


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


A   :					Union[str, Any]	=      logging.get_logger(__name__)







@add_end_docstrings(lowercase__)
class     _lowercase				(							lowercase__):


       """simple docstring"""




       def __init__(     self							:    str      ,   **__lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase )

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





       def __call__(     self							:    Dict      ,   __lowerCamelCase							:    Union[np.ndarray, bytes, str]      ,   **__lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    Any      ,   **__lowerCamelCase							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						{}
             if "candidate_labels" in kwargs:
                   lowerCamelCase__	:       Union[str, Any]					=						kwargs["candidate_labels"]
             if "hypothesis_template" in kwargs:
                   lowerCamelCase__	:       List[Any]					=						kwargs["hypothesis_template"]

             return preprocess_params, {}, {}





       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Union[str, Any]=None      ,   __lowerCamelCase							:    List[str]="This is a sound of {}." ):


             '''simple docstring'''



             if isinstance(__lowerCamelCase      ,   __lowerCamelCase ):
                   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__	:       Union[str, Any]					=						requests.get(__lowerCamelCase ).content
                   else:
                         with open(__lowerCamelCase      ,   "rb" ) as f:
                               lowerCamelCase__	:       List[Any]					=						f.read()

             if isinstance(__lowerCamelCase      ,   __lowerCamelCase ):
                   lowerCamelCase__	:       Tuple					=						ffmpeg_read(__lowerCamelCase      ,   self.feature_extractor.sampling_rate )

             if not isinstance(__lowerCamelCase      ,   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__	:       List[str]					=						self.feature_extractor(
                 [audio]      ,   sampling_rate=self.feature_extractor.sampling_rate      ,   return_tensors="pt" )
             lowerCamelCase__	:       Dict					=						candidate_labels
             lowerCamelCase__	:       List[Any]					=						[hypothesis_template.format(__lowerCamelCase ) for x in candidate_labels]
             lowerCamelCase__	:       List[str]					=						self.tokenizer(__lowerCamelCase      ,   return_tensors=self.framework      ,   padding=__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						[text_inputs]
             return inputs





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						model_inputs.pop("candidate_labels" )
             lowerCamelCase__	:       str					=						model_inputs.pop("text_inputs" )
             if isinstance(text_inputs[0]      ,   __lowerCamelCase ):
                   lowerCamelCase__	:       int					=						text_inputs[0]
             else:
                   # Batching case.
                   lowerCamelCase__	:       List[str]					=						text_inputs[0][0]

             lowerCamelCase__	:       List[Any]					=						self.model(**__lowerCamelCase      ,   **__lowerCamelCase )

             lowerCamelCase__	:       Union[str, Any]					=						{
                 "candidate_labels": candidate_labels,
                 "logits": outputs.logits_per_audio,
             }
             return model_outputs





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						model_outputs.pop("candidate_labels" )
             lowerCamelCase__	:       int					=						model_outputs["logits"][0]

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

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






import os

def    lowercase_	(	_A    :					str = "input.txt"		):

      """simple docstring"""




      with open(os.path.join(os.path.dirname(_A		)					,      _A		)		) as input_file:
            lowerCamelCase__	:       List[Any]					=						[
                [int(_A		) for element in line.split(","		)]
                for line in input_file.readlines()
            ]

      lowerCamelCase__	:       Optional[Any]					=						len(_A		)
      lowerCamelCase__	:       Union[str, Any]					=						len(matrix[0]		)

      lowerCamelCase__	:       Union[str, Any]					=						[[-1 for _ in range(_A		)] for _ in range(_A		)]
      for i in range(_A		):
            lowerCamelCase__	:       Optional[Any]					=						matrix[i][0]

      for j in range(1					,      _A		):
            for i in range(_A		):
                  lowerCamelCase__	:       int					=						minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1					,      _A		):
                  lowerCamelCase__	:       Tuple					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i - 1][j] + matrix[i][j]		)

            for i in range(rows - 2					,      -1					,      -1		):
                  lowerCamelCase__	:       str					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i + 1][j] + matrix[i][j]		)

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


if __name__ == "__main__":
    print(f'{solution() = }')


