

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

SCREAMING_SNAKE_CASE						:Dict      =			logging.get_logger(__name__)




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		np.array:



    """simple docstring"""






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

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

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






@add_end_docstrings(snake_case							)
class 		__magic_name__ (				snake_case							):






     def __init__(							self						,		*_lowercase						,		**_lowercase     )->			List[str]:
         # Default, might be overriden by the model.config.
         UpperCamelCase_          =		5
         super().__init__(*_lowercase						,		**_lowercase     )

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

         self.check_model_type(_lowercase     )






     def __call__(							self						,		_lowercase						,		**_lowercase						,		)->			Any:
         return super().__call__(_lowercase						,		**_lowercase     )






     def      UpperCAmelCase_							(							self						,		_lowercase=None						,		**_lowercase     )->			Any:
         # No parameters on this pipeline right now
         UpperCamelCase_          =		{}
         if top_k is not None:
             if top_k > self.model.config.num_labels:
                 UpperCamelCase_          =		self.model.config.num_labels
             UpperCamelCase_          =		top_k
         return {}, {}, postprocess_params






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			List[Any]:
         if isinstance(_lowercase						,		_lowercase     ):
             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
                 UpperCamelCase_          =		requests.get(_lowercase     ).content
             else:
                 with open(_lowercase						,		"rb"     ) as f:
                     UpperCamelCase_          =		f.read()

         if isinstance(_lowercase						,		_lowercase     ):
             UpperCamelCase_          =		ffmpeg_read(_lowercase						,		self.feature_extractor.sampling_rate     )

         if isinstance(_lowercase						,		_lowercase     ):
             # 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"     )

             UpperCamelCase_          =		inputs.pop("raw"						,		_lowercase     )
             if _inputs is None:
                 # Remove path which will not be used from `datasets`.
                 inputs.pop("path"						,		_lowercase     )
                 UpperCamelCase_          =		inputs.pop("array"						,		_lowercase     )
             UpperCamelCase_          =		inputs.pop("sampling_rate"     )
             UpperCamelCase_          =		_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`."     )

                 UpperCamelCase_          =		F.resample(
                     torch.from_numpy(_lowercase     )						,		_lowercase						,		self.feature_extractor.sampling_rate     ).numpy()

         if not isinstance(_lowercase						,		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"     )

         UpperCamelCase_          =		self.feature_extractor(
             _lowercase						,		sampling_rate=self.feature_extractor.sampling_rate						,		return_tensors="pt"     )
         return processed






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			List[Any]:
         UpperCamelCase_          =		self.model(**_lowercase     )
         return model_outputs






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase=5     )->			List[str]:
         UpperCamelCase_          =		model_outputs.logits[0].softmax(-1     )
         UpperCamelCase_   ,    UpperCamelCase_          =		probs.topk(_lowercase     )

         UpperCamelCase_          =		scores.tolist()
         UpperCamelCase_          =		ids.tolist()

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

         return labels



from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    if not inplace:
        UpperCamelCase_          =		ma + ma
    else:
        ma += ma

    return ma




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		-1 * len(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		list(range(len(tensor.shape[:zero_index]       )       )       )
    return tensor.permute(first_inds + [zero_index + i for i in inds]       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    return t.reshape(t.shape[:-no_dims] + (-1,)       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1E-4       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		mask.expand(*value.shape       )
    return torch.sum(mask * value       ,		dim=SCREAMING_SNAKE_CASE_       ) / (eps + torch.sum(SCREAMING_SNAKE_CASE_       ,		dim=SCREAMING_SNAKE_CASE_       ))




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 2.3125       ,		SCREAMING_SNAKE_CASE_ = 21.6875       ,		SCREAMING_SNAKE_CASE_ = 6_4       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		torch.linspace(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		no_bins - 1       ,		device=pts.device       )
    UpperCamelCase_          =		torch.sqrt(torch.sum((pts.unsqueeze(-2       ) - pts.unsqueeze(-3       )) ** 2       ,		dim=-1       )       )
    return torch.bucketize(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		dict:



    """simple docstring"""






    UpperCamelCase_          =		dicts[0]
    UpperCamelCase_          =		{}
    for k, v in first.items():
        UpperCamelCase_          =		[d[k] for d in dicts]
        if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
            UpperCamelCase_          =		dict_multimap(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
        else:
            UpperCamelCase_          =		fn(SCREAMING_SNAKE_CASE_       )

    return new_dict




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		v_bins.view(((1,) * len(x.shape       )) + (len(SCREAMING_SNAKE_CASE_       ),)       )
    UpperCamelCase_          =		x[..., None] - reshaped_bins
    UpperCamelCase_          =		torch.argmin(torch.abs(SCREAMING_SNAKE_CASE_       )       ,		dim=-1       )
    return nn.functional.one_hot(SCREAMING_SNAKE_CASE_       ,		num_classes=len(SCREAMING_SNAKE_CASE_       )       ).float()




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 0       ,		SCREAMING_SNAKE_CASE_ = 0       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		[]
    for i, s in enumerate(data.shape[:no_batch_dims]       ):
        UpperCamelCase_          =		torch.arange(SCREAMING_SNAKE_CASE_       )
        UpperCamelCase_          =		r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape       ) - i - 1)))       )
        ranges.append(SCREAMING_SNAKE_CASE_       )

    UpperCamelCase_          =		[slice(SCREAMING_SNAKE_CASE_       ) for _ in range(len(data.shape       ) - no_batch_dims       )]
    UpperCamelCase_          =		inds
    ranges.extend(SCREAMING_SNAKE_CASE_       )
    # Matt note: Editing this to get around the behaviour of using a list as an array index changing
    # in recent Numpy versions
    return data[tuple(SCREAMING_SNAKE_CASE_       )]


SCREAMING_SNAKE_CASE						:Dict      =			TypeVar("""T""")




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Dict[Any, Union[dict, list, tuple, Any]]:



    """simple docstring"""






    UpperCamelCase_          =		{}
    for k, v in dic.items():
        if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
            UpperCamelCase_          =		dict_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
        else:
            UpperCamelCase_          =		tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )

    return new_dict




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		dict:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		list:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		tuple:



    """simple docstring"""






    ...




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[int]:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return dict_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return [tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ) for x in tree]
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return tuple(tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ) for x in tree       )
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return fn(SCREAMING_SNAKE_CASE_       )
    else:
        print(type(SCREAMING_SNAKE_CASE_       )       )
        raise ValueError("Not supported"       )
SCREAMING_SNAKE_CASE						:Dict      =			partial(tree_map, leaf_type=torch.Tensor)
