





"""simple docstring"""







import functools
import operator

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


a__      :	Dict       				=      logging.get_logger(__name__)

a__      :	Optional[Any]       				=      {
    '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 __magic_name__   (       A__		):
       UpperCamelCase    :						Tuple      =	"wavlm"
       def __init__(     self   ,  __magic_name__=3_2   ,  __magic_name__=7_6_8   ,  __magic_name__=1_2   ,  __magic_name__=1_2   ,  __magic_name__=3_0_7_2   ,  __magic_name__="gelu"   ,  __magic_name__=0.1   ,  __magic_name__=0.1   ,  __magic_name__=0.1   ,  __magic_name__=0.0   ,  __magic_name__=0.1   ,  __magic_name__=0.1   ,  __magic_name__=0.02   ,  __magic_name__=1e-5   ,  __magic_name__="group"   ,  __magic_name__="gelu"   ,  __magic_name__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)   ,  __magic_name__=(5, 2, 2, 2, 2, 2, 2)   ,  __magic_name__=(1_0, 3, 3, 3, 3, 2, 2)   ,  __magic_name__=False   ,  __magic_name__=1_2_8   ,  __magic_name__=1_6   ,  __magic_name__=3_2_0   ,  __magic_name__=8_0_0   ,  __magic_name__=False   ,  __magic_name__=True   ,  __magic_name__=0.05   ,  __magic_name__=1_0   ,  __magic_name__=2   ,  __magic_name__=0.0   ,  __magic_name__=1_0   ,  __magic_name__=3_2_0   ,  __magic_name__=2   ,  __magic_name__=0.1   ,  __magic_name__=1_0_0   ,  __magic_name__=2_5_6   ,  __magic_name__=2_5_6   ,  __magic_name__=0.1   ,  __magic_name__="mean"   ,  __magic_name__=False   ,  __magic_name__=False   ,  __magic_name__=2_5_6   ,  __magic_name__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)   ,  __magic_name__=(5, 3, 3, 1, 1)   ,  __magic_name__=(1, 2, 3, 1, 1)   ,  __magic_name__=5_1_2   ,  __magic_name__=8_0   ,  __magic_name__=0   ,  __magic_name__=1   ,  __magic_name__=2   ,  __magic_name__=False   ,  __magic_name__=3   ,  __magic_name__=2   ,  __magic_name__=3   ,  __magic_name__=None   ,  **__magic_name__   ,  ):





         """simple docstring"""


         super().__init__(**__magic_name__   ,  pad_token_id=__magic_name__   ,  bos_token_id=__magic_name__   ,  eos_token_id=__magic_name__	)
         _lowerCAmelCase	      =					hidden_size
         _lowerCAmelCase	      =					feat_extract_norm
         _lowerCAmelCase	      =					feat_extract_activation
         _lowerCAmelCase	      =					list(__magic_name__	)
         _lowerCAmelCase	      =					list(__magic_name__	)
         _lowerCAmelCase	      =					list(__magic_name__	)
         _lowerCAmelCase	      =					conv_bias
         _lowerCAmelCase	      =					num_buckets
         _lowerCAmelCase	      =					max_bucket_distance
         _lowerCAmelCase	      =					num_conv_pos_embeddings
         _lowerCAmelCase	      =					num_conv_pos_embedding_groups
         _lowerCAmelCase	      =					len(self.conv_dim	)
         _lowerCAmelCase	      =					num_hidden_layers
         _lowerCAmelCase	      =					intermediate_size
         _lowerCAmelCase	      =					hidden_act
         _lowerCAmelCase	      =					num_attention_heads
         _lowerCAmelCase	      =					hidden_dropout
         _lowerCAmelCase	      =					attention_dropout
         _lowerCAmelCase	      =					activation_dropout
         _lowerCAmelCase	      =					feat_proj_dropout
         _lowerCAmelCase	      =					final_dropout
         _lowerCAmelCase	      =					layerdrop
         _lowerCAmelCase	      =					layer_norm_eps
         _lowerCAmelCase	      =					initializer_range
         _lowerCAmelCase	      =					num_ctc_classes
         _lowerCAmelCase	      =					vocab_size
         _lowerCAmelCase	      =					do_stable_layer_norm
         _lowerCAmelCase	      =					use_weighted_layer_sum
         _lowerCAmelCase	      =					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	      =					apply_spec_augment
         _lowerCAmelCase	      =					mask_time_prob
         _lowerCAmelCase	      =					mask_time_length
         _lowerCAmelCase	      =					mask_time_min_masks
         _lowerCAmelCase	      =					mask_feature_prob
         _lowerCAmelCase	      =					mask_feature_length

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

         # ctc loss
         _lowerCAmelCase	      =					ctc_loss_reduction
         _lowerCAmelCase	      =					ctc_zero_infinity

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

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

         # XVector-specific parameters. Feel free to ignore for other classes.
         _lowerCAmelCase	      =					list(__magic_name__	)
         _lowerCAmelCase	      =					list(__magic_name__	)
         _lowerCAmelCase	      =					list(__magic_name__	)
         _lowerCAmelCase	      =					xvector_output_dim
       @property
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


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











# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async






def     a     (		lowerCamelCase_=None							):






				'''simple docstring'''
				if subparsers is not None:
								lowercase__  					=     subparsers.add_parser('''test'''							)
				else:
								lowercase__  					=     argparse.ArgumentParser('''Accelerate test command'''							)

				parser.add_argument(
				    '''--config_file'''						,  default=lowerCamelCase_						,  help=(
				        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
				        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
				        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
				        '''with \'huggingface\'.'''
				    )						,  )

				if subparsers is not None:
								parser.set_defaults(func=lowerCamelCase_							)
				return parser






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     os.path.sep.join(__file__.split(os.path.sep							)[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']							)

				if args.config_file is None:
								lowercase__  					=     script_name
				else:
								lowercase__  					=     F"""--config_file={args.config_file} {script_name}"""

				lowercase__  					=     ['''accelerate-launch'''] + test_args.split()
				lowercase__  					=     execute_subprocess_async(lowerCamelCase_						,  env=os.environ.copy()							)
				if result.returncode == 0:
								print('''Test is a success! You are ready for your distributed training!'''							)






def     a     (		):






				'''simple docstring'''
				lowercase__  					=     test_command_parser()
				lowercase__  					=     parser.parse_args()
				test_command(lowerCamelCase_							)


if __name__ == "__main__":
							main()




