
from typing import Mapping

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


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






class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	'''mt5'''
					__magic_name__						:							str     	=	['''past_key_values''']


					def __init__(	self       ,       a__=2_5_0_1_1_2       ,       a__=5_1_2       ,       a__=6_4       ,       a__=1_0_2_4       ,       a__=8       ,       a__=None       ,       a__=6       ,       a__=3_2       ,       a__=1_2_8       ,       a__=0.1       ,       a__=1E-6       ,       a__=1.0       ,       a__="gated-gelu"       ,       a__=True       ,       a__=True       ,       a__="T5Tokenizer"       ,       a__=False       ,       a__=0       ,       a__=1       ,       a__=0       ,       **a__       ,       )		->							Optional[int]:





									'''simple docstring'''
									super().__init__(
									    is_encoder_decoder=a__       ,       tokenizer_class=a__       ,       tie_word_embeddings=a__       ,       pad_token_id=a__       ,       eos_token_id=a__       ,       decoder_start_token_id=a__       ,       **a__       ,       )
									A_       	=  vocab_size
									A_       	=  d_model
									A_       	=  d_kv
									A_       	=  d_ff
									A_       	=  num_layers
									A_       	=  (
									    num_decoder_layers if num_decoder_layers is not None else self.num_layers
									)  # default = symmetry
									A_       	=  num_heads
									A_       	=  relative_attention_num_buckets
									A_       	=  relative_attention_max_distance
									A_       	=  dropout_rate
									A_       	=  layer_norm_epsilon
									A_       	=  initializer_factor
									A_       	=  feed_forward_proj
									A_       	=  use_cache

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


					@property
					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									return self.d_model


					@property
					def        lowerCAmelCase_    (	self						)		->							Any:





									'''simple docstring'''
									return self.num_heads


					@property
					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									return self.num_layers






class 					__lowercase		(					A       ):


					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
					def        lowerCAmelCase_    (	self						)		->							Mapping[str, Mapping[int, str]]:





									'''simple docstring'''
									A_       	=  {
									    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
									    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
									}
									if self.use_past:
													A_       	=  '''past_encoder_sequence + sequence'''
													A_       	=  {0: '''batch'''}
													A_       	=  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
									else:
													A_       	=  {0: '''batch''', 1: '''decoder_sequence'''}
													A_       	=  {0: '''batch''', 1: '''decoder_sequence'''}

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

									return common_inputs


					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									return 1_3


					@property
					def        lowerCAmelCase_    (	self						)		->							float:





									'''simple docstring'''
									return 5E-4

from collections import namedtuple

SCREAMING_SNAKE_CASE   :		Any					 =       namedtuple("from_to", "from_ to")

SCREAMING_SNAKE_CASE   :		str					 =       {
    "atm": from_to(1, 1),
    "pascal": from_to(0.0000098, 10_1325),
    "bar": from_to(0.986923, 1.01325),
    "kilopascal": from_to(0.00986923, 101.325),
    "megapascal": from_to(9.86923, 0.101325),
    "psi": from_to(0.068046, 14.6959),
    "inHg": from_to(0.0334211, 29.9213),
    "torr": from_to(0.00131579, 760),
}



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				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()