


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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


_lowercase				     = logging.get_logger(__name__)

_lowercase				     = {"tokenizer_file": "tokenizer.json"}

_lowercase				     = {
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}



class 	lowerCamelCase__			(      _a      ):
   __lowerCamelCase			      =  VOCAB_FILES_NAMES
   __lowerCamelCase			      =  PRETRAINED_VOCAB_FILES_MAP
   __lowerCamelCase			      =  ["""input_ids""", """attention_mask"""]
   __lowerCamelCase			      =  None




   def __init__(   self						:						Union[str, Any]    ,    __a						:						Optional[Any]=None    ,    __a						:						List[str]=None    ,    __a						:						Union[str, Any]=None    ,    __a						:						List[Any]="<unk>"    ,    __a						:						Optional[int]="<s>"    ,    __a						:						str="</s>"    ,    __a						:						Tuple="<pad>"    ,    __a						:						List[str]=False    ,    __a						:						Optional[Any]=False    ,    **__a						:						List[Any]    ,    ):




         '''simple docstring'''







         super().__init__(
             __a    ,    __a    ,    tokenizer_file=__a    ,    unk_token=__a    ,    bos_token=__a    ,    eos_token=__a    ,    pad_token=__a    ,    add_prefix_space=__a    ,    clean_up_tokenization_spaces=__a    ,    **__a    ,    )
         lowerCamelCase__:						Dict							   = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
         if pre_tok_state.get("""add_prefix_space"""    ,    __a							) != add_prefix_space:
               lowerCamelCase__:						Dict							   = getattr(__a    ,    pre_tok_state.pop("""type"""							)							)
               lowerCamelCase__:						Optional[int]							   = add_prefix_space
               lowerCamelCase__:						List[Any]							   = pre_tok_class(**__a							)

         lowerCamelCase__:						Tuple							   = add_prefix_space




   def 							lowerCamelCase_   (   self						:						Optional[int]    ,    *__a						:						List[Any]    ,    **__a						:						Dict							):




         '''simple docstring'''







         lowerCamelCase__:						Dict							   = kwargs.get("""is_split_into_words"""    ,    __a							)
         if not (self.add_prefix_space or not is_split_into_words):
               raise Exception(
                   f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                   """ pretokenized inputs."""							)

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




   def 							lowerCamelCase_   (   self						:						int    ,    *__a						:						Union[str, Any]    ,    **__a						:						Any							):




         '''simple docstring'''







         lowerCamelCase__:						Dict							   = kwargs.get("""is_split_into_words"""    ,    __a							)

         if not (self.add_prefix_space or not is_split_into_words):
               raise Exception(
                   f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                   """ pretokenized inputs."""							)

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




   def 							lowerCamelCase_   (   self						:						str    ,    __a						:						Optional[int]    ,    __a						:						str = None							):




         '''simple docstring'''







         lowerCamelCase__:						Union[str, Any]							   = self._tokenizer.model.save(__a    ,    name=__a							)
         return tuple(__a							)




   def 							lowerCamelCase_   (   self						:						Union[str, Any]    ,    __a						:						Union[str, Any]							):




         '''simple docstring'''







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

         if len(__a							) > self.model_max_length:
               lowerCamelCase__:						Optional[Any]							   = input_ids[-self.model_max_length :]
         return input_ids










import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    # Initialise PyTorch model
    _lowercase			:				Optional[int] 						=						TaConfig.from_json_file(lowerCamelCase_					)
    print(F'''Building PyTorch model from configuration: {config}'''					)
    _lowercase			:				Union[str, Any] 						=						TaForConditionalGeneration(lowerCamelCase_					)

    # Load weights from tf checkpoint
    load_tf_weights_in_ta(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)

    # Save pytorch-model
    print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
    model.save_pretrained(lowerCamelCase_					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 SCREAMING_SNAKE_CASE			: Any				   =					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


