from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


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


class 						lowercase    (     a     ):
							lowercase__						:       str								=							["""input_values""", """padding_mask"""]




							def __init__(						self   :					int	,							_UpperCamelCase   :					int = 1	,							_UpperCamelCase   :					int = 24_000	,							_UpperCamelCase   :					float = 0.0	,							_UpperCamelCase   :					float = None	,							_UpperCamelCase   :					float = None	,							**_UpperCamelCase   :					Optional[int]	,							)					->				Any:



												'''simple docstring'''

												super().__init__(feature_size=_UpperCamelCase	,							sampling_rate=_UpperCamelCase	,							padding_value=_UpperCamelCase	,							**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       chunk_length_s
												SCREAMING_SNAKE_CASE    			=       overlap




							@property
							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[int]:



												'''simple docstring'''

												if self.chunk_length_s is None:
																	return None
												else:
																	return int(self.chunk_length_s * self.sampling_rate      )




							@property
							def 	__snake_case(						self   :					Any      )					->				Optional[int]:



												'''simple docstring'''

												if self.chunk_length_s is None or self.overlap is None:
																	return None
												else:
																	return max(1	,							int((1.0 - self.overlap) * self.chunk_length      )      )





							def __call__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,							_UpperCamelCase   :					Optional[Union[bool, str, PaddingStrategy]] = None	,							_UpperCamelCase   :					Optional[bool] = False	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							_UpperCamelCase   :					Optional[int] = None	,							)					->				BatchFeature:



												'''simple docstring'''

												if sampling_rate is not None:
																	if sampling_rate != self.sampling_rate:
																						raise ValueError(
																						    F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
																						    F" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
																						    F" {self.sampling_rate} and not {sampling_rate}."      )
												else:
																	logger.warning(
																	    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
																	    "Failing to do so can result in silent errors that might be hard to debug."      )

												if padding and truncation:
																	raise ValueError("Both padding and truncation were set. Make sure you only set one."      )
												elif padding is None:
																	# by default let's pad the inputs
																	SCREAMING_SNAKE_CASE    			=       True

												SCREAMING_SNAKE_CASE    			=       bool(
												    isinstance(_UpperCamelCase	,							(list, tuple)      ) and (isinstance(raw_audio[0]	,							(np.ndarray, tuple, list)      ))      )

												if is_batched:
																	SCREAMING_SNAKE_CASE    			=       [np.asarray(_UpperCamelCase	,							dtype=np.floataa      ).T for audio in raw_audio]
												elif not is_batched and not isinstance(_UpperCamelCase	,							np.ndarray      ):
																	SCREAMING_SNAKE_CASE    			=       np.asarray(_UpperCamelCase	,							dtype=np.floataa      )
												elif isinstance(_UpperCamelCase	,							np.ndarray      ) and raw_audio.dtype is np.dtype(np.floataa      ):
																	SCREAMING_SNAKE_CASE    			=       raw_audio.astype(np.floataa      )

												# always return batch
												if not is_batched:
																	SCREAMING_SNAKE_CASE    			=       [np.asarray(_UpperCamelCase      ).T]

												# verify inputs are valid
												for idx, example in enumerate(_UpperCamelCase      ):
																	if example.ndim > 2:
																						raise ValueError(F"Expected input shape (channels, length) but got shape {example.shape}"      )
																	if self.feature_size == 1 and example.ndim != 1:
																						raise ValueError(F"Expected mono audio but example has {example.shape[-1]} channels"      )
																	if self.feature_size == 2 and example.shape[-1] != 2:
																						raise ValueError(F"Expected stereo audio but example has {example.shape[-1]} channels"      )

												SCREAMING_SNAKE_CASE    			=       None
												SCREAMING_SNAKE_CASE    			=       BatchFeature({"input_values": raw_audio}      )
												if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
																	if truncation:
																						SCREAMING_SNAKE_CASE    			=       min(array.shape[0] for array in raw_audio      )
																						SCREAMING_SNAKE_CASE    			=       int(np.floor(max_length / self.chunk_stride      )      )
																						SCREAMING_SNAKE_CASE    			=       (nb_step - 1) * self.chunk_stride + self.chunk_length
																	elif padding:
																						SCREAMING_SNAKE_CASE    			=       max(array.shape[0] for array in raw_audio      )
																						SCREAMING_SNAKE_CASE    			=       int(np.ceil(max_length / self.chunk_stride      )      )
																						SCREAMING_SNAKE_CASE    			=       (nb_step - 1) * self.chunk_stride + self.chunk_length
																						SCREAMING_SNAKE_CASE    			=       "max_length"
																	else:
																						SCREAMING_SNAKE_CASE    			=       input_values

        # normal padding on batch
												if padded_inputs is None:
																	SCREAMING_SNAKE_CASE    			=       self.pad(
																	    _UpperCamelCase	,							max_length=_UpperCamelCase	,							truncation=_UpperCamelCase	,							padding=_UpperCamelCase	,							return_attention_mask=_UpperCamelCase	,							)
																	if padding:
																						SCREAMING_SNAKE_CASE    			=       padded_inputs.pop("attention_mask"      )

												SCREAMING_SNAKE_CASE    			=       []
												for example in padded_inputs.pop("input_values"      ):
																	if self.feature_size == 1:
																						SCREAMING_SNAKE_CASE    			=       example[..., None]
																	input_values.append(example.T      )

												SCREAMING_SNAKE_CASE    			=       input_values
												if return_tensors is not None:
																	SCREAMING_SNAKE_CASE    			=       padded_inputs.convert_to_tensors(_UpperCamelCase      )

												return padded_inputs





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment


class 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Union[str, Any]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       inspect.getfile(accelerate.test_utils      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_script.py"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(
												    mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_distributed_data_loop.py"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_ops.py"]      )




							@require_multi_gpu
							def 	__snake_case(						self   :					Optional[int]      )					->				Any:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices."      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )




							@require_multi_gpu
							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices."      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
												print(F"Command: {cmd}"      )
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )




							@require_multi_gpu
							def 	__snake_case(						self   :					int      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__      )]
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )





							@require_multi_gpu
							def 	__snake_case(						self   :					int      )					->				int:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
												with patch_environment(omp_num_threads=1	,							cuda_visible_devices="0,1"      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )


if __name__ == "__main__":
							_lowerCamelCase					:   str							=		Accelerator()
							_lowerCamelCase					:   List[str]							=		(accelerator.state.process_index + 2, 10)
							_lowerCamelCase					:   str							=		torch.randint(0, 10, shape).to(accelerator.device)

							_lowerCamelCase					:   Optional[Any]							=		''''''

							_lowerCamelCase					:   str							=		accelerator.pad_across_processes(tensor)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
														error_msg += "Padding was not done with the right value (0)."

							_lowerCamelCase					:   Any							=		accelerator.pad_across_processes(tensor, pad_first=True)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							_lowerCamelCase					:   int							=		accelerator.state.num_processes - accelerator.state.process_index - 1
							if not torch.equal(tensora[index:], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[:index] == 0):
														error_msg += "Padding was not done with the right value (0)."

							# Raise error at the end to make sure we don't stop at the first failure.
							if len(error_msg) > 0:
														raise ValueError(error_msg)




