
'''simple docstring'''



def A (__lowerCamelCase :int = 10					,      __lowerCamelCase :int = 1000					,      __lowerCamelCase :bool = True						):
						assert (
						    isinstance(__lowerCamelCase					,      __lowerCamelCase						)
						    and isinstance(__lowerCamelCase					,      __lowerCamelCase						)
						    and isinstance(__lowerCamelCase					,      __lowerCamelCase						)
						), "Invalid type of value(s) specified to function!"

						if min_val > max_val:
												raise ValueError("""Invalid value for min_val or max_val (min_value < max_value)"""						)
						return min_val if option else max_val


def A (__lowerCamelCase :int					,      __lowerCamelCase :int						):
						return int((number_a + number_a) / 2						)


def A (__lowerCamelCase :int					,      __lowerCamelCase :int					,      __lowerCamelCase :int						):
						assert (
						    isinstance(__lowerCamelCase					,      __lowerCamelCase						) and isinstance(__lowerCamelCase					,      __lowerCamelCase						) and isinstance(__lowerCamelCase					,      __lowerCamelCase						)
						), 'argument values must be type of "int"'

						if lower > higher:
												raise ValueError("""argument value for lower and higher must be(lower > higher)"""						)

						if not lower < to_guess < higher:
												raise ValueError(
												    """guess value must be within the range of lower and higher value"""						)

						def answer(__lowerCamelCase :int						) -> str:
												if number > to_guess:
																		return "high"
												elif number < to_guess:
																		return "low"
												else:
																		return "same"

						print("""started..."""						)

						_lowerCAmelCase    		=					lower
						_lowerCAmelCase    		=					higher

						_lowerCAmelCase    		=					[]

						while True:
												_lowerCAmelCase    		=					get_avg(__lowerCamelCase					,      __lowerCamelCase						)
												last_numbers.append(__lowerCamelCase						)

												if answer(__lowerCamelCase						) == "low":
																		_lowerCAmelCase    		=					number
												elif answer(__lowerCamelCase						) == "high":
																		_lowerCAmelCase    		=					number
												else:
																		break

						print(f'guess the number : {last_numbers[-1]}'						)
						print(f'details : {last_numbers!s}'						)


def A ():
						_lowerCAmelCase    		=					int(input("""Enter lower value : """						).strip()						)
						_lowerCAmelCase    		=					int(input("""Enter high value : """						).strip()						)
						_lowerCAmelCase    		=					int(input("""Enter value to guess : """						).strip()						)
						guess_the_number(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)


if __name__ == "__main__":
						main()


'''simple docstring'''



from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


_lowercase          =						logging.get_logger(__name__)




@add_end_docstrings(_SCREAMING_SNAKE_CASE					)
class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  **_lowercase ):

												"""simple docstring"""



												super().__init__(**_lowercase )

												if self.framework != "pt":
																		raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
												# No specific FOR_XXX available yet




						def __call__(   self       ,  _lowercase       ,  **_lowercase ):

												"""simple docstring"""



												return super().__call__(_lowercase       ,  **_lowercase )




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					{}
												if "candidate_labels" in kwargs:
																		_lowerCAmelCase    		=					kwargs["""candidate_labels"""]
												if "hypothesis_template" in kwargs:
																		_lowerCAmelCase    		=					kwargs["""hypothesis_template"""]

												return preprocess_params, {}, {}




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase=None       ,  _lowercase="This is a sound of {}." ):

												"""simple docstring"""



												if isinstance(_lowercase       ,  _lowercase ):
																		if audio.startswith("""http://""" ) or audio.startswith("""https://""" ):
																								# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																								# like http_huggingface_co.png
																								_lowerCAmelCase    		=					requests.get(_lowercase ).content
																		else:
																								with open(_lowercase       ,  """rb""" ) as f:
																														_lowerCAmelCase    		=					f.read()

												if isinstance(_lowercase       ,  _lowercase ):
																		_lowerCAmelCase    		=					ffmpeg_read(_lowercase       ,  self.feature_extractor.sampling_rate )

												if not isinstance(_lowercase       ,  np.ndarray ):
																		raise ValueError("""We expect a numpy ndarray as input""" )
												if len(audio.shape ) != 1:
																		raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline""" )

												_lowerCAmelCase    		=					self.feature_extractor(
												    [audio]       ,  sampling_rate=self.feature_extractor.sampling_rate       ,  return_tensors="""pt""" )
												_lowerCAmelCase    		=					candidate_labels
												_lowerCAmelCase    		=					[hypothesis_template.format(_lowercase ) for x in candidate_labels]
												_lowerCAmelCase    		=					self.tokenizer(_lowercase       ,  return_tensors=self.framework       ,  padding=_lowercase )
												_lowerCAmelCase    		=					[text_inputs]
												return inputs




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					model_inputs.pop("""candidate_labels""" )
												_lowerCAmelCase    		=					model_inputs.pop("""text_inputs""" )
												if isinstance(text_inputs[0]       ,  _lowercase ):
																		_lowerCAmelCase    		=					text_inputs[0]
												else:
																		# Batching case.
																		_lowerCAmelCase    		=					text_inputs[0][0]

												_lowerCAmelCase    		=					self.model(**_lowercase       ,  **_lowercase )

												_lowerCAmelCase    		=					{
												    """candidate_labels""": candidate_labels,
												    """logits""": outputs.logits_per_audio,
												}
												return model_outputs




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					model_outputs.pop("""candidate_labels""" )
												_lowerCAmelCase    		=					model_outputs["""logits"""][0]

												if self.framework == "pt":
																		_lowerCAmelCase    		=					logits.softmax(dim=0 )
																		_lowerCAmelCase    		=					probs.tolist()
												else:
																		raise ValueError("""`tf` framework not supported.""" )

												_lowerCAmelCase    		=					[
												    {"""score""": score, """label""": candidate_label}
												    for score, candidate_label in sorted(zip(_lowercase       ,  _lowercase )       ,  key=lambda _lowercase : -x[0] )
												]
												return result
