"""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     :				Dict    				=    logging.get_logger(__name__)

@add_end_docstrings(__UpperCamelCase      )
class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  Optional[Any]	,    **snake_case__    :  List[Any]  ):
							super().__init__(**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    :  Union[str, Any]	,    snake_case__    :  Union[np.ndarray, bytes, str]	,    **snake_case__    :  int  ):
							return super().__call__(snake_case__	,    **snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any	,    **snake_case__    :  Any  ):
							lowerCAmelCase__   = {}
							if "candidate_labels" in kwargs:
									lowerCAmelCase__   = kwargs["""candidate_labels"""]
							if "hypothesis_template" in kwargs:
									lowerCAmelCase__   = kwargs["""hypothesis_template"""]

							return preprocess_params, {}, {}




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]	,    snake_case__    :  str	,    snake_case__    :  int=None	,    snake_case__    :  List[str]="This is a sound of {}."  ):
							if isinstance(snake_case__	,    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__   = requests.get(snake_case__  ).content
									else:
											with open(snake_case__	,    """rb"""  ) as f:
													lowerCAmelCase__   = f.read()

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

							if not isinstance(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__   = self.feature_extractor(
							    [audio]	,    sampling_rate=self.feature_extractor.sampling_rate	,    return_tensors="""pt"""  )
							lowerCAmelCase__   = candidate_labels
							lowerCAmelCase__   = [hypothesis_template.format(snake_case__  ) for x in candidate_labels]
							lowerCAmelCase__   = self.tokenizer(snake_case__	,    return_tensors=self.framework	,    padding=snake_case__  )
							lowerCAmelCase__   = [text_inputs]
							return inputs




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]	,    snake_case__    :  int  ):
							lowerCAmelCase__   = model_inputs.pop("""candidate_labels"""  )
							lowerCAmelCase__   = model_inputs.pop("""text_inputs"""  )
							if isinstance(text_inputs[0]	,    snake_case__  ):
									lowerCAmelCase__   = text_inputs[0]
							else:
									# Batching case.
									lowerCAmelCase__   = text_inputs[0][0]

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

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  str  ):
							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(snake_case__	,    snake_case__  )	,    key=lambda snake_case__  : -x[0]  )
							]
							return result





"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return " ".join(
		    """""".join(word[::-1]  ) if len(lowerCamelCase__  ) > 4 else word for word in sentence.split()  )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(reverse_long_words("Hey wollef sroirraw"))




