

"""simple docstring"""


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def 			_lowercase       (     __lowerCAmelCase				)							->					List[Any]:
      # 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 			_lowercase       (     __lowerCAmelCase				)							->					Dict:
      # word like '180' or '身高' or '神'
      for char in word:
            SCREAMING_SNAKE_CASE__ :   str   		=						ord(snake_case__				)
            if not _is_chinese_char(snake_case__				):
                  return 0
      return 1




def 			_lowercase       (     __lowerCAmelCase				)							->					List[Any]:
      SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						set()

      for token in tokens:
            SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						len(snake_case__				) > 1 and is_chinese(snake_case__				)
            if chinese_word:
                  word_set.add(snake_case__				)
      SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						list(snake_case__				)
      return word_list




def 			_lowercase       (     __lowerCAmelCase	,       __lowerCAmelCase				)							->					List[Any]:
      if not chinese_word_set:
            return bert_tokens
      SCREAMING_SNAKE_CASE__ :   Tuple   		=						max([len(snake_case__				) for w in chinese_word_set]				)

      SCREAMING_SNAKE_CASE__ :   int   		=						bert_tokens
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__ :   Dict   		=						0, len(snake_case__				)
      while start < end:
            SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						True
            if is_chinese(bert_word[start]				):
                  SCREAMING_SNAKE_CASE__ :   Any   		=						min(end - start	,       snake_case__				)
                  for i in range(snake_case__	,       1	,       -1				):
                        SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						"""""".join(bert_word[start : start + i]				)
                        if whole_word in chinese_word_set:
                              for j in range(start + 1	,       start + i				):
                                    SCREAMING_SNAKE_CASE__ :   int   		=						"""##""" + bert_word[j]
                              SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						start + i
                              SCREAMING_SNAKE_CASE__ :   Tuple   		=						False
                              break
            if single_word:
                  start += 1
      return bert_word




def 			_lowercase       (     __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase				)							->					str:
      SCREAMING_SNAKE_CASE__ :   List[Any]   		=						[]

      for i in range(0	,       len(snake_case__				)	,       100				):
            SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						ltp_tokenizer.pipeline(lines[i : i + 100]	,       tasks=["""cws"""]				).cws
            SCREAMING_SNAKE_CASE__ :   Any   		=						[get_chinese_word(snake_case__				) for r in res]
            ltp_res.extend(snake_case__				)
      assert len(snake_case__				) == len(snake_case__				)

      SCREAMING_SNAKE_CASE__ :   Tuple   		=						[]
      for i in range(0	,       len(snake_case__				)	,       100				):
            SCREAMING_SNAKE_CASE__ :   str   		=						bert_tokenizer(lines[i : i + 100]	,       add_special_tokens=snake_case__	,       truncation=snake_case__	,       max_length=512				)
            bert_res.extend(res["""input_ids"""]				)
      assert len(snake_case__				) == len(snake_case__				)

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

      assert len(snake_case__				) == len(snake_case__				)

      return ref_ids




def 			_lowercase       (     __lowerCAmelCase				)							->					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:
            SCREAMING_SNAKE_CASE__ :   str   		=						f.readlines()
      SCREAMING_SNAKE_CASE__ :   int   		=						[line.strip() for line in data if len(snake_case__				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      SCREAMING_SNAKE_CASE__ :   str   		=						LTP(args.ltp				)  # faster in GPU device
      SCREAMING_SNAKE_CASE__ :   int   		=						BertTokenizer.from_pretrained(args.bert				)

      SCREAMING_SNAKE_CASE__ :   int   		=						prepare_ref(snake_case__	,       snake_case__	,       snake_case__				)

      with open(args.save_path	,       """w"""	,       encoding="""utf-8"""				) as f:
            SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						[json.dumps(snake_case__				) + """\n""" for ref in ref_ids]
            f.writelines(snake_case__				)


if __name__ == "__main__":
  a       :Optional[int]     =			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       :List[str]     =			parser.parse_args()
  main(args)






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				if "cls_token" in name:
								lowercase_	    =   name.replace('''cls_token'''		,  '''vit.embeddings.cls_token'''   )
				if "mask_token" in name:
								lowercase_	    =   name.replace('''mask_token'''		,  '''decoder.mask_token'''   )
				if "decoder_pos_embed" in name:
								lowercase_	    =   name.replace('''decoder_pos_embed'''		,  '''decoder.decoder_pos_embed'''   )
				if "pos_embed" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''pos_embed'''		,  '''vit.embeddings.position_embeddings'''   )
				if "patch_embed.proj" in name:
								lowercase_	    =   name.replace('''patch_embed.proj'''		,  '''vit.embeddings.patch_embeddings.projection'''   )
				if "patch_embed.norm" in name:
								lowercase_	    =   name.replace('''patch_embed.norm'''		,  '''vit.embeddings.norm'''   )
				if "decoder_blocks" in name:
								lowercase_	    =   name.replace('''decoder_blocks'''		,  '''decoder.decoder_layers'''   )
				if "blocks" in name:
								lowercase_	    =   name.replace('''blocks'''		,  '''vit.encoder.layer'''   )
				if "attn.proj" in name:
								lowercase_	    =   name.replace('''attn.proj'''		,  '''attention.output.dense'''   )
				if "attn" in name:
								lowercase_	    =   name.replace('''attn'''		,  '''attention.self'''   )
				if "norm1" in name:
								lowercase_	    =   name.replace('''norm1'''		,  '''layernorm_before'''   )
				if "norm2" in name:
								lowercase_	    =   name.replace('''norm2'''		,  '''layernorm_after'''   )
				if "mlp.fc1" in name:
								lowercase_	    =   name.replace('''mlp.fc1'''		,  '''intermediate.dense'''   )
				if "mlp.fc2" in name:
								lowercase_	    =   name.replace('''mlp.fc2'''		,  '''output.dense'''   )
				if "decoder_embed" in name:
								lowercase_	    =   name.replace('''decoder_embed'''		,  '''decoder.decoder_embed'''   )
				if "decoder_norm" in name:
								lowercase_	    =   name.replace('''decoder_norm'''		,  '''decoder.decoder_norm'''   )
				if "decoder_pred" in name:
								lowercase_	    =   name.replace('''decoder_pred'''		,  '''decoder.decoder_pred'''   )
				if "norm.weight" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''norm.weight'''		,  '''vit.layernorm.weight'''   )
				if "norm.bias" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''norm.bias'''		,  '''vit.layernorm.bias'''   )

				return name




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Any   ):


				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase_	    =   orig_state_dict.pop(snake_case__   )

								if "qkv" in key:
												lowercase_	    =   key.split('''.'''   )
												lowercase_	    =   int(key_split[1]   )
												if "decoder_blocks" in key:
																lowercase_	    =   config.decoder_hidden_size
																lowercase_	    =   '''decoder.decoder_layers.'''
																if "weight" in key:
																				lowercase_	    =   val[:dim, :]
																				lowercase_	    =   val[dim : dim * 2, :]
																				lowercase_	    =   val[-dim:, :]
																elif "bias" in key:
																				lowercase_	    =   val[:dim]
																				lowercase_	    =   val[dim : dim * 2]
																				lowercase_	    =   val[-dim:]
												else:
																lowercase_	    =   config.hidden_size
																lowercase_	    =   '''vit.encoder.layer.'''
																if "weight" in key:
																				lowercase_	    =   val[:dim, :]
																				lowercase_	    =   val[dim : dim * 2, :]
																				lowercase_	    =   val[-dim:, :]
																elif "bias" in key:
																				lowercase_	    =   val[:dim]
																				lowercase_	    =   val[dim : dim * 2]
																				lowercase_	    =   val[-dim:]

								else:
												lowercase_	    =   val

				return orig_state_dict




def 							a		(				snake_case__:   str		,  snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   ViTMAEConfig()
				if "large" in checkpoint_url:
								lowercase_	    =   1_024
								lowercase_	    =   4_096
								lowercase_	    =   24
								lowercase_	    =   16
				elif "huge" in checkpoint_url:
								lowercase_	    =   14
								lowercase_	    =   1_280
								lowercase_	    =   5_120
								lowercase_	    =   32
								lowercase_	    =   16

				lowercase_	    =   ViTMAEForPreTraining(snake_case__   )

				lowercase_	    =   torch.hub.load_state_dict_from_url(snake_case__		,  map_location='''cpu'''   )['''model''']

				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )

				lowercase_	    =   convert_state_dict(snake_case__		,  snake_case__   )

				model.load_state_dict(snake_case__   )
				model.eval()

				lowercase_	    =   '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

				lowercase_	    =   Image.open(requests.get(snake_case__		,  stream=snake_case__   ).raw   )
				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )
				lowercase_	    =   image_processor(images=snake_case__		,  return_tensors='''pt'''   )

				# forward pass
				torch.manual_seed(2   )
				lowercase_	    =   model(**snake_case__   )
				lowercase_	    =   outputs.logits

				if "large" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]   )
				elif "huge" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]   )
				else:
								lowercase_	    =   torch.tensor(
								    [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]   )

				# verify logits
				assert torch.allclose(logits[0, :3, :3]		,  snake_case__		,  atol=1e-4   )

				print(F'''Saving model to {pytorch_dump_folder_path}'''   )
				model.save_pretrained(snake_case__   )

				print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
				image_processor.save_pretrained(snake_case__   )


if __name__ == "__main__":
						__a         =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_url',
						    default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
						    type=str,
						    help='URL of the checkpoint you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)

						__a         =   parser.parse_args()
						convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




