import math

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


__lowercase										= logging.get_logger(__name__)

__lowercase										= {
    '''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						int      =				"""data2vec-audio"""







    def __init__(     self			,			__lowercase=32			,			__lowercase=768			,			__lowercase=12			,			__lowercase=12			,			__lowercase=3_072			,			__lowercase="gelu"			,			__lowercase=0.1			,			__lowercase=0.1			,			__lowercase=0.1			,			__lowercase=0.0			,			__lowercase=0.1			,			__lowercase=0.1			,			__lowercase=0.02			,			__lowercase=1E-5			,			__lowercase="gelu"			,			__lowercase=(512, 512, 512, 512, 512, 512, 512)			,			__lowercase=(5, 2, 2, 2, 2, 2, 2)			,			__lowercase=(10, 3, 3, 3, 3, 2, 2)			,			__lowercase=False			,			__lowercase=16			,			__lowercase=19			,			__lowercase=5			,			__lowercase=0.05			,			__lowercase=10			,			__lowercase=2			,			__lowercase=0.0			,			__lowercase=10			,			__lowercase=0			,			__lowercase="sum"			,			__lowercase=False			,			__lowercase=False			,			__lowercase=256			,			__lowercase=(512, 512, 512, 512, 1_500)			,			__lowercase=(5, 3, 3, 1, 1)			,			__lowercase=(1, 2, 3, 1, 1)			,			__lowercase=512			,			__lowercase=0			,			__lowercase=1			,			__lowercase=2			,			__lowercase=False			,			__lowercase=3			,			__lowercase=2			,			__lowercase=3			,			__lowercase=None			,			**__lowercase			,			)	->     int:
        super().__init__(**__lowercase			,			pad_token_id=__lowercase			,			bos_token_id=__lowercase			,			eos_token_id=__lowercase)
        __UpperCamelCase					:int        =		hidden_size
        __UpperCamelCase					:Optional[int]        =		feat_extract_activation
        __UpperCamelCase					:Optional[Any]        =		list(__lowercase)
        __UpperCamelCase					:int        =		list(__lowercase)
        __UpperCamelCase					:Tuple        =		list(__lowercase)
        __UpperCamelCase					:Optional[int]        =		conv_bias
        __UpperCamelCase					:int        =		num_conv_pos_embeddings
        __UpperCamelCase					:Optional[Any]        =		num_conv_pos_embedding_groups
        __UpperCamelCase					:int        =		conv_pos_kernel_size
        __UpperCamelCase					:Tuple        =		len(self.conv_dim)
        __UpperCamelCase					:Dict        =		num_hidden_layers
        __UpperCamelCase					:Union[str, Any]        =		intermediate_size
        __UpperCamelCase					:Optional[Any]        =		hidden_act
        __UpperCamelCase					:Optional[Any]        =		num_attention_heads
        __UpperCamelCase					:Dict        =		hidden_dropout
        __UpperCamelCase					:str        =		attention_dropout
        __UpperCamelCase					:Union[str, Any]        =		activation_dropout
        __UpperCamelCase					:Any        =		feat_proj_dropout
        __UpperCamelCase					:List[str]        =		final_dropout
        __UpperCamelCase					:str        =		layerdrop
        __UpperCamelCase					:Any        =		layer_norm_eps
        __UpperCamelCase					:Tuple        =		initializer_range
        __UpperCamelCase					:Tuple        =		vocab_size
        __UpperCamelCase					:List[Any]        =		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					:Optional[Any]        =		mask_time_prob
        __UpperCamelCase					:List[Any]        =		mask_time_length
        __UpperCamelCase					:int        =		mask_time_min_masks
        __UpperCamelCase					:Dict        =		mask_feature_prob
        __UpperCamelCase					:List[Any]        =		mask_feature_length
        __UpperCamelCase					:str        =		mask_feature_min_masks

        # ctc loss
        __UpperCamelCase					:Optional[int]        =		ctc_loss_reduction
        __UpperCamelCase					:Dict        =		ctc_zero_infinity

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

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

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







    @property
    def       UpperCamelCase__       (     self)	->     int:
        return math.prod(self.conv_stride)






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:int        =		ArgumentParser(
        description=(
            '''PyTorch TPU distributed training launch '''
            '''helper utility that will spawn up '''
            '''multiple distributed processes'''
        )     )

    # Optional arguments for the launch helper
    parser.add_argument('''--num_cores'''  ,      type=SCREAMING_SNAKE_CASE  ,      default=1  ,      help='''Number of TPU cores to use (1 or 8).'''     )

    # positional
    parser.add_argument(
        '''training_script'''  ,      type=SCREAMING_SNAKE_CASE  ,      help=(
            '''The full path to the single TPU training '''
            '''program/script to be launched in parallel, '''
            '''followed by all the arguments for the '''
            '''training script'''
        )  ,      )

    # rest from the training program
    parser.add_argument('''training_script_args'''  ,      nargs=SCREAMING_SNAKE_CASE     )

    return parser.parse_args()



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Tuple        =		parse_args()

    # Import training_script as a module.
    __UpperCamelCase					:Dict        =		Path(args.training_script     )
    sys.path.append(str(script_fpath.parent.resolve()     )     )
    __UpperCamelCase					:Any        =		script_fpath.stem
    __UpperCamelCase					:Union[str, Any]        =		importlib.import_module(SCREAMING_SNAKE_CASE     )

    # Patch sys.argv
    __UpperCamelCase					:Dict        =		[args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores     )]
    xmp.spawn(mod._mp_fn  ,      args=()  ,      nprocs=args.num_cores     )


if __name__ == "__main__":
    main()





