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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any]	)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      model.config

  SCREAMING_SNAKE_CASE__										=      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 ,     )
  SCREAMING_SNAKE_CASE__										=      MBartConfig(
      is_decoder=__UpperCamelCase ,     is_encoder_decoder=__UpperCamelCase ,     add_cross_attention=__UpperCamelCase ,     decoder_layers=original_config.decoder_layer ,     max_position_embeddings=original_config.max_position_embeddings ,     vocab_size=len(
          model.decoder.tokenizer	) ,     scale_embedding=__UpperCamelCase ,     add_final_layer_norm=__UpperCamelCase ,     )

  return encoder_config, decoder_config



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[str]	)							->					int:







  """simple docstring"""





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

    if name == "encoder.norm.weight":
      SCREAMING_SNAKE_CASE__										=      """encoder.layernorm.weight"""
    if name == "encoder.norm.bias":
      SCREAMING_SNAKE_CASE__										=      """encoder.layernorm.bias"""

  return name



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Union[str, Any]	)							->					Dict:







  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    SCREAMING_SNAKE_CASE__										=      orig_state_dict.pop(__UpperCamelCase	)

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

      if "weight" in key:
        SCREAMING_SNAKE_CASE__										=      val[:dim, :]
        SCREAMING_SNAKE_CASE__										=      val[dim : dim * 2, :]
        SCREAMING_SNAKE_CASE__										=      val[-dim:, :]
      else:
        SCREAMING_SNAKE_CASE__										=      val[:dim]
        SCREAMING_SNAKE_CASE__										=      val[dim : dim * 2]
        SCREAMING_SNAKE_CASE__										=      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:
      SCREAMING_SNAKE_CASE__										=      val

  return orig_state_dict



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       Dict=None ,     __UpperCamelCase    :       int=False	)							->					Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      DonutModel.from_pretrained(__UpperCamelCase	).eval()

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

  SCREAMING_SNAKE_CASE__										=      original_model.state_dict()
  SCREAMING_SNAKE_CASE__										=      convert_state_dict(__UpperCamelCase ,     __UpperCamelCase	)
  model.load_state_dict(__UpperCamelCase	)

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

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

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

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

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

  # verify decoder hidden states
  SCREAMING_SNAKE_CASE__										=      original_model(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	).logits
  SCREAMING_SNAKE_CASE__										=      model(__UpperCamelCase ,     decoder_input_ids=__UpperCamelCase	).logits
  assert torch.allclose(__UpperCamelCase ,     __UpperCamelCase ,     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(__UpperCamelCase	)
    processor.save_pretrained(__UpperCamelCase	)

  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__":
   __lowerCamelCase			: Tuple							   =		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.''',
   )

   __lowerCamelCase			: Tuple							   =		parser.parse_args()
   convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowerCamelCase			: Any							   =		abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int]	)							->					Any:







  """simple docstring"""





  from diffusers.utils.testing_utils import pytest_addoption_shared

  pytest_addoption_shared(__UpperCamelCase	)






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict	)							->					Optional[Any]:







  """simple docstring"""





  from diffusers.utils.testing_utils import pytest_terminal_summary_main

  SCREAMING_SNAKE_CASE__										=      terminalreporter.config.getoption("""--make-reports"""	)
  if make_reports:
    pytest_terminal_summary_main(__UpperCamelCase ,     id=__UpperCamelCase	)
