





'''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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _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=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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       for key in orig_state_dict.copy().keys():
              _UpperCamelCase              =						orig_state_dict.pop(lowerCAmelCase					)

              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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase=None    ,       lowerCAmelCase=False					):
       # load original model
       _UpperCamelCase              =						DonutModel.from_pretrained(lowerCAmelCase					).eval()

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

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

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

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

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

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

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

       lowercase    : int    	=  parser.parse_args()
       convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase    : Optional[Any]    	=  logging.getLogger(__name__)

class  __A(						__UpperCAmelCase ):

     def __init__(      self,       A=-1      ):





            """simple docstring"""

            _UpperCamelCase              =						label_idx

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            if isinstance(A,       A      ):
                   _UpperCamelCase              =						mode.value
            _UpperCamelCase              =						os.path.join(A,       F'''{mode}.txt'''      )
            _UpperCamelCase              =						1
            _UpperCamelCase              =						[]
            with open(A,       encoding='''utf-8'''      ) as f:
                   _UpperCamelCase              =						[]
                   _UpperCamelCase              =						[]
                   for line in f:
                          if line.startswith('''-DOCSTART-'''      ) or line == "" or line == "\n":
                                 if words:
                                        examples.append(InputExample(guid=F'''{mode}-{guid_index}''',       words=A,       labels=A      )      )
                                        guid_index += 1
                                        _UpperCamelCase              =						[]
                                        _UpperCamelCase              =						[]
                          else:
                                 _UpperCamelCase              =						line.split(''' '''      )
                                 words.append(splits[0]      )
                                 if len(A      ) > 1:
                                        labels.append(splits[self.label_idx].replace('''\n''',       ''''''      )      )
                                 else:
                                        # Examples could have no label for mode = "test"
                                        labels.append('''O'''      )
                   if words:
                          examples.append(InputExample(guid=F'''{mode}-{guid_index}''',       words=A,       labels=A      )      )
            return examples

     def 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						0
            for line in test_input_reader:
                   if line.startswith('''-DOCSTART-'''      ) or line == "" or line == "\n":
                          writer.write(A      )
                          if not preds_list[example_id]:
                                 example_id += 1
                   elif preds_list[example_id]:
                          _UpperCamelCase              =						line.split()[0] + ''' ''' + preds_list[example_id].pop(0      ) + '''\n'''
                          writer.write(A      )
                   else:
                          logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''',       line.split()[0]      )



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if path:
                   with open(A,       '''r'''      ) as f:
                          _UpperCamelCase              =						f.read().splitlines()
                   if "O" not in labels:
                          _UpperCamelCase              =						['''O'''] + labels
                   return labels
            else:
                   return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class  __A(						__UpperCAmelCase ):

     def __init__(      self      ):





            """simple docstring"""

            super().__init__(label_idx=-2      )



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if path:
                   with open(A,       '''r'''      ) as f:
                          _UpperCamelCase              =						f.read().splitlines()
                   if "O" not in labels:
                          _UpperCamelCase              =						['''O'''] + labels
                   return labels
            else:
                   return [
                       "O",
                       "B-ADVP",
                       "B-INTJ",
                       "B-LST",
                       "B-PRT",
                       "B-NP",
                       "B-SBAR",
                       "B-VP",
                       "B-ADJP",
                       "B-CONJP",
                       "B-PP",
                       "I-ADVP",
                       "I-INTJ",
                       "I-LST",
                       "I-PRT",
                       "I-NP",
                       "I-SBAR",
                       "I-VP",
                       "I-ADJP",
                       "I-CONJP",
                       "I-PP",
                   ]






class  __A(						__UpperCAmelCase ):

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            if isinstance(A,       A      ):
                   _UpperCamelCase              =						mode.value
            _UpperCamelCase              =						os.path.join(A,       F'''{mode}.txt'''      )
            _UpperCamelCase              =						1
            _UpperCamelCase              =						[]

            with open(A,       encoding='''utf-8'''      ) as f:
                   for sentence in parse_incr(A      ):
                          _UpperCamelCase              =						[]
                          _UpperCamelCase              =						[]
                          for token in sentence:
                                 words.append(token['''form''']      )
                                 labels.append(token['''upos''']      )
                          assert len(A      ) == len(A      )
                          if words:
                                 examples.append(InputExample(guid=F'''{mode}-{guid_index}''',       words=A,       labels=A      )      )
                                 guid_index += 1
            return examples

     def 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						0
            for sentence in parse_incr(A      ):
                   _UpperCamelCase              =						preds_list[example_id]
                   _UpperCamelCase              =						''''''
                   for token in sentence:
                          out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0      )}) '''
                   out += "\n"
                   writer.write(A      )
                   example_id += 1



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if path:
                   with open(A,       '''r'''      ) as f:
                          return f.read().splitlines()
            else:
                   return [
                       "ADJ",
                       "ADP",
                       "ADV",
                       "AUX",
                       "CCONJ",
                       "DET",
                       "INTJ",
                       "NOUN",
                       "NUM",
                       "PART",
                       "PRON",
                       "PROPN",
                       "PUNCT",
                       "SCONJ",
                       "SYM",
                       "VERB",
                       "X",
                   ]






