


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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)


@add_end_docstrings(__A							)
class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


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


     def 	snake_case_  (	self  ,       **__A    ):
        __a					=  {}
        if "candidate_labels" in kwargs:
           __a					=  kwargs["""candidate_labels"""]
        if "hypothesis_template" in kwargs:
           __a					=  kwargs["""hypothesis_template"""]

        return preprocess_params, {}, {}


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

        if isinstance(__A  ,       __A    ):
           __a					=  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"""    )

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


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  model_inputs.pop("""candidate_labels"""    )
        __a					=  model_inputs.pop("""text_inputs"""    )
        if isinstance(text_inputs[0]  ,       __A    ):
           __a					=  text_inputs[0]
        else:
           # Batching case.
           __a					=  text_inputs[0][0]

        __a					=  self.model(**__A  ,       **__A    )

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


     def 	snake_case_  (	self  ,       __A    ):
        __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(__A  ,       __A    )  ,       key=lambda __A    : -x[0]    )
        ]
        return result











def     a     (		lowerCamelCase_							):






				'''simple docstring'''

				if p < 2:
								raise ValueError('''p should not be less than 2!'''							)
				elif p == 2:
								return True

				lowercase__  					=     4
				lowercase__  					=     (1 << p) - 1
				for _ in range(p - 2							):
								lowercase__  					=     ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
							print(lucas_lehmer_test(7))
							print(lucas_lehmer_test(11))




