






"""simple docstring"""





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

import sentencepiece as spm

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


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

A__       :			Optional[Any]=						logging.get_logger(__name__)

A__       :			Optional[int]=						{"vocab_file": "tokenizer.model"}

A__       :			Optional[int]=						{
    "vocab_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
    },
    "tokenizer_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
    },
}
A__       :			Optional[int]=						{
    "hf-internal-testing/llama-tokenizer": 20_48,
}
A__       :			str=						"▁"

A__       :			str=						"[INST]", "[/INST]"
A__       :			Dict=						"<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
A__       :			List[Any]=						"You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."
class  __lowerCamelCase     (	_a	):
   a   :					List[str]			    =VOCAB_FILES_NAMES
   a   :					List[Any]			    =PRETRAINED_VOCAB_FILES_MAP
   a   :					Optional[Any]			    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a   :					Tuple			    =["""input_ids""", """attention_mask"""]

   def __init__(       self			,					snake_case_			,					snake_case_="<unk>"			,					snake_case_="<s>"			,					snake_case_="</s>"			,					snake_case_=None			,					snake_case_ = None			,					snake_case_=True			,					snake_case_=False			,					snake_case_=False			,					snake_case_=True			,					**snake_case_			,					)		->							Union[str, Any]:
       UpperCamelCase__								=     {} if sp_model_kwargs is None else sp_model_kwargs
       UpperCamelCase__								=     AddedToken(_UpperCAmelCase			,					lstrip=_UpperCAmelCase			,					rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase			,					_UpperCAmelCase   ) else bos_token
       UpperCamelCase__								=     AddedToken(_UpperCAmelCase			,					lstrip=_UpperCAmelCase			,					rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase			,					_UpperCAmelCase   ) else eos_token
       UpperCamelCase__								=     AddedToken(_UpperCAmelCase			,					lstrip=_UpperCAmelCase			,					rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase			,					_UpperCAmelCase   ) else unk_token
       UpperCamelCase__								=     AddedToken(_UpperCAmelCase			,					lstrip=_UpperCAmelCase			,					rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase			,					_UpperCAmelCase   ) else pad_token
       super().__init__(
           bos_token=_UpperCAmelCase			,					eos_token=_UpperCAmelCase			,					unk_token=_UpperCAmelCase			,					pad_token=_UpperCAmelCase			,					add_bos_token=_UpperCAmelCase			,					add_eos_token=_UpperCAmelCase			,					sp_model_kwargs=self.sp_model_kwargs			,					clean_up_tokenization_spaces=_UpperCAmelCase			,					legacy=_UpperCAmelCase			,					**_UpperCAmelCase			,					)
       if legacy:
           logger.warning_once(
               F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
               ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'   )
       UpperCamelCase__								=     legacy
       UpperCamelCase__								=     vocab_file
       UpperCamelCase__								=     add_bos_token
       UpperCamelCase__								=     add_eos_token
       UpperCamelCase__								=     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(_UpperCAmelCase   )

   def __getstate__(       self   )		->							Optional[int]:
       UpperCamelCase__								=     self.__dict__.copy()
       UpperCamelCase__								=     None
       UpperCamelCase__								=     self.sp_model.serialized_model_proto()
       return state

   def __setstate__(       self			,					snake_case_   )		->							Any:
       UpperCamelCase__								=     d
       UpperCamelCase__								=     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[Any]:
       return self.sp_model.get_piece_size()

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[str]:
       UpperCamelCase__								=     {self.convert_ids_to_tokens(_UpperCAmelCase   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					**snake_case_   )		->							List[str]:



       if not self.legacy:
           UpperCamelCase__								=     SPIECE_UNDERLINE + text.replace(_UpperCAmelCase			,					' '   )
       return super().tokenize(_UpperCAmelCase			,					**_UpperCAmelCase   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Optional[Any]:



       if not self.legacy:
           UpperCamelCase__								=     text.startswith(_UpperCAmelCase   )
           if is_first:
               UpperCamelCase__								=     text[1:]

       UpperCamelCase__								=     self.sp_model.encode(_UpperCAmelCase			,					out_type=_UpperCAmelCase   )

       if not self.legacy and not is_first and not text.startswith(' '   ) and tokens[0].startswith(_UpperCAmelCase   ):
           UpperCamelCase__								=     ([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
       return tokens

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Optional[int]:
       return self.sp_model.piece_to_id(_UpperCAmelCase   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							List[str]:
       UpperCamelCase__								=     self.sp_model.IdToPiece(_UpperCAmelCase   )
       return token

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							str:
       UpperCamelCase__								=     []
       UpperCamelCase__								=     ''
       UpperCamelCase__								=     False
       for i, token in enumerate(_UpperCAmelCase   ):
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               if not prev_is_special and i != 0:
                   out_string += " "
               out_string += self.sp_model.decode(_UpperCAmelCase   ) + token
               UpperCamelCase__								=     True
               UpperCamelCase__								=     []
           else:
               current_sub_tokens.append(_UpperCAmelCase   )
               UpperCamelCase__								=     False
       out_string += self.sp_model.decode(_UpperCAmelCase   )
       return out_string

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_ = None   )		->							int:



       if not os.path.isdir(_UpperCAmelCase   ):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
           return
       UpperCamelCase__								=     os.path.join(
           _UpperCAmelCase			,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

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

       return (out_vocab_file,)

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_=None   )		->							str:
       UpperCamelCase__								=     [self.bos_token_id] if self.add_bos_token else []
       UpperCamelCase__								=     [self.eos_token_id] if self.add_eos_token else []

       UpperCamelCase__								=     bos_token_id + token_ids_a + eos_token_id

       if token_ids_a is not None:
           UpperCamelCase__								=     output + bos_token_id + token_ids_a + eos_token_id

       return output

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_ = None			,					snake_case_ = False   )		->							Union[str, Any]:



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

       UpperCamelCase__								=     [1] if self.add_bos_token else []
       UpperCamelCase__								=     [1] if self.add_eos_token else []

       if token_ids_a is None:
           return bos_token_id + ([0] * len(_UpperCAmelCase   )) + eos_token_id
       return (
           bos_token_id
           + ([0] * len(_UpperCAmelCase   ))
           + eos_token_id
           + bos_token_id
           + ([0] * len(_UpperCAmelCase   ))
           + eos_token_id
       )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_ = None   )		->							Dict:
       UpperCamelCase__								=     [self.bos_token_id] if self.add_bos_token else []
       UpperCamelCase__								=     [self.eos_token_id] if self.add_eos_token else []

       UpperCamelCase__								=     [0] * len(bos_token_id + token_ids_a + eos_token_id   )

       if token_ids_a is not None:
           output += [1] * len(bos_token_id + token_ids_a + eos_token_id   )

       return output







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Optional[Any]:
       UpperCamelCase__								=     list(conversation.iter_texts()   )
       if not all([is_user for is_user, msg in dialogue[::2]]   ) or not all(
           [not is_user for is_user, msg in dialogue[1::2]]   ):
           raise ValueError(
               'The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'   )

       UpperCamelCase__								=     []
       if len(conversation.past_user_inputs   ) > 0:
           if not conversation.past_user_inputs[0].startswith(_UpperCAmelCase   ) or E_SYS not in conversation.past_user_inputs[0]:
               UpperCamelCase__								=     (
                   B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
               )
       elif not dialogue[0][1].startswith(_UpperCAmelCase   ) or E_SYS not in dialogue[0][1]:
           UpperCamelCase__								=     (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

       dialog_tokens += sum(
           [
               [self.bos_token_id]
               + self.encode(
                   F'{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '			,					add_special_tokens=_UpperCAmelCase   )
               + [self.eos_token_id]
               for prompt, answer in zip(dialogue[::2]			,					dialogue[1::2]   )
           ]			,					[]			,					)
       if not (dialogue[-1][0]):
           raise ValueError(F'Last message must be from user, got {dialogue[-1]["role"]}'   )
       dialog_tokens += [self.bos_token_id] + self.encode(
           F'{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'			,					add_special_tokens=_UpperCAmelCase   )
       return dialog_tokens





"""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)




