
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def        __lowerCAmelCase     (     a__    )			->    Dict:
				# This defines a "chinese character" as anything in the CJK Unicode block:
				#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
				#
				# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
				# despite its name. The modern Korean Hangul alphabet is a different block,
				# as is Japanese Hiragana and Katakana. Those alphabets are used to write
				# space-separated words, so they are not treated specially and handled
				# like the all of the other languages.
				if (
				    (cp >= 0X4E00 and cp <= 0X9FFF)
				    or (cp >= 0X3400 and cp <= 0X4DBF)  #
				    or (cp >= 0X2_0000 and cp <= 0X2_A6DF)  #
				    or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
				    or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
				    or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
				    or (cp >= 0XF900 and cp <= 0XFAFF)
				    or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
				):  #
								return True

				return False


def        __lowerCAmelCase     (     a__    )			->    str:
				# word like '180' or '身高' or '神'
				for char in word:
								__a     				=       ord(__lowerCAmelCase    )
								if not _is_chinese_char(__lowerCAmelCase    ):
												return 0
				return 1


def        __lowerCAmelCase     (     a__    )			->    int:
				__a     				=       set()

				for token in tokens:
								__a     				=       len(__lowerCAmelCase    ) > 1 and is_chinese(__lowerCAmelCase    )
								if chinese_word:
												word_set.add(__lowerCAmelCase    )
				__a     				=       list(__lowerCAmelCase    )
				return word_list


def        __lowerCAmelCase     (     a__		,       a__    )			->    Any:
				if not chinese_word_set:
								return bert_tokens
				__a     				=       max([len(__lowerCAmelCase    ) for w in chinese_word_set]    )

				__a     				=       bert_tokens
				__a				,	__a     				=       0, len(__lowerCAmelCase    )
				while start < end:
								__a     				=       True
								if is_chinese(bert_word[start]    ):
												__a     				=       min(end - start		,       __lowerCAmelCase    )
												for i in range(__lowerCAmelCase		,       1		,       -1    ):
																__a     				=       ''''''.join(bert_word[start : start + i]    )
																if whole_word in chinese_word_set:
																				for j in range(start + 1		,       start + i    ):
																								__a     				=       '''##''' + bert_word[j]
																				__a     				=       start + i
																				__a     				=       False
																				break
								if single_word:
												start += 1
				return bert_word


def        __lowerCAmelCase     (     a__		,       a__		,       a__    )			->    Optional[int]:
				__a     				=       []

				for i in range(0		,       len(__lowerCAmelCase    )		,       100    ):
								__a     				=       ltp_tokenizer.pipeline(lines[i : i + 100]		,       tasks=['''cws''']    ).cws
								__a     				=       [get_chinese_word(__lowerCAmelCase    ) for r in res]
								ltp_res.extend(__lowerCAmelCase    )
				assert len(__lowerCAmelCase    ) == len(__lowerCAmelCase    )

				__a     				=       []
				for i in range(0		,       len(__lowerCAmelCase    )		,       100    ):
								__a     				=       bert_tokenizer(lines[i : i + 100]		,       add_special_tokens=__lowerCAmelCase		,       truncation=__lowerCAmelCase		,       max_length=512    )
								bert_res.extend(res['''input_ids''']    )
				assert len(__lowerCAmelCase    ) == len(__lowerCAmelCase    )

				__a     				=       []
				for input_ids, chinese_word in zip(__lowerCAmelCase		,       __lowerCAmelCase    ):
								__a     				=       []
								for id in input_ids:
												__a     				=       bert_tokenizer._convert_id_to_token(__lowerCAmelCase    )
												input_tokens.append(__lowerCAmelCase    )
								__a     				=       add_sub_symbol(__lowerCAmelCase		,       __lowerCAmelCase    )
								__a     				=       []
								# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
								for i, token in enumerate(__lowerCAmelCase    ):
												if token[:2] == "##":
																__a     				=       token[2:]
																# save chinese tokens' pos
																if len(__lowerCAmelCase    ) == 1 and _is_chinese_char(ord(__lowerCAmelCase    )    ):
																				ref_id.append(__lowerCAmelCase    )
								ref_ids.append(__lowerCAmelCase    )

				assert len(__lowerCAmelCase    ) == len(__lowerCAmelCase    )

				return ref_ids


def        __lowerCAmelCase     (     a__    )			->    List[str]:
				# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
				# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
				with open(args.file_name		,       '''r'''		,       encoding='''utf-8'''    ) as f:
								__a     				=       f.readlines()
				__a     				=       [line.strip() for line in data if len(__lowerCAmelCase    ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				__a     				=       LTP(args.ltp    )  # faster in GPU device
				__a     				=       BertTokenizer.from_pretrained(args.bert    )

				__a     				=       prepare_ref(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )

				with open(args.save_path		,       '''w'''		,       encoding='''utf-8'''    ) as f:
								__a     				=       [json.dumps(__lowerCAmelCase    ) + '''\n''' for ref in ref_ids]
								f.writelines(__lowerCAmelCase    )


if __name__ == "__main__":
					A	:      Any  					=					argparse.ArgumentParser(description='prepare_chinese_ref')
					parser.add_argument(
					    '--file_name',
					    required=False,
					    type=str,
					    default='./resources/chinese-demo.txt',
					    help='file need process, same as training data in lm',
					)
					parser.add_argument(
					    '--ltp',
					    required=False,
					    type=str,
					    default='./resources/ltp',
					    help='resources for LTP tokenizer, usually a path',
					)
					parser.add_argument(
					    '--bert',
					    required=False,
					    type=str,
					    default='./resources/robert',
					    help='resources for Bert tokenizer',
					)
					parser.add_argument(
					    '--save_path',
					    required=False,
					    type=str,
					    default='./resources/ref.txt',
					    help='path to save res',
					)

					A	:      Tuple  					=					parser.parse_args()
					main(args)





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_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      model.config

  lowerCAmelCase__										=      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   , )
  lowerCAmelCase__										=      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 		a_							( __lowerCAmelCase				):
  if "encoder.model" in name:
    lowerCAmelCase__										=      name.replace('''encoder.model'''   , '''encoder'''				)
  if "decoder.model" in name:
    lowerCAmelCase__										=      name.replace('''decoder.model'''   , '''decoder'''				)
  if "patch_embed.proj" in name:
    lowerCAmelCase__										=      name.replace('''patch_embed.proj'''   , '''embeddings.patch_embeddings.projection'''				)
  if "patch_embed.norm" in name:
    lowerCAmelCase__										=      name.replace('''patch_embed.norm'''   , '''embeddings.norm'''				)
  if name.startswith('''encoder'''				):
    if "layers" in name:
      lowerCAmelCase__										=      '''encoder.''' + name
    if "attn.proj" in name:
      lowerCAmelCase__										=      name.replace('''attn.proj'''   , '''attention.output.dense'''				)
    if "attn" in name and "mask" not in name:
      lowerCAmelCase__										=      name.replace('''attn'''   , '''attention.self'''				)
    if "norm1" in name:
      lowerCAmelCase__										=      name.replace('''norm1'''   , '''layernorm_before'''				)
    if "norm2" in name:
      lowerCAmelCase__										=      name.replace('''norm2'''   , '''layernorm_after'''				)
    if "mlp.fc1" in name:
      lowerCAmelCase__										=      name.replace('''mlp.fc1'''   , '''intermediate.dense'''				)
    if "mlp.fc2" in name:
      lowerCAmelCase__										=      name.replace('''mlp.fc2'''   , '''output.dense'''				)

    if name == "encoder.norm.weight":
      lowerCAmelCase__										=      '''encoder.layernorm.weight'''
    if name == "encoder.norm.bias":
      lowerCAmelCase__										=      '''encoder.layernorm.bias'''

  return name
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  for key in orig_state_dict.copy().keys():
    lowerCAmelCase__										=      orig_state_dict.pop(__lowerCAmelCase				)

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

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

  return orig_state_dict
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase=None   , __lowerCAmelCase=False				):
  # load original model
  lowerCAmelCase__										=      DonutModel.from_pretrained(__lowerCAmelCase				).eval()

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

  lowerCAmelCase__										=      original_model.state_dict()
  lowerCAmelCase__										=      convert_state_dict(__lowerCAmelCase   , __lowerCAmelCase				)
  model.load_state_dict(__lowerCAmelCase				)

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

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

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

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

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

  # verify decoder hidden states
  lowerCAmelCase__										=      original_model(__lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				).logits
  lowerCAmelCase__										=      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__":
      __magic_name__  :  Union[str, Any]					=       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.""",
      )

      __magic_name__  :  Any					=       parser.parse_args()
      convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

