import functools
import operator

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


SCREAMING_SNAKE_CASE		:     List[str]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     List[Any]         =	{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class        UpperCamelCase						(				__a				):
						a__		:List[Any]      =			'''sew'''





						def __init__(self			,     __UpperCamelCase=32			,     __UpperCamelCase=768			,     __UpperCamelCase=12			,     __UpperCamelCase=12			,     __UpperCamelCase=3_072			,     __UpperCamelCase=2			,     __UpperCamelCase="gelu"			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.0			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.02			,     __UpperCamelCase=1E-5			,     __UpperCamelCase="group"			,     __UpperCamelCase="gelu"			,     __UpperCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)			,     __UpperCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)			,     __UpperCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)			,     __UpperCamelCase=False			,     __UpperCamelCase=128			,     __UpperCamelCase=16			,     __UpperCamelCase=True			,     __UpperCamelCase=0.05			,     __UpperCamelCase=10			,     __UpperCamelCase=2			,     __UpperCamelCase=0.0			,     __UpperCamelCase=10			,     __UpperCamelCase=0			,     __UpperCamelCase="mean"			,     __UpperCamelCase=False			,     __UpperCamelCase=False			,     __UpperCamelCase=256			,     __UpperCamelCase=0			,     __UpperCamelCase=1			,     __UpperCamelCase=2			,     **__UpperCamelCase			,     )				->				str:
												super().__init__(**__UpperCamelCase			,     pad_token_id=__UpperCamelCase			,     bos_token_id=__UpperCamelCase			,     eos_token_id=__UpperCamelCase						)
												UpperCamelCase_		:							Any  =		hidden_size
												UpperCamelCase_		:							List[Any]  =		feat_extract_norm
												UpperCamelCase_		:							Union[str, Any]  =		feat_extract_activation
												UpperCamelCase_		:							List[Any]  =		list(__UpperCamelCase						)
												UpperCamelCase_		:							List[Any]  =		list(__UpperCamelCase						)
												UpperCamelCase_		:							int  =		list(__UpperCamelCase						)
												UpperCamelCase_		:							int  =		conv_bias
												UpperCamelCase_		:							Union[str, Any]  =		num_conv_pos_embeddings
												UpperCamelCase_		:							Union[str, Any]  =		num_conv_pos_embedding_groups
												UpperCamelCase_		:							List[Any]  =		len(self.conv_dim						)
												UpperCamelCase_		:							Dict  =		num_hidden_layers
												UpperCamelCase_		:							Dict  =		intermediate_size
												UpperCamelCase_		:							int  =		squeeze_factor
												UpperCamelCase_		:							Optional[Any]  =		hidden_act
												UpperCamelCase_		:							Tuple  =		num_attention_heads
												UpperCamelCase_		:							Tuple  =		hidden_dropout
												UpperCamelCase_		:							List[str]  =		attention_dropout
												UpperCamelCase_		:							str  =		activation_dropout
												UpperCamelCase_		:							Optional[Any]  =		feat_proj_dropout
												UpperCamelCase_		:							Tuple  =		final_dropout
												UpperCamelCase_		:							int  =		layerdrop
												UpperCamelCase_		:							Tuple  =		layer_norm_eps
												UpperCamelCase_		:							Dict  =		initializer_range
												UpperCamelCase_		:							Dict  =		vocab_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)`,"""
																		    f'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
																		    f'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

												# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
												UpperCamelCase_		:							Any  =		apply_spec_augment
												UpperCamelCase_		:							int  =		mask_time_prob
												UpperCamelCase_		:							Dict  =		mask_time_length
												UpperCamelCase_		:							Optional[int]  =		mask_time_min_masks
												UpperCamelCase_		:							int  =		mask_feature_prob
												UpperCamelCase_		:							Dict  =		mask_feature_length
												UpperCamelCase_		:							Tuple  =		mask_feature_min_masks

												# ctc loss
												UpperCamelCase_		:							Optional[Any]  =		ctc_loss_reduction
												UpperCamelCase_		:							List[str]  =		ctc_zero_infinity

												# sequence classification
												UpperCamelCase_		:							Any  =		use_weighted_layer_sum
												UpperCamelCase_		:							Any  =		classifier_proj_size





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







import math
from typing import Optional

import numpy as np

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


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

SCREAMING_SNAKE_CASE		:     Tuple         =	{
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class        UpperCamelCase						(				__a				):
						a__		:int      =			'''encodec'''





						def __init__(self			,     __UpperCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0]			,     __UpperCamelCase=24_000			,     __UpperCamelCase=1			,     __UpperCamelCase=False			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=128			,     __UpperCamelCase=32			,     __UpperCamelCase=1			,     __UpperCamelCase=[8, 5, 4, 2]			,     __UpperCamelCase="weight_norm"			,     __UpperCamelCase=7			,     __UpperCamelCase=7			,     __UpperCamelCase=3			,     __UpperCamelCase=2			,     __UpperCamelCase=True			,     __UpperCamelCase="reflect"			,     __UpperCamelCase=2			,     __UpperCamelCase=2			,     __UpperCamelCase=1.0			,     __UpperCamelCase=1_024			,     __UpperCamelCase=None			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				Dict:
												UpperCamelCase_		:							str  =		target_bandwidths
												UpperCamelCase_		:							Tuple  =		sampling_rate
												UpperCamelCase_		:							str  =		audio_channels
												UpperCamelCase_		:							Any  =		normalize
												UpperCamelCase_		:							Union[str, Any]  =		chunk_length_s
												UpperCamelCase_		:							Optional[Any]  =		overlap
												UpperCamelCase_		:							Any  =		hidden_size
												UpperCamelCase_		:							Dict  =		num_filters
												UpperCamelCase_		:							List[str]  =		num_residual_layers
												UpperCamelCase_		:							Optional[int]  =		upsampling_ratios
												UpperCamelCase_		:							str  =		norm_type
												UpperCamelCase_		:							str  =		kernel_size
												UpperCamelCase_		:							Tuple  =		last_kernel_size
												UpperCamelCase_		:							int  =		residual_kernel_size
												UpperCamelCase_		:							str  =		dilation_growth_rate
												UpperCamelCase_		:							str  =		use_causal_conv
												UpperCamelCase_		:							str  =		pad_mode
												UpperCamelCase_		:							Union[str, Any]  =		compress
												UpperCamelCase_		:							Dict  =		num_lstm_layers
												UpperCamelCase_		:							Optional[int]  =		trim_right_ratio
												UpperCamelCase_		:							int  =		codebook_size
												UpperCamelCase_		:							str  =		codebook_dim if codebook_dim is not None else hidden_size
												UpperCamelCase_		:							Optional[int]  =		use_conv_shortcut

												if self.norm_type not in ["weight_norm", "time_group_norm"]:
																		raise ValueError(
																		    f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''						)

												super().__init__(**__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Optional[int]:
												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate						)





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





						@property
						def        A_							(self						)				->				int:
												UpperCamelCase_		:							int  =		np.prod(self.upsampling_ratios						)
												return math.ceil(self.sampling_rate / hop_length						)





						@property
						def        A_							(self						)				->				int:
												return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)						)






