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  :					Any          =  logging.get_logger(__name__)





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



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

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

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




@add_end_docstrings(_UpperCAmelCase			)
class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       *lowerCAmelCase__				,       **lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							5
							super().__init__(*lowerCAmelCase__				,       **lowerCAmelCase__						)

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

							self.check_model_type(lowerCAmelCase__						)





			def __call__(       self				,       lowerCAmelCase__				,       **lowerCAmelCase__				,       )					-> Optional[int]:

							'''simple docstring'''


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





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							{}
							if top_k is not None:
											if top_k > self.model.config.num_labels:
															__lowercase			      =							self.model.config.num_labels
											__lowercase			      =							top_k
							return {}, {}, postprocess_params





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Dict:

							'''simple docstring'''


							if isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):
											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
															__lowercase			      =							requests.get(lowerCAmelCase__						).content
											else:
															with open(lowerCAmelCase__				,       '''rb'''						) as f:
																			__lowercase			      =							f.read()

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

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

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

															__lowercase			      =							F.resample(
															    torch.from_numpy(lowerCAmelCase__						)				,       lowerCAmelCase__				,       self.feature_extractor.sampling_rate						).numpy()

							if not isinstance(lowerCAmelCase__				,       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'''						)

							__lowercase			      =							self.feature_extractor(
							    lowerCAmelCase__				,       sampling_rate=self.feature_extractor.sampling_rate				,       return_tensors='''pt'''						)
							return processed





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							self.model(**lowerCAmelCase__						)
							return model_outputs





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=5						)					-> str:

							'''simple docstring'''


							__lowercase			      =							model_outputs.logits[0].softmax(-1						)
							__lowercase      ,					__lowercase			      =							probs.topk(lowerCAmelCase__						)

							__lowercase			      =							scores.tolist()
							__lowercase			      =							ids.tolist()

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

							return labels
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__a  :					int          =  5_0_0_0_0_0

__a   ,						__a  :					Any          =  os.path.split(__file__)
__a  :					Dict          =  os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							dataset.select(range(0							,      len(lowercase							)							,      2							)							)





@get_duration
def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							dataset.sort('''numbers'''							)





@get_duration
def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							dataset.shuffle()





@get_duration
def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							dataset.train_test_split(0.1							)





@get_duration
def     UpperCAmelCase						(   lowercase							,      lowercase=10							):







				"""simple docstring"""



				for shard_id in range(lowercase							):
								__lowercase			      =							dataset.shard(lowercase							,      lowercase							)





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							{'''num examples''': SPEED_TEST_N_EXAMPLES}
				__lowercase			      =							(select, sort, shuffle, train_test_split, shard)
				with tempfile.TemporaryDirectory() as tmp_dir:
								print('''generating dataset'''							)
								__lowercase			      =							datasets.Features({'''text''': datasets.Value('''string'''							), '''numbers''': datasets.Value('''float32'''							)}							)
								__lowercase			      =							generate_example_dataset(
								    os.path.join(lowercase							,      '''dataset.arrow'''							)							,      lowercase							,      num_examples=lowercase							)
								print('''Functions'''							)
								for func in functions:
												print(func.__name__							)
												__lowercase			      =							func(lowercase							)

				with open(lowercase							,      '''wb'''							) as f:
								f.write(json.dumps(lowercase							).encode('''utf-8'''							)							)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_indices_mapping()