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   :				int     			=						logging.get_logger(__name__)


@add_end_docstrings(a       )
class   lowercase      (       a       ):




							def __init__(  self				:   Union[str, Any]		,							**_UpperCamelCase				:   Tuple )   ->			List[Any]:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase )

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




							def __call__(  self				:   Optional[Any]		,							_UpperCamelCase				:   Union[np.ndarray, bytes, str]		,							**_UpperCamelCase				:   Dict )   ->			Dict:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Any		,							**_UpperCamelCase				:   Dict )   ->			Optional[Any]:



												'''simple docstring'''

												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 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   List[str]=None		,							_UpperCamelCase				:   Optional[int]="This is a sound of {}." )   ->			Tuple:



												'''simple docstring'''

												if isinstance(_UpperCamelCase		,							_UpperCamelCase ):
																	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(_UpperCamelCase ).content
																	else:
																						with open(_UpperCamelCase		,							"rb" ) as f:
																											SCREAMING_SNAKE_CASE           =    f.read()

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

												if not isinstance(_UpperCamelCase		,							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(_UpperCamelCase ) for x in candidate_labels]
												SCREAMING_SNAKE_CASE           =    self.tokenizer(_UpperCamelCase		,							return_tensors=self.framework		,							padding=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    [text_inputs]
												return inputs




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   Tuple )   ->			List[str]:



												'''simple docstring'''

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

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

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





							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   Dict )   ->			List[str]:



												'''simple docstring'''

												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(_UpperCamelCase		,							_UpperCamelCase )		,							key=lambda _UpperCamelCase : -x[0] )
												]
												return result





from __future__ import annotations

from collections.abc import Iterator


class   lowercase      :




							def __init__(  self				:   str		,							_UpperCamelCase				:   int )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    value
												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    None



class   lowercase      :




							def __init__(  self				:   str		,							_UpperCamelCase				:   Node )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    tree




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Node | None )   ->			int:



												'''simple docstring'''

												if node is None:
																	return 0
												return node.value + (
												    self.depth_first_search(node.left ) + self.depth_first_search(node.right )
												)





							def __iter__(  self				:   List[Any] )   ->			Iterator[int]:



												'''simple docstring'''

												yield self.depth_first_search(self.tree )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




