





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

import sentencepiece as spm

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


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

__A							:			List[Any]         =			{"vocab_file": "sentencepiece.bpe.model"}

__A							:			str         =			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

__A							:			Dict         =			{
    "moussaKam/mbarthez": 1_024,
    "moussaKam/barthez": 1_024,
    "moussaKam/barthez-orangesum-title": 1_024,
}

__A							:			Dict         =			"▁"







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


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




     def __init__(    self				,     snake_case_				,     snake_case_="<s>"				,     snake_case_="</s>"				,     snake_case_="</s>"				,     snake_case_="<s>"				,     snake_case_="<unk>"				,     snake_case_="<pad>"				,     snake_case_="<mask>"				,     snake_case_ = None				,     **snake_case_				,     ):
         # Mask token behave like a normal word, i.e. include the space before it
         _A     =							AddedToken(snake_case_				,     lstrip=snake_case_				,     rstrip=snake_case_							) if isinstance(snake_case_				,     snake_case_							) else mask_token

         _A     =							{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             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_				,     sp_model_kwargs=self.sp_model_kwargs				,     **snake_case_				,     )

         _A     =							vocab_file
         _A     =							spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(str(snake_case_							)							)

         _A     =							{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

         _A     =							len(self.sp_model							) - 1
         _A     =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):

         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _A     =							[self.cls_token_id]
         _A     =							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None				,     snake_case_ = False							):
         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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):
         _A     =							[self.sep_token_id]
         _A     =							[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]




     @property
     def        lowerCAmelCase__		(    self							):
         return len(self.sp_model							)




     def        lowerCAmelCase__		(    self							):
         _A     =							{self.convert_ids_to_tokens(snake_case_							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         return self.sp_model.encode(snake_case_				,     out_type=snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         _A     =							self.sp_model.PieceToId(snake_case_							)

         return spm_id if spm_id else self.unk_token_id




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							[]
         _A     =							''
         _A     =							False
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 if not prev_is_special:
                     out_string += " "
                 out_string += self.sp_model.decode(snake_case_							) + token
                 _A     =							True
                 _A     =							[]
             else:
                 current_sub_tokens.append(snake_case_							)
                 _A     =							False
         out_string += self.sp_model.decode(snake_case_							)
         return out_string.strip()




     def __getstate__(    self							):
         _A     =							self.__dict__.copy()
         _A     =							None
         return state




     def __setstate__(    self				,     snake_case_							):
         _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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):
         if not os.path.isdir(snake_case_							):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
             return
         _A     =							os.path.join(
             snake_case_				,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

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

         return (out_vocab_file,)









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,
   )




