



"""simple docstring"""







from typing import Mapping

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


A     =      logging.get_logger(__name__)






class   UpperCAmelCase__		(   UpperCamelCase  ):
		lowerCAmelCase_							:						Union[str, Any]							=  """mt5"""
		lowerCAmelCase_							:						Union[str, Any]							=  ["""past_key_values"""]



		def __init__(			self	:     List[str]   ,				snake_case	:     List[str]=250_112   ,				snake_case	:     List[Any]=512   ,				snake_case	:     Any=64   ,				snake_case	:     List[str]=1_024   ,				snake_case	:     Any=8   ,				snake_case	:     Optional[Any]=None   ,				snake_case	:     Optional[int]=6   ,				snake_case	:     Union[str, Any]=32   ,				snake_case	:     Any=128   ,				snake_case	:     Tuple=0.1   ,				snake_case	:     List[str]=1E-6   ,				snake_case	:     Dict=1.0   ,				snake_case	:     List[str]="gated-gelu"   ,				snake_case	:     int=True   ,				snake_case	:     List[str]=True   ,				snake_case	:     Tuple="T5Tokenizer"   ,				snake_case	:     List[str]=False   ,				snake_case	:     str=0   ,				snake_case	:     str=1   ,				snake_case	:     Optional[Any]=0   ,				**snake_case	:     Any   ,				)      ->    List[str]:






									'''simple docstring'''

									super().__init__(
									    is_encoder_decoder=snake_case   ,				tokenizer_class=snake_case   ,				tie_word_embeddings=snake_case   ,				pad_token_id=snake_case   ,				eos_token_id=snake_case   ,				decoder_start_token_id=snake_case   ,				**snake_case   ,				)
									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(snake_case					) > 1 and act_info[0] != "gated" or len(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    =       'gelu_new'



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






									'''simple docstring'''

									return self.d_model



		@property
		def        A_		(			self	:     Optional[int]					)      ->    Optional[int]:






									'''simple docstring'''

									return self.num_heads



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






									'''simple docstring'''

									return self.num_layers






class   UpperCAmelCase__		(   UpperCamelCase  ):



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def        A_		(			self	:     int					)      ->    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_(snake_case   ,				direction='inputs'					)

									return common_inputs



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def        A_		(			self	:     List[Any]					)      ->    int:






									'''simple docstring'''

									return 13



		@property
		def        A_		(			self	:     List[str]					)      ->    float:






									'''simple docstring'''

									return 5E-4








"""simple docstring"""







from collections import namedtuple

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

A     =      {
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

A     =      {
    'mm': from_to(0.001, 1_0_0_0),
    'cm': from_to(0.01, 1_0_0),
    'm': from_to(1, 1),
    'km': from_to(1_0_0_0, 0.001),
    'in': from_to(0.0254, 39.3701),
    'ft': from_to(0.3048, 3.2_8084),
    'yd': from_to(0.9144, 1.0_9361),
    'mi': from_to(1609.34, 0.0_0062_1371),
}

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				float:
							A    =       from_type.lower().rstrip('s'      )
							A    =       TYPE_CONVERSION.get(lowerCamelCase__					,   lowerCamelCase__      )
							A    =       to_type.lower().rstrip('s'      )
							A    =       TYPE_CONVERSION.get(lowerCamelCase__					,   lowerCamelCase__      )
							if new_from not in METRIC_CONVERSION:
														A    =       (
														    f"""Invalid 'from_type' value: {from_type!r}.\n"""
														    f"""Conversion abbreviations are: {', '.join(lowerCamelCase__      )}"""
														)
														raise ValueError(lowerCamelCase__      )
							if new_to not in METRIC_CONVERSION:
														A    =       (
														    f"""Invalid 'to_type' value: {to_type!r}.\n"""
														    f"""Conversion abbreviations are: {', '.join(lowerCamelCase__      )}"""
														)
														raise ValueError(lowerCamelCase__      )
							return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
							import doctest

							doctest.testmod()



