


_lowerCAmelCase:						dict[str, float]   				=       {
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.609_344,
    "knot": 1.852,
}

_lowerCAmelCase:						dict[str, float]   				=       {
    "km/h": 1.0,
    "m/s": 0.277_777_778,
    "mph": 0.621_371_192,
    "knot": 0.539_956_803,
}






def   _lowercase(					__a				:						float    ,     __a				:						str    ,     __a				:						str     ):
							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(__a     )}"""
														)
														raise ValueError(__a     )
							return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]    ,     3     )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class       lowercase_					(unittest.TestCase   ):





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'       ,  return_dict=lowercase_).to(lowercase_)
											a__      =AutoTokenizer.from_pretrained('google/mt5-small')

											a__      =tokenizer('Hello there'       ,  return_tensors='pt').input_ids
											a__      =tokenizer('Hi I am'       ,  return_tensors='pt').input_ids

											a__      =model(input_ids.to(lowercase_)       ,  labels=labels.to(lowercase_)).loss
											a__      =-(labels.shape[-1] * loss.item())

											a__      =-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

