





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


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

_lowerCAmelCase							:					List[Any]		    =		{
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}


class     __magic_name__     (							lowercase_							):





     """simple docstring"""



     __UpperCamelCase    			=   '''t5'''
     __UpperCamelCase    			=   ['''past_key_values''']
     __UpperCamelCase    			=   {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



     def __init__(							self				:int	,					snake_case				:int=32_128	,					snake_case				:List[str]=512	,					snake_case				:Tuple=64	,					snake_case				:Dict=2_048	,					snake_case				:Optional[int]=6	,					snake_case				:Any=None	,					snake_case				:Optional[int]=8	,					snake_case				:List[Any]=32	,					snake_case				:Union[str, Any]=128	,					snake_case				:int=0.1	,					snake_case				:str=1e-6	,					snake_case				:List[Any]=1.0	,					snake_case				:Tuple="relu"	,					snake_case				:str=True	,					snake_case				:Optional[int]=True	,					snake_case				:Dict=0	,					snake_case				:List[Any]=1	,					**snake_case				:Tuple	,					):





        '''simple docstring'''




        A_    : Any								=	vocab_size
        A_    : Union[str, Any]								=	d_model
        A_    : List[Any]								=	d_kv
        A_    : str								=	d_ff
        A_    : Union[str, Any]								=	num_layers
        A_    : Optional[int]								=	(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A_    : Optional[int]								=	num_heads
        A_    : Tuple								=	relative_attention_num_buckets
        A_    : Optional[Any]								=	relative_attention_max_distance
        A_    : Union[str, Any]								=	dropout_rate
        A_    : Any								=	layer_norm_epsilon
        A_    : List[Any]								=	initializer_factor
        A_    : int								=	feed_forward_proj
        A_    : Optional[Any]								=	use_cache

        A_    : Tuple								=	self.feed_forward_proj.split("-"     )
        A_    : Union[str, Any]								=	act_info[-1]
        A_    : List[Any]								=	act_info[0] == 'gated'

        if len(SCREAMING_SNAKE_CASE_     ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE_     ) > 2:
           raise ValueError(
               f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "\'gated-gelu\' or \'relu\'"     )

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
           A_    : List[Any]								=	'gelu_new'

        super().__init__(
            pad_token_id=SCREAMING_SNAKE_CASE_	,					eos_token_id=SCREAMING_SNAKE_CASE_	,					is_encoder_decoder=SCREAMING_SNAKE_CASE_	,					**SCREAMING_SNAKE_CASE_	,					)




class     __magic_name__     (							lowercase_							):





     """simple docstring"""



     @property
     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]     ):





        '''simple docstring'''




        A_    : Any								=	{
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
           A_    : List[str]								=	'past_encoder_sequence + sequence'
           A_    : List[str]								=	{0: 'batch'}
           A_    : Tuple								=	{0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
           A_    : int								=	{0: 'batch', 1: 'decoder_sequence'}
           A_    : Optional[Any]								=	{0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
           self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_	,					direction="inputs"     )

        return common_inputs





     @property
     def   SCREAMING_SNAKE_CASE						(							self				:Any     ):





        '''simple docstring'''




        return 13





"""simple docstring"""







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

lowercase__		:				List[str]          =	{"""tokenizer_file""": """tokenizer.json"""}

lowercase__		:				List[str]          =	{
    """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       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE  				=     PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE  				=     ["""input_ids""", """attention_mask"""]
      _SCREAMING_SNAKE_CASE  				=     None



      def __init__(      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=None    ,			SCREAMING_SNAKE_CASE_				:      Tuple=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]="<unk>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="<s>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="</s>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="<pad>"    ,			SCREAMING_SNAKE_CASE_				:      List[str]=False    ,			SCREAMING_SNAKE_CASE_				:      Any=False    ,			**SCREAMING_SNAKE_CASE_				:      Any    ,			):
        super().__init__(
            SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			tokenizer_file=SCREAMING_SNAKE_CASE_    ,			unk_token=SCREAMING_SNAKE_CASE_    ,			bos_token=SCREAMING_SNAKE_CASE_    ,			eos_token=SCREAMING_SNAKE_CASE_    ,			pad_token=SCREAMING_SNAKE_CASE_    ,			add_prefix_space=SCREAMING_SNAKE_CASE_    ,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_    ,			)
        lowerCAmelCase_   :				Optional[Any]								= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
        if pre_tok_state.get('add_prefix_space'    ,			SCREAMING_SNAKE_CASE_       ) != add_prefix_space:
          lowerCAmelCase_   :				str								= getattr(SCREAMING_SNAKE_CASE_    ,			pre_tok_state.pop('type'       )       )
          lowerCAmelCase_   :				str								= add_prefix_space
          lowerCAmelCase_   :				str								= pre_tok_class(**SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				str								= add_prefix_space



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			*SCREAMING_SNAKE_CASE_				:      List[Any]    ,			**SCREAMING_SNAKE_CASE_				:      Tuple       ):
        lowerCAmelCase_   :				List[Any]								= kwargs.get('is_split_into_words'    ,			SCREAMING_SNAKE_CASE_       )
        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(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			*SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			**SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				List[Any]								= kwargs.get('is_split_into_words'    ,			SCREAMING_SNAKE_CASE_       )

        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(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[str] = None       ):
        lowerCAmelCase_   :				Optional[int]								= self._tokenizer.model.save(SCREAMING_SNAKE_CASE_    ,			name=SCREAMING_SNAKE_CASE_       )
        return tuple(SCREAMING_SNAKE_CASE_       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      "Conversation"       ):
        lowerCAmelCase_   :				int								= []
        for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_    ,			add_special_tokens=SCREAMING_SNAKE_CASE_       ) + [self.eos_token_id]       )

        if len(SCREAMING_SNAKE_CASE_       ) > self.model_max_length:
          lowerCAmelCase_   :				Any								= input_ids[-self.model_max_length :]
        return input_ids





