
lowerCamelCase_  :	Tuple 				=					{
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
lowerCamelCase_  :	Dict 				=					{
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		float:
				UpperCamelCase_:							Optional[int]		=  from_type.lower().strip("""s"""  )
				UpperCamelCase_:							str		=  to_type.lower().strip("""s"""  )

				UpperCamelCase_:							str		=  UNIT_SYMBOL.get(lowerCamelCase       ,      lowerCamelCase  )
				UpperCamelCase_:							Optional[Any]		=  UNIT_SYMBOL.get(lowerCamelCase       ,      lowerCamelCase  )

				if from_sanitized not in METRIC_CONVERSION:
								UpperCamelCase_:							Any		=  (
								    F'''Invalid \'from_type\' value: {from_type!r}.\n'''
								    F'''Conversion abbreviations are: {", ".join(lowerCamelCase  )}'''
								)
								raise ValueError(lowerCamelCase  )
				if to_sanitized not in METRIC_CONVERSION:
								UpperCamelCase_:							str		=  (
								    F'''Invalid \'to_type\' value: {to_type!r}.\n'''
								    F'''Conversion abbreviations are: {", ".join(lowerCamelCase  )}'''
								)
								raise ValueError(lowerCamelCase  )
				UpperCamelCase_:							Union[str, Any]		=  METRIC_CONVERSION[from_sanitized]
				UpperCamelCase_:							Optional[Any]		=  METRIC_CONVERSION[to_sanitized]
				UpperCamelCase_:							Optional[int]		=  1

				if from_exponent > to_exponent:
								UpperCamelCase_:							Union[str, Any]		=  from_exponent - to_exponent
				else:
								UpperCamelCase_:							Dict		=  -(to_exponent - from_exponent)

				return value * pow(10       ,      lowerCamelCase  )


if __name__ == "__main__":
							from doctest import testmod

							testmod()








import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow

@is_pipeline_test
@require_torch
class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			@require_torch
			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Any		=  pipeline(
							    task="""zero-shot-audio-classification"""  ,		model="""hf-internal-testing/tiny-clap-htsat-unfused""" )
							UpperCamelCase_:							str		=  load_dataset("""ashraq/esc50""" )
							UpperCamelCase_:							Optional[int]		=  dataset["""train"""]["""audio"""][-1]["""array"""]
							UpperCamelCase_:							Optional[int]		=  audio_classifier(snake_case_  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[{"""score""": 0.501, """label""": """Sound of a dog"""}, {"""score""": 0.499, """label""": """Sound of vaccum cleaner"""}]  ,		)

			@unittest.skip("""No models are available in TF""" )
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							pass

			@slow
			@require_torch
			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							List[str]		=  pipeline(
							    task="""zero-shot-audio-classification"""  ,		model="""laion/clap-htsat-unfused"""  ,		)
							# This is an audio of a dog
							UpperCamelCase_:							Tuple		=  load_dataset("""ashraq/esc50""" )
							UpperCamelCase_:							Optional[int]		=  dataset["""train"""]["""audio"""][-1]["""array"""]
							UpperCamelCase_:							int		=  audio_classifier(snake_case_  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )

							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[
							        {"""score""": 0.999, """label""": """Sound of a dog"""},
							        {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
							    ]  ,		)

							UpperCamelCase_:							Any		=  audio_classifier([audio] * 5  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[
							        [
							            {"""score""": 0.999, """label""": """Sound of a dog"""},
							            {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
							        ],
							    ]
							    * 5  ,		)
							UpperCamelCase_:							Any		=  audio_classifier(
							    [audio] * 5  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]  ,		batch_size=5 )
							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[
							        [
							            {"""score""": 0.999, """label""": """Sound of a dog"""},
							            {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
							        ],
							    ]
							    * 5  ,		)




			@unittest.skip("""No models are available in TF""" )
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							pass






