"""simple docstring"""


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


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

lowerCAmelCase_:       List[Any]			     =       {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_:       str			     =       {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

lowerCAmelCase_:       Optional[int]			     =       {
    "microsoft/deberta-base": 5_1_2,
    "microsoft/deberta-large": 5_1_2,
    "microsoft/deberta-xlarge": 5_1_2,
    "microsoft/deberta-base-mnli": 5_1_2,
    "microsoft/deberta-large-mnli": 5_1_2,
    "microsoft/deberta-xlarge-mnli": 5_1_2,
}

lowerCAmelCase_:       Optional[int]			     =       {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}






class  a__     ( _a						):
   snake_case_				=   VOCAB_FILES_NAMES
   snake_case_				=   PRETRAINED_VOCAB_FILES_MAP
   snake_case_				=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_				=   ["input_ids", "attention_mask", "token_type_ids"]
   snake_case_				=   DebertaTokenizer


   def __init__(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=None,					_UpperCAmelCase=None,					_UpperCAmelCase="replace",					_UpperCAmelCase="[CLS]",					_UpperCAmelCase="[SEP]",					_UpperCAmelCase="[SEP]",					_UpperCAmelCase="[CLS]",					_UpperCAmelCase="[UNK]",					_UpperCAmelCase="[PAD]",					_UpperCAmelCase="[MASK]",					_UpperCAmelCase=False,					**_UpperCAmelCase,					):
        '''simple docstring'''



        super().__init__(
            _UpperCAmelCase,					_UpperCAmelCase,					tokenizer_file=_UpperCAmelCase,					errors=_UpperCAmelCase,					bos_token=_UpperCAmelCase,					eos_token=_UpperCAmelCase,					unk_token=_UpperCAmelCase,					sep_token=_UpperCAmelCase,					cls_token=_UpperCAmelCase,					pad_token=_UpperCAmelCase,					mask_token=_UpperCAmelCase,					add_prefix_space=_UpperCAmelCase,					**_UpperCAmelCase,					)
        lowercase__								=				kwargs.pop("add_bos_token",					_UpperCAmelCase	)

        lowercase__								=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
        if pre_tok_state.get("add_prefix_space",					_UpperCAmelCase	) != add_prefix_space:
             lowercase__								=				getattr(_UpperCAmelCase,					pre_tok_state.pop("type"	)	)
             lowercase__								=				add_prefix_space
             lowercase__								=				pre_tok_class(**_UpperCAmelCase	)

        lowercase__								=				add_prefix_space


   @property
   def 						snake_case__						(  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 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				AddedToken(_UpperCAmelCase,					lstrip=_UpperCAmelCase,					rstrip=_UpperCAmelCase	) if isinstance(_UpperCAmelCase,					_UpperCAmelCase	) else value
        lowercase__								=				value


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None	):
        '''simple docstring'''



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


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None	):
        '''simple docstring'''



        lowercase__								=				[self.sep_token_id]
        lowercase__								=				[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	) * [0] + len(token_ids_a + sep	) * [1]


   def 						snake_case__						(  self,					*_UpperCAmelCase,					**_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				kwargs.get("is_split_into_words",					_UpperCAmelCase	)
        assert self.add_prefix_space or not is_split_into_words, (
            F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*_UpperCAmelCase,					**_UpperCAmelCase	)


   def 						snake_case__						(  self,					*_UpperCAmelCase,					**_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				kwargs.get("is_split_into_words",					_UpperCAmelCase	)

        assert self.add_prefix_space or not is_split_into_words, (
            F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*_UpperCAmelCase,					**_UpperCAmelCase	)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None	):
        '''simple docstring'''



        lowercase__								=				self._tokenizer.model.save(_UpperCAmelCase,					name=_UpperCAmelCase	)
        return tuple(_UpperCAmelCase	)


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				[]
        for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(_UpperCAmelCase,					add_special_tokens=_UpperCAmelCase	) + [self.eos_token_id]	)

        if len(_UpperCAmelCase	) > self.model_max_length:
             lowercase__								=				input_ids[-self.model_max_length :]
        return input_ids





"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				TaConfig.from_pretrained(A   )
     lowercase__								=				FlaxTaForConditionalGeneration(config=A   )
     lowercase__								=				checkpoints.load_tax_checkpoint(A   )

     lowercase__								=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

     # Encoder
     for layer_index in range(config.num_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

          # Self-Attention
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

          if split_mlp_wi:
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

          lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_attention_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo
          lowercase__								=				tax_mlp_layer_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_encoder_rel_embedding

     # Assigning
     lowercase__								=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
     lowercase__								=				tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

          # Self-Attention
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
              "scale"
          ]

          # Encoder-Decoder-Attention
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
              "kernel"
          ]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

          # MLP
          if split_mlp_wi:
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

          lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_pre_attention_layer_norm

          lowercase__								=				tax_enc_dec_attention_key
          lowercase__								=				tax_enc_dec_attention_out
          lowercase__								=				tax_enc_dec_attention_query
          lowercase__								=				tax_enc_dec_attention_value

          lowercase__								=				tax_cross_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo

          lowercase__								=				txa_mlp_layer_norm

     # Decoder Normalization
     lowercase__								=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
     lowercase__								=				txa_decoder_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_decoder_rel_embedding

     # Token Embeddings
     lowercase__								=				tax_model["target"]["token_embedder"]["embedding"]
     lowercase__								=				txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          lowercase__								=				tax_model["target"]["decoder"]["logits_dense"]["kernel"]

     flax_model.save_pretrained(A   )
     print("T5X Model was sucessfully converted!"   )


if __name__ == "__main__":
 lowerCAmelCase_:       List[Any]			     =       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_:       Dict			     =       parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




