

from typing import Mapping

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


lowercase_             =     logging.get_logger(__name__)

lowercase_             =     {
    '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 				__lowerCAmelCase  (      __SCREAMING_SNAKE_CASE							):
  _a		    =						"""t5"""
  _a		    =						["""past_key_values"""]
  _a		    =						{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




  def __init__(       self							,     lowerCAmelCase=32_128							,     lowerCAmelCase=512							,     lowerCAmelCase=64							,     lowerCAmelCase=2_048							,     lowerCAmelCase=6							,     lowerCAmelCase=None							,     lowerCAmelCase=8							,     lowerCAmelCase=32							,     lowerCAmelCase=128							,     lowerCAmelCase=0.1							,     lowerCAmelCase=1e-6							,     lowerCAmelCase=1.0							,     lowerCAmelCase="relu"							,     lowerCAmelCase=True							,     lowerCAmelCase=True							,     lowerCAmelCase=0							,     lowerCAmelCase=1							,     **lowerCAmelCase							,     )						->				Dict:
        '''simple docstring'''






        _lowercase			 =vocab_size
        _lowercase			 =d_model
        _lowercase			 =d_kv
        _lowercase			 =d_ff
        _lowercase			 =num_layers
        _lowercase			 =(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        _lowercase			 =num_heads
        _lowercase			 =relative_attention_num_buckets
        _lowercase			 =relative_attention_max_distance
        _lowercase			 =dropout_rate
        _lowercase			 =layer_norm_epsilon
        _lowercase			 =initializer_factor
        _lowercase			 =feed_forward_proj
        _lowercase			 =use_cache

        _lowercase			 =self.feed_forward_proj.split('-'      )
        _lowercase			 =act_info[-1]
        _lowercase			 =act_info[0] == """gated"""

        if len(_a      ) > 1 and act_info[0] != "gated" or len(_a      ) > 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":
              _lowercase			 ="""gelu_new"""

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





class 				__lowerCAmelCase  (      __SCREAMING_SNAKE_CASE							):




  @property
  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        _lowercase			 ={
            """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
            """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
        }
        if self.use_past:
              _lowercase			 ="""past_encoder_sequence + sequence"""
              _lowercase			 ={0: """batch"""}
              _lowercase			 ={0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
              _lowercase			 ={0: """batch""", 1: """decoder_sequence"""}
              _lowercase			 ={0: """batch""", 1: """decoder_sequence"""}

        if self.use_past:
              self.fill_with_past_key_values_(_a							,     direction='inputs'      )

        return common_inputs




  @property
  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        return 13





def   a   (    A__			:     str   ,     A__			:     int  )					->   list[str]:





      """simple docstring"""



      return [sentence[i : i + ngram_size] for i in range(len(A__  ) - ngram_size + 1  )]


if __name__ == "__main__":
  from doctest import testmod

  testmod()


