


"""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


__UpperCamelCase						=      logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase_    )
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		def __init__(	self						,       **lowerCAmelCase__				)     ->							List[Any]:
								super().__init__(**lowerCAmelCase__				)

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







		def __call__(	self						,       lowerCAmelCase__						,       **lowerCAmelCase__				)     ->							Optional[Any]:
								return super().__call__(lowerCAmelCase__						,       **lowerCAmelCase__				)







		def 							__A						(	self						,       **lowerCAmelCase__				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					{}
								if "candidate_labels" in kwargs:
														SCREAMING_SNAKE_CASE													=					kwargs['candidate_labels']
								if "hypothesis_template" in kwargs:
														SCREAMING_SNAKE_CASE													=					kwargs['hypothesis_template']

								return preprocess_params, {}, {}







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=None						,       lowerCAmelCase__="This is a sound of {}."				)     ->							Dict:
								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
														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
																				SCREAMING_SNAKE_CASE													=					requests.get(lowerCAmelCase__				).content
														else:
																				with open(lowerCAmelCase__						,       'rb'				) as f:
																										SCREAMING_SNAKE_CASE													=					f.read()

								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					ffmpeg_read(lowerCAmelCase__						,       self.feature_extractor.sampling_rate				)

								if not isinstance(lowerCAmelCase__						,       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'				)

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







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					model_inputs.pop('candidate_labels'				)
								SCREAMING_SNAKE_CASE													=					model_inputs.pop('text_inputs'				)
								if isinstance(text_inputs[0]						,       lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					text_inputs[0]
								else:
														# Batching case.
														SCREAMING_SNAKE_CASE													=					text_inputs[0][0]

								SCREAMING_SNAKE_CASE													=					self.model(**lowerCAmelCase__						,       **lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					{
								    'candidate_labels': candidate_labels,
								    'logits': outputs.logits_per_audio,
								}
								return model_outputs







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					model_outputs.pop('candidate_labels'				)
								SCREAMING_SNAKE_CASE													=					model_outputs['logits'][0]

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

								SCREAMING_SNAKE_CASE													=					[
								    {'score': score, 'label': candidate_label}
								    for score, candidate_label in sorted(zip(lowerCAmelCase__						,       lowerCAmelCase__				)						,       key=lambda lowerCAmelCase__				: -x[0]				)
								]
								return result






"""simple docstring"""




from __future__ import annotations


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str  )   ->						list[int]:
						return [ord(SCREAMING_SNAKE_CASE_  ) - 96 for elem in plain]


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list[int]  )   ->						str:
						return "".join(chr(elem + 96  ) for elem in encoded  )


def     lowercase			()   ->						None:
						SCREAMING_SNAKE_CASE													=					encode(input('-> '  ).strip().lower()  )
						print('Encoded: ' ,			SCREAMING_SNAKE_CASE_  )
						print('Decoded:' ,			decode(SCREAMING_SNAKE_CASE_  )  )


if __name__ == "__main__":
	main()


