


'''simple docstring'''






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
a_   :      Union[str, Any]				    =      {
    "/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 		_A			(lowerCAmelCase__					:Union[str, Any]						)				->					Dict:




     '''simple docstring'''



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

          _a      =							r'(encoder|decoder)\/'

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

               elif groups[0] == "decoder":
                    _a      =							re.sub(r'/mlp/'			,  r'/2/mlp/'			,  lowerCAmelCase__						)
                    _a      =							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:
                    _a      =							new_key.replace(lowerCAmelCase__			,  lowerCAmelCase__						)

          print(f'{key} -> {new_key}'						)
          _a      =							s_dict.pop(lowerCAmelCase__						)

     if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
          _a      =							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:
          _a      =							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:
               _a      =							s_dict[key].shape[0]
               _a      =							s_dict[key]
               for idx in range(lowerCAmelCase__						):
                    _a      =							expert_weihts[idx]
                    print(f'{key} -> {key.replace("expert/"			,  "nested fstring"						)}'						)

               s_dict.pop(lowerCAmelCase__						)

     return s_dict


a_   :      Dict				    =      {
    "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 		_A			(lowerCAmelCase__					:List[Any]			,  lowerCAmelCase__					:Optional[Any]						)				->					str:




     '''simple docstring'''



     import regex as re

     with open(lowerCAmelCase__			,  'r'						) as f:
          _a      =							f.read()

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

     _a      =							re.findall(r'(.*activations) = \(\'(.*)\',\)'			,  lowerCAmelCase__						)[0]
     _a      =							str(activation[1]						)

     _a      =							num_experts
     _a      =							SwitchTransformersConfig(**lowerCAmelCase__						)
     return config






def 		_A			(lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Optional[Any]=None			,  lowerCAmelCase__					:Any="./"			,  lowerCAmelCase__					:Tuple=8						)				->					Dict:




     '''simple docstring'''



     print(f'Loading flax weights from : {flax_checkpoint_path}'						)
     _a      =							checkpoints.load_tax_checkpoint(lowerCAmelCase__						)

     if gin_file is not None:
          _a      =							convert_gin_to_config(lowerCAmelCase__			,  lowerCAmelCase__						)
     else:
          _a      =							SwitchTransformersConfig.from_pretrained(lowerCAmelCase__						)

     _a      =							SwitchTransformersForConditionalGeneration(lowerCAmelCase__						)

     _a      =							flax_params['target']
     _a      =							flatten_dict(lowerCAmelCase__			,  sep='/'						)
     _a      =							rename_keys(lowerCAmelCase__						)
     _a      =							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__":
 a_   :      str				    =      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")
 a_   :      Any				    =      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,
 )





'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_   :      Union[str, Any]				    =      logging.get_logger(__name__)

a_   :      Union[str, Any]				    =      {"vocab_file": "spiece.model"}

a_   :      List[Any]				    =      {
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

a_   :      Dict				    =      {"bert_for_seq_generation": 5_1_2}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		VOCAB_FILES_NAMES
 _lowerCAmelCase	   =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	   =		[]
 _lowerCAmelCase	   =		["""input_ids""", """attention_mask"""]
 def __init__(	self ,			__magic_name__ ,			__magic_name__="<s>" ,			__magic_name__="</s>" ,			__magic_name__="<unk>" ,			__magic_name__="<pad>" ,			__magic_name__="<::::>" ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							None:
      _a      =							{} if sp_model_kwargs is None else sp_model_kwargs

      # Add extra_ids to the special token list
      super().__init__(
          bos_token=__magic_name__ ,			eos_token=__magic_name__ ,			unk_token=__magic_name__ ,			pad_token=__magic_name__ ,			sep_token=__magic_name__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**__magic_name__ ,			)

      _a      =							vocab_file

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(__magic_name__  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      return self.sp_model.get_piece_size()
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							{self.convert_ids_to_tokens(__magic_name__  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab
 def __getstate__(	self  )   ->							Any:
      _a      =							self.__dict__.copy()
      _a      =							None
      return state
 def __setstate__(	self ,			__magic_name__  )   ->							int:
      _a      =							d

      # for backward compatibility
      if not hasattr(self ,			'sp_model_kwargs'  ):
           _a      =							{}

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(self.vocab_file  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.encode(__magic_name__ ,			out_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.piece_to_id(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      _a      =							self.sp_model.IdToPiece(__magic_name__  )
      return token
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[Any]:
      _a      =							[]
      _a      =							''
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                out_string += self.sp_model.decode(__magic_name__  ) + token
                _a      =							[]
           else:
                current_sub_tokens.append(__magic_name__  )
      out_string += self.sp_model.decode(__magic_name__  )
      return out_string.strip()




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							Tuple[str]:
      if not os.path.isdir(__magic_name__  ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
           return
      _a      =							os.path.join(
          __magic_name__ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

      if os.path.abspath(self.vocab_file  ) != os.path.abspath(__magic_name__  ) and os.path.isfile(self.vocab_file  ):
           copyfile(self.vocab_file ,			__magic_name__  )
      elif not os.path.isfile(self.vocab_file  ):
           with open(__magic_name__ ,			'wb'  ) as fi:
                _a      =							self.sp_model.serialized_model_proto()
                fi.write(__magic_name__  )

      return (out_vocab_file,)

