"""simple docstring"""


import argparse
import json
from pathlib import Path

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

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


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






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      SwinConfig(
      embed_dim=1_92   ,depths=(2, 2, 18, 2)   ,num_heads=(6, 12, 24, 48)   ,window_size=12   ,out_features=["""stage2""", """stage3""", """stage4"""]   ,)

  _UpperCAmelCase						 =      DetaConfig(
      backbone_config=_lowerCamelCase   ,num_queries=9_00   ,encoder_ffn_dim=20_48   ,decoder_ffn_dim=20_48   ,num_feature_levels=5   ,assign_first_stage=_lowerCamelCase   ,with_box_refine=_lowerCamelCase   ,two_stage=_lowerCamelCase   ,)

  # set labels
  _UpperCAmelCase						 =      'huggingface/label-files'
  if "o365" in model_name:
    _UpperCAmelCase						 =      3_66
    _UpperCAmelCase						 =      'object365-id2label.json'
  else:
    _UpperCAmelCase						 =      91
    _UpperCAmelCase						 =      'coco-detection-id2label.json'

  _UpperCAmelCase						 =      num_labels
  _UpperCAmelCase						 =      json.load(open(cached_download(hf_hub_url(_lowerCamelCase   ,_lowerCamelCase   ,repo_type="""dataset"""		)		)   ,"""r"""		)		)
  _UpperCAmelCase						 =      {int(_lowerCamelCase		): v for k, v in idalabel.items()}
  _UpperCAmelCase						 =      idalabel
  _UpperCAmelCase						 =      {v: k for k, v in idalabel.items()}

  return config






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      []

  # stem
  # fmt: off
  rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")		)
  rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")		)
  rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")		)
  rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.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.0.body.layers.{i}.blocks.{j}.norm1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')		)

    if i < 3:
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.reduction.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.bias''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')		)

  rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")		)
  rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")		)
  rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")		)
  rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")		)
  rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")		)
  rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")		)

  # transformer encoder
  for i in range(config.encoder_layers		):
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')		)

  # transformer decoder
  for i in range(config.decoder_layers		):
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')		)

  # fmt: on

  return rename_keys






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      dct.pop(_lowerCamelCase		)
  _UpperCAmelCase						 =      val






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      [int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
  for i in range(len(backbone_config.depths		)		):
    _UpperCAmelCase						 =      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)
      _UpperCAmelCase						 =      state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''		)
      _UpperCAmelCase						 =      state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''		)
      # next, add query, keys and values (in that order) to the state dict
      _UpperCAmelCase						 =      in_proj_weight[:dim, :]
      _UpperCAmelCase						 =      in_proj_bias[: dim]
      _UpperCAmelCase						 =      in_proj_weight[
          dim : dim * 2, :
      ]
      _UpperCAmelCase						 =      in_proj_bias[
          dim : dim * 2
      ]
      _UpperCAmelCase						 =      in_proj_weight[
          -dim :, :
      ]
      _UpperCAmelCase						 =      in_proj_bias[-dim :]
      # fmt: on






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      config.d_model
  for i in range(config.decoder_layers		):
    # read in weights + bias of input projection layer of self-attention
    _UpperCAmelCase						 =      state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''		)
    _UpperCAmelCase						 =      state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''		)
    # next, add query, keys and values (in that order) to the state dict
    _UpperCAmelCase						 =      in_proj_weight[:hidden_size, :]
    _UpperCAmelCase						 =      in_proj_bias[:hidden_size]
    _UpperCAmelCase						 =      in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    _UpperCAmelCase						 =      in_proj_bias[hidden_size : hidden_size * 2]
    _UpperCAmelCase						 =      in_proj_weight[-hidden_size:, :]
    _UpperCAmelCase						 =      in_proj_bias[-hidden_size:]






def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      'http://images.cocodataset.org/val2017/000000039769.jpg'
  _UpperCAmelCase						 =      Image.open(requests.get(_lowerCamelCase   ,stream=_lowerCamelCase		).raw		)

  return im






@torch.no_grad()
def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      get_deta_config(_lowerCamelCase		)

  # load original state dict
  if model_name == "deta-swin-large":
    _UpperCAmelCase						 =      hf_hub_download(repo_id="""nielsr/deta-checkpoints"""   ,filename="""adet_swin_ft.pth"""		)
  elif model_name == "deta-swin-large-o365":
    _UpperCAmelCase						 =      hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""   ,filename="""deta_swin_pt_o365.pth"""		)
  else:
    raise ValueError(f'''Model name {model_name} not supported'''		)

  _UpperCAmelCase						 =      torch.load(_lowerCamelCase   ,map_location="""cpu"""		)['model']

  # original state dict
  for name, param in state_dict.items():
    print(_lowerCamelCase   ,param.shape		)

  # rename keys
  _UpperCAmelCase						 =      create_rename_keys(_lowerCamelCase		)
  for src, dest in rename_keys:
    rename_key(_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase		)
  read_in_swin_q_k_v(_lowerCamelCase   ,config.backbone_config		)
  read_in_decoder_q_k_v(_lowerCamelCase   ,_lowerCamelCase		)

  # fix some prefixes
  for key in state_dict.copy().keys():
    if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
      _UpperCAmelCase						 =      state_dict.pop(_lowerCamelCase		)
      _UpperCAmelCase						 =      val
    if "input_proj" in key:
      _UpperCAmelCase						 =      state_dict.pop(_lowerCamelCase		)
      _UpperCAmelCase						 =      val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      _UpperCAmelCase						 =      state_dict.pop(_lowerCamelCase		)
      _UpperCAmelCase						 =      val

    # finally, create HuggingFace model and load state dict
  _UpperCAmelCase						 =      DetaForObjectDetection(_lowerCamelCase		)
  model.load_state_dict(_lowerCamelCase		)
  model.eval()

  _UpperCAmelCase						 =      'cuda' if torch.cuda.is_available() else 'cpu'
  model.to(_lowerCamelCase		)

  # load image processor
  _UpperCAmelCase						 =      DetaImageProcessor(format="""coco_detection"""		)

  # verify our conversion on image
  _UpperCAmelCase						 =      prepare_img()
  _UpperCAmelCase						 =      processor(images=_lowerCamelCase   ,return_tensors="""pt"""		)
  _UpperCAmelCase						 =      encoding['pixel_values']
  _UpperCAmelCase						 =      model(pixel_values.to(_lowerCamelCase		)		)

  # verify logits
  print("""Logits:"""   ,outputs.logits[0, :3, :3]		)
  print("""Boxes:"""   ,outputs.pred_boxes[0, :3, :3]		)
  if model_name == "deta-swin-large":
    _UpperCAmelCase						 =      torch.tensor(
        [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]		)
    _UpperCAmelCase						 =      torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]		)
  elif model_name == "deta-swin-large-o365":
    _UpperCAmelCase						 =      torch.tensor(
        [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]		)
    _UpperCAmelCase						 =      torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]		)
  assert torch.allclose(outputs.logits[0, :3, :3]   ,expected_logits.to(_lowerCamelCase		)   ,atol=1E-4		)
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,expected_boxes.to(_lowerCamelCase		)   ,atol=1E-4		)
  print("""Everything ok!"""		)

  if pytorch_dump_folder_path:
    # Save model and processor
    logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''		)
    Path(_lowerCamelCase		).mkdir(exist_ok=_lowerCamelCase		)
    model.save_pretrained(_lowerCamelCase		)
    processor.save_pretrained(_lowerCamelCase		)

  # Push to hub
  if push_to_hub:
    print("""Pushing model and processor to hub..."""		)
    model.push_to_hub(f'''jozhang97/{model_name}'''		)
    processor.push_to_hub(f'''jozhang97/{model_name}'''		)


if __name__ == "__main__":
    UpperCAmelCase__  		=						argparse.ArgumentParser()

    parser.add_argument(
        """--model_name""",
        type=str,
        default="""deta-swin-large""",
        choices=["""deta-swin-large""", """deta-swin-large-o365"""],
        help="""Name of the model you'd like to convert.""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default=None,
        type=str,
        help="""Path to the folder to output PyTorch model.""",
    )
    parser.add_argument(
        """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
    )
    UpperCAmelCase__  		=						parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_pytesseract_available():
    from PIL import Image

    from transformers import LayoutLMvaImageProcessor


class        a				(		unittest.TestCase   ):


    def __init__(					self	:  int   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  str=7   ,				__lowerCAmelCase	:  Dict=3   ,				__lowerCAmelCase	:  List[Any]=18   ,				__lowerCAmelCase	:  int=30   ,				__lowerCAmelCase	:  Any=400   ,				__lowerCAmelCase	:  Tuple=True   ,				__lowerCAmelCase	:  str=None   ,				__lowerCAmelCase	:  Optional[Any]=True   ,				):
      _UpperCAmelCase						 =      size if size is not None else {"""height""": 18, """width""": 18}
      _UpperCAmelCase						 =      parent
      _UpperCAmelCase						 =      batch_size
      _UpperCAmelCase						 =      num_channels
      _UpperCAmelCase						 =      image_size
      _UpperCAmelCase						 =      min_resolution
      _UpperCAmelCase						 =      max_resolution
      _UpperCAmelCase						 =      do_resize
      _UpperCAmelCase						 =      size
      _UpperCAmelCase						 =      apply_ocr


    def      lowerCAmelCase_  (					self	:  int					):
      return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}



@require_torch
@require_pytesseract
class        a				(		lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  List[str]     							=							LayoutLMvaImageProcessor if is_pytesseract_available() else None


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      LayoutLMvaImageProcessingTester(self					)


    @property
    def      lowerCAmelCase_  (					self	:  Dict					):
      return self.image_processor_tester.prepare_image_processor_dict()


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(__lowerCAmelCase   ,				"""do_resize"""					)					)
      self.assertTrue(hasattr(__lowerCAmelCase   ,				"""size"""					)					)
      self.assertTrue(hasattr(__lowerCAmelCase   ,				"""apply_ocr"""					)					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size   ,				{"""height""": 18, """width""": 18}					)

      _UpperCAmelCase						 =      self.image_processing_class.from_dict(self.image_processor_dict   ,				size=42					)
      self.assertEqual(image_processor.size   ,				{"""height""": 42, """width""": 42}					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      pass


    def      lowerCAmelCase_  (					self	:  List[str]					):
      # Initialize image_processing
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      _UpperCAmelCase						 =      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__lowerCAmelCase					)
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase   ,				Image.Image					)

      # Test not batched input
      _UpperCAmelCase						 =      image_processing(image_inputs[0]   ,				return_tensors="""pt"""					)
      self.assertEqual(
          encoding.pixel_values.shape   ,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )   ,				)

      self.assertIsInstance(encoding.words   ,				__lowerCAmelCase					)
      self.assertIsInstance(encoding.boxes   ,				__lowerCAmelCase					)

      # Test batched
      _UpperCAmelCase						 =      image_processing(__lowerCAmelCase   ,				return_tensors="""pt"""					).pixel_values
      self.assertEqual(
          encoded_images.shape   ,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )   ,				)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      # Initialize image_processing
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      _UpperCAmelCase						 =      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__lowerCAmelCase   ,				numpify=__lowerCAmelCase					)
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase   ,				np.ndarray					)

      # Test not batched input
      _UpperCAmelCase						 =      image_processing(image_inputs[0]   ,				return_tensors="""pt"""					).pixel_values
      self.assertEqual(
          encoded_images.shape   ,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )   ,				)

      # Test batched
      _UpperCAmelCase						 =      image_processing(__lowerCAmelCase   ,				return_tensors="""pt"""					).pixel_values
      self.assertEqual(
          encoded_images.shape   ,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )   ,				)


    def      lowerCAmelCase_  (					self	:  Any					):
      # Initialize image_processing
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      _UpperCAmelCase						 =      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__lowerCAmelCase   ,				torchify=__lowerCAmelCase					)
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase   ,				torch.Tensor					)

      # Test not batched input
      _UpperCAmelCase						 =      image_processing(image_inputs[0]   ,				return_tensors="""pt"""					).pixel_values
      self.assertEqual(
          encoded_images.shape   ,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )   ,				)

      # Test batched
      _UpperCAmelCase						 =      image_processing(__lowerCAmelCase   ,				return_tensors="""pt"""					).pixel_values
      self.assertEqual(
          encoded_images.shape   ,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["""height"""],
              self.image_processor_tester.size["""width"""],
          )   ,				)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      # with apply_OCR = True
      _UpperCAmelCase						 =      LayoutLMvaImageProcessor()

      from datasets import load_dataset

      _UpperCAmelCase						 =      load_dataset("""hf-internal-testing/fixtures_docvqa"""   ,				split="""test"""					)

      _UpperCAmelCase						 =      Image.open(ds[0]["""file"""]					).convert("""RGB"""					)

      _UpperCAmelCase						 =      image_processing(__lowerCAmelCase   ,				return_tensors="""pt"""					)

      self.assertEqual(encoding.pixel_values.shape   ,				(1, 3, 224, 224)					)
      self.assertEqual(len(encoding.words					)   ,				len(encoding.boxes					)					)

      # fmt: off
      # the words and boxes were obtained with Tesseract 4.1.1
      _UpperCAmelCase						 =      [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
      _UpperCAmelCase						 =      [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
      # fmt: on

      self.assertListEqual(encoding.words   ,				__lowerCAmelCase					)
      self.assertListEqual(encoding.boxes   ,				__lowerCAmelCase					)

      # with apply_OCR = False
      _UpperCAmelCase						 =      LayoutLMvaImageProcessor(apply_ocr=__lowerCAmelCase					)

      _UpperCAmelCase						 =      image_processing(__lowerCAmelCase   ,				return_tensors="""pt"""					)

      self.assertEqual(encoding.pixel_values.shape   ,				(1, 3, 224, 224)					)
