





"""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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int			):







      '''simple docstring'''




      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=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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




      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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						int			):







      '''simple docstring'''




      for key in orig_state_dict.copy().keys():
            lowerCAmelCase  						=       orig_state_dict.pop(SCREAMING_SNAKE_CASE			)

            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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						int=None							,						SCREAMING_SNAKE_CASE     :						Tuple=False			):







      '''simple docstring'''




      lowerCAmelCase  						=       DonutModel.from_pretrained(SCREAMING_SNAKE_CASE			).eval()

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

      lowerCAmelCase  						=       original_model.state_dict()
      lowerCAmelCase  						=       convert_state_dict(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      model.load_state_dict(SCREAMING_SNAKE_CASE			)

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

      lowerCAmelCase  						=       XLMRobertaTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE							,						from_slow=SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       DonutImageProcessor(
          do_align_long_axis=original_model.config.align_long_axis							,						size=original_model.config.input_size[::-1]			)
      lowerCAmelCase  						=       DonutProcessor(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       processor(SCREAMING_SNAKE_CASE							,						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}"""							,						SCREAMING_SNAKE_CASE			)
      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(SCREAMING_SNAKE_CASE							,						add_special_tokens=SCREAMING_SNAKE_CASE							,						return_tensors="""pt"""			)[
          """input_ids"""
      ]

      lowerCAmelCase  						=       original_model.encoder.model.patch_embed(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase						,		lowerCAmelCase  						=       model.encoder.embeddings(SCREAMING_SNAKE_CASE			)
      assert torch.allclose(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						atol=1e-3			)

      # verify encoder hidden states
      lowerCAmelCase  						=       original_model.encoder(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       model.encoder(SCREAMING_SNAKE_CASE			).last_hidden_state
      assert torch.allclose(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						atol=1e-2			)

      # verify decoder hidden states
      lowerCAmelCase  						=       original_model(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			).logits
      lowerCAmelCase  						=       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__":
       SCREAMING_SNAKE_CASE__					=   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.",
       )

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











"""simple docstring"""
from __future__ import annotations

import math





def  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		bool     ,       UpperCamelCase__		:		list[int]     ,       UpperCamelCase__		:		float							):

    if depth < 0:
        raise ValueError('''Depth cannot be less than 0'''							)

    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Scores cannot be empty'''							)

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1     ,       node_index * 2     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)     ,       minimax(depth + 1     ,       node_index * 2 + 1     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)     ,       )

    return min(
        minimax(depth + 1     ,       node_index * 2     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)     ,       minimax(depth + 1     ,       node_index * 2 + 1     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)     ,       )





def  lowerCamelCase_						():
    _UpperCAmelCase							:  Any											=    [90, 23, 6, 33, 21, 65, 123, 3_4423]
    _UpperCAmelCase							:  Any											=    math.log(len(UpperCamelCase__							)     ,       2							)
    print('''Optimal value : '''     ,       end=''''''							)
    print(minimax(0     ,       0     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()


