




from scipy.stats import pearsonr

import datasets


__magic_name__  :  Union[str, Any]					=       """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
"""


__magic_name__  :  Tuple					=       """
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


__magic_name__  :  int					=       """
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,					_KWARGS_DESCRIPTION			)
class     SCREAMING_SNAKE_CASE__		(datasets.Metric			):
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       return datasets.MetricInfo(
           description=_DESCRIPTION       ,						citation=_CITATION       ,						inputs_description=_KWARGS_DESCRIPTION       ,						features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''float'''						),
                   '''references''': datasets.Value('''float'''						),
               }						)       ,						reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']       ,						)



     def        A__      (						self	:		Any       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Dict=False						):



       """simple docstring"""
       if return_pvalue:
         lowerCAmelCase__										=      pearsonr(__lowerCamelCase       ,						__lowerCamelCase						)
         return {"pearsonr": results[0], "p-value": results[1]}
       else:
         return {"pearsonr": float(pearsonr(__lowerCamelCase       ,						__lowerCamelCase						)[0]						)}







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)

