

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							:     List[Any] 						=	logging.get_logger(__name__)


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



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

			try:
						a_		:		List[Any]				    =				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""")
			a_		:		Dict				    =				ffmpeg_process.communicate(a__)
			a_		:		Any				    =				output_stream[0]

			a_		:		List[Any]				    =				np.frombuffer(a__       ,					np.floataa)
			if audio.shape[0] == 0:
						raise ValueError("""Malformed soundfile""")
			return audio



@add_end_docstrings(a_    )
class    A__(a_    ):
						"""simple docstring"""






						def __init__(  self       ,     *_lowercase       ,     **_lowercase  )      ->				Any:
									# Default, might be overriden by the model.config.
									a_		:		Optional[Any]				    =				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       ,     )      ->				List[str]:
									return super().__call__(_lowercase       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase=None       ,     **_lowercase  )      ->				int:
									# No parameters on this pipeline right now
									a_		:		int				    =				{}
									if top_k is not None:
												if top_k > self.model.config.num_labels:
															a_		:		Dict				    =				self.model.config.num_labels
												a_		:		Dict				    =				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
															a_		:		str				    =				requests.get(_lowercase  ).content
												else:
															with open(_lowercase       ,     """rb"""  ) as f:
																		a_		:		List[Any]				    =				f.read()

									if isinstance(_lowercase       ,     _lowercase  ):
												a_		:		Union[str, Any]				    =				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"""  )

												a_		:		Tuple				    =				inputs.pop("""raw"""       ,     _lowercase  )
												if _inputs is None:
															# Remove path which will not be used from `datasets`.
															inputs.pop("""path"""       ,     _lowercase  )
															a_		:		Any				    =				inputs.pop("""array"""       ,     _lowercase  )
												a_		:		List[str]				    =				inputs.pop("""sampling_rate"""  )
												a_		:		int				    =				_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_		:		str				    =				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"""  )

									a_		:		List[str]				    =				self.feature_extractor(
									    _lowercase       ,     sampling_rate=self.feature_extractor.sampling_rate       ,     return_tensors="""pt"""  )
									return processed





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				int:
									a_		:		Union[str, Any]				    =				self.model(**_lowercase  )
									return model_outputs





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase=5  )      ->				Tuple:
									a_		:		int				    =				model_outputs.logits[0].softmax(-1  )
									a_    ,			a_		:		Union[str, Any]				    =				probs.topk(_lowercase  )

									a_		:		Any				    =				scores.tolist()
									a_		:		Any				    =				ids.tolist()

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

									return labels





import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin


def 			_UpperCAmelCase							(		a__=2       ,					a__=True       ,					a__=False):

			'''simple docstring'''



			a_		:		Optional[Any]				    =				Accelerator(step_scheduler_with_optimizer=a__       ,					split_batches=a__)
			a_		:		Dict				    =				torch.nn.Linear(2       ,					4)
			a_		:		int				    =				torch.optim.AdamW(model.parameters()       ,					lr=1.0)
			a_		:		Optional[int]				    =				torch.optim.lr_scheduler.OneCycleLR(a__       ,					max_lr=0.01       ,					steps_per_epoch=2       ,					epochs=1)
			a_    ,			a_    ,			a_		:		List[Any]				    =				accelerator.prepare(a__       ,					a__       ,					a__)

			# Optimizer has stepped
			scheduler.step()
			if step_scheduler_with_optimizer or (num_processes == 1):
						assert (
						    scheduler.scheduler.last_epoch == num_processes
						), f'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
			else:
						assert (
						    scheduler.scheduler.last_epoch != num_processes
						), f'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''


def 			_UpperCAmelCase							(		a__=2       ,					a__=True       ,					a__=False):

			'''simple docstring'''



			a_		:		int				    =				Accelerator(step_scheduler_with_optimizer=a__       ,					split_batches=a__)
			a_		:		Tuple				    =				torch.nn.Linear(2       ,					4)
			a_		:		str				    =				torch.optim.AdamW(model.parameters()       ,					lr=1.0)
			a_		:		Optional[int]				    =				torch.optim.lr_scheduler.LambdaLR(a__       ,					lr_lambda=lambda a__: 1 - n / 1_0)
			a_    ,			a_    ,			a_		:		List[str]				    =				accelerator.prepare(a__       ,					a__       ,					a__)

			# Optimizer has stepped
			a_		:		Optional[Any]				    =				False
			scheduler.step()
			a_		:		Union[str, Any]				    =				1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
			assert (
			    scheduler.get_last_lr()[0] == expected_lr
			), f'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

			# Optimizer has not stepped
			a_		:		Any				    =				True
			scheduler.step()
			if not step_scheduler_with_optimizer:
						a_		:		Any				    =				1 - 2 / 1_0
			assert (
			    scheduler.get_last_lr()[0] == expected_lr
			), f'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''


def 			_UpperCAmelCase							(		a__ = 2):

			'''simple docstring'''



			from transformers import get_linear_schedule_with_warmup

			a_		:		int				    =				[1, 2, 4]
			for num_steps in steps:
						a_		:		List[str]				    =				GradientAccumulationPlugin(num_steps=a__       ,					adjust_scheduler=num_steps > 1)
						a_		:		List[str]				    =				Accelerator(gradient_accumulation_plugin=a__)
						a_		:		int				    =				torch.nn.Linear(2       ,					4)
						a_		:		Any				    =				torch.optim.AdamW(model.parameters()       ,					lr=10.0)
						a_		:		List[Any]				    =				get_linear_schedule_with_warmup(optimizer=a__       ,					num_warmup_steps=0       ,					num_training_steps=2_0)

						a_    ,			a_    ,			a_		:		int				    =				accelerator.prepare(a__       ,					a__       ,					a__)

						for i in range(1_0 * num_steps):
									with accelerator.accumulate(a__):
												optimizer.step()
												scheduler.step()

									if i == (1_0 * num_steps - 2):
												assert (
												    scheduler.get_last_lr()[0] != 0
												), f'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
						assert (
						    scheduler.get_last_lr()[0] == 0
						), f'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
						GradientState._reset_state()



@require_cpu
class    A__(unittest.TestCase    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				str:
									debug_launcher(partial(_lowercase       ,     num_processes=1  )       ,     num_processes=1  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									debug_launcher(partial(_lowercase       ,     num_processes=1  )       ,     num_processes=1  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     step_scheduler_with_optimizer=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     step_scheduler_with_optimizer=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(_lowercase  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(_lowercase  )
									debug_launcher(partial(_lowercase       ,     num_processes=1       ,     split_batches=_lowercase  )       ,     num_processes=1  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(partial(_lowercase       ,     step_scheduler_with_optimizer=_lowercase  )  )





						def 	UpperCamelCase__			(  self  )      ->				str:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(partial(_lowercase       ,     step_scheduler_with_optimizer=_lowercase  )  )





						@require_huggingface_suite
						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									AcceleratorState._reset_state(_lowercase  )
									debug_launcher(partial(_lowercase       ,     num_processes=1  )  )
									debug_launcher(_lowercase  )


