=str,
      required=False,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument(
      '--push_to_hub',
      action='store_true',
  )
  parser.add_argument(
      '--model_name',
      default='dpt-large',
      type=str,
      help='Name of the model, in case you\'re pushing to the hub.',
  )
  parser.add_argument(
      '--show_prediction',
      action='store_true',
  )

  __UpperCAmelCase    				=     parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )









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

__UpperCAmelCase    				=     logging.get_logger(__name__)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     return [
         int(1000 * (box[0] / width)				),
         int(1000 * (box[1] / height)				),
         int(1000 * (box[2] / width)				),
         int(1000 * (box[3] / height)				),
     ]







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = None				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else ""

     # apply OCR
     _lowerCAmelCase   :					List[str]        =    to_pil_image(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    pil_image.size
     _lowerCAmelCase   :					Union[str, Any]        =    pytesseract.image_to_data(SCREAMING_SNAKE_CASE		,							lang=SCREAMING_SNAKE_CASE		,							output_type="dict"		,							config=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    data["text"], data["left"], data["top"], data["width"], data["height"]

     # filter empty words and corresponding coordinates
     _lowerCAmelCase   :					Dict        =    [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if not word.strip()]
     _lowerCAmelCase   :					Any        =    [word for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Dict        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[int]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]

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

     # finally, normalize the bounding boxes
     _lowerCAmelCase   :					Dict        =    []
     for box in actual_boxes:
          normalized_boxes.append(normalize_box(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)				)

     assert len(SCREAMING_SNAKE_CASE				) == len(SCREAMING_SNAKE_CASE				), "Not as many words as there are bounding boxes"

     return words, normalized_boxes





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

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






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					int        =    size if size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )

      _lowerCAmelCase   :					Any        =    do_resize
      _lowerCAmelCase   :					Any        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Optional[int]        =    apply_ocr
      _lowerCAmelCase   :					Dict        =    ocr_lang
      _lowerCAmelCase   :					Optional[int]        =    tesseract_config

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






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    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()}'''       )
      _lowerCAmelCase   :					Dict        =    (size["height"], size["width"])
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )


 def    __magic_name__       (		self :	Dict     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[Any]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					Union[str, Any]        =    apply_ocr if apply_ocr is not None else self.apply_ocr
      _lowerCAmelCase   :					Tuple        =    ocr_lang if ocr_lang is not None else self.ocr_lang
      _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else self.tesseract_config

      _lowerCAmelCase   :					Union[str, 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."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Dict        =    [to_numpy_array(A_       ) for image in images]

      if apply_ocr:
           requires_backends(self     ,						"pytesseract"       )
           _lowerCAmelCase   :					Any        =    []
           _lowerCAmelCase   :					List[Any]        =    []
           for image in images:
                _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    apply_tesseract(A_     ,						A_     ,						A_       )
                words_batch.append(A_       )
                boxes_batch.append(A_       )

      if do_resize:
           _lowerCAmelCase   :					Union[str, Any]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      # flip color channels from RGB to BGR (as Detectron2 requires this)
      _lowerCAmelCase   :					Optional[int]        =    [flip_channel_order(A_       ) for image in images]
      _lowerCAmelCase   :					List[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    BatchFeature(data={"pixel_values": images}     ,						tensor_type=A_       )

      if apply_ocr:
           _lowerCAmelCase   :					Tuple        =    words_batch
           _lowerCAmelCase   :					Any        =    boxes_batch
      return data






