




"""simple docstring"""






import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
lowercase__  		=		logging.getLogger(__name__)







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    Optional[Any]					=					git.Repo(search_parent_directories=lowercase__	)
				_lowerCamelCase  :    List[str]					=					{
				    'repo_id': str(lowercase__	),
				    'repo_sha': str(repo.head.object.hexsha	),
				    'repo_branch': str(repo.active_branch	),
				}

				with open(os.path.join(lowercase__							,					'git_log.json'	)							,					'w'	) as f:
								json.dump(lowercase__							,					lowercase__							,					indent=4	)







def   _snake_case    (       lowercase__	):
				if params.n_gpu <= 0:
								_lowerCamelCase  :    str					=					0
								_lowerCamelCase  :    str					=					-1
								_lowerCamelCase  :    int					=					True
								_lowerCamelCase  :    Optional[int]					=					False
								return

				assert torch.cuda.is_available()

				logger.info('Initializing GPUs'	)
				if params.n_gpu > 1:
								assert params.local_rank != -1

								_lowerCamelCase  :    Optional[Any]					=					int(os.environ['WORLD_SIZE']	)
								_lowerCamelCase  :    Union[str, Any]					=					int(os.environ['N_GPU_NODE']	)
								_lowerCamelCase  :    Tuple					=					int(os.environ['RANK']	)

								# number of nodes / node ID
								_lowerCamelCase  :    int					=					params.world_size // params.n_gpu_per_node
								_lowerCamelCase  :    Union[str, Any]					=					params.global_rank // params.n_gpu_per_node
								_lowerCamelCase  :    Dict					=					True

								assert params.n_nodes == int(os.environ['N_NODES']	)
								assert params.node_id == int(os.environ['NODE_RANK']	)

				# local job (single GPU)
				else:
								assert params.local_rank == -1

								_lowerCamelCase  :    Tuple					=					1
								_lowerCamelCase  :    Dict					=					0
								_lowerCamelCase  :    str					=					0
								_lowerCamelCase  :    int					=					0
								_lowerCamelCase  :    Any					=					1
								_lowerCamelCase  :    Optional[Any]					=					1
								_lowerCamelCase  :    str					=					False

				# sanity checks
				assert params.n_nodes >= 1
				assert 0 <= params.node_id < params.n_nodes
				assert 0 <= params.local_rank <= params.global_rank < params.world_size
				assert params.world_size == params.n_nodes * params.n_gpu_per_node

				# define whether this is the master process / if we are in multi-node distributed mode
				_lowerCamelCase  :    int					=					params.node_id == 0 and params.local_rank == 0
				_lowerCamelCase  :    int					=					params.n_nodes > 1

				# summary
				_lowerCamelCase  :    Any					=					f'''--- Global rank: {params.global_rank} - '''
				logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes	)
				logger.info(PREFIX + 'Node ID        : %i' % params.node_id	)
				logger.info(PREFIX + 'Local rank     : %i' % params.local_rank	)
				logger.info(PREFIX + 'World size     : %i' % params.world_size	)
				logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node	)
				logger.info(PREFIX + 'Master         : %s' % str(params.is_master	)	)
				logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node	)	)
				logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu	)	)
				logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()	)

				# set GPU device
				torch.cuda.set_device(params.local_rank	)

				# initialize multi-GPU
				if params.multi_gpu:
								logger.info('Initializing PyTorch distributed'	)
								torch.distributed.init_process_group(
								    init_method='env://'							,					backend='nccl'							,					)







def   _snake_case    (       lowercase__	):
				np.random.seed(args.seed	)
				torch.manual_seed(args.seed	)
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed	)





"""simple docstring"""






import functools
import operator

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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """sew-d"""
			def __init__(						self						,   lowercase=32						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=3072						,   lowercase=2						,   lowercase=512						,   lowercase=256						,   lowercase=True						,   lowercase=True						,   lowercase=("p2c", "c2p")						,   lowercase="layer_norm"						,   lowercase="gelu_python"						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=0.0						,   lowercase=0.1						,   lowercase=0.02						,   lowercase=1E-7						,   lowercase=1E-5						,   lowercase="group"						,   lowercase="gelu"						,   lowercase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)						,   lowercase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,   lowercase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,   lowercase=False						,   lowercase=128						,   lowercase=16						,   lowercase=True						,   lowercase=0.05						,   lowercase=10						,   lowercase=2						,   lowercase=0.0						,   lowercase=10						,   lowercase=0						,   lowercase="mean"						,   lowercase=False						,   lowercase=False						,   lowercase=256						,   lowercase=0						,   lowercase=1						,   lowercase=2						,   **lowercase						,   ):
							super().__init__(**lowercase						,   pad_token_id=lowercase						,   bos_token_id=lowercase						,   eos_token_id=lowercase						)
							_lowerCamelCase  :    List[str]					=					hidden_size
							_lowerCamelCase  :    int					=					feat_extract_norm
							_lowerCamelCase  :    Optional[Any]					=					feat_extract_activation
							_lowerCamelCase  :    Optional[Any]					=					list(lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					list(lowercase						)
							_lowerCamelCase  :    Optional[Any]					=					list(lowercase						)
							_lowerCamelCase  :    List[str]					=					conv_bias
							_lowerCamelCase  :    str					=					num_conv_pos_embeddings
							_lowerCamelCase  :    Optional[Any]					=					num_conv_pos_embedding_groups
							_lowerCamelCase  :    Tuple					=					len(self.conv_dim						)
							_lowerCamelCase  :    Optional[int]					=					num_hidden_layers
							_lowerCamelCase  :    List[str]					=					intermediate_size
							_lowerCamelCase  :    Dict					=					squeeze_factor
							_lowerCamelCase  :    Any					=					max_position_embeddings
							_lowerCamelCase  :    List[str]					=					position_buckets
							_lowerCamelCase  :    Dict					=					share_att_key
							_lowerCamelCase  :    List[str]					=					relative_attention
							_lowerCamelCase  :    Optional[Any]					=					norm_rel_ebd
							_lowerCamelCase  :    List[Any]					=					list(lowercase						)
							_lowerCamelCase  :    List[Any]					=					hidden_act
							_lowerCamelCase  :    Optional[Any]					=					num_attention_heads
							_lowerCamelCase  :    Optional[int]					=					hidden_dropout
							_lowerCamelCase  :    Optional[int]					=					attention_dropout
							_lowerCamelCase  :    Any					=					activation_dropout
							_lowerCamelCase  :    Tuple					=					feat_proj_dropout
							_lowerCamelCase  :    Any					=					final_dropout
							_lowerCamelCase  :    str					=					layer_norm_eps
							_lowerCamelCase  :    Tuple					=					feature_layer_norm_eps
							_lowerCamelCase  :    Any					=					initializer_range
							_lowerCamelCase  :    Dict					=					vocab_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)`,'
											    F'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
											    F'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							_lowerCamelCase  :    Dict					=					apply_spec_augment
							_lowerCamelCase  :    int					=					mask_time_prob
							_lowerCamelCase  :    Union[str, Any]					=					mask_time_length
							_lowerCamelCase  :    Optional[Any]					=					mask_time_min_masks
							_lowerCamelCase  :    List[Any]					=					mask_feature_prob
							_lowerCamelCase  :    Union[str, Any]					=					mask_feature_length
							_lowerCamelCase  :    List[Any]					=					mask_feature_min_masks

							# ctc loss
							_lowerCamelCase  :    List[str]					=					ctc_loss_reduction
							_lowerCamelCase  :    List[str]					=					ctc_zero_infinity

							# sequence classification
							_lowerCamelCase  :    Dict					=					use_weighted_layer_sum
							_lowerCamelCase  :    List[Any]					=					classifier_proj_size






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