

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 						_lowercase					(	lowercase__		):
      __lowerCAmelCase   :		Optional[int]   =   model.config

      __lowerCAmelCase   :		str   =   DonutSwinConfig(
          image_size=original_config.input_size  ,      patch_size=4  ,      depths=original_config.encoder_layer  ,      num_heads=[4, 8, 1_6, 3_2]  ,      window_size=original_config.window_size  ,      embed_dim=1_2_8  ,      )
      __lowerCAmelCase   :		List[str]   =   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 						_lowercase					(	lowercase__		):
      if "encoder.model" in name:
            __lowerCAmelCase   :		str   =   name.replace('''encoder.model'''  ,      '''encoder'''		)
      if "decoder.model" in name:
            __lowerCAmelCase   :		Tuple   =   name.replace('''decoder.model'''  ,      '''decoder'''		)
      if "patch_embed.proj" in name:
            __lowerCAmelCase   :		List[Any]   =   name.replace('''patch_embed.proj'''  ,      '''embeddings.patch_embeddings.projection'''		)
      if "patch_embed.norm" in name:
            __lowerCAmelCase   :		Dict   =   name.replace('''patch_embed.norm'''  ,      '''embeddings.norm'''		)
      if name.startswith('''encoder'''		):
            if "layers" in name:
                  __lowerCAmelCase   :		Optional[Any]   =   '''encoder.''' + name
            if "attn.proj" in name:
                  __lowerCAmelCase   :		Dict   =   name.replace('''attn.proj'''  ,      '''attention.output.dense'''		)
            if "attn" in name and "mask" not in name:
                  __lowerCAmelCase   :		Tuple   =   name.replace('''attn'''  ,      '''attention.self'''		)
            if "norm1" in name:
                  __lowerCAmelCase   :		List[str]   =   name.replace('''norm1'''  ,      '''layernorm_before'''		)
            if "norm2" in name:
                  __lowerCAmelCase   :		int   =   name.replace('''norm2'''  ,      '''layernorm_after'''		)
            if "mlp.fc1" in name:
                  __lowerCAmelCase   :		int   =   name.replace('''mlp.fc1'''  ,      '''intermediate.dense'''		)
            if "mlp.fc2" in name:
                  __lowerCAmelCase   :		Optional[int]   =   name.replace('''mlp.fc2'''  ,      '''output.dense'''		)

            if name == "encoder.norm.weight":
                  __lowerCAmelCase   :		str   =   '''encoder.layernorm.weight'''
            if name == "encoder.norm.bias":
                  __lowerCAmelCase   :		Union[str, Any]   =   '''encoder.layernorm.bias'''

      return name







def 						_lowercase					(	lowercase__  ,      lowercase__		):
      for key in orig_state_dict.copy().keys():
            __lowerCAmelCase   :		str   =   orig_state_dict.pop(__lowerCAmelCase		)

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

                  if "weight" in key:
                        __lowerCAmelCase   :		Dict   =   val[:dim, :]
                        __lowerCAmelCase   :		Dict   =   val[dim : dim * 2, :]
                        __lowerCAmelCase   :		Any   =   val[-dim:, :]
                  else:
                        __lowerCAmelCase   :		int   =   val[:dim]
                        __lowerCAmelCase   :		Optional[Any]   =   val[dim : dim * 2]
                        __lowerCAmelCase   :		Optional[int]   =   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   :		Tuple   =   val

      return orig_state_dict







def 						_lowercase					(	lowercase__  ,      lowercase__=None  ,      lowercase__=False		):
      # load original model
      __lowerCAmelCase   :		str   =   DonutModel.from_pretrained(__lowerCAmelCase		).eval()

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

      __lowerCAmelCase   :		List[Any]   =   original_model.state_dict()
      __lowerCAmelCase   :		Dict   =   convert_state_dict(__lowerCAmelCase  ,      __lowerCAmelCase		)
      model.load_state_dict(__lowerCAmelCase		)

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

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

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

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

      # verify encoder hidden states
      __lowerCAmelCase   :		List[Any]   =   original_model.encoder(__lowerCAmelCase		)
      __lowerCAmelCase   :		Dict   =   model.encoder(__lowerCAmelCase		).last_hidden_state
      assert torch.allclose(__lowerCAmelCase  ,      __lowerCAmelCase  ,      atol=1E-2		)

      # verify decoder hidden states
      __lowerCAmelCase   :		Optional[Any]   =   original_model(__lowerCAmelCase  ,      __lowerCAmelCase  ,      __lowerCAmelCase		).logits
      __lowerCAmelCase   :		Tuple   =   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__":
 _UpperCamelCase					     =						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.",
 )

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








from __future__ import annotations
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase = None				):
  lowerCAmelCase__										=      word_bank or []
  # create a table
  lowerCAmelCase__										=      len(__lowerCAmelCase				) + 1

  lowerCAmelCase__										=      []
  for _ in range(__lowerCAmelCase				):
    table.append([]				)
  # seed value
  lowerCAmelCase__										=      [[]]  # because empty string has empty combination

  # iterate through the indices
  for i in range(__lowerCAmelCase				):
    # condition
    if table[i] != []:
      for word in word_bank:
        # slice condition
        if target[i : i + len(__lowerCAmelCase				)] == word:
          lowerCAmelCase__										=      [
              [word, *way] for way in table[i]
          ]
          # adds the word to every combination the current position holds
          # now,push that combination to the table[i+len(word)]
          table[i + len(__lowerCAmelCase				)] += new_combinations

    # combinations are in reverse order so reverse for better output
  for combination in table[len(__lowerCAmelCase				)]:
    combination.reverse()

  return table[len(__lowerCAmelCase				)]


if __name__ == "__main__":
      print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
      print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
      print(
          all_construct(
              """hexagonosaurus""",
              ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
          )
      )

