
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					( _lowercase			)       ->      int:
     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=_lowercase						,     is_encoder_decoder=_lowercase						,     add_cross_attention=_lowercase						,     decoder_layers=original_config.decoder_layer						,     max_position_embeddings=original_config.max_position_embeddings						,     vocab_size=len(
             model.decoder.tokenizer			)						,     scale_embedding=_lowercase						,     add_final_layer_norm=_lowercase						,     )

     return encoder_config, decoder_config

def  __lowerCamelCase					( _lowercase			)       ->      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					( _lowercase						,     _lowercase			)       ->      str:
     for key in orig_state_dict.copy().keys():
          UpperCamelCase						    =					orig_state_dict.pop(_lowercase			)

          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					( _lowercase						,     _lowercase=None						,     _lowercase=False			)       ->      str:
     # load original model
     UpperCamelCase						    =					DonutModel.from_pretrained(_lowercase			).eval()

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

     UpperCamelCase						    =					original_model.state_dict()
     UpperCamelCase						    =					convert_state_dict(_lowercase						,     _lowercase			)
     model.load_state_dict(_lowercase			)

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

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

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

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

     # verify decoder hidden states
     UpperCamelCase						    =					original_model(_lowercase						,     _lowercase						,     _lowercase			).logits
     UpperCamelCase						    =					model(_lowercase						,     decoder_input_ids=_lowercase			).logits
     assert torch.allclose(_lowercase						,     _lowercase						,     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(_lowercase			)
          processor.save_pretrained(_lowercase			)

     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__":
 _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.''',
 )

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




from functools import lru_cache

@lru_cache
def  __lowerCamelCase					( _lowercase			)       ->      int:
     if num < 0:
          raise ValueError('Number should not be negative.'			)

     return 1 if num in (0, 1) else num * factorial(num - 1			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


