





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_ (							UpperCamelCase_  )     ->		Optional[Any]:
       UpperCamelCase_          =  model.config

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

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

       return name




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple:
       for key in orig_state_dict.copy().keys():
              UpperCamelCase_          =  orig_state_dict.pop(UpperCamelCase_  )

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

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

       return orig_state_dict




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=None					,		UpperCamelCase_=False  )     ->		Any:
       # load original model
       UpperCamelCase_          =  DonutModel.from_pretrained(UpperCamelCase_  ).eval()

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

       UpperCamelCase_          =  original_model.state_dict()
       UpperCamelCase_          =  convert_state_dict(UpperCamelCase_					,		UpperCamelCase_  )
       model.load_state_dict(UpperCamelCase_  )

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

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

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

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

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

       # verify decoder hidden states
       UpperCamelCase_          =  original_model(UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  ).logits
       UpperCamelCase_          =  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__":
     _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)








import sys




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Union[str, Any]:
       UpperCamelCase_          =  len(UpperCamelCase_  )
       UpperCamelCase_          =  [[0 for x in range(UpperCamelCase_  )] for x in range(UpperCamelCase_  )]
       UpperCamelCase_          =  [[0 for x in range(UpperCamelCase_  )] for x in range(UpperCamelCase_  )]

       for chain_length in range(2					,		UpperCamelCase_  ):
              for a in range(1					,		n - chain_length + 1  ):
                     UpperCamelCase_          =  a + chain_length - 1

                     UpperCamelCase_          =  sys.maxsize
                     for c in range(UpperCamelCase_					,		UpperCamelCase_  ):
                            UpperCamelCase_          =  (
                                matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
                            )
                            if cost < matrix[a][b]:
                                   UpperCamelCase_          =  cost
                                   UpperCamelCase_          =  c
       return matrix, sol




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[str]:
       if i == j:
              print("A" + str(UpperCamelCase_  )					,		end=" "  )
       else:
              print("("					,		end=" "  )
              print_optiomal_solution(UpperCamelCase_					,		UpperCamelCase_					,		optimal_solution[i][j]  )
              print_optiomal_solution(UpperCamelCase_					,		optimal_solution[i][j] + 1					,		UpperCamelCase_  )
              print(")"					,		end=" "  )




def  lowerCAmelCase_ (							)     ->		Any:
       UpperCamelCase_          =  [30, 35, 15, 5, 10, 20, 25]
       UpperCamelCase_          =  len(UpperCamelCase_  )
       # Size of matrix created from above array will be
       # 30*35 35*15 15*5 5*10 10*20 20*25
       UpperCamelCase_       , UpperCamelCase_          =  matrix_chain_order(UpperCamelCase_  )

       print("No. of Operation required: " + str(matrix[1][n - 1]  )  )
       print_optiomal_solution(UpperCamelCase_					,		1					,		n - 1  )


if __name__ == "__main__":
     main()

