
"""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


lowerCamelCase__           =	logging.get_logger(__name__)




@add_end_docstrings(_lowerCamelCase)
class 				A__ (			_lowerCamelCase):

		def __init__(   self ,	**_SCREAMING_SNAKE_CASE				):
			super().__init__(**_SCREAMING_SNAKE_CASE				)

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

		def __call__(   self ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return super().__call__(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	**_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		str		    =   {}
			if "candidate_labels" in kwargs:
				__lowerCAmelCase    :		Optional[Any]		    =   kwargs['candidate_labels']
			if "hypothesis_template" in kwargs:
				__lowerCAmelCase    :		Any		    =   kwargs['hypothesis_template']

			return preprocess_params, {}, {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE="This is a sound of {}."				):
			if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				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    :		Dict		    =   requests.get(_SCREAMING_SNAKE_CASE				).content
				else:
					with open(_SCREAMING_SNAKE_CASE ,	'rb'				) as f:
						__lowerCAmelCase    :		str		    =   f.read()

			if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Dict		    =   ffmpeg_read(_SCREAMING_SNAKE_CASE ,	self.feature_extractor.sampling_rate				)

			if not isinstance(_SCREAMING_SNAKE_CASE ,	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    :		Optional[Any]		    =   self.feature_extractor(
			    [audio] ,	sampling_rate=self.feature_extractor.sampling_rate ,	return_tensors='pt'				)
			__lowerCAmelCase    :		Union[str, Any]		    =   candidate_labels
			__lowerCAmelCase    :		Optional[int]		    =   [hypothesis_template.format(_SCREAMING_SNAKE_CASE				) for x in candidate_labels]
			__lowerCAmelCase    :		Dict		    =   self.tokenizer(_SCREAMING_SNAKE_CASE ,	return_tensors=self.framework ,	padding=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Tuple		    =   [text_inputs]
			return inputs

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		str		    =   model_inputs.pop('candidate_labels'				)
			__lowerCAmelCase    :		List[str]		    =   model_inputs.pop('text_inputs'				)
			if isinstance(text_inputs[0] ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		List[str]		    =   text_inputs[0]
			else:
				# Batching case.
				__lowerCAmelCase    :		List[Any]		    =   text_inputs[0][0]

			__lowerCAmelCase    :		Any		    =   self.model(**_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   {
			    'candidate_labels': candidate_labels,
			    'logits': outputs.logits_per_audio,
			}
			return model_outputs







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Any		    =   model_outputs.pop('candidate_labels'				)
			__lowerCAmelCase    :		Union[str, Any]		    =   model_outputs['logits'][0]

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

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

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase = False	):

	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		str		    =   F"Expected string as input, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)
	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		Union[str, Any]		    =   F"Expected boolean as use_pascal parameter, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)

	__lowerCAmelCase    :		Tuple		    =   input_str.split('_'	)

	__lowerCAmelCase    :		int		    =   0 if use_pascal else 1

	__lowerCAmelCase    :		Any		    =   words[start_index:]

	__lowerCAmelCase    :		Any		    =   [word[0].upper() + word[1:] for word in words_to_capitalize]

	__lowerCAmelCase    :		Any		    =   '' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]	)


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

							testmod()