




'''simple docstring'''

import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   np.array:

					'''simple docstring'''

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

					try:
										with subprocess.Popen(__UpperCAmelCase, stdin=subprocess.PIPE, stdout=subprocess.PIPE      ) as ffmpeg_process:
															snake_case_		     =							ffmpeg_process.communicate(__UpperCAmelCase      )
					except FileNotFoundError as error:
										raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''      ) from error
					snake_case_		     =							output_stream[0]
					snake_case_		     =							np.frombuffer(__UpperCAmelCase, np.floataa      )
					if audio.shape[0] == 0:
										raise ValueError('''Malformed soundfile'''      )
					return audio


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = "f32le", )						->   Optional[int]:

					'''simple docstring'''

					snake_case_		     =							F"{sampling_rate}"
					snake_case_		     =							'''1'''
					if format_for_conversion == "s16le":
										snake_case_		     =							2
					elif format_for_conversion == "f32le":
										snake_case_		     =							4
					else:
										raise ValueError(F"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"      )

					snake_case_		     =							platform.system()
					if system == "Linux":
										snake_case_		     =							'''alsa'''
										snake_case_		     =							'''default'''
					elif system == "Darwin":
										snake_case_		     =							'''avfoundation'''
										snake_case_		     =							''':0'''
					elif system == "Windows":
										snake_case_		     =							'''dshow'''
										snake_case_		     =							'''default'''

					snake_case_		     =							[
					    '''ffmpeg''',
					    '''-f''',
					    format_,
					    '''-i''',
					    input_,
					    '''-ac''',
					    ac,
					    '''-ar''',
					    ar,
					    '''-f''',
					    format_for_conversion,
					    '''-fflags''',
					    '''nobuffer''',
					    '''-hide_banner''',
					    '''-loglevel''',
					    '''quiet''',
					    '''pipe:1''',
					]
					snake_case_		     =							int(round(sampling_rate * chunk_length_s      )      ) * size_of_sample
					snake_case_		     =							_ffmpeg_stream(__UpperCAmelCase, __UpperCAmelCase      )
					for item in iterator:
										yield item


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = "f32le", )						->   List[Any]:

					'''simple docstring'''

					if stream_chunk_s is not None:
										snake_case_		     =							stream_chunk_s
					else:
										snake_case_		     =							chunk_length_s

					snake_case_		     =							ffmpeg_microphone(__UpperCAmelCase, __UpperCAmelCase, format_for_conversion=__UpperCAmelCase      )
					if format_for_conversion == "s16le":
										snake_case_		     =							np.intaa
										snake_case_		     =							2
					elif format_for_conversion == "f32le":
										snake_case_		     =							np.floataa
										snake_case_		     =							4
					else:
										raise ValueError(F"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"      )

					if stride_length_s is None:
										snake_case_		     =							chunk_length_s / 6
					snake_case_		     =							int(round(sampling_rate * chunk_length_s      )      ) * size_of_sample
					if isinstance(__UpperCAmelCase, (int, float)      ):
										snake_case_		     =							[stride_length_s, stride_length_s]

					snake_case_		     =							int(round(sampling_rate * stride_length_s[0]      )      ) * size_of_sample
					snake_case_		     =							int(round(sampling_rate * stride_length_s[1]      )      ) * size_of_sample
					snake_case_		     =							datetime.datetime.now()
					snake_case_		     =							datetime.timedelta(seconds=__UpperCAmelCase      )
					for item in chunk_bytes_iter(__UpperCAmelCase, __UpperCAmelCase, stride=(stride_left, stride_right), stream=__UpperCAmelCase      ):
										# Put everything back in numpy scale
										snake_case_		     =							np.frombuffer(item['''raw'''], dtype=__UpperCAmelCase      )
										snake_case_		     =							(
										    item['''stride'''][0] // size_of_sample,
										    item['''stride'''][1] // size_of_sample,
										)
										snake_case_		     =							sampling_rate
										audio_time += delta
										if datetime.datetime.now() > audio_time + 10 * delta:
															# We're late !! SKIP
															continue
										yield item


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = False      )						->   int:

					'''simple docstring'''

					snake_case_		     =							b''''''
					snake_case_     ,snake_case_		     =							stride
					if stride_left + stride_right >= chunk_len:
										raise ValueError(
										    F"Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"      )
					snake_case_		     =							0
					for raw in iterator:
										acc += raw
										if stream and len(__UpperCAmelCase      ) < chunk_len:
															snake_case_		     =							(_stride_left, 0)
															yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
										else:
															while len(__UpperCAmelCase      ) >= chunk_len:
																				# We are flushing the accumulator
																				snake_case_		     =							(_stride_left, stride_right)
																				snake_case_		     =							{'''raw''': acc[:chunk_len], '''stride''': stride}
																				if stream:
																									snake_case_		     =							False
																				yield item
																				snake_case_		     =							stride_left
																				snake_case_		     =							acc[chunk_len - stride_left - stride_right :]
    # Last chunk
					if len(__UpperCAmelCase      ) > stride_left:
										snake_case_		     =							{'''raw''': acc, '''stride''': (_stride_left, 0)}
										if stream:
															snake_case_		     =							False
										yield item


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

					snake_case_		     =							2**24  # 16Mo
					try:
										with subprocess.Popen(__UpperCAmelCase, stdout=subprocess.PIPE, bufsize=__UpperCAmelCase      ) as ffmpeg_process:
															while True:
																				snake_case_		     =							ffmpeg_process.stdout.read(__UpperCAmelCase      )
																				if raw == b"":
																									break
																				yield raw
					except FileNotFoundError as error:
										raise ValueError('''ffmpeg was not found but is required to stream audio files from filename'''      ) from error







'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

a    :							List[str]										=    {
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}






class 		a							(  _lowerCamelCase	):
				snake_case_           =						"wavlm"


				def __init__(						self       :   Union[str, Any]			,							lowercase_       :   Union[str, Any]=32			,							lowercase_       :   Optional[int]=768			,							lowercase_       :   List[str]=12			,							lowercase_       :   Dict=12			,							lowercase_       :   Any=3072			,							lowercase_       :   List[Any]="gelu"			,							lowercase_       :   int=0.1			,							lowercase_       :   Dict=0.1			,							lowercase_       :   int=0.1			,							lowercase_       :   int=0.0			,							lowercase_       :   List[Any]=0.1			,							lowercase_       :   int=0.1			,							lowercase_       :   Optional[int]=0.02			,							lowercase_       :   Dict=1e-5			,							lowercase_       :   Tuple="group"			,							lowercase_       :   str="gelu"			,							lowercase_       :   Any=(512, 512, 512, 512, 512, 512, 512)			,							lowercase_       :   str=(5, 2, 2, 2, 2, 2, 2)			,							lowercase_       :   Any=(10, 3, 3, 3, 3, 2, 2)			,							lowercase_       :   Tuple=False			,							lowercase_       :   List[Any]=128			,							lowercase_       :   int=16			,							lowercase_       :   Tuple=320			,							lowercase_       :   Union[str, Any]=800			,							lowercase_       :   List[str]=False			,							lowercase_       :   str=True			,							lowercase_       :   List[Any]=0.05			,							lowercase_       :   Optional[Any]=10			,							lowercase_       :   Optional[Any]=2			,							lowercase_       :   Optional[int]=0.0			,							lowercase_       :   List[str]=10			,							lowercase_       :   Union[str, Any]=320			,							lowercase_       :   Optional[Any]=2			,							lowercase_       :   List[str]=0.1			,							lowercase_       :   int=100			,							lowercase_       :   Tuple=256			,							lowercase_       :   Tuple=256			,							lowercase_       :   Dict=0.1			,							lowercase_       :   Any="mean"			,							lowercase_       :   Tuple=False			,							lowercase_       :   Dict=False			,							lowercase_       :   str=256			,							lowercase_       :   Optional[int]=(512, 512, 512, 512, 1500)			,							lowercase_       :   List[str]=(5, 3, 3, 1, 1)			,							lowercase_       :   Any=(1, 2, 3, 1, 1)			,							lowercase_       :   int=512			,							lowercase_       :   List[Any]=80			,							lowercase_       :   Optional[Any]=0			,							lowercase_       :   int=1			,							lowercase_       :   List[str]=2			,							lowercase_       :   List[str]=False			,							lowercase_       :   Dict=3			,							lowercase_       :   List[Any]=2			,							lowercase_       :   Optional[Any]=3			,							lowercase_       :   int=None			,							**lowercase_       :   List[str]			,							):
									super().__init__(**lowercase_			,							pad_token_id=lowercase_			,							bos_token_id=lowercase_			,							eos_token_id=lowercase_   )
									snake_case_		     =							hidden_size
									snake_case_		     =							feat_extract_norm
									snake_case_		     =							feat_extract_activation
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							conv_bias
									snake_case_		     =							num_buckets
									snake_case_		     =							max_bucket_distance
									snake_case_		     =							num_conv_pos_embeddings
									snake_case_		     =							num_conv_pos_embedding_groups
									snake_case_		     =							len(self.conv_dim   )
									snake_case_		     =							num_hidden_layers
									snake_case_		     =							intermediate_size
									snake_case_		     =							hidden_act
									snake_case_		     =							num_attention_heads
									snake_case_		     =							hidden_dropout
									snake_case_		     =							attention_dropout
									snake_case_		     =							activation_dropout
									snake_case_		     =							feat_proj_dropout
									snake_case_		     =							final_dropout
									snake_case_		     =							layerdrop
									snake_case_		     =							layer_norm_eps
									snake_case_		     =							initializer_range
									snake_case_		     =							num_ctc_classes
									snake_case_		     =							vocab_size
									snake_case_		     =							do_stable_layer_norm
									snake_case_		     =							use_weighted_layer_sum
									snake_case_		     =							classifier_proj_size

									if (
									    (len(self.conv_stride   ) != self.num_feat_extract_layers)
									    or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
									    or (len(self.conv_dim   ) != self.num_feat_extract_layers)
									):
														raise ValueError(
														    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
														    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
														    F" {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,"
														    F" `len(config.conv_kernel) = {len(self.conv_kernel   )}`."   )

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									snake_case_		     =							apply_spec_augment
									snake_case_		     =							mask_time_prob
									snake_case_		     =							mask_time_length
									snake_case_		     =							mask_time_min_masks
									snake_case_		     =							mask_feature_prob
									snake_case_		     =							mask_feature_length

									# parameters for pretraining with codevector quantized representations
									snake_case_		     =							num_codevectors_per_group
									snake_case_		     =							num_codevector_groups
									snake_case_		     =							contrastive_logits_temperature
									snake_case_		     =							num_negatives
									snake_case_		     =							codevector_dim
									snake_case_		     =							proj_codevector_dim
									snake_case_		     =							diversity_loss_weight

									# ctc loss
									snake_case_		     =							ctc_loss_reduction
									snake_case_		     =							ctc_zero_infinity

									# adapter
									snake_case_		     =							add_adapter
									snake_case_		     =							adapter_kernel_size
									snake_case_		     =							adapter_stride
									snake_case_		     =							num_adapter_layers
									snake_case_		     =							output_hidden_size or hidden_size

									# SequenceClassification-specific parameter. Feel free to ignore for other classes.
									snake_case_		     =							classifier_proj_size

									# XVector-specific parameters. Feel free to ignore for other classes.
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							xvector_output_dim


				@property
				def     A_					(						self       :   Tuple   ):
									return functools.reduce(operator.mul			,							self.conv_stride			,							1   )

