





"""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


__magic_name__       :					Dict						=			logging.get_logger(__name__)






@add_end_docstrings(__SCREAMING_SNAKE_CASE	)
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   **_A    ):






									'''simple docstring'''







									super().__init__(**_A    )

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







			def __call__(      self						,   _A						,   **_A    ):






									'''simple docstring'''







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







			def   _a      (      self						,   **_A    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			{}
									if "candidate_labels" in kwargs:
															UpperCamelCase				:		Optional[int]				   =			kwargs["""candidate_labels"""]
									if "hypothesis_template" in kwargs:
															UpperCamelCase				:		Optional[int]				   =			kwargs["""hypothesis_template"""]

									return preprocess_params, {}, {}







			def   _a      (      self						,   _A						,   _A=None						,   _A="This is a sound of {}."    ):






									'''simple docstring'''







									if isinstance(_A						,   _A    ):
															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
																					UpperCamelCase				:		Optional[Any]				   =			requests.get(_A    ).content
															else:
																					with open(_A						,   """rb"""    ) as f:
																											UpperCamelCase				:		Any				   =			f.read()

									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		Any				   =			ffmpeg_read(_A						,   self.feature_extractor.sampling_rate    )

									if not isinstance(_A						,   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"""    )

									UpperCamelCase				:		Any				   =			self.feature_extractor(
									    [audio]						,   sampling_rate=self.feature_extractor.sampling_rate						,   return_tensors="""pt"""    )
									UpperCamelCase				:		List[Any]				   =			candidate_labels
									UpperCamelCase				:		Optional[int]				   =			[hypothesis_template.format(_A    ) for x in candidate_labels]
									UpperCamelCase				:		Optional[int]				   =			self.tokenizer(_A						,   return_tensors=self.framework						,   padding=_A    )
									UpperCamelCase				:		Optional[Any]				   =			[text_inputs]
									return inputs







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			model_inputs.pop("""candidate_labels"""    )
									UpperCamelCase				:		Optional[Any]				   =			model_inputs.pop("""text_inputs"""    )
									if isinstance(text_inputs[0]						,   _A    ):
															UpperCamelCase				:		str				   =			text_inputs[0]
									else:
															# Batching case.
															UpperCamelCase				:		Tuple				   =			text_inputs[0][0]

									UpperCamelCase				:		Optional[Any]				   =			self.model(**_A						,   **_A    )

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







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			model_outputs.pop("""candidate_labels"""    )
									UpperCamelCase				:		Optional[int]				   =			model_outputs["""logits"""][0]

									if self.framework == "pt":
															UpperCamelCase				:		Optional[int]				   =			logits.softmax(dim=0    )
															UpperCamelCase				:		Tuple				   =			probs.tolist()
									else:
															raise ValueError("""`tf` framework not supported."""    )

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









class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     {}



 def  lowerCAmelCase_    (				self    :						List[str]		):
       print(self.vertex		)
       for i in self.vertex:
             print(_lowerCAmelCase							,							' -> '							,							' -> '.join([str(_lowerCAmelCase		) for j in self.vertex[i]]		)		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int		):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase		)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_      		=     [to_vertex]



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_      		=     [False] * len(self.vertex		)

       # call the recursive helper function
       for i in range(len(self.vertex		)		):
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase							,							_lowerCAmelCase		)




 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						list		):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_      		=     True

       print(_lowerCAmelCase							,							end=' '		)

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase							,							_lowerCAmelCase		)


if __name__ == "__main__":
   lowerCamelCase__					:  List[Any]      							=				Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print('DFS:')
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3