

'''simple docstring'''
import os
import unicodedata
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 SPIECE_UNDERLINE, logging


_lowerCamelCase      					=     logging.get_logger(__name__)

_lowerCamelCase      					=     {"""vocab_file""": """spiece.model"""}

_lowerCamelCase      					=     {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}
class 					_snake_case		(__SCREAMING_SNAKE_CASE):






			def __init__(  self			,_snake_case			,_snake_case=False			,_snake_case=True			,_snake_case=False			,_snake_case="<s>"			,_snake_case="</s>"			,_snake_case="<unk>"			,_snake_case="<sep>"			,_snake_case="<pad>"			,_snake_case="<cls>"			,_snake_case="<mask>"			,_snake_case=["<eop>", "<eod>"]			,_snake_case = None			,**_snake_case			,):
				UpperCAmelCase_     :      List[str]						=  AddedToken(_snake_case			,lstrip=_snake_case			,rstrip=_snake_case     ) if isinstance(_snake_case			,_snake_case     ) else mask_token

				UpperCAmelCase_     :      Dict						=  {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    do_lower_case=_snake_case			,remove_space=_snake_case			,keep_accents=_snake_case			,bos_token=_snake_case			,eos_token=_snake_case			,unk_token=_snake_case			,sep_token=_snake_case			,pad_token=_snake_case			,cls_token=_snake_case			,mask_token=_snake_case			,additional_special_tokens=_snake_case			,sp_model_kwargs=self.sp_model_kwargs			,**_snake_case			,)

				UpperCAmelCase_     :      Dict						=  3

				UpperCAmelCase_     :      Union[str, Any]						=  do_lower_case
				UpperCAmelCase_     :      Any						=  remove_space
				UpperCAmelCase_     :      Optional[Any]						=  keep_accents
				UpperCAmelCase_     :      Optional[Any]						=  vocab_file

				UpperCAmelCase_     :      List[Any]						=  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
				self.sp_model.Load(_snake_case     )

				try:
					import jieba
				except ModuleNotFoundError as error:
					raise error.__class__(
					    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
					    "See https://pypi.org/project/jieba/ for installation."     )
				UpperCAmelCase_     :      List[str]						=  jieba
				UpperCAmelCase_     :      Any						=  str.maketrans(" \n"			,"\u2582\u2583"     )






			@property
			# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
			def 		UpperCamelCase__							(  self     ):
				return len(self.sp_model     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      str						=  {self.convert_ids_to_tokens(_snake_case     ): i for i in range(self.vocab_size     )}
				vocab.update(self.added_tokens_encoder     )
				return vocab






			def __getstate__(  self     ):
				UpperCAmelCase_     :      List[Any]						=  self.__dict__.copy()
				UpperCAmelCase_     :      Optional[int]						=  None
				return state






			def __setstate__(  self			,_snake_case     ):
				UpperCAmelCase_     :      Union[str, Any]						=  d

				# for backward compatibility
				if not hasattr(self			,"sp_model_kwargs"     ):
					UpperCAmelCase_     :      Tuple						=  {}

				UpperCAmelCase_     :      int						=  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
				self.sp_model.Load(self.vocab_file     )






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				if self.remove_space:
					UpperCAmelCase_     :      Union[str, Any]						=  " ".join(inputs.strip().split()     )
				else:
					UpperCAmelCase_     :      str						=  inputs
				UpperCAmelCase_     :      List[str]						=  outputs.replace("``"			,"\""     ).replace("''"			,"\""     )

				if not self.keep_accents:
					UpperCAmelCase_     :      int						=  unicodedata.normalize("NFKD"			,_snake_case     )
					UpperCAmelCase_     :      int						=  "".join([c for c in outputs if not unicodedata.combining(_snake_case     )]     )
				if self.do_lower_case:
					UpperCAmelCase_     :      Dict						=  outputs.lower()

				return outputs






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      Any						=  self.preprocess_text(_snake_case     )
				UpperCAmelCase_     :      Optional[int]						=  self.sp_model.encode(_snake_case			,out_type=_snake_case     )
				UpperCAmelCase_     :      List[Any]						=  []
				for piece in pieces:
					if len(_snake_case     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
						UpperCAmelCase_     :      Optional[int]						=  self.sp_model.EncodeAsPieces(piece[:-1].replace(_snake_case			,""     )     )
						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
							if len(cur_pieces[0]     ) == 1:
								UpperCAmelCase_     :      Any						=  cur_pieces[1:]
							else:
								UpperCAmelCase_     :      int						=  cur_pieces[0][1:]
						cur_pieces.append(piece[-1]     )
						new_pieces.extend(_snake_case     )
					else:
						new_pieces.append(_snake_case     )

				return new_pieces






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				return self.sp_model.PieceToId(_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				return self.sp_model.IdToPiece(_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      Dict						=  "".join(_snake_case     ).replace(_snake_case			," "     ).strip()
				return out_string






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None     ):
				UpperCAmelCase_     :      Dict						=  [self.sep_token_id]
				UpperCAmelCase_     :      Optional[int]						=  [self.cls_token_id]
				if token_ids_a is None:
					return token_ids_a + sep + cls
				return token_ids_a + sep + token_ids_a + sep + cls






			def 		UpperCamelCase__							(  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 not None:
					return ([0] * len(_snake_case     )) + [1] + ([0] * len(_snake_case     )) + [1, 1]
				return ([0] * len(_snake_case     )) + [1, 1]






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None     ):
				UpperCAmelCase_     :      List[Any]						=  [self.sep_token_id]
				UpperCAmelCase_     :      Union[str, Any]						=  [2]

				if token_ids_a is None:
					return len(token_ids_a + sep     ) * [0] + cls_segment_id
				return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id






			def 		UpperCamelCase__							(  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
				UpperCAmelCase_     :      Optional[int]						=  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:
						UpperCAmelCase_     :      int						=  self.sp_model.serialized_model_proto()
						fi.write(_snake_case     )

				return (out_vocab_file,)






			def 		UpperCamelCase__							(  self			,*_snake_case			,**_snake_case     ):
				UpperCAmelCase_     :      Tuple						=  super()._decode(*_snake_case			,**_snake_case     )
				UpperCAmelCase_     :      List[str]						=  text.replace(" "			,""     ).replace("\u2582"			," "     ).replace("\u2583"			,"\n"     )
				return text








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,
   )




