


import functools
import operator

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


__UpperCamelCase    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[int]    =		'unispeech-sat'




			def __init__(							self      ,						snake_case=32      ,						snake_case=768      ,						snake_case=12      ,						snake_case=12      ,						snake_case=3072      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=0.0      ,						snake_case=0.0      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=0.02      ,						snake_case=1E-5      ,						snake_case="group"      ,						snake_case="gelu"      ,						snake_case=(512, 512, 512, 512, 512, 512, 512)      ,						snake_case=(5, 2, 2, 2, 2, 2, 2)      ,						snake_case=(10, 3, 3, 3, 3, 2, 2)      ,						snake_case=False      ,						snake_case=128      ,						snake_case=16      ,						snake_case=False      ,						snake_case=True      ,						snake_case=0.05      ,						snake_case=10      ,						snake_case=2      ,						snake_case=0.0      ,						snake_case=10      ,						snake_case=0      ,						snake_case=320      ,						snake_case=2      ,						snake_case=0.1      ,						snake_case=100      ,						snake_case=256      ,						snake_case=256      ,						snake_case=0.1      ,						snake_case="mean"      ,						snake_case=False      ,						snake_case=False      ,						snake_case=256      ,						snake_case=(512, 512, 512, 512, 1500)      ,						snake_case=(5, 3, 3, 1, 1)      ,						snake_case=(1, 2, 3, 1, 1)      ,						snake_case=512      ,						snake_case=0      ,						snake_case=1      ,						snake_case=2      ,						snake_case=504      ,						**snake_case      ,						):

							'''simple docstring'''


							super().__init__(**snake_case      ,						pad_token_id=snake_case      ,						bos_token_id=snake_case      ,						eos_token_id=snake_case     )
							UpperCamelCase__					=       hidden_size
							UpperCamelCase__					=       feat_extract_norm
							UpperCamelCase__					=       feat_extract_activation
							UpperCamelCase__					=       list(snake_case     )
							UpperCamelCase__					=       list(snake_case     )
							UpperCamelCase__					=       list(snake_case     )
							UpperCamelCase__					=       conv_bias
							UpperCamelCase__					=       num_conv_pos_embeddings
							UpperCamelCase__					=       num_conv_pos_embedding_groups
							UpperCamelCase__					=       len(self.conv_dim     )
							UpperCamelCase__					=       num_hidden_layers
							UpperCamelCase__					=       intermediate_size
							UpperCamelCase__					=       hidden_act
							UpperCamelCase__					=       num_attention_heads
							UpperCamelCase__					=       hidden_dropout
							UpperCamelCase__					=       attention_dropout
							UpperCamelCase__					=       activation_dropout
							UpperCamelCase__					=       feat_proj_dropout
							UpperCamelCase__					=       final_dropout
							UpperCamelCase__					=       layerdrop
							UpperCamelCase__					=       layer_norm_eps
							UpperCamelCase__					=       initializer_range
							UpperCamelCase__					=       vocab_size
							UpperCamelCase__					=       num_clusters
							UpperCamelCase__					=       do_stable_layer_norm
							UpperCamelCase__					=       use_weighted_layer_sum

							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
							UpperCamelCase__					=       apply_spec_augment
							UpperCamelCase__					=       mask_time_prob
							UpperCamelCase__					=       mask_time_length
							UpperCamelCase__					=       mask_time_min_masks
							UpperCamelCase__					=       mask_feature_prob
							UpperCamelCase__					=       mask_feature_length
							UpperCamelCase__					=       mask_feature_min_masks

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

							# ctc loss
							UpperCamelCase__					=       ctc_loss_reduction
							UpperCamelCase__					=       ctc_zero_infinity

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

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







			@property
			def 				snake_case__				(							self     ):

							'''simple docstring'''


							return functools.reduce(operator.mul      ,						self.conv_stride      ,						1     )






from __future__ import annotations

import numpy as np
def        UpperCamelCase_(				_A   :list[float]  )->		Union[str, Any]:
			return np.maximum(0				,     _A  )


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


