

import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig

def 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[Any]          =       TaConfig.from_pretrained(a					)
      SCREAMING_SNAKE_CASE_			:					List[str]          =       FlaxTaForConditionalGeneration(config=a					)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       checkpoints.load_tax_checkpoint(a					)

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

      # Encoder
      for layer_index in range(config.num_layers					):
            SCREAMING_SNAKE_CASE_			:					List[str]          =       f"layers_{str(a					)}"

            # Self-Attention
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
            SCREAMING_SNAKE_CASE_			:					str          =       tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
            SCREAMING_SNAKE_CASE_			:					Dict          =       tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

            # Layer Normalization
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE_			:					int          =       tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
            else:
                  SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

            SCREAMING_SNAKE_CASE_			:					Any          =       tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

            # Layer Normalization
            SCREAMING_SNAKE_CASE_			:					Tuple          =       tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

            # Assigning
            SCREAMING_SNAKE_CASE_			:					Tuple          =       tax_attention_key
            SCREAMING_SNAKE_CASE_			:					List[str]          =       tax_attention_out
            SCREAMING_SNAKE_CASE_			:					str          =       tax_attention_query
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tax_attention_value

            SCREAMING_SNAKE_CASE_			:					List[Any]          =       tax_attention_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE_			:					List[Any]          =       tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE_			:					List[str]          =       tax_mlp_wi

            SCREAMING_SNAKE_CASE_			:					int          =       tax_mlp_wo
            SCREAMING_SNAKE_CASE_			:					str          =       tax_mlp_layer_norm

      # Only for layer 0:
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
      SCREAMING_SNAKE_CASE_			:					Any          =       tax_encoder_rel_embedding

      # Assigning
      SCREAMING_SNAKE_CASE_			:					Dict          =       tax_model['target']['encoder']['encoder_norm']['scale']
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_decoder_layers					):
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       f"layers_{str(a					)}"

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

            # Layer Normalization
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       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']['key'][
                'kernel'
            ]
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
                'kernel'
            ]
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
                'kernel'
            ]
            SCREAMING_SNAKE_CASE_			:					Tuple          =       tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['value'][
                'kernel'
            ]

            # Layer Normalization
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

            # MLP
            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE_			:					List[str]          =       tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
                  SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
            else:
                  SCREAMING_SNAKE_CASE_			:					str          =       tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

            SCREAMING_SNAKE_CASE_			:					Tuple          =       tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

            # Layer Normalization
            SCREAMING_SNAKE_CASE_			:					str          =       tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

            # Assigning
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_attention_key
            SCREAMING_SNAKE_CASE_			:					List[str]          =       tax_attention_out
            SCREAMING_SNAKE_CASE_			:					Dict          =       tax_attention_query
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       tax_attention_value

            SCREAMING_SNAKE_CASE_			:					str          =       tax_pre_attention_layer_norm

            SCREAMING_SNAKE_CASE_			:					Tuple          =       tax_enc_dec_attention_key
            SCREAMING_SNAKE_CASE_			:					Dict          =       tax_enc_dec_attention_out
            SCREAMING_SNAKE_CASE_			:					str          =       tax_enc_dec_attention_query
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tax_enc_dec_attention_value

            SCREAMING_SNAKE_CASE_			:					Tuple          =       tax_cross_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_mlp_wi

            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tax_mlp_wo

            SCREAMING_SNAKE_CASE_			:					List[Any]          =       txa_mlp_layer_norm

      # Decoder Normalization
      SCREAMING_SNAKE_CASE_			:					Dict          =       tax_model['target']['decoder']['decoder_norm']['scale']
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       txa_decoder_norm

      # Only for layer 0:
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
      SCREAMING_SNAKE_CASE_			:					Any          =       tax_decoder_rel_embedding

      # Token Embeddings
      SCREAMING_SNAKE_CASE_			:					List[str]          =       tax_model['target']['token_embedder']['embedding']
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       txa_token_embeddings

      # LM Head (only in v1.1 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_model['target']['decoder']['logits_dense']['kernel']

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


if __name__ == "__main__":
    lowerCAmelCase       :				int         =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
    )
    parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
    parser.add_argument(
        '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
    )
    lowerCAmelCase       :				Tuple         =					parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


lowerCAmelCase       :				List[Any]         =					logging.get_logger(__name__)

lowerCAmelCase       :				Any         =					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase       :				Any         =					{
    'vocab_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase       :				str         =					{
    'microsoft/mpnet-base': 5_12,
}

lowerCAmelCase       :				Optional[Any]         =					{
    'microsoft/mpnet-base': {'do_lower_case': True},
}
class 	_A						(					__magic_name__):
  SCREAMING_SNAKE_CASE      : Tuple    =					VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE      : List[Any]    =					PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE      : Dict    =					PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE      : str    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE      : Dict    =					MPNetTokenizer
  SCREAMING_SNAKE_CASE      : Any    =					['''input_ids''', '''attention_mask''']

  def __init__(			self	,   _SCREAMING_SNAKE_CASE=None	,   _SCREAMING_SNAKE_CASE=None	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE="<s>"	,   _SCREAMING_SNAKE_CASE="</s>"	,   _SCREAMING_SNAKE_CASE="</s>"	,   _SCREAMING_SNAKE_CASE="<s>"	,   _SCREAMING_SNAKE_CASE="[UNK]"	,   _SCREAMING_SNAKE_CASE="<pad>"	,   _SCREAMING_SNAKE_CASE="<mask>"	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        super().__init__(
            _SCREAMING_SNAKE_CASE	,   tokenizer_file=_SCREAMING_SNAKE_CASE	,   do_lower_case=_SCREAMING_SNAKE_CASE	,   bos_token=_SCREAMING_SNAKE_CASE	,   eos_token=_SCREAMING_SNAKE_CASE	,   sep_token=_SCREAMING_SNAKE_CASE	,   cls_token=_SCREAMING_SNAKE_CASE	,   unk_token=_SCREAMING_SNAKE_CASE	,   pad_token=_SCREAMING_SNAKE_CASE	,   mask_token=_SCREAMING_SNAKE_CASE	,   tokenize_chinese_chars=_SCREAMING_SNAKE_CASE	,   strip_accents=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE	,   )

        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
        if (
            pre_tok_state.get('lowercase'	,   _SCREAMING_SNAKE_CASE   ) != do_lower_case
            or pre_tok_state.get('strip_accents'	,   _SCREAMING_SNAKE_CASE   ) != strip_accents
        ):
              SCREAMING_SNAKE_CASE_			:					Any          =       getattr(_SCREAMING_SNAKE_CASE	,   pre_tok_state.pop('type'   )   )
              SCREAMING_SNAKE_CASE_			:					Dict          =       do_lower_case
              SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       strip_accents
              SCREAMING_SNAKE_CASE_			:					Tuple          =       pre_tok_class(**_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       do_lower_case

  @property
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        if self._mask_token is None:
              if self.verbose:
                    logger.error('Using mask_token, but it is not set yet.'   )
              return None
        return str(self._mask_token   )

  @mask_token.setter
  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       AddedToken(_SCREAMING_SNAKE_CASE	,   lstrip=_SCREAMING_SNAKE_CASE	,   rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ) else value
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       value

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE=None   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[str]          =       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
              return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = None   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       [self.sep_token_id]
        SCREAMING_SNAKE_CASE_			:					int          =       [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 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = None   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       self._tokenizer.model.save(_SCREAMING_SNAKE_CASE	,   name=_SCREAMING_SNAKE_CASE   )
        return tuple(_SCREAMING_SNAKE_CASE   )






