


'''simple docstring'''


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   lowerCamelCase       (   lowerCamelCase	:   Union[str, Any]):
      A_    :	Optional[int]							 =					model.config

      A_    :	str							 =					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=128				,    )
      A_    :	List[Any]							 =					MBartConfig(
          is_decoder=SCREAMING_SNAKE_CASE__				,    is_encoder_decoder=SCREAMING_SNAKE_CASE__				,    add_cross_attention=SCREAMING_SNAKE_CASE__				,    decoder_layers=original_config.decoder_layer				,    max_position_embeddings=original_config.max_position_embeddings				,    vocab_size=len(
              model.decoder.tokenizer)				,    scale_embedding=SCREAMING_SNAKE_CASE__				,    add_final_layer_norm=SCREAMING_SNAKE_CASE__				,    )

      return encoder_config, decoder_config




def   lowerCamelCase       (   lowerCamelCase	:   Union[str, Any]):


      if "encoder.model" in name:
            A_    :	Optional[Any]							 =					name.replace("""encoder.model"""				,    """encoder""")
      if "decoder.model" in name:
            A_    :	Optional[int]							 =					name.replace("""decoder.model"""				,    """decoder""")
      if "patch_embed.proj" in name:
            A_    :	Tuple							 =					name.replace("""patch_embed.proj"""				,    """embeddings.patch_embeddings.projection""")
      if "patch_embed.norm" in name:
            A_    :	Dict							 =					name.replace("""patch_embed.norm"""				,    """embeddings.norm""")
      if name.startswith("""encoder"""):
            if "layers" in name:
                  A_    :	List[str]							 =					'''encoder.''' + name
            if "attn.proj" in name:
                  A_    :	Union[str, Any]							 =					name.replace("""attn.proj"""				,    """attention.output.dense""")
            if "attn" in name and "mask" not in name:
                  A_    :	List[Any]							 =					name.replace("""attn"""				,    """attention.self""")
            if "norm1" in name:
                  A_    :	Optional[int]							 =					name.replace("""norm1"""				,    """layernorm_before""")
            if "norm2" in name:
                  A_    :	Dict							 =					name.replace("""norm2"""				,    """layernorm_after""")
            if "mlp.fc1" in name:
                  A_    :	str							 =					name.replace("""mlp.fc1"""				,    """intermediate.dense""")
            if "mlp.fc2" in name:
                  A_    :	Tuple							 =					name.replace("""mlp.fc2"""				,    """output.dense""")

            if name == "encoder.norm.weight":
                  A_    :	Any							 =					'''encoder.layernorm.weight'''
            if name == "encoder.norm.bias":
                  A_    :	Tuple							 =					'''encoder.layernorm.bias'''

      return name




def   lowerCamelCase       (   lowerCamelCase	:   Union[str, Any]				,    lowerCamelCase	:   List[str]):


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

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

                  if "weight" in key:
                        A_    :	str							 =					val[:dim, :]
                        A_    :	Any							 =					val[dim : dim * 2, :]
                        A_    :	List[str]							 =					val[-dim:, :]
                  else:
                        A_    :	Optional[int]							 =					val[:dim]
                        A_    :	List[str]							 =					val[dim : dim * 2]
                        A_    :	List[str]							 =					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:
                  A_    :	List[str]							 =					val

      return orig_state_dict




def   lowerCamelCase       (   lowerCamelCase	:   Dict				,    lowerCamelCase	:   str=None				,    lowerCamelCase	:   Any=False):
      A_    :	Any							 =					DonutModel.from_pretrained(SCREAMING_SNAKE_CASE__).eval()

      # load HuggingFace model
      A_    :	int							 =					get_configs(SCREAMING_SNAKE_CASE__)
      A_    :	str							 =					DonutSwinModel(SCREAMING_SNAKE_CASE__)
      A_    :	List[str]							 =					MBartForCausalLM(SCREAMING_SNAKE_CASE__)
      A_    :	Union[str, Any]							 =					VisionEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__				,    decoder=SCREAMING_SNAKE_CASE__)
      model.eval()

      A_    :	Dict							 =					original_model.state_dict()
      A_    :	int							 =					convert_state_dict(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__)
      model.load_state_dict(SCREAMING_SNAKE_CASE__)

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

      A_    :	Dict							 =					XLMRobertaTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE__				,    from_slow=SCREAMING_SNAKE_CASE__)
      A_    :	List[Any]							 =					DonutImageProcessor(
          do_align_long_axis=original_model.config.align_long_axis				,    size=original_model.config.input_size[::-1])
      A_    :	str							 =					DonutProcessor(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__)
      A_    :	int							 =					processor(SCREAMING_SNAKE_CASE__				,    return_tensors="""pt""").pixel_values

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

      A_    :	str							 =					original_model.encoder.model.patch_embed(SCREAMING_SNAKE_CASE__)
      A_    :	Optional[int]							 =					model.encoder.embeddings(SCREAMING_SNAKE_CASE__)
      assert torch.allclose(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    atol=1E-3)

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

      # verify decoder hidden states
      A_    :	Any							 =					original_model(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__).logits
      A_    :	List[str]							 =					model(SCREAMING_SNAKE_CASE__				,    decoder_input_ids=SCREAMING_SNAKE_CASE__).logits
      assert torch.allclose(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    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(SCREAMING_SNAKE_CASE__)
            processor.save_pretrained(SCREAMING_SNAKE_CASE__)

      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__								=   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__								=   parser.parse_args()
     convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


'''simple docstring'''
import os
from collections.abc import Iterator




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = "."			)  ->							Iterator[str]:


		'''simple docstring'''


		for dir_path, dir_names, filenames in os.walk(SCREAMING_SNAKE_CASE__			):
				snake_case  :       Optional[Any]     			=						[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
				for filename in filenames:
						if filename == "__init__.py":
								continue
						if os.path.splitext(SCREAMING_SNAKE_CASE__			)[1] in (".py", ".ipynb"):
								yield os.path.join(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			).lstrip('''./'''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		return F'{i * "  "}*' if i else "\n##"




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							str:


		'''simple docstring'''


		snake_case  :       Union[str, Any]     			=						old_path.split(os.sep			)
		for i, new_part in enumerate(new_path.split(os.sep			)			):
				if (i + 1 > len(SCREAMING_SNAKE_CASE__			) or old_parts[i] != new_part) and new_part:
						print(F'{md_prefix(SCREAMING_SNAKE_CASE__			)} {new_part.replace("_" ,       " "			).title()}'			)
		return new_path




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = "."			)  ->							None:


		'''simple docstring'''


		snake_case  :       List[Any]     			=						''''''
		for filepath in sorted(good_file_paths(SCREAMING_SNAKE_CASE__			)			):
				snake_case			,snake_case  :       Optional[Any]     			=						os.path.split(SCREAMING_SNAKE_CASE__			)
				if filepath != old_path:
						snake_case  :       Dict     			=						print_path(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
				snake_case  :       int     			=						(filepath.count(os.sep			) + 1) if filepath else 0
				snake_case  :       int     			=						F'{filepath}/{filename}'.replace(''' ''' ,       '''%20'''			)
				snake_case  :       int     			=						os.path.splitext(filename.replace('''_''' ,       ''' '''			).title()			)[0]
				print(F'{md_prefix(SCREAMING_SNAKE_CASE__			)} [{filename}]({url})'			)


if __name__ == "__main__":
						print_directory_md(".")


