import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


__lowercase											=      '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''



def lowerCamelCase						(     SCREAMING_SNAKE_CASE=None			):
				'''simple docstring'''







				if subparsers is not None:
								__UpperCamelCase  :Dict    						=	subparsers.add_parser('''tpu-config'''						,    description=_description			)
				else:
								__UpperCamelCase  :Optional[int]    						=	argparse.ArgumentParser('''Accelerate tpu-config command'''						,    description=_description			)
				# Core arguments
				__UpperCamelCase  :Tuple    						=	parser.add_argument_group(
				    '''Config Arguments'''						,    '''Arguments that can be configured through `accelerate config`.'''			)
				config_args.add_argument(
				    '''--config_file'''						,    type=SCREAMING_SNAKE_CASE						,    default=SCREAMING_SNAKE_CASE						,    help='''Path to the config file to use for accelerate.'''						,    )
				config_args.add_argument(
				    '''--tpu_name'''						,    default=SCREAMING_SNAKE_CASE						,    help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''						,    )
				config_args.add_argument(
				    '''--tpu_zone'''						,    default=SCREAMING_SNAKE_CASE						,    help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''						,    )
				__UpperCamelCase  :Tuple    						=	parser.add_argument_group('''TPU Arguments'''						,    '''Arguments for options ran inside the TPU.'''			)
				pod_args.add_argument(
				    '''--use_alpha'''						,    action='''store_true'''						,    help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''						,    )
				pod_args.add_argument(
				    '''--command_file'''						,    default=SCREAMING_SNAKE_CASE						,    help='''The path to the file containing the commands to run on the pod on startup.'''						,    )
				pod_args.add_argument(
				    '''--command'''						,    action='''append'''						,    nargs='''+'''						,    help='''A command to run on the pod. Can be passed multiple times.'''						,    )
				pod_args.add_argument(
				    '''--install_accelerate'''						,    action='''store_true'''						,    help='''Whether to install accelerate on the pod. Defaults to False.'''						,    )
				pod_args.add_argument(
				    '''--accelerate_version'''						,    default='''latest'''						,    help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''						,    )
				pod_args.add_argument(
				    '''--debug'''						,    action='''store_true'''						,    help='''If set, will print the command that would be run instead of running it.'''			)

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



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







				__UpperCamelCase  :Any    						=	None

				# Get the default from the config file if it exists.
				if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE			):
								__UpperCamelCase  :Tuple    						=	load_config_from_file(args.config_file			)
								if not args.command_file and defaults.command_file is not None and not args.command:
												__UpperCamelCase  :Any    						=	defaults.command_file
								if not args.command and defaults.commands is not None:
												__UpperCamelCase  :Union[str, Any]    						=	defaults.commands
								if not args.tpu_name:
												__UpperCamelCase  :Optional[int]    						=	defaults.tpu_name
								if not args.tpu_zone:
												__UpperCamelCase  :Union[str, Any]    						=	defaults.tpu_zone
				if args.accelerate_version == "dev":
								__UpperCamelCase  :List[Any]    						=	"""git+https://github.com/huggingface/accelerate.git"""
				elif args.accelerate_version == "latest":
								__UpperCamelCase  :int    						=	"""accelerate -U"""
				elif isinstance(parse(args.accelerate_version			)						,    SCREAMING_SNAKE_CASE			):
								__UpperCamelCase  :Tuple    						=	f"""accelerate=={args.accelerate_version}"""

				if not args.command_file and not args.command:
								raise ValueError('''You must specify either a command file or a command to run on the pod.'''			)

				if args.command_file:
								with open(args.command_file						,    '''r'''			) as f:
												__UpperCamelCase  :int    						=	[f.read().splitlines()]

    # To turn list of lists into list of strings
				if isinstance(args.command[0]						,    SCREAMING_SNAKE_CASE			):
								__UpperCamelCase  :List[Any]    						=	[line for cmd in args.command for line in cmd]
				# Default to the shared folder and install accelerate
				__UpperCamelCase  :int    						=	["""cd /usr/share"""]
				if args.install_accelerate:
								new_cmd += [f"""pip install {args.accelerate_version}"""]
				new_cmd += args.command
				__UpperCamelCase  :List[Any]    						=	"""; """.join(SCREAMING_SNAKE_CASE			)

				# Then send it to gcloud
				# Eventually try to use google-api-core to do this instead of subprocess
				__UpperCamelCase  :Union[str, Any]    						=	["""gcloud"""]
				if args.use_alpha:
								cmd += ["alpha"]
				cmd += [
				    "compute",
				    "tpus",
				    "tpu-vm",
				    "ssh",
				    args.tpu_name,
				    "--zone",
				    args.tpu_zone,
				    "--command",
				    args.command,
				    "--worker",
				    "all",
				]
				if args.debug:
								print(f"""Running {' '.join(SCREAMING_SNAKE_CASE			)}"""			)
								return
				subprocess.run(SCREAMING_SNAKE_CASE			)
				print('''Successfully setup pod.'''			)



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







				__UpperCamelCase  :Tuple    						=	tpu_command_parser()
				__UpperCamelCase  :str    						=	parser.parse_args()

				tpu_command_launcher(SCREAMING_SNAKE_CASE			)






import functools
import operator

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


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''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 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		List[Any]      			=   """wavlm"""







    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="group"				,				__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=128				,				__lowercase=16				,				__lowercase=320				,				__lowercase=800				,				__lowercase=False				,				__lowercase=True				,				__lowercase=0.05				,				__lowercase=10				,				__lowercase=2				,				__lowercase=0.0				,				__lowercase=10				,				__lowercase=320				,				__lowercase=2				,				__lowercase=0.1				,				__lowercase=100				,				__lowercase=256				,				__lowercase=256				,				__lowercase=0.1				,				__lowercase="mean"				,				__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=80				,				__lowercase=0				,				__lowercase=1				,				__lowercase=2				,				__lowercase=False				,				__lowercase=3				,				__lowercase=2				,				__lowercase=3				,				__lowercase=None				,				**__lowercase				,				) ->			Dict:
        super().__init__(**__lowercase				,				pad_token_id=__lowercase				,				bos_token_id=__lowercase				,				eos_token_id=__lowercase)
        __UpperCamelCase  :Any    						=	hidden_size
        __UpperCamelCase  :Tuple    						=	feat_extract_norm
        __UpperCamelCase  :List[str]    						=	feat_extract_activation
        __UpperCamelCase  :int    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	conv_bias
        __UpperCamelCase  :Tuple    						=	num_buckets
        __UpperCamelCase  :Optional[int]    						=	max_bucket_distance
        __UpperCamelCase  :Union[str, Any]    						=	num_conv_pos_embeddings
        __UpperCamelCase  :Optional[Any]    						=	num_conv_pos_embedding_groups
        __UpperCamelCase  :List[Any]    						=	len(self.conv_dim)
        __UpperCamelCase  :Tuple    						=	num_hidden_layers
        __UpperCamelCase  :str    						=	intermediate_size
        __UpperCamelCase  :Union[str, Any]    						=	hidden_act
        __UpperCamelCase  :Optional[int]    						=	num_attention_heads
        __UpperCamelCase  :str    						=	hidden_dropout
        __UpperCamelCase  :int    						=	attention_dropout
        __UpperCamelCase  :Optional[int]    						=	activation_dropout
        __UpperCamelCase  :str    						=	feat_proj_dropout
        __UpperCamelCase  :List[Any]    						=	final_dropout
        __UpperCamelCase  :int    						=	layerdrop
        __UpperCamelCase  :List[Any]    						=	layer_norm_eps
        __UpperCamelCase  :Optional[int]    						=	initializer_range
        __UpperCamelCase  :Any    						=	num_ctc_classes
        __UpperCamelCase  :Optional[int]    						=	vocab_size
        __UpperCamelCase  :List[Any]    						=	do_stable_layer_norm
        __UpperCamelCase  :str    						=	use_weighted_layer_sum
        __UpperCamelCase  :Any    						=	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
        __UpperCamelCase  :Union[str, Any]    						=	apply_spec_augment
        __UpperCamelCase  :Optional[Any]    						=	mask_time_prob
        __UpperCamelCase  :Union[str, Any]    						=	mask_time_length
        __UpperCamelCase  :Optional[int]    						=	mask_time_min_masks
        __UpperCamelCase  :str    						=	mask_feature_prob
        __UpperCamelCase  :Tuple    						=	mask_feature_length

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

        # ctc loss
        __UpperCamelCase  :int    						=	ctc_loss_reduction
        __UpperCamelCase  :Any    						=	ctc_zero_infinity

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

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

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







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





