


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


SCREAMING_SNAKE_CASE						:Optional[int]			=  logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE						:int			=  {
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
}

SCREAMING_SNAKE_CASE						:Tuple			=  {
    '''facebook/bart-base''': 10_24,
    '''facebook/bart-large''': 10_24,
    '''facebook/bart-large-mnli''': 10_24,
    '''facebook/bart-large-cnn''': 10_24,
    '''facebook/bart-large-xsum''': 10_24,
    '''yjernite/bart_eli5''': 10_24,
}



@lru_cache()
def       _lowerCAmelCase    (				)->Any:
   '''simple docstring'''






   snake_case_            =       (
       list(range(ord("!"				)		,    ord("~"				) + 1				)				) + list(range(ord("¡"				)		,    ord("¬"				) + 1				)				) + list(range(ord("®"				)		,    ord("ÿ"				) + 1				)				)
   )
   snake_case_            =       bs[:]
   snake_case_            =       0
   for b in range(2**8				):
      if b not in bs:
         bs.append(lowerCAmelCase_				)
         cs.append(2**8 + n				)
         n += 1
   snake_case_            =       [chr(lowerCAmelCase_				) for n in cs]
   return dict(zip(lowerCAmelCase_		,    lowerCAmelCase_				)				)



def       _lowerCAmelCase    (				lowerCAmelCase_				:Dict				)->List[str]:
   '''simple docstring'''






   snake_case_            =       set()
   snake_case_            =       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      snake_case_            =       char
   return pairs



class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





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



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




     """simple docstring"""
     snake_case_            =       AddedToken(_lowerCAmelCase     ,					lstrip=_lowerCAmelCase     ,					rstrip=_lowerCAmelCase	) if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) else bos_token
     snake_case_            =       AddedToken(_lowerCAmelCase     ,					lstrip=_lowerCAmelCase     ,					rstrip=_lowerCAmelCase	) if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) else eos_token
     snake_case_            =       AddedToken(_lowerCAmelCase     ,					lstrip=_lowerCAmelCase     ,					rstrip=_lowerCAmelCase	) if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) else sep_token
     snake_case_            =       AddedToken(_lowerCAmelCase     ,					lstrip=_lowerCAmelCase     ,					rstrip=_lowerCAmelCase	) if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) else cls_token
     snake_case_            =       AddedToken(_lowerCAmelCase     ,					lstrip=_lowerCAmelCase     ,					rstrip=_lowerCAmelCase	) if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) else unk_token
     snake_case_            =       AddedToken(_lowerCAmelCase     ,					lstrip=_lowerCAmelCase     ,					rstrip=_lowerCAmelCase	) if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) else pad_token

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

     super().__init__(
         errors=_lowerCAmelCase     ,					bos_token=_lowerCAmelCase     ,					eos_token=_lowerCAmelCase     ,					unk_token=_lowerCAmelCase     ,					sep_token=_lowerCAmelCase     ,					cls_token=_lowerCAmelCase     ,					pad_token=_lowerCAmelCase     ,					mask_token=_lowerCAmelCase     ,					add_prefix_space=_lowerCAmelCase     ,					**_lowerCAmelCase     ,					)

     with open(_lowerCAmelCase     ,					encoding="utf-8"	) as vocab_handle:
        snake_case_            =       json.load(_lowerCAmelCase	)
     snake_case_            =       {v: k for k, v in self.encoder.items()}
     snake_case_            =       errors  # how to handle errors in decoding
     snake_case_            =       bytes_to_unicode()
     snake_case_            =       {v: k for k, v in self.byte_encoder.items()}
     with open(_lowerCAmelCase     ,					encoding="utf-8"	) as merges_handle:
        snake_case_            =       merges_handle.read().split("\n"	)[1:-1]
     snake_case_            =       [tuple(merge.split()	) for merge in bpe_merges]
     snake_case_            =       dict(zip(_lowerCAmelCase     ,					range(len(_lowerCAmelCase	)	)	)	)
     snake_case_            =       {}
     snake_case_            =       add_prefix_space

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



  @property
  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			int:




     """simple docstring"""
     return len(self.encoder	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			int:




     """simple docstring"""
     return dict(self.encoder     ,					**self.added_tokens_encoder	)



  def        lowerCAmelCase__	(  self							:							Any     ,					_lowerCAmelCase							:							int	) ->			List[Any]:




     """simple docstring"""
     if token in self.cache:
        return self.cache[token]
     snake_case_            =       tuple(_lowerCAmelCase	)
     snake_case_            =       get_pairs(_lowerCAmelCase	)

     if not pairs:
        return token

     while True:
        snake_case_            =       min(_lowerCAmelCase     ,					key=lambda _lowerCAmelCase	: self.bpe_ranks.get(_lowerCAmelCase     ,					float("inf"	)	)	)
        if bigram not in self.bpe_ranks:
           break
        snake_case_     ,     snake_case_            =       bigram
        snake_case_            =       []
        snake_case_            =       0
        while i < len(_lowerCAmelCase	):
           try:
              snake_case_            =       word.index(_lowerCAmelCase     ,					_lowerCAmelCase	)
           except ValueError:
              new_word.extend(word[i:]	)
              break
           else:
              new_word.extend(word[i:j]	)
              snake_case_            =       j

           if word[i] == first and i < len(_lowerCAmelCase	) - 1 and word[i + 1] == second:
              new_word.append(first + second	)
              i += 2
           else:
              new_word.append(word[i]	)
              i += 1
        snake_case_            =       tuple(_lowerCAmelCase	)
        snake_case_            =       new_word
        if len(_lowerCAmelCase	) == 1:
           break
        else:
           snake_case_            =       get_pairs(_lowerCAmelCase	)
     snake_case_            =       " ".join(_lowerCAmelCase	)
     snake_case_            =       word
     return word



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Any	) ->			Optional[Any]:




     """simple docstring"""
     snake_case_            =       []
     for token in re.findall(self.pat     ,					_lowerCAmelCase	):
        snake_case_            =       "".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(_lowerCAmelCase	).split(" "	)	)
     return bpe_tokens



  def        lowerCAmelCase__	(  self							:							Dict     ,					_lowerCAmelCase							:							Dict	) ->			Optional[Any]:




     """simple docstring"""
     return self.encoder.get(_lowerCAmelCase     ,					self.encoder.get(self.unk_token	)	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Optional[Any]	) ->			List[Any]:




     """simple docstring"""
     return self.decoder.get(_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							Tuple	) ->			Dict:




     """simple docstring"""
     snake_case_            =       "".join(_lowerCAmelCase	)
     snake_case_            =       bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8"     ,					errors=self.errors	)
     return text



  def        lowerCAmelCase__	(  self							:							Any     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[str] = None	) ->			Tuple[str]:




     """simple docstring"""
     if not os.path.isdir(_lowerCAmelCase	):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
        return
     snake_case_            =       os.path.join(
         _lowerCAmelCase     ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
     snake_case_            =       os.path.join(
         _lowerCAmelCase     ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)

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

     snake_case_            =       0
     with open(_lowerCAmelCase     ,					"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 _lowerCAmelCase	: 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!"	)
              snake_case_            =       token_index
           writer.write(" ".join(_lowerCAmelCase	) + "\n"	)
           index += 1

     return vocab_file, merge_file



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[int]     ,					_lowerCAmelCase							:							Optional[List[int]] = None	) ->			List[int]:




     """simple docstring"""
     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     snake_case_            =       [self.cls_token_id]
     snake_case_            =       [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							List[int]     ,					_lowerCAmelCase							:							Optional[List[int]] = None     ,					_lowerCAmelCase							:							bool = False	) ->			List[int]:




     """simple docstring"""
     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=_lowerCAmelCase     ,					token_ids_a=_lowerCAmelCase     ,					already_has_special_tokens=_lowerCAmelCase	)

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



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							List[int]     ,					_lowerCAmelCase							:							Optional[List[int]] = None	) ->			List[int]:




     """simple docstring"""
     snake_case_            =       [self.sep_token_id]
     snake_case_            =       [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        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							int=False     ,					**_lowerCAmelCase							:							int	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       kwargs.pop("add_prefix_space"     ,					self.add_prefix_space	)
     if (is_split_into_words or add_prefix_space) and (len(_lowerCAmelCase	) > 0 and not text[0].isspace()):
        snake_case_            =       " " + text
     return (text, kwargs)








import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
SCREAMING_SNAKE_CASE						:Tuple			=  {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}



def       _lowerCAmelCase    (				lowerCAmelCase_				:Any				)->Any:
   '''simple docstring'''






   snake_case_            =       list(s_dict.keys()				)
   for key in keys:
      snake_case_            =       r".*/layers_(\d+)"
      snake_case_            =       key
      if re.match(lowerCAmelCase_		,    lowerCAmelCase_				):
         snake_case_            =       re.sub(r"layers_(\d+)"		,    r"block/\1/layer"		,    lowerCAmelCase_				)

      snake_case_            =       r"(encoder|decoder)\/"

      if re.match(lowerCAmelCase_		,    lowerCAmelCase_				):
         snake_case_            =       re.match(lowerCAmelCase_		,    lowerCAmelCase_				).groups()
         if groups[0] == "encoder":
            snake_case_            =       re.sub(r"/mlp/"		,    r"/1/mlp/"		,    lowerCAmelCase_				)
            snake_case_            =       re.sub(r"/pre_mlp_layer_norm/"		,    r"/1/layer_norm/"		,    lowerCAmelCase_				)

         elif groups[0] == "decoder":
            snake_case_            =       re.sub(r"/mlp/"		,    r"/2/mlp/"		,    lowerCAmelCase_				)
            snake_case_            =       re.sub(r"/pre_mlp_layer_norm/"		,    r"/2/layer_norm/"		,    lowerCAmelCase_				)

        # 2. Convert other classic mappings
      for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
         if old_key in new_key:
            snake_case_            =       new_key.replace(lowerCAmelCase_		,    lowerCAmelCase_				)

      print(F'''{key} -> {new_key}'''				)
      snake_case_            =       s_dict.pop(lowerCAmelCase_				)

   if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
      snake_case_            =       s_dict[
          "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
      ].T
   if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
      snake_case_            =       s_dict[
          "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
      ].T

   # 3. Take extra care of the EXPERTS layer
   for key in list(s_dict.keys()				):
      if "expert" in key:
         snake_case_            =       s_dict[key].shape[0]
         snake_case_            =       s_dict[key]
         for idx in range(lowerCAmelCase_				):
            snake_case_            =       expert_weihts[idx]
            print(F'''{key} -> {key.replace('expert/'		,    'nested fstring'				)}'''				)

         s_dict.pop(lowerCAmelCase_				)

   return s_dict


SCREAMING_SNAKE_CASE						:Optional[Any]			=  {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Any				)->Optional[int]:
   '''simple docstring'''






   import regex as re

   with open(lowerCAmelCase_		,    "r"				) as f:
      snake_case_            =       f.read()

   snake_case_            =       re.findall(r"(.*) = ([0-9.]*)"		,    lowerCAmelCase_				)
   snake_case_            =       {}
   for param, value in regex_match:
      if param in GIN_TO_CONFIG_MAPPING and value != "":
         snake_case_            =       float(lowerCAmelCase_				) if "." in value else int(lowerCAmelCase_				)

   snake_case_            =       re.findall(r"(.*activations) = \(\'(.*)\',\)"		,    lowerCAmelCase_				)[0]
   snake_case_            =       str(activation[1]				)

   snake_case_            =       num_experts
   snake_case_            =       SwitchTransformersConfig(**lowerCAmelCase_				)
   return config



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:Dict=None		,    lowerCAmelCase_				:str="./"		,    lowerCAmelCase_				:Optional[int]=8				)->List[str]:
   '''simple docstring'''






   print(F'''Loading flax weights from : {flax_checkpoint_path}'''				)
   snake_case_            =       checkpoints.load_tax_checkpoint(lowerCAmelCase_				)

   if gin_file is not None:
      snake_case_            =       convert_gin_to_config(lowerCAmelCase_		,    lowerCAmelCase_				)
   else:
      snake_case_            =       SwitchTransformersConfig.from_pretrained(lowerCAmelCase_				)

   snake_case_            =       SwitchTransformersForConditionalGeneration(lowerCAmelCase_				)

   snake_case_            =       flax_params["target"]
   snake_case_            =       flatten_dict(lowerCAmelCase_		,    sep="/"				)
   snake_case_            =       rename_keys(lowerCAmelCase_				)
   snake_case_            =       unflatten_dict(lowerCAmelCase_		,    sep="/"				)

   # Load the flax params in the PT model
   load_flax_weights_in_pytorch_model(lowerCAmelCase_		,    lowerCAmelCase_				)

   print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
   pt_model.save_pretrained(lowerCAmelCase_				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:int			=  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--switch_t5x_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
           ''' model architecture. If not provided, a `gin_file` has to be provided.'''
       ),
   )
   parser.add_argument(
       '''--gin_file''',
       default=None,
       type=str,
       required=False,
       help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
   )
   parser.add_argument(
       '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
   )
   parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
   SCREAMING_SNAKE_CASE						:int			=  parser.parse_args()
   convert_flax_checkpoint_to_pytorch(
       args.switch_tax_checkpoint_path,
       args.config_name,
       args.gin_file,
       args.pytorch_dump_folder_path,
       args.num_experts,
   )




