




import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__magic_name__  :  Tuple					=       logging.get_logger(__name__)





class     SCREAMING_SNAKE_CASE__		(_a			):
     def __init__(						self	:		List[str]       ,						*__lowerCamelCase	:		int       ,						**__lowerCamelCase	:		Any						):



       """simple docstring"""
       warnings.warn(
           '''The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
           ''' Please use DeformableDetrImageProcessor instead.'''       ,						__lowerCamelCase       ,						)
       super().__init__(*__lowerCamelCase       ,						**__lowerCamelCase						)







import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)
def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      model.config

  lowerCAmelCase__										=      DonutSwinConfig(
      image_size=original_config.input_size   , patch_size=4   , depths=original_config.encoder_layer   , num_heads=[4, 8, 16, 32]   , window_size=original_config.window_size   , embed_dim=1_28   , )
  lowerCAmelCase__										=      MBartConfig(
      is_decoder=__lowerCAmelCase   , is_encoder_decoder=__lowerCAmelCase   , add_cross_attention=__lowerCAmelCase   , decoder_layers=original_config.decoder_layer   , max_position_embeddings=original_config.max_position_embeddings   , vocab_size=len(
          model.decoder.tokenizer				)   , scale_embedding=__lowerCAmelCase   , add_final_layer_norm=__lowerCAmelCase   , )

  return encoder_config, decoder_config
def 		a_							( __lowerCAmelCase				):
  if "encoder.model" in name:
    lowerCAmelCase__										=      name.replace('''encoder.model'''   , '''encoder'''				)
  if "decoder.model" in name:
    lowerCAmelCase__										=      name.replace('''decoder.model'''   , '''decoder'''				)
  if "patch_embed.proj" in name:
    lowerCAmelCase__										=      name.replace('''patch_embed.proj'''   , '''embeddings.patch_embeddings.projection'''				)
  if "patch_embed.norm" in name:
    lowerCAmelCase__										=      name.replace('''patch_embed.norm'''   , '''embeddings.norm'''				)
  if name.startswith('''encoder'''				):
    if "layers" in name:
      lowerCAmelCase__										=      '''encoder.''' + name
    if "attn.proj" in name:
      lowerCAmelCase__										=      name.replace('''attn.proj'''   , '''attention.output.dense'''				)
    if "attn" in name and "mask" not in name:
      lowerCAmelCase__										=      name.replace('''attn'''   , '''attention.self'''				)
    if "norm1" in name:
      lowerCAmelCase__										=      name.replace('''norm1'''   , '''layernorm_before'''				)
    if "norm2" in name:
      lowerCAmelCase__										=      name.replace('''norm2'''   , '''layernorm_after'''				)
    if "mlp.fc1" in name:
      lowerCAmelCase__										=      name.replace('''mlp.fc1'''   , '''intermediate.dense'''				)
    if "mlp.fc2" in name:
      lowerCAmelCase__										=      name.replace('''mlp.fc2'''   , '''output.dense'''				)

    if name == "encoder.norm.weight":
      lowerCAmelCase__										=      '''encoder.layernorm.weight'''
    if name == "encoder.norm.bias":
      lowerCAmelCase__										=      '''encoder.layernorm.bias'''

  return name
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  for key in orig_state_dict.copy().keys():
    lowerCAmelCase__										=      orig_state_dict.pop(__lowerCAmelCase				)

    if "qkv" in key:
      lowerCAmelCase__										=      key.split('''.'''				)
      lowerCAmelCase__										=      int(key_split[3]				)
      lowerCAmelCase__										=      int(key_split[5]				)
      lowerCAmelCase__										=      model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        lowerCAmelCase__										=      val[:dim, :]
        lowerCAmelCase__										=      val[dim : dim * 2, :]
        lowerCAmelCase__										=      val[-dim:, :]
      else:
        lowerCAmelCase__										=      val[:dim]
        lowerCAmelCase__										=      val[dim : dim * 2]
        lowerCAmelCase__										=      val[-dim:]
    elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
      # HuggingFace implementation doesn't use attn_mask buffer
      # and model doesn't use final LayerNorms for the encoder
      pass
    else:
      lowerCAmelCase__										=      val

  return orig_state_dict
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase=None   , __lowerCAmelCase=False				):
  # load original model
  lowerCAmelCase__										=      DonutModel.from_pretrained(__lowerCAmelCase				).eval()

  # load HuggingFace model
  lowerCAmelCase__				,      lowerCAmelCase__										=      get_configs(__lowerCAmelCase				)
  lowerCAmelCase__										=      DonutSwinModel(__lowerCAmelCase				)
  lowerCAmelCase__										=      MBartForCausalLM(__lowerCAmelCase				)
  lowerCAmelCase__										=      VisionEncoderDecoderModel(encoder=__lowerCAmelCase   , decoder=__lowerCAmelCase				)
  model.eval()

  lowerCAmelCase__										=      original_model.state_dict()
  lowerCAmelCase__										=      convert_state_dict(__lowerCAmelCase   , __lowerCAmelCase				)
  model.load_state_dict(__lowerCAmelCase				)

  # verify results on scanned document
  lowerCAmelCase__										=      load_dataset('''hf-internal-testing/example-documents'''				)
  lowerCAmelCase__										=      dataset['''test'''][0]['''image'''].convert('''RGB'''				)

  lowerCAmelCase__										=      XLMRobertaTokenizerFast.from_pretrained(__lowerCAmelCase   , from_slow=__lowerCAmelCase				)
  lowerCAmelCase__										=      DonutImageProcessor(
      do_align_long_axis=original_model.config.align_long_axis   , size=original_model.config.input_size[::-1]				)
  lowerCAmelCase__										=      DonutProcessor(__lowerCAmelCase   , __lowerCAmelCase				)
  lowerCAmelCase__										=      processor(__lowerCAmelCase   , return_tensors='''pt'''				).pixel_values

  if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
    lowerCAmelCase__										=      '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
    lowerCAmelCase__										=      '''When is the coffee break?'''
    lowerCAmelCase__										=      task_prompt.replace('''{user_input}'''   , __lowerCAmelCase				)
  elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
    lowerCAmelCase__										=      '''<s_rvlcdip>'''
  elif model_name in [
      "naver-clova-ix/donut-base-finetuned-cord-v1",
      "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
  ]:
    lowerCAmelCase__										=      '''<s_cord>'''
  elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
    lowerCAmelCase__										=      '''s_cord-v2>'''
  elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
    lowerCAmelCase__										=      '''<s_zhtrainticket>'''
  elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
    # use a random prompt
    lowerCAmelCase__										=      '''hello world'''
  else:
    raise ValueError('''Model name not supported'''				)
  lowerCAmelCase__										=      original_model.decoder.tokenizer(__lowerCAmelCase   , add_special_tokens=__lowerCAmelCase   , return_tensors='''pt'''				)[
      '''input_ids'''
  ]

  lowerCAmelCase__										=      original_model.encoder.model.patch_embed(__lowerCAmelCase				)
  lowerCAmelCase__				,      lowerCAmelCase__										=      model.encoder.embeddings(__lowerCAmelCase				)
  assert torch.allclose(__lowerCAmelCase   , __lowerCAmelCase   , atol=1E-3				)

  # verify encoder hidden states
  lowerCAmelCase__										=      original_model.encoder(__lowerCAmelCase				)
  lowerCAmelCase__										=      model.encoder(__lowerCAmelCase				).last_hidden_state
  assert torch.allclose(__lowerCAmelCase   , __lowerCAmelCase   , atol=1E-2				)

  # verify decoder hidden states
  lowerCAmelCase__										=      original_model(__lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				).logits
  lowerCAmelCase__										=      model(__lowerCAmelCase   , decoder_input_ids=__lowerCAmelCase				).logits
  assert torch.allclose(__lowerCAmelCase   , __lowerCAmelCase   , atol=1E-3				)
  print('''Looks ok!'''				)

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

  if push_to_hub:
    model.push_to_hub('''nielsr/''' + model_name.split('''/'''				)[-1]   , commit_message='''Update model'''				)
    processor.push_to_hub('''nielsr/''' + model_name.split('''/'''				)[-1]   , commit_message='''Update model'''				)


if __name__ == "__main__":
      __magic_name__  :  Union[str, Any]					=       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""naver-clova-ix/donut-base-finetuned-docvqa""",
          required=False,
          type=str,
          help="""Name of the original model you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          required=False,
          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 and processor to the 🤗 hub.""",
      )

      __magic_name__  :  Any					=       parser.parse_args()
      convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

