


'''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					:      Optional[Any]       ):
       _A					:     Optional[Any]     = model.config

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

              if name == "encoder.norm.weight":
                     _A					:     Optional[int]     = 'encoder.layernorm.weight'
              if name == "encoder.norm.bias":
                     _A					:     Dict     = 'encoder.layernorm.bias'

       return name






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Any       ):
       for key in orig_state_dict.copy().keys():
              _A					:     Tuple     = orig_state_dict.pop(lowerCamelCase       )

              if "qkv" in key:
                     _A					:     str     = key.split('.'       )
                     _A					:     Any     = int(key_split[3]       )
                     _A					:     Dict     = int(key_split[5]       )
                     _A					:     Union[str, Any]     = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

       return orig_state_dict






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      Union[str, Any]=None  ,lowerCamelCase					:      Any=False       ):
       # load original model
       _A					:     Optional[Any]     = DonutModel.from_pretrained(lowerCamelCase       ).eval()

       # load HuggingFace model
       _A  ,			_A					:     Tuple     = get_configs(lowerCamelCase       )
       _A					:     List[Any]     = DonutSwinModel(lowerCamelCase       )
       _A					:     Any     = MBartForCausalLM(lowerCamelCase       )
       _A					:     Tuple     = VisionEncoderDecoderModel(encoder=lowerCamelCase  ,decoder=lowerCamelCase       )
       model.eval()

       _A					:     str     = original_model.state_dict()
       _A					:     Any     = convert_state_dict(lowerCamelCase  ,lowerCamelCase       )
       model.load_state_dict(lowerCamelCase       )

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

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

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

       _A					:     List[Any]     = original_model.encoder.model.patch_embed(lowerCamelCase       )
       _A  ,			_A					:     Dict     = model.encoder.embeddings(lowerCamelCase       )
       assert torch.allclose(lowerCamelCase  ,lowerCamelCase  ,atol=1E-3       )

       # verify encoder hidden states
       _A					:     Optional[int]     = original_model.encoder(lowerCamelCase       )
       _A					:     Tuple     = model.encoder(lowerCamelCase       ).last_hidden_state
       assert torch.allclose(lowerCamelCase  ,lowerCamelCase  ,atol=1E-2       )

       # verify decoder hidden states
       _A					:     int     = original_model(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       ).logits
       _A					:     List[Any]     = 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__":
   A				:					Optional[int]    						=			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.''',
   )

   A				:					Dict    						=			parser.parse_args()
   convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):

       def count_of_possible_combinations(lowerCamelCase					:      int       ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              return sum(count_of_possible_combinations(target - item       ) for item in array       )

       return count_of_possible_combinations(lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):

       def count_of_possible_combinations_with_dp_array(
           lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]       ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              if dp_array[target] != -1:
                     return dp_array[target]
              _A					:     Optional[Any]     = sum(
                  count_of_possible_combinations_with_dp_array(target - item  ,lowerCamelCase       )
                  for item in array       )
              _A					:     List[str]     = answer
              return answer

       _A					:     Optional[int]     = [-1] * (target + 1)
       return count_of_possible_combinations_with_dp_array(lowerCamelCase  ,lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):
       _A					:     Dict     = [0] * (target + 1)
       _A					:     List[str]     = 1

       for i in range(1  ,target + 1       ):
              for j in range(lowerCamelCase       ):
                     if i - array[j] >= 0:
                            dp_array[i] += dp_array[i - array[j]]

       return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   A				:					Dict    						=			3
   A				:					Union[str, Any]    						=			5
   A				:					Union[str, Any]    						=			[1, 2, 5]
   print(combination_sum_iv(n, array, target))






