






'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
  import jieba

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


a_					=	logging.get_logger(__name__)

a_					=	{'vocab_file': 'vocab.txt'}

a_					=	{
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

a_					=	{
    'openbmb/cpm-ant-10b': 1_0_2_4,
}





def 	_a(					UpperCamelCase__			:  str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple									=collections.OrderedDict()
      with open(UpperCamelCase__,		'''r''',		encoding='''utf-8'''							) as reader:
            SCREAMING_SNAKE_CASE__       :			str									=reader.readlines()
      for index, token in enumerate(UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__       :			Optional[Any]									=token.rstrip('''\n'''							)
            SCREAMING_SNAKE_CASE__       :			int									=index
      return vocab






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							Any   , __lowercase		:							Optional[Any]   , __lowercase		:							List[str]="<unk>"   , __lowercase		:							Optional[Any]=2_00 )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			int									=vocab
       SCREAMING_SNAKE_CASE__       :			int									=unk_token
       SCREAMING_SNAKE_CASE__       :			int									=max_input_chars_per_word




 def        __magic_name__		( self		:							Tuple   , __lowercase		:							str )		->  Any:
       SCREAMING_SNAKE_CASE__       :			int									=list(__lowercase )
       if len(__lowercase ) > self.max_input_chars_per_word:
             return [self.unk_token]

       SCREAMING_SNAKE_CASE__       :			Optional[Any]									=0
       SCREAMING_SNAKE_CASE__       :			int									=[]
       while start < len(__lowercase ):
             SCREAMING_SNAKE_CASE__       :			Optional[int]									=len(__lowercase )
             SCREAMING_SNAKE_CASE__       :			Tuple									=None
             while start < end:
                   SCREAMING_SNAKE_CASE__       :			List[Any]									=''''''.join(chars[start:end] )
                   if substr in self.vocab:
                         SCREAMING_SNAKE_CASE__       :			str									=substr
                         break
                   end -= 1
             if cur_substr is None:
                   sub_tokens.append(self.unk_token )
                   start += 1
             else:
                   sub_tokens.append(__lowercase )
                   SCREAMING_SNAKE_CASE__       :			List[str]									=end

       return sub_tokens






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							VOCAB_FILES_NAMES
 snake_case_												=							PRETRAINED_VOCAB_FILES_MAP
 snake_case_												=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_												=							["""input_ids""", """attention_mask"""]
 snake_case_												=							False




 def __init__( self		:							Optional[int]   , __lowercase		:							Dict   , __lowercase		:							Tuple="<d>"   , __lowercase		:							int="</d>"   , __lowercase		:							str="<s>"   , __lowercase		:							Optional[int]="</s>"   , __lowercase		:							List[Any]="<pad>"   , __lowercase		:							List[Any]="<unk>"   , __lowercase		:							Optional[Any]="</n>"   , __lowercase		:							Tuple="</_>"   , __lowercase		:							List[Any]="left"   , **__lowercase		:							Any   , )		->  Union[str, Any]:
       requires_backends(self   , ['''jieba'''] )
       super().__init__(
           bod_token=__lowercase   , eod_token=__lowercase   , bos_token=__lowercase   , eos_token=__lowercase   , pad_token=__lowercase   , unk_token=__lowercase   , line_token=__lowercase   , space_token=__lowercase   , padding_side=__lowercase   , **__lowercase   , )
       SCREAMING_SNAKE_CASE__       :			Any									=bod_token
       SCREAMING_SNAKE_CASE__       :			int									=eod_token
       SCREAMING_SNAKE_CASE__       :			Optional[int]									=load_vocab(__lowercase )
       SCREAMING_SNAKE_CASE__       :			List[Any]									=self.encoder[space_token]
       SCREAMING_SNAKE_CASE__       :			List[str]									=self.encoder[line_token]

       del self.encoder[space_token]
       del self.encoder[line_token]

       SCREAMING_SNAKE_CASE__       :			Optional[int]									=collections.OrderedDict(sorted(self.encoder.items()   , key=lambda __lowercase : x[1] ) )
       SCREAMING_SNAKE_CASE__       :			List[str]									={v: k for k, v in self.encoder.items()}

       SCREAMING_SNAKE_CASE__       :			str									=WordpieceTokenizer(vocab=self.encoder   , unk_token=self.unk_token )




 @property
 def        __magic_name__		( self		:							Dict )		->  List[str]:
       return self.encoder[self.bod_token]




 @property
 def        __magic_name__		( self		:							List[str] )		->  Optional[Any]:
       return self.encoder[self.eod_token]




 @property
 def        __magic_name__		( self		:							int )		->  Union[str, Any]:
       return self.encoder["\n"]




 @property
 def        __magic_name__		( self		:							Union[str, Any] )		->  int:
       return len(self.encoder )




 def        __magic_name__		( self		:							Union[str, Any] )		->  str:
       return dict(self.encoder   , **self.added_tokens_encoder )




 def        __magic_name__		( self		:							int   , __lowercase		:							Dict )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Tuple									=[]
       for x in jieba.cut(__lowercase   , cut_all=__lowercase ):
             output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowercase ) )
       return output_tokens




 def        __magic_name__		( self		:							str   , __lowercase		:							int   , **__lowercase		:							List[Any] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]									=[i for i in token_ids if i >= 0]
       SCREAMING_SNAKE_CASE__       :			str									=[
           x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
       ]
       return super()._decode(__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Tuple   , __lowercase		:							List[str] )		->  Union[str, Any]:
       return token in self.encoder




 def        __magic_name__		( self		:							int   , __lowercase		:							List[str] )		->  str:
       return "".join(__lowercase )




 def        __magic_name__		( self		:							List[str]   , __lowercase		:							Tuple )		->  Any:
       return self.encoder.get(__lowercase   , self.encoder.get(self.unk_token ) )




 def        __magic_name__		( self		:							int   , __lowercase		:							int )		->  Dict:
       return self.decoder.get(__lowercase   , self.unk_token )




 def        __magic_name__		( self		:							Dict   , __lowercase		:							str   , __lowercase		:							Optional[str] = None )		->  Tuple[str]:
       if os.path.isdir(__lowercase ):
             SCREAMING_SNAKE_CASE__       :			List[str]									=os.path.join(
                 __lowercase   , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
       else:
             SCREAMING_SNAKE_CASE__       :			Dict									=(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
       SCREAMING_SNAKE_CASE__       :			Optional[int]									=0
       if " " in self.encoder:
             SCREAMING_SNAKE_CASE__       :			Optional[Any]									=self.encoder[''' ''']
             del self.encoder[" "]
       if "\n" in self.encoder:
             SCREAMING_SNAKE_CASE__       :			List[Any]									=self.encoder['''\n''']
             del self.encoder["\n"]
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]									=collections.OrderedDict(sorted(self.encoder.items()   , key=lambda __lowercase : x[1] ) )
       with open(__lowercase   , '''w'''   , encoding='''utf-8''' ) as writer:
             for token, token_index in self.encoder.items():
                   if index != token_index:
                         logger.warning(
                             F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                             ''' Please check that the vocabulary is not corrupted!''' )
                         SCREAMING_SNAKE_CASE__       :			Any									=token_index
                   writer.write(token + '''\n''' )
                   index += 1
       return (vocab_file,)




 def        __magic_name__		( self		:							Any   , __lowercase		:							List[int]   , __lowercase		:							List[int] = None )		->  List[int]:
       if token_ids_a is None:
             return [self.bos_token_id] + token_ids_a
       return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




 def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							List[int]   , __lowercase		:							Optional[List[int]] = None   , __lowercase		:							bool = False )		->  List[int]:

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

       if token_ids_a is not None:
             return [1] + ([0] * len(__lowercase )) + [1] + ([0] * len(__lowercase ))
       return [1] + ([0] * len(__lowercase ))







'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Any													=AutoConfig.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=checkpoints.load_tax_checkpoint(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

      if config.model_type == "t5":
            SCREAMING_SNAKE_CASE__       :			str													='''SelfAttention'''
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''LocalSelfAttention'''
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Dict													='''TransientGlobalSelfAttention'''
      else:
            raise ValueError(
                '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
                ''' attribute with a value from [\'local\', \'transient-global].'''							)

      # Encoder
      for layer_index in range(config.num_layers							):
            SCREAMING_SNAKE_CASE__       :			Dict													=f"layers_{str(UpperCamelCase__							)}"

            # Self-Attention
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''encoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			Dict													=tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_global_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			Any													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wo
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=flax_model_encoder_layer_block

      # Only for layer 0:
      SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
      SCREAMING_SNAKE_CASE__       :			Any													=tax_encoder_rel_embedding

      # Side/global relative position_bias + layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_encoder_global_rel_embedding

      # Assigning
      SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			int													=tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers							):
            SCREAMING_SNAKE_CASE__       :			int													=f"layers_{str(UpperCamelCase__							)}"

            # Self-Attention
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                '''scale'''
            ]

            # Encoder-Decoder-Attention
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_enc_dec_attention_module['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			Any													=tax_enc_dec_attention_module['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_enc_dec_attention_module['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_enc_dec_attention_module['''value''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

            # MLP
            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''decoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_pre_attention_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_enc_dec_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_enc_dec_attention_out
            SCREAMING_SNAKE_CASE__       :			str													=tax_enc_dec_attention_query
            SCREAMING_SNAKE_CASE__       :			str													=tax_enc_dec_attention_value

            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_cross_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_mlp_wo

            SCREAMING_SNAKE_CASE__       :			str													=txa_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[Any]													=flax_model_decoder_layer_block

      # Decoder Normalization
      SCREAMING_SNAKE_CASE__       :			Any													=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=txa_decoder_norm

      # Only for layer 0:
      SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
      SCREAMING_SNAKE_CASE__       :			Tuple													=tax_decoder_rel_embedding

      # Token Embeddings
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''token_embedder''']['''embedding''']
      SCREAMING_SNAKE_CASE__       :			List[Any]													=txa_token_embeddings

      # LM Head (only in v1.1 and LongT5 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

      flax_model.save_pretrained(UpperCamelCase__							)
      print('''T5X Model was sucessfully converted!'''							)


if __name__ == "__main__":
  a_					=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
  )
  parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
  parser.add_argument(
      '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
  )
  a_					=	parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)