





"""simple docstring"""
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

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






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  F"{sampling_rate}"
							A		=  "1"
							A		=  "f32le"
							A		=  [
							    "ffmpeg",
							    "-i",
							    "pipe:0",
							    "-ac",
							    ac,
							    "-ar",
							    ar,
							    "-f",
							    format_for_conversion,
							    "-hide_banner",
							    "-loglevel",
							    "quiet",
							    "pipe:1",
							]

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

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






@add_end_docstrings(SCREAMING_SNAKE_CASE__			)
class  _UpperCAmelCase      (     SCREAMING_SNAKE_CASE__			):


					def __init__( self      :Union[str, Any]					,	*__UpperCamelCase      :Union[str, Any]					,	**__UpperCamelCase      :Optional[int]		):
												A		=  5
												super().__init__(*__UpperCamelCase					,	**__UpperCamelCase		)

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

												self.check_model_type(__UpperCamelCase		)


					def __call__( self      :Union[str, Any]					,	__UpperCamelCase      :Union[np.ndarray, bytes, str]					,	**__UpperCamelCase      :Optional[int]					,	):
												return super().__call__(__UpperCamelCase					,	**__UpperCamelCase		)


					def      lowerCamelCase	( self      :int					,	__UpperCamelCase      :Optional[Any]=None					,	**__UpperCamelCase      :Any		):
												A		=  {}
												if top_k is not None:
																			if top_k > self.model.config.num_labels:
																										A		=  self.model.config.num_labels
																			A		=  top_k
												return {}, {}, postprocess_params


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :Union[str, Any]		):

												if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																			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
																										A		=  requests.get(__UpperCamelCase		).content
																			else:
																										with open(__UpperCamelCase					,	"rb"		) as f:
																																	A		=  f.read()

												if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																			A		=  ffmpeg_read(__UpperCamelCase					,	self.feature_extractor.sampling_rate		)

												if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																			# 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"		)

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

																										A		=  F.resample(
																										    torch.from_numpy(__UpperCamelCase		)					,	__UpperCamelCase					,	self.feature_extractor.sampling_rate		).numpy()

												if not isinstance(__UpperCamelCase					,	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"		)

												A		=  self.feature_extractor(
												    __UpperCamelCase					,	sampling_rate=self.feature_extractor.sampling_rate					,	return_tensors="pt"		)
												return processed


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :Tuple		):
												A		=  self.model(**__UpperCamelCase		)
												return model_outputs


					def      lowerCamelCase	( self      :Dict					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :Optional[int]=5		):
												A		=  model_outputs.logits[0].softmax(-1		)
												A,     A		=  probs.topk(__UpperCamelCase		)

												A		=  scores.tolist()
												A		=  ids.tolist()

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

												return labels









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Optional[int]							=			logging.get_logger(__name__)

_snake_case					:		List[Any]							=			{
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''tvlt'''


					def __init__( self      :Dict					,	__UpperCamelCase      :Tuple=2_24					,	__UpperCamelCase      :Dict=20_48					,	__UpperCamelCase      :Optional[Any]=1_28					,	__UpperCamelCase      :Optional[int]=[16, 16]					,	__UpperCamelCase      :Any=[16, 16]					,	__UpperCamelCase      :List[str]=3					,	__UpperCamelCase      :str=1					,	__UpperCamelCase      :List[str]=8					,	__UpperCamelCase      :str=7_68					,	__UpperCamelCase      :List[str]=12					,	__UpperCamelCase      :Tuple=12					,	__UpperCamelCase      :Union[str, Any]=30_72					,	__UpperCamelCase      :List[Any]="gelu"					,	__UpperCamelCase      :List[str]=0.0					,	__UpperCamelCase      :Union[str, Any]=0.0					,	__UpperCamelCase      :List[str]=0.02					,	__UpperCamelCase      :Dict=1e-6					,	__UpperCamelCase      :Any=True					,	__UpperCamelCase      :List[str]=False					,	__UpperCamelCase      :str=16					,	__UpperCamelCase      :List[str]=5_12					,	__UpperCamelCase      :int=8					,	__UpperCamelCase      :int=20_48					,	__UpperCamelCase      :int=0.75					,	__UpperCamelCase      :Tuple=0.15					,	__UpperCamelCase      :Optional[Any]="frame-level"					,	__UpperCamelCase      :int=True					,	__UpperCamelCase      :str=True					,	__UpperCamelCase      :Tuple="classification"					,	**__UpperCamelCase      :Any					,	):
												super().__init__(**__UpperCamelCase		)

												if audio_mask_type not in ("frame-level", "patch_level"):
																			raise ValueError(
																			    "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
																			    f"got {audio_mask_type}"		)

												A		=  image_size
												A		=  spectrogram_length
												A		=  frequency_length
												A		=  image_patch_size
												A		=  audio_patch_size
												A		=  num_image_channels
												A		=  num_audio_channels
												A		=  num_frames

												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  initializer_range
												A		=  layer_norm_eps
												A		=  qkv_bias
												A		=  use_mean_pooling

												A		=  decoder_num_attention_heads
												A		=  decoder_hidden_size
												A		=  decoder_num_hidden_layers
												A		=  decoder_intermediate_size
												A		=  pixel_mask_ratio
												A		=  audio_mask_ratio
												A		=  audio_mask_type

												A		=  task_matching
												A		=  task_mae
												A		=  loss_type


