
"""simple docstring"""







import functools
import operator

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


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """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):
		A_		: Tuple            =			'wavlm'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=32 ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=30_72 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1E-5 ,	_SCREAMING_SNAKE_CASE="group" ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,	_SCREAMING_SNAKE_CASE=(5, 2, 2, 2, 2, 2, 2) ,	_SCREAMING_SNAKE_CASE=(10, 3, 3, 3, 3, 2, 2) ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=1_28 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=3_20 ,	_SCREAMING_SNAKE_CASE=8_00 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=0.05 ,	_SCREAMING_SNAKE_CASE=10 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=10 ,	_SCREAMING_SNAKE_CASE=3_20 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=1_00 ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE="mean" ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=(5_12, 5_12, 5_12, 5_12, 15_00) ,	_SCREAMING_SNAKE_CASE=(5, 3, 3, 1, 1) ,	_SCREAMING_SNAKE_CASE=(1, 2, 3, 1, 1) ,	_SCREAMING_SNAKE_CASE=5_12 ,	_SCREAMING_SNAKE_CASE=80 ,	_SCREAMING_SNAKE_CASE=0 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=None ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_SCREAMING_SNAKE_CASE ,	pad_token_id=_SCREAMING_SNAKE_CASE ,	bos_token_id=_SCREAMING_SNAKE_CASE ,	eos_token_id=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_size
			__lowerCAmelCase    :		List[str]		    =   feat_extract_norm
			__lowerCAmelCase    :		str		    =   feat_extract_activation
			__lowerCAmelCase    :		Union[str, Any]		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[int]		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   conv_bias
			__lowerCAmelCase    :		Tuple		    =   num_buckets
			__lowerCAmelCase    :		List[Any]		    =   max_bucket_distance
			__lowerCAmelCase    :		Union[str, Any]		    =   num_conv_pos_embeddings
			__lowerCAmelCase    :		Optional[int]		    =   num_conv_pos_embedding_groups
			__lowerCAmelCase    :		Dict		    =   len(self.conv_dim				)
			__lowerCAmelCase    :		List[Any]		    =   num_hidden_layers
			__lowerCAmelCase    :		Tuple		    =   intermediate_size
			__lowerCAmelCase    :		int		    =   hidden_act
			__lowerCAmelCase    :		Any		    =   num_attention_heads
			__lowerCAmelCase    :		Optional[int]		    =   hidden_dropout
			__lowerCAmelCase    :		Optional[Any]		    =   attention_dropout
			__lowerCAmelCase    :		Dict		    =   activation_dropout
			__lowerCAmelCase    :		Tuple		    =   feat_proj_dropout
			__lowerCAmelCase    :		List[str]		    =   final_dropout
			__lowerCAmelCase    :		Optional[int]		    =   layerdrop
			__lowerCAmelCase    :		int		    =   layer_norm_eps
			__lowerCAmelCase    :		List[Any]		    =   initializer_range
			__lowerCAmelCase    :		str		    =   num_ctc_classes
			__lowerCAmelCase    :		Tuple		    =   vocab_size
			__lowerCAmelCase    :		List[Any]		    =   do_stable_layer_norm
			__lowerCAmelCase    :		int		    =   use_weighted_layer_sum
			__lowerCAmelCase    :		Tuple		    =   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
			__lowerCAmelCase    :		Tuple		    =   apply_spec_augment
			__lowerCAmelCase    :		int		    =   mask_time_prob
			__lowerCAmelCase    :		List[str]		    =   mask_time_length
			__lowerCAmelCase    :		str		    =   mask_time_min_masks
			__lowerCAmelCase    :		Optional[Any]		    =   mask_feature_prob
			__lowerCAmelCase    :		Dict		    =   mask_feature_length

			# parameters for pretraining with codevector quantized representations
			__lowerCAmelCase    :		Optional[int]		    =   num_codevectors_per_group
			__lowerCAmelCase    :		Dict		    =   num_codevector_groups
			__lowerCAmelCase    :		Optional[int]		    =   contrastive_logits_temperature
			__lowerCAmelCase    :		int		    =   num_negatives
			__lowerCAmelCase    :		str		    =   codevector_dim
			__lowerCAmelCase    :		Any		    =   proj_codevector_dim
			__lowerCAmelCase    :		Union[str, Any]		    =   diversity_loss_weight

			# ctc loss
			__lowerCAmelCase    :		Tuple		    =   ctc_loss_reduction
			__lowerCAmelCase    :		Dict		    =   ctc_zero_infinity

			# adapter
			__lowerCAmelCase    :		str		    =   add_adapter
			__lowerCAmelCase    :		List[Any]		    =   adapter_kernel_size
			__lowerCAmelCase    :		List[str]		    =   adapter_stride
			__lowerCAmelCase    :		List[Any]		    =   num_adapter_layers
			__lowerCAmelCase    :		Union[str, Any]		    =   output_hidden_size or hidden_size

			# SequenceClassification-specific parameter. Feel free to ignore for other classes.
			__lowerCAmelCase    :		Dict		    =   classifier_proj_size

			# XVector-specific parameters. Feel free to ignore for other classes.
			__lowerCAmelCase    :		Optional[Any]		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Tuple		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   xvector_output_dim







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

"""simple docstring"""







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Tuple		    =   HfArgumentParser(_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[Any]		    =   parser.parse_args_into_dataclasses()[0]
	__lowerCAmelCase    :		int		    =   TensorFlowBenchmark(args=_UpperCamelCase	)
	try:
		__lowerCAmelCase    :		Dict		    =   parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		__lowerCAmelCase    :		List[Any]		    =   'Arg --no_{0} is no longer used, please use --no-{0} instead.'
		__lowerCAmelCase    :		Union[str, Any]		    =   ' '.join(str(_UpperCamelCase	).split(' '	)[:-1]	)
		__lowerCAmelCase    :		List[str]		    =   ''
		__lowerCAmelCase    :		Any		    =   eval(str(_UpperCamelCase	).split(' '	)[-1]	)
		__lowerCAmelCase    :		Dict		    =   []
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]	)
			else:
				wrong_args.append(_UpperCamelCase	)
		if len(_UpperCamelCase	) > 0:
			__lowerCAmelCase    :		Any		    =   full_error_msg + begin_error_msg + str(_UpperCamelCase	)
		raise ValueError(_UpperCamelCase	)
	benchmark.run()


if __name__ == "__main__":
							main()