

from typing import Mapping

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


__A    	=			logging.get_logger(__name__)

__A    	=			{
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}



class 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	List[Any]		   =       "longt5"
      UpperCamelCase_    :	Union[str, Any]		   =       ["past_key_values"]
      UpperCamelCase_    :	List[Any]		   =       {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



      def __init__(    self   :       List[Any]    ,      A__   :       int=32128    ,      A__   :       Optional[int]=512    ,      A__   :       int=64    ,      A__   :       Optional[Any]=2048    ,      A__   :       Union[str, Any]=6    ,      A__   :       Optional[Any]=None    ,      A__   :       Any=8    ,      A__   :       int=127    ,      A__   :       Dict=16    ,      A__   :       Tuple=32    ,      A__   :       List[str]=128    ,      A__   :       Tuple=0.1    ,      A__   :       int=1e-6    ,      A__   :       Tuple=1.0    ,      A__   :       Optional[int]="relu"    ,      A__   :       Optional[int]=True    ,      A__   :       Optional[int]="local"    ,      A__   :       int=True    ,      A__   :       Union[str, Any]=0    ,      A__   :       List[Any]=1    ,      **A__   :       int    ,      )					->  Optional[int]:
        _snake_case		=							vocab_size
        _snake_case		=							d_model
        _snake_case		=							d_kv
        _snake_case		=							d_ff
        _snake_case		=							num_layers
        # default = symmetry
        _snake_case		=							num_decoder_layers if num_decoder_layers is not None else self.num_layers
        _snake_case		=							num_heads
        _snake_case		=							local_radius
        _snake_case		=							global_block_size
        _snake_case		=							relative_attention_num_buckets
        _snake_case		=							relative_attention_max_distance
        _snake_case		=							dropout_rate
        _snake_case		=							layer_norm_epsilon
        _snake_case		=							initializer_factor
        _snake_case		=							feed_forward_proj
        _snake_case		=							encoder_attention_type
        _snake_case		=							use_cache

        _snake_case		=							self.feed_forward_proj.split('''-'''	)
        _snake_case		=							act_info[-1]
        _snake_case		=							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":
          _snake_case		=							'''gelu_new'''

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



class 						lowercase_					(						__lowercase  ):



      @property
      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  Mapping[str, Mapping[int, str]]:
        _snake_case		=							{
            '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
            '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
        }
        if self.use_past:
          _snake_case		=							'''past_encoder_sequence + sequence'''
          _snake_case		=							{0: '''batch'''}
          _snake_case		=							{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
        else:
          _snake_case		=							{0: '''batch''', 1: '''decoder_sequence'''}
          _snake_case		=							{0: '''batch''', 1: '''decoder_sequence'''}

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

        return common_inputs







      @property
      def 		UpperCamelCase_	(    self   :       Tuple	)					->  int:
        return 13




from collections import namedtuple

__A    	=			namedtuple('''from_to''', '''from_ to''')

__A    	=			{
    '''atm''': from_to(1, 1),
    '''pascal''': from_to(0.000_0098, 10_13_25),
    '''bar''': from_to(0.98_6923, 1.0_1325),
    '''kilopascal''': from_to(0.0098_6923, 101.325),
    '''megapascal''': from_to(9.8_6923, 0.10_1325),
    '''psi''': from_to(0.06_8046, 14.6959),
    '''inHg''': from_to(0.033_4211, 29.9213),
    '''torr''': from_to(0.0013_1579, 7_60),
}



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			float:



  """simple docstring"""






  if from_type not in PRESSURE_CONVERSION:
    raise ValueError(
        F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
        + ''', '''.join(_UpperCamelCase  )  )
  if to_type not in PRESSURE_CONVERSION:
    raise ValueError(
        F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
        + ''', '''.join(_UpperCamelCase  )  )
  return (
      value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

