
'''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(UpperCamelCase__					)
class   a_ (      UpperCamelCase__					):




			def __init__(       self					,   **UpperCAmelCase	):
				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					,   UpperCAmelCase					,   **UpperCAmelCase	):
				return super().__call__(UpperCAmelCase					,   **UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   **UpperCAmelCase	):
				a_      				=						{}
				if "candidate_labels" in kwargs:
					a_      				=						kwargs["""candidate_labels"""]
				if "hypothesis_template" in kwargs:
					a_      				=						kwargs["""hypothesis_template"""]

				return preprocess_params, {}, {}




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase=None					,   UpperCAmelCase="This is a sound of {}."	):
				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
						a_      				=						requests.get(UpperCAmelCase	).content
					else:
						with open(UpperCAmelCase					,   """rb"""	) as f:
							a_      				=						f.read()

				if isinstance(UpperCAmelCase					,   UpperCAmelCase	):
					a_      				=						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"""	)

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




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						model_inputs.pop("""candidate_labels"""	)
				a_      				=						model_inputs.pop("""text_inputs"""	)
				if isinstance(text_inputs[0]					,   UpperCAmelCase	):
					a_      				=						text_inputs[0]
				else:
					# Batching case.
					a_      				=						text_inputs[0][0]

				a_      				=						self.model(**UpperCAmelCase					,   **UpperCAmelCase	)

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






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						model_outputs.pop("""candidate_labels"""	)
				a_      				=						model_outputs["""logits"""][0]

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

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


'''simple docstring'''
from typing import Any







class   a_ :




			def __init__(       self					,   UpperCAmelCase	):
				a_      				=						data
				a_      				=						None







class   a_ :




			def __init__(       self	):
				a_      				=						None




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.head
				while temp is not None:
					print(temp.data					,   end=""" """	)
					a_      				=						temp.next
				print()




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						Node(UpperCAmelCase	)
				a_      				=						self.head
				a_      				=						new_node






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				if node_data_a == node_data_a:
					return
				else:
					a_      				=						self.head
					while node_a is not None and node_a.data != node_data_a:
						a_      				=						node_a.next

					a_      				=						self.head
					while node_a is not None and node_a.data != node_data_a:
						a_      				=						node_a.next

					if node_a is None or node_a is None:
						return

					a_				,  a_      				=						node_a.data, node_a.data


if __name__ == "__main__":
		lowercase__											=LinkedList()
		for i in range(5, 0, -1):
				ll.push(i)

		ll.print_list()

		ll.swap_nodes(1, 4)
		print('After swapping')
		ll.print_list()
