
"""simple docstring"""




a		:       dict[str, float]     						=       {
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.60_9344,
    "knot": 1.852,
}

a		:       dict[str, float]     						=       {
    "km/h": 1.0,
    "m/s": 0.2_7777_7778,
    "mph": 0.6_2137_1192,
    "knot": 0.5_3995_6803,
}



def     __magic_name__		(					UpperCamelCase     :		float							,		UpperCamelCase     :		str							,		UpperCamelCase     :		str     )     ->						float:
    if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
        a__		=			(
            f'Incorrect \'from_type\' or \'to_type\' value: {unit_from!r}, {unit_to!r}\n'
            f'Valid values are: {", ".join(UpperCamelCase     )}'
        )
        raise ValueError(UpperCamelCase     )
    return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]							,		3     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




"""simple docstring"""




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class     lowercase(_lowercase    ,						_lowercase    ,						_lowercase       ):



     @register_to_config
     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = False     ,				)		->     Union[str, Any]:







         """simple docstring"""



         super().__init__()

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			False

         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )

         a__		=			TaConfig(
             vocab_size=__SCREAMING_SNAKE_CASE     ,				d_model=__SCREAMING_SNAKE_CASE     ,				num_heads=__SCREAMING_SNAKE_CASE     ,				d_kv=__SCREAMING_SNAKE_CASE     ,				d_ff=__SCREAMING_SNAKE_CASE     ,				dropout_rate=__SCREAMING_SNAKE_CASE     ,				feed_forward_proj=__SCREAMING_SNAKE_CASE     ,				is_decoder=__SCREAMING_SNAKE_CASE     ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,				)

         a__		=			nn.ModuleList()
         for lyr_num in range(__SCREAMING_SNAKE_CASE   ):
             a__		=			TaBlock(__SCREAMING_SNAKE_CASE   )
             self.encoders.append(__SCREAMING_SNAKE_CASE   )

         a__		=			TaLayerNorm(__SCREAMING_SNAKE_CASE   )
         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			self.token_embedder(__SCREAMING_SNAKE_CASE   )

         a__		=			encoder_input_tokens.shape[1]
         a__		=			torch.arange(__SCREAMING_SNAKE_CASE     ,				device=encoder_input_tokens.device   )
         x += self.position_encoding(__SCREAMING_SNAKE_CASE   )

         a__		=			self.dropout_pre(__SCREAMING_SNAKE_CASE   )

         # inverted the attention mask
         a__		=			encoder_input_tokens.size()
         a__		=			self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         for lyr in self.encoders:
             a__		=			lyr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )[0]
         a__		=			self.layer_norm(__SCREAMING_SNAKE_CASE   )

         return self.dropout_post(__SCREAMING_SNAKE_CASE   ), encoder_inputs_mask


