
'''simple docstring'''






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowercase	=    logging.get_logger(__name__)


__lowercase	=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
__lowercase	=    {
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
}

__lowercase	=    {
    '''RUCAIBox/mvp''': 10_24,
}







@lru_cache()
def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       Tuple    							=(
		    list(range(ord('''!'''  )  ,					ord('''~'''  ) + 1  )  ) + list(range(ord('''¡'''  )  ,					ord('''¬'''  ) + 1  )  ) + list(range(ord('''®'''  )  ,					ord('''ÿ'''  ) + 1  )  )
		)
		lowerCAmelCase_   :       int    							=bs[:]
		lowerCAmelCase_   :       Optional[Any]    							=0
		for b in range(2**8  ):
				if b not in bs:
						bs.append(_SCREAMING_SNAKE_CASE  )
						cs.append(2**8 + n  )
						n += 1
		lowerCAmelCase_   :       Tuple    							=[chr(_SCREAMING_SNAKE_CASE  ) for n in cs]
		return dict(zip(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )  )







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Union[str, Any]    							=set()
		lowerCAmelCase_   :       Any    							=word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)  )
				lowerCAmelCase_   :       List[str]    							=char
		return pairs






class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  List[Any]								=		VOCAB_FILES_NAMES
	_UpperCamelCase							:  List[Any]								=		PRETRAINED_VOCAB_FILES_MAP
	_UpperCamelCase							:  Any								=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCamelCase							:  str								=		['''input_ids''', '''attention_mask''']




	def __init__(						self				: Union[str, Any]					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: int					,						UpperCamelCase_				: List[str]="replace"					,						UpperCamelCase_				: List[str]="<s>"					,						UpperCamelCase_				: List[str]="</s>"					,						UpperCamelCase_				: Tuple="</s>"					,						UpperCamelCase_				: Tuple="<s>"					,						UpperCamelCase_				: str="<unk>"					,						UpperCamelCase_				: Any="<pad>"					,						UpperCamelCase_				: Union[str, Any]="<mask>"					,						UpperCamelCase_				: Union[str, Any]=False					,						**UpperCamelCase_				: Optional[Any]					,						):
			lowerCAmelCase_   :       Dict    							=AddedToken(UpperCamelCase_					,						lstrip=UpperCamelCase_					,						rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_					,						UpperCamelCase_      ) else bos_token
			lowerCAmelCase_   :       Optional[int]    							=AddedToken(UpperCamelCase_					,						lstrip=UpperCamelCase_					,						rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_					,						UpperCamelCase_      ) else eos_token
			lowerCAmelCase_   :       Optional[Any]    							=AddedToken(UpperCamelCase_					,						lstrip=UpperCamelCase_					,						rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_					,						UpperCamelCase_      ) else sep_token
			lowerCAmelCase_   :       List[Any]    							=AddedToken(UpperCamelCase_					,						lstrip=UpperCamelCase_					,						rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_					,						UpperCamelCase_      ) else cls_token
			lowerCAmelCase_   :       Dict    							=AddedToken(UpperCamelCase_					,						lstrip=UpperCamelCase_					,						rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_					,						UpperCamelCase_      ) else unk_token
			lowerCAmelCase_   :       Dict    							=AddedToken(UpperCamelCase_					,						lstrip=UpperCamelCase_					,						rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_					,						UpperCamelCase_      ) else pad_token

			# Mask token behave like a normal word, i.e. include the space before it
			lowerCAmelCase_   :       List[Any]    							=AddedToken(UpperCamelCase_					,						lstrip=UpperCamelCase_					,						rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_					,						UpperCamelCase_      ) else mask_token

			super().__init__(
			    errors=UpperCamelCase_					,						bos_token=UpperCamelCase_					,						eos_token=UpperCamelCase_					,						unk_token=UpperCamelCase_					,						sep_token=UpperCamelCase_					,						cls_token=UpperCamelCase_					,						pad_token=UpperCamelCase_					,						mask_token=UpperCamelCase_					,						add_prefix_space=UpperCamelCase_					,						**UpperCamelCase_					,						)
			with open(UpperCamelCase_					,						encoding='''utf-8'''      ) as vocab_handle:
					lowerCAmelCase_   :       Optional[int]    							=json.load(UpperCamelCase_      )
			lowerCAmelCase_   :       Union[str, Any]    							={v: k for k, v in self.encoder.items()}
			lowerCAmelCase_   :       List[str]    							=errors  # how to handle errors in decoding
			lowerCAmelCase_   :       Union[str, Any]    							=bytes_to_unicode()
			lowerCAmelCase_   :       str    							={v: k for k, v in self.byte_encoder.items()}
			with open(UpperCamelCase_					,						encoding='''utf-8'''      ) as merges_handle:
					lowerCAmelCase_   :       Optional[Any]    							=merges_handle.read().split('''\n'''      )[1:-1]
			lowerCAmelCase_   :       List[Any]    							=[tuple(merge.split()      ) for merge in bpe_merges]
			lowerCAmelCase_   :       str    							=dict(zip(UpperCamelCase_					,						range(len(UpperCamelCase_      )      )      )      )
			lowerCAmelCase_   :       Any    							={}
			lowerCAmelCase_   :       int    							=add_prefix_space

			# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
			lowerCAmelCase_   :       Optional[Any]    							=re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''      )




	@property
	def 	__A      (						self				: Union[str, Any]      ):
			return len(self.encoder      )




	def 	__A      (						self				: List[Any]      ):
			return dict(self.encoder					,						**self.added_tokens_encoder      )




	def 	__A      (						self				: str					,						UpperCamelCase_				: List[str]      ):
			if token in self.cache:
					return self.cache[token]
			lowerCAmelCase_   :       List[str]    							=tuple(UpperCamelCase_      )
			lowerCAmelCase_   :       Dict    							=get_pairs(UpperCamelCase_      )

			if not pairs:
					return token

			while True:
					lowerCAmelCase_   :       Optional[Any]    							=min(UpperCamelCase_					,						key=lambda UpperCamelCase_      : self.bpe_ranks.get(UpperCamelCase_					,						float('''inf'''      )      )      )
					if bigram not in self.bpe_ranks:
							break
					lowerCAmelCase_							,		lowerCAmelCase_   :       List[str]    							=bigram
					lowerCAmelCase_   :       List[Any]    							=[]
					lowerCAmelCase_   :       Tuple    							=0
					while i < len(UpperCamelCase_      ):
							try:
									lowerCAmelCase_   :       str    							=word.index(UpperCamelCase_					,						UpperCamelCase_      )
							except ValueError:
									new_word.extend(word[i:]      )
									break
							else:
									new_word.extend(word[i:j]      )
									lowerCAmelCase_   :       Dict    							=j

							if word[i] == first and i < len(UpperCamelCase_      ) - 1 and word[i + 1] == second:
									new_word.append(first + second      )
									i += 2
							else:
									new_word.append(word[i]      )
									i += 1
					lowerCAmelCase_   :       Union[str, Any]    							=tuple(UpperCamelCase_      )
					lowerCAmelCase_   :       Union[str, Any]    							=new_word
					if len(UpperCamelCase_      ) == 1:
							break
					else:
							lowerCAmelCase_   :       Tuple    							=get_pairs(UpperCamelCase_      )
			lowerCAmelCase_   :       Optional[Any]    							=''' '''.join(UpperCamelCase_      )
			lowerCAmelCase_   :       str    							=word
			return word




	def 	__A      (						self				: Optional[int]					,						UpperCamelCase_				: str      ):
			lowerCAmelCase_   :       int    							=[]
			for token in re.findall(self.pat					,						UpperCamelCase_      ):
					lowerCAmelCase_   :       str    							=''''''.join(
					    self.byte_encoder[b] for b in token.encode('''utf-8'''      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
					bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase_      ).split(''' '''      )      )
			return bpe_tokens




	def 	__A      (						self				: Tuple					,						UpperCamelCase_				: Optional[Any]      ):
			return self.encoder.get(UpperCamelCase_					,						self.encoder.get(self.unk_token      )      )




	def 	__A      (						self				: int					,						UpperCamelCase_				: List[str]      ):
			return self.decoder.get(UpperCamelCase_      )




	def 	__A      (						self				: Union[str, Any]					,						UpperCamelCase_				: Union[str, Any]      ):
			lowerCAmelCase_   :       Union[str, Any]    							=''''''.join(UpperCamelCase_      )
			lowerCAmelCase_   :       Union[str, Any]    							=bytearray([self.byte_decoder[c] for c in text]      ).decode('''utf-8'''					,						errors=self.errors      )
			return text




	def 	__A      (						self				: Union[str, Any]					,						UpperCamelCase_				: str					,						UpperCamelCase_				: Optional[str] = None      ):
			if not os.path.isdir(UpperCamelCase_      ):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
					return
			lowerCAmelCase_   :       Optional[int]    							=os.path.join(
			    UpperCamelCase_					,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
			lowerCAmelCase_   :       List[Any]    							=os.path.join(
			    UpperCamelCase_					,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']      )

			with open(UpperCamelCase_					,						'''w'''					,						encoding='''utf-8'''      ) as f:
					f.write(json.dumps(self.encoder					,						indent=2					,						sort_keys=UpperCamelCase_					,						ensure_ascii=UpperCamelCase_      ) + '''\n'''      )

			lowerCAmelCase_   :       Any    							=0
			with open(UpperCamelCase_					,						'''w'''					,						encoding='''utf-8'''      ) as writer:
					writer.write('''#version: 0.2\n'''      )
					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,						key=lambda UpperCamelCase_      : kv[1]      ):
							if index != token_index:
									logger.warning(
									    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
									    ''' Please check that the tokenizer is not corrupted!'''      )
									lowerCAmelCase_   :       List[Any]    							=token_index
							writer.write(''' '''.join(UpperCamelCase_      ) + '''\n'''      )
							index += 1

			return vocab_file, merge_file




	def 	__A      (						self				: Union[str, Any]					,						UpperCamelCase_				: List[int]					,						UpperCamelCase_				: Optional[List[int]] = None      ):
			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			lowerCAmelCase_   :       Any    							=[self.cls_token_id]
			lowerCAmelCase_   :       Union[str, Any]    							=[self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep




	def 	__A      (						self				: Union[str, Any]					,						UpperCamelCase_				: List[int]					,						UpperCamelCase_				: Optional[List[int]] = None					,						UpperCamelCase_				: bool = False      ):
			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=UpperCamelCase_					,						token_ids_a=UpperCamelCase_					,						already_has_special_tokens=UpperCamelCase_      )

			if token_ids_a is None:
					return [1] + ([0] * len(UpperCamelCase_      )) + [1]
			return [1] + ([0] * len(UpperCamelCase_      )) + [1, 1] + ([0] * len(UpperCamelCase_      )) + [1]




	def 	__A      (						self				: str					,						UpperCamelCase_				: List[int]					,						UpperCamelCase_				: Optional[List[int]] = None      ):
			lowerCAmelCase_   :       Tuple    							=[self.sep_token_id]
			lowerCAmelCase_   :       int    							=[self.cls_token_id]

			if token_ids_a is None:
					return len(cls + token_ids_a + sep      ) * [0]
			return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]





	def 	__A      (						self				: Optional[Any]					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: Any=False					,						**UpperCamelCase_				: List[str]      ):
			lowerCAmelCase_   :       Any    							=kwargs.pop('''add_prefix_space'''					,						self.add_prefix_space      )
			if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase_      ) > 0 and not text[0].isspace()):
					lowerCAmelCase_   :       Union[str, Any]    							=''' ''' + text
			return (text, kwargs)








'''simple docstring'''






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		if "model" in orig_key:
				lowerCAmelCase_   :       Union[str, Any]    							=orig_key.replace('''model.'''  ,					''''''  )
		if "norm1" in orig_key:
				lowerCAmelCase_   :       List[Any]    							=orig_key.replace('''norm1'''  ,					'''attention.output.LayerNorm'''  )
		if "norm2" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm2'''  ,					'''output.LayerNorm'''  )
		if "norm" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm'''  ,					'''LayerNorm'''  )
		if "transformer" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.split('''.'''  )[0].split('''_'''  )[-1]
				lowerCAmelCase_   :       Optional[Any]    							=orig_key.replace(f'transformer_{layer_num}'  ,					f'encoder.layer.{layer_num}'  )
		if "mha.attn" in orig_key:
				lowerCAmelCase_   :       Optional[Any]    							=orig_key.replace('''mha.attn'''  ,					'''attention.self'''  )
		if "mha" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mha'''  ,					'''attention'''  )
		if "W_q" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''W_q'''  ,					'''self.query'''  )
		if "W_k" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''W_k'''  ,					'''self.key'''  )
		if "W_v" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''W_v'''  ,					'''self.value'''  )
		if "ff.0" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''ff.0'''  ,					'''intermediate.dense'''  )
		if "ff.2" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.replace('''ff.2'''  ,					'''output.dense'''  )
		if "ff" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''ff'''  ,					'''output.dense'''  )
		if "mlm_class" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mlm.mlm_class'''  ,					'''cls.predictions.decoder'''  )
		if "mlm" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''mlm'''  ,					'''cls.predictions.transform'''  )
		if "backbone.backbone.encoders" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''backbone.backbone.encoders'''  ,					'''encoder.layer'''  )
		if "cls" not in orig_key:
				lowerCAmelCase_   :       List[Any]    							='''mra.''' + orig_key

		return orig_key







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_   :       Tuple    							=orig_state_dict.pop(_SCREAMING_SNAKE_CASE  )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowerCAmelCase_   :       Dict    							=val

		lowerCAmelCase_   :       Union[str, Any]    							=orig_state_dict['''cls.predictions.decoder.bias''']
		lowerCAmelCase_   :       int    							=torch.arange(_SCREAMING_SNAKE_CASE  ).expand((1, -1)  ) + 2

		return orig_state_dict







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Tuple    							=torch.load(_SCREAMING_SNAKE_CASE  ,					map_location='''cpu'''  )['''model_state_dict''']
		lowerCAmelCase_   :       Optional[int]    							=MraConfig.from_json_file(_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Optional[Any]    							=MraForMaskedLM(_SCREAMING_SNAKE_CASE  )

		lowerCAmelCase_   :       str    							=convert_checkpoint_helper(config.max_position_embeddings  ,					_SCREAMING_SNAKE_CASE  )

		print(model.load_state_dict(_SCREAMING_SNAKE_CASE  )  )
		model.eval()
		model.save_pretrained(_SCREAMING_SNAKE_CASE  )

		print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'  )


if __name__ == "__main__":
							__lowercase	=    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
							)
							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The json file for Mra model config.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							__lowercase	=    parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






