





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


_lowerCAmelCase						=	logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
_lowerCAmelCase						=	{
    '''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''',
    },
}

_lowerCAmelCase						=	{
    '''RUCAIBox/mvp''': 1_024,
}

@lru_cache()
def      __snake_case						(					) ->						List[Any]:
   A_					:				List[Any]          =			(
       list(range(ord("!"	)				,				ord("~"	) + 1	)	) + list(range(ord("¡"	)				,				ord("¬"	) + 1	)	) + list(range(ord("®"	)				,				ord("ÿ"	) + 1	)	)
   )
   A_					:				int          =			bs[:]
   A_					:				List[str]          =			0
   for b in range(2**8	):
      if b not in bs:
         bs.append(_lowerCAmelCase	)
         cs.append(2**8 + n	)
         n += 1
   A_					:				List[Any]          =			[chr(_lowerCAmelCase	) for n in cs]
   return dict(zip(_lowerCAmelCase				,				_lowerCAmelCase	)	)

def      __snake_case						(					_lowerCAmelCase : Dict	) ->						Any:
   A_					:				Dict          =			set()
   A_					:				Optional[int]          =			word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)	)
      A_					:				Union[str, Any]          =			char
   return pairs





class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     __UpperCamelCase					    =     VOCAB_FILES_NAMES
     __UpperCamelCase					    =     PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase					    =     ['''input_ids''', '''attention_mask''']



     def __init__(							self       :int			,      snake_case       :Optional[int]			,      snake_case       :int			,      snake_case       :List[str]="replace"			,      snake_case       :int="<s>"			,      snake_case       :int="</s>"			,      snake_case       :List[str]="</s>"			,      snake_case       :List[Any]="<s>"			,      snake_case       :str="<unk>"			,      snake_case       :Dict="<pad>"			,      snake_case       :List[str]="<mask>"			,      snake_case       :Optional[int]=False			,      **snake_case       :Optional[int]			,      ):





        '''simple docstring'''




        A_					:				Any          =			AddedToken(snake_case			,      lstrip=snake_case			,      rstrip=snake_case  ) if isinstance(snake_case			,      snake_case  ) else bos_token
        A_					:				List[Any]          =			AddedToken(snake_case			,      lstrip=snake_case			,      rstrip=snake_case  ) if isinstance(snake_case			,      snake_case  ) else eos_token
        A_					:				Any          =			AddedToken(snake_case			,      lstrip=snake_case			,      rstrip=snake_case  ) if isinstance(snake_case			,      snake_case  ) else sep_token
        A_					:				List[str]          =			AddedToken(snake_case			,      lstrip=snake_case			,      rstrip=snake_case  ) if isinstance(snake_case			,      snake_case  ) else cls_token
        A_					:				Dict          =			AddedToken(snake_case			,      lstrip=snake_case			,      rstrip=snake_case  ) if isinstance(snake_case			,      snake_case  ) else unk_token
        A_					:				Optional[Any]          =			AddedToken(snake_case			,      lstrip=snake_case			,      rstrip=snake_case  ) if isinstance(snake_case			,      snake_case  ) else pad_token

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

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

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        A_					:				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 						SCREAMING_SNAKE_CASE		(							self       :Any  ):





        '''simple docstring'''




        return len(self.encoder  )



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]  ):





        '''simple docstring'''




        return dict(self.encoder			,      **self.added_tokens_encoder  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict			,      snake_case       :Tuple  ):





        '''simple docstring'''




        if token in self.cache:
           return self.cache[token]
        A_					:				List[str]          =			tuple(snake_case  )
        A_					:				Optional[int]          =			get_pairs(snake_case  )

        if not pairs:
           return token

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

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



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple			,      snake_case       :Union[str, Any]  ):





        '''simple docstring'''




        A_					:				List[Any]          =			[]
        for token in re.findall(self.pat			,      snake_case  ):
           A_					:				Optional[int]          =			"".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(snake_case  ).split(" "  )  )
        return bpe_tokens



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :List[str]  ):





        '''simple docstring'''




        return self.encoder.get(snake_case			,      self.encoder.get(self.unk_token  )  )



     def 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :List[Any]  ):





        '''simple docstring'''




        return self.decoder.get(snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]			,      snake_case       :str  ):





        '''simple docstring'''




        A_					:				Tuple          =			"".join(snake_case  )
        A_					:				Optional[int]          =			bytearray([self.byte_decoder[c] for c in text]  ).decode("utf-8"			,      errors=self.errors  )
        return text



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict			,      snake_case       :str			,      snake_case       :Optional[str] = None  ):





        '''simple docstring'''




        if not os.path.isdir(snake_case  ):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"  )
           return
        A_					:				List[Any]          =			os.path.join(
            snake_case			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )
        A_					:				Optional[int]          =			os.path.join(
            snake_case			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]  )

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

        A_					:				List[str]          =			0
        with open(snake_case			,      "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 snake_case  : 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!"  )
                 A_					:				Any          =			token_index
              writer.write(" ".join(snake_case  ) + "\n"  )
              index += 1

        return vocab_file, merge_file



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]			,      snake_case       :List[int]			,      snake_case       :Optional[List[int]] = None  ):





        '''simple docstring'''




        if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        A_					:				Tuple          =			[self.cls_token_id]
        A_					:				List[str]          =			[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]			,      snake_case       :List[int]			,      snake_case       :Optional[List[int]] = None			,      snake_case       :bool = False  ):





        '''simple docstring'''




        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=snake_case			,      token_ids_a=snake_case			,      already_has_special_tokens=snake_case  )

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



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]			,      snake_case       :List[int]			,      snake_case       :Optional[List[int]] = None  ):





        '''simple docstring'''




        A_					:				int          =			[self.sep_token_id]
        A_					:				str          =			[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 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :List[str]			,      snake_case       :int=False			,      **snake_case       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Tuple          =			kwargs.pop("add_prefix_space"			,      self.add_prefix_space  )
        if (is_split_into_words or add_prefix_space) and (len(snake_case  ) > 0 and not text[0].isspace()):
           A_					:				Union[str, Any]          =			" " + text
        return (text, kwargs)







import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()

def      __snake_case						(					_lowerCAmelCase : Optional[int]				,				_lowerCAmelCase : List[str]				,				_lowerCAmelCase : List[Any]	) ->						Any:
   # Initialise PyTorch model
   A_					:				Tuple          =			FNetConfig.from_json_file(_lowerCAmelCase	)
   print(f"Building PyTorch model from configuration: {config}"	)
   A_					:				Tuple          =			FNetForPreTraining(_lowerCAmelCase	)

   A_					:				List[str]          =			restore_checkpoint(_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Any          =			checkpoint_dict["target"]

   # Embeddings
   # Position IDs
   A_					:				Optional[Any]          =			fnet_pretraining_model.state_dict()

   A_					:				Optional[int]          =			state_dict["fnet.embeddings.position_ids"]
   A_					:				Union[str, Any]          =			{"fnet.embeddings.position_ids": position_ids}
   # Embedding Layers
   A_					:				str          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]	)
   A_					:				List[Any]          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]	)
   A_					:				int          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]	)
   A_					:				List[Any]          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]	).T
   A_					:				Dict          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]	)
   A_					:				int          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]	)
   A_					:				Any          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]	)

   # Encoder Layers
   for layer in range(config.num_hidden_layers	):
      A_					:				List[str]          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["scale"]	)
      A_					:				Union[str, Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["bias"]	)

      A_					:				Union[str, Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["kernel"]	).T
      A_					:				List[Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["bias"]	)

      A_					:				Optional[int]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["kernel"]	).T
      A_					:				int          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["bias"]	)

      A_					:				Any          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["scale"]	)
      A_					:				Tuple          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["bias"]	)

   # Pooler Layers
   A_					:				Optional[Any]          =			torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]	).T
   A_					:				List[Any]          =			torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]	)

   # Masked LM Layers
   A_					:				Optional[Any]          =			torch.tensor(
       pretrained_model_params["predictions_dense"]["kernel"]	).T
   A_					:				int          =			torch.tensor(
       pretrained_model_params["predictions_dense"]["bias"]	)
   A_					:				str          =			torch.tensor(
       pretrained_model_params["predictions_layer_norm"]["scale"]	)
   A_					:				List[Any]          =			torch.tensor(
       pretrained_model_params["predictions_layer_norm"]["bias"]	)
   A_					:				Tuple          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]	)
   A_					:				Any          =			torch.tensor(
       pretrained_model_params["predictions_output"]["output_bias"]	)
   A_					:				List[str]          =			torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]	)

   # Seq Relationship Layers
   A_					:				str          =			torch.tensor(
       pretrained_model_params["classification"]["output_kernel"]	)
   A_					:				str          =			torch.tensor(
       pretrained_model_params["classification"]["output_bias"]	)

   # Load State Dict
   fnet_pretraining_model.load_state_dict(_lowerCAmelCase	)

   # Save PreTrained
   print(f"Saving pretrained model to {save_path}"	)
   fnet_pretraining_model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
 _lowerCAmelCase							:      Tuple						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--fnet_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained FNet model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 _lowerCAmelCase							:      Optional[int]						=	parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
