

import functools
import operator

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


lowerCAmelCase__		      =       logging.get_logger(__name__)

lowerCAmelCase__		      =       {
    """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     a__  (    snake_case	):
			"""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					,					)     ->  str:
							'''simple docstring'''






							super().__init__(**lowercase					,					pad_token_id=lowercase					,					bos_token_id=lowercase					,					eos_token_id=lowercase				)
							A__						=	hidden_size
							A__						=	feat_extract_norm
							A__						=	feat_extract_activation
							A__						=	list(lowercase				)
							A__						=	list(lowercase				)
							A__						=	list(lowercase				)
							A__						=	conv_bias
							A__						=	num_conv_pos_embeddings
							A__						=	num_conv_pos_embedding_groups
							A__						=	len(self.conv_dim				)
							A__						=	num_hidden_layers
							A__						=	intermediate_size
							A__						=	squeeze_factor
							A__						=	max_position_embeddings
							A__						=	position_buckets
							A__						=	share_att_key
							A__						=	relative_attention
							A__						=	norm_rel_ebd
							A__						=	list(lowercase				)
							A__						=	hidden_act
							A__						=	num_attention_heads
							A__						=	hidden_dropout
							A__						=	attention_dropout
							A__						=	activation_dropout
							A__						=	feat_proj_dropout
							A__						=	final_dropout
							A__						=	layer_norm_eps
							A__						=	feature_layer_norm_eps
							A__						=	initializer_range
							A__						=	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
							A__						=	apply_spec_augment
							A__						=	mask_time_prob
							A__						=	mask_time_length
							A__						=	mask_time_min_masks
							A__						=	mask_feature_prob
							A__						=	mask_feature_length
							A__						=	mask_feature_min_masks

							# ctc loss
							A__						=	ctc_loss_reduction
							A__						=	ctc_zero_infinity

							# sequence classification
							A__						=	use_weighted_layer_sum
							A__						=	classifier_proj_size


			@property
			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






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





'''simple docstring'''



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class 							__UpperCamelCase				(		unittest.TestCase					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  inspect.getfile(accelerate.test_utils							)
						__a			:       List[str]					    =  os.path.sep.join(
						    mod_file.split(os.path.sep							)[:-1] + ['scripts', 'external_deps', 'test_metrics.py']							)

						from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

						__a			:       Union[str, Any]					    =  test_metrics




	@require_cpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						debug_launcher(self.test_metrics.main						,				num_processes=1							)




	@require_cpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						debug_launcher(self.test_metrics.main							)




	@require_single_gpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.test_metrics.main()





	@require_multi_gpu
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						print(f"""Found {torch.cuda.device_count()} devices."""							)
						__a			:       List[Any]					    =  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
						with patch_environment(omp_num_threads=1							):
											execute_subprocess_async(__a						,				env=os.environ.copy()							)



