



'''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   a_   (		_UpperCAmelCase    :      str				)    ->  List[Any]:
      __snake_case :   List[str]													=    model.config

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

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

      return name





def   a_   (		_UpperCAmelCase    :      List[str] ,_UpperCAmelCase    :      str				)    ->  Union[str, Any]:
      for key in orig_state_dict.copy().keys():
            __snake_case :   Any													=    orig_state_dict.pop(_UpperCAmelCase				)

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

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

      return orig_state_dict





def   a_   (		_UpperCAmelCase    :      str ,_UpperCAmelCase    :      int=None ,_UpperCAmelCase    :      Dict=False				)    ->  int:
      # load original model
      __snake_case :   str													=    DonutModel.from_pretrained(_UpperCAmelCase				).eval()

      # load HuggingFace model
      __snake_case							,     __snake_case :   Dict													=    get_configs(_UpperCAmelCase				)
      __snake_case :   Optional[int]													=    DonutSwinModel(_UpperCAmelCase				)
      __snake_case :   Dict													=    MBartForCausalLM(_UpperCAmelCase				)
      __snake_case :   List[Any]													=    VisionEncoderDecoderModel(encoder=_UpperCAmelCase ,decoder=_UpperCAmelCase				)
      model.eval()

      __snake_case :   str													=    original_model.state_dict()
      __snake_case :   List[Any]													=    convert_state_dict(_UpperCAmelCase ,_UpperCAmelCase				)
      model.load_state_dict(_UpperCAmelCase				)

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

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

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

      __snake_case :   Union[str, Any]													=    original_model.encoder.model.patch_embed(_UpperCAmelCase				)
      __snake_case							,     __snake_case :   Union[str, Any]													=    model.encoder.embeddings(_UpperCAmelCase				)
      assert torch.allclose(_UpperCAmelCase ,_UpperCAmelCase ,atol=1E-3				)

      # verify encoder hidden states
      __snake_case :   Tuple													=    original_model.encoder(_UpperCAmelCase				)
      __snake_case :   str													=    model.encoder(_UpperCAmelCase				).last_hidden_state
      assert torch.allclose(_UpperCAmelCase ,_UpperCAmelCase ,atol=1E-2				)

      # verify decoder hidden states
      __snake_case :   Any													=    original_model(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase				).logits
      __snake_case :   List[str]													=    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__":
    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__    :   str           =					parser.parse_args()
    convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= BlenderbotSmallTokenizer
 A__				= False
 def A_   (   self						:							Any  )      ->   str:







       '''simple docstring'''
       super().setUp()

       __snake_case :   Any													=    ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
       __snake_case :   str													=    dict(zip(__a      ,     range(len(__a  )  )  )  )

       __snake_case :   List[Any]													=    ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
       __snake_case :   Tuple													=    {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

       __snake_case :   str													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['vocab_file']  )
       __snake_case :   Union[str, Any]													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['merges_file']  )
       with open(self.vocab_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write(json.dumps(__a  ) + '\n'  )
       with open(self.merges_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write('\n'.join(__a  )  )
 def A_   (   self						:							Tuple      ,     **__a						:							Optional[int]  )      ->   List[str]:







       '''simple docstring'''
       kwargs.update(self.special_tokens_map  )
       return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname      ,     **__a  )
 def A_   (   self						:							int      ,     __a						:							int  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   List[str]													=    'adapt act apte'
       __snake_case :   List[str]													=    'adapt act apte'
       return input_text, output_text
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       __snake_case :   Dict													=    BlenderbotSmallTokenizer(self.vocab_file      ,     self.merges_file      ,     **self.special_tokens_map  )
       __snake_case :   List[str]													=    'adapt act apte'
       __snake_case :   List[Any]													=    ['adapt', 'act', 'ap@@', 'te']
       __snake_case :   Optional[int]													=    tokenizer.tokenize(__a  )
       self.assertListEqual(__a      ,     __a  )

       __snake_case :   List[str]													=    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

       __snake_case :   List[str]													=    [0, 1, 2, 3, 4, 5]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__a  )      ,     __a  )
 def A_   (   self						:							int  )      ->   str:







       '''simple docstring'''
       __snake_case :   int													=    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'  )
       assert tok('sam'  ).input_ids == [1384]
       __snake_case :   List[str]													=    'I am a small frog.'
       __snake_case :   List[Any]													=    tok([src_text]      ,     padding=__a      ,     truncation=__a  )['input_ids']
       __snake_case :   int													=    tok.batch_decode(__a      ,     skip_special_tokens=__a      ,     clean_up_tokenization_spaces=__a  )[0]
       assert src_text != decoded  # I wish it did!
       assert decoded == "i am a small frog ."





 def A_   (   self						:							List[str]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'  )
       __snake_case :   Optional[Any]													=    'I am a small frog .'
       __snake_case :   List[str]													=    '.'
       __snake_case :   List[str]													=    tok(__a  )['input_ids']
       __snake_case :   str													=    tok(__a  )['input_ids']

       assert encoded[-1] == encoded_dot[0]



