


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

lowerCamelCase		=						logging.get_logger(__name__)






def 	lowerCamelCase_       (			_a      ,  _a			):




		"""simple docstring"""






		lowerCAmelCase__  :     str       	=   f'{sampling_rate}'
		lowerCAmelCase__  :     Dict       	=   '''1'''
		lowerCAmelCase__  :     List[str]       	=   '''f32le'''
		lowerCAmelCase__  :     Dict       	=   [
		    '''ffmpeg''',
		    '''-i''',
		    '''pipe:0''',
		    '''-ac''',
		    ac,
		    '''-ar''',
		    ar,
		    '''-f''',
		    format_for_conversion,
		    '''-hide_banner''',
		    '''-loglevel''',
		    '''quiet''',
		    '''pipe:1''',
		]

		try:
				lowerCAmelCase__  :     List[str]       	=   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'''			)
		lowerCAmelCase__  :     Union[str, Any]       	=   ffmpeg_process.communicate(_a			)
		lowerCAmelCase__  :     Optional[Any]       	=   output_stream[0]

		lowerCAmelCase__  :     Tuple       	=   np.frombuffer(_a      ,  np.floataa			)
		if audio.shape[0] == 0:
				raise ValueError('''Malformed soundfile'''			)
		return audio





@add_end_docstrings(_lowercase)
class    _a				(							_lowercase):




	def __init__(   self		:						Dict						,   *_SCREAMING_SNAKE_CASE		:						Any						,   **_SCREAMING_SNAKE_CASE		:						List[str]						)->			int:
			# Default, might be overriden by the model.config.
			lowerCAmelCase__  :     Union[str, Any]       	=   5
			super().__init__(*_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)

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

			self.check_model_type(_SCREAMING_SNAKE_CASE						)




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




	def 				UpperCAmelCase__(   self		:						Tuple						,   _SCREAMING_SNAKE_CASE		:						str=None						,   **_SCREAMING_SNAKE_CASE		:						Tuple						)->			str:
			# No parameters on this pipeline right now
			lowerCAmelCase__  :     Dict       	=   {}
			if top_k is not None:
					if top_k > self.model.config.num_labels:
							lowerCAmelCase__  :     List[str]       	=   self.model.config.num_labels
					lowerCAmelCase__  :     Tuple       	=   top_k
			return {}, {}, postprocess_params




	def 				UpperCAmelCase__(   self		:						Optional[int]						,   _SCREAMING_SNAKE_CASE		:						List[Any]						)->			List[str]:
			if isinstance(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						):
					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
							lowerCAmelCase__  :     List[str]       	=   requests.get(_SCREAMING_SNAKE_CASE						).content
					else:
							with open(_SCREAMING_SNAKE_CASE						,   '''rb'''						) as f:
									lowerCAmelCase__  :     Union[str, Any]       	=   f.read()

			if isinstance(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						):
					lowerCAmelCase__  :     Tuple       	=   ffmpeg_read(_SCREAMING_SNAKE_CASE						,   self.feature_extractor.sampling_rate						)

			if isinstance(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						):
					# 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'''						)

					lowerCAmelCase__  :     Optional[int]       	=   inputs.pop('''raw'''						,   _SCREAMING_SNAKE_CASE						)
					if _inputs is None:
							# Remove path which will not be used from `datasets`.
							inputs.pop('''path'''						,   _SCREAMING_SNAKE_CASE						)
							lowerCAmelCase__  :     Any       	=   inputs.pop('''array'''						,   _SCREAMING_SNAKE_CASE						)
					lowerCAmelCase__  :     int       	=   inputs.pop('''sampling_rate'''						)
					lowerCAmelCase__  :     str       	=   _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`.'''						)

							lowerCAmelCase__  :     Dict       	=   F.resample(
							    torch.from_numpy(_SCREAMING_SNAKE_CASE						)						,   _SCREAMING_SNAKE_CASE						,   self.feature_extractor.sampling_rate						).numpy()

			if not isinstance(_SCREAMING_SNAKE_CASE						,   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'''						)

			lowerCAmelCase__  :     str       	=   self.feature_extractor(
			    _SCREAMING_SNAKE_CASE						,   sampling_rate=self.feature_extractor.sampling_rate						,   return_tensors='''pt'''						)
			return processed




	def 				UpperCAmelCase__(   self		:						Union[str, Any]						,   _SCREAMING_SNAKE_CASE		:						Optional[Any]						)->			Optional[int]:
			lowerCAmelCase__  :     str       	=   self.model(**_SCREAMING_SNAKE_CASE						)
			return model_outputs




	def 				UpperCAmelCase__(   self		:						List[Any]						,   _SCREAMING_SNAKE_CASE		:						Any						,   _SCREAMING_SNAKE_CASE		:						Optional[Any]=5						)->			Any:
			lowerCAmelCase__  :     Optional[int]       	=   model_outputs.logits[0].softmax(-1						)
			lowerCAmelCase__	,      lowerCAmelCase__  :     Optional[int]       	=   probs.topk(_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     List[Any]       	=   scores.tolist()
			lowerCAmelCase__  :     List[str]       	=   ids.tolist()

			lowerCAmelCase__  :     List[Any]       	=   [{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)]

			return labels








from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims




