
import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

_A       =					logging.get_logger(__name__)



def 		lowercase_     (  A__					,						A__						)				->				Union[str, Any]:
    """simple docstring"""

    snake_case						=   F'{sampling_rate}'
    snake_case						=   "1"
    snake_case						=   "f32le"
    snake_case						=   [
        "ffmpeg",
        "-i",
        "pipe:0",
        "-ac",
        ac,
        "-ar",
        ar,
        "-f",
        format_for_conversion,
        "-hide_banner",
        "-loglevel",
        "quiet",
        "pipe:1",
    ]

    try:
        snake_case						=   subprocess.Popen(_A					,						stdin=subprocess.PIPE					,						stdout=subprocess.PIPE						)
    except FileNotFoundError:
        raise ValueError("ffmpeg was not found but is required to load audio files from filename"						)
    snake_case						=   ffmpeg_process.communicate(_A						)
    snake_case						=   output_stream[0]

    snake_case						=   np.frombuffer(_A					,						np.floataa						)
    if audio.shape[0] == 0:
        raise ValueError("Malformed soundfile"						)
    return audio







@add_end_docstrings(A_    )
class 	lowerCamelCase						(    A_    ):





     def __init__(self :       str							,       *_A :       Optional[Any]							,       **_A :       int  )		->		Optional[Any]:
         snake_case						=   5
         super().__init__(*_A							,       **_A  )

         if self.framework != "pt":
             raise ValueError(f'The {self.__class__} is only available in PyTorch.'  )

         self.check_model_type(_A  )





     def __call__(self :       Tuple							,       _A :       Union[np.ndarray, bytes, str]							,       **_A :       Optional[int]							,       )		->		int:
         return super().__call__(_A							,       **_A  )





     def 						UpperCAmelCase(self :       Optional[int]							,       _A :       List[Any]=None							,       **_A :       Tuple  )		->		Any:
         snake_case						=   {}
         if top_k is not None:
             if top_k > self.model.config.num_labels:
                 snake_case						=   self.model.config.num_labels
             snake_case						=   top_k
         return {}, {}, postprocess_params





     def 						UpperCAmelCase(self :       Union[str, Any]							,       _A :       Tuple  )		->		Any:







         if isinstance(_A							,       _A  ):
             if inputs.startswith("http://"  ) or inputs.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
                 snake_case						=   requests.get(_A  ).content
             else:
                 with open(_A							,       "rb"  ) as f:
                     snake_case						=   f.read()

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

         if isinstance(_A							,       _A  ):
             # Accepting `"array"` which is the key defined in `datasets` for
             # better integration
             if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                 raise ValueError(
                     "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
                     "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
                     "containing the sampling_rate associated with that array"  )

             snake_case						=   inputs.pop("raw"							,       _A  )
             if _inputs is None:
                 # Remove path which will not be used from `datasets`.
                 inputs.pop("path"							,       _A  )
                 snake_case						=   inputs.pop("array"							,       _A  )
             snake_case						=   inputs.pop("sampling_rate"  )
             snake_case						=   _inputs
             if in_sampling_rate != self.feature_extractor.sampling_rate:
                 import torch

                 if is_torchaudio_available():
                     from torchaudio import functional as F
                 else:
                     raise ImportError(
                         "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
                         "The torchaudio package can be installed through: `pip install torchaudio`."  )

                 snake_case						=   F.resample(
                     torch.from_numpy(_A  )							,       _A							,       self.feature_extractor.sampling_rate  ).numpy()

         if not isinstance(_A							,       np.ndarray  ):
             raise ValueError("We expect a numpy ndarray as input"  )
         if len(inputs.shape  ) != 1:
             raise ValueError("We expect a single channel audio input for AudioClassificationPipeline"  )

         snake_case						=   self.feature_extractor(
             _A							,       sampling_rate=self.feature_extractor.sampling_rate							,       return_tensors="pt"  )
         return processed





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       Union[str, Any]  )		->		List[str]:
         snake_case						=   self.model(**_A  )
         return model_outputs





     def 						UpperCAmelCase(self :       List[Any]							,       _A :       str							,       _A :       List[str]=5  )		->		str:
         snake_case						=   model_outputs.logits[0].softmax(-1  )
         snake_case	,				snake_case						=   probs.topk(_A  )

         snake_case						=   scores.tolist()
         snake_case						=   ids.tolist()

         snake_case						=   [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_A							,       _A  )]

         return labels









'''simple docstring'''


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				Union[List[PIL.Image.Image], np.ndarray]
			__a				:				Optional[List[bool]]


if is_transformers_available() and is_torch_available():
					from .modeling_roberta_series import RobertaSeriesModelWithTransformation
					from .pipeline_alt_diffusion import AltDiffusionPipeline
					from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
