


'''simple docstring'''



import functools
import operator

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


__lowercase     :						Any	=     logging.get_logger(__name__)

__lowercase     :						str	=     {
    '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 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "sew-d"




	def __init__(       self						,    __a=32						,    __a=768						,    __a=12						,    __a=12						,    __a=3072						,    __a=2						,    __a=512						,    __a=256						,    __a=True						,    __a=True						,    __a=("p2c", "c2p")						,    __a="layer_norm"						,    __a="gelu_python"						,    __a=0.1						,    __a=0.1						,    __a=0.1						,    __a=0.0						,    __a=0.1						,    __a=0.02						,    __a=1E-7						,    __a=1E-5						,    __a="group"						,    __a="gelu"						,    __a=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)						,    __a=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,    __a=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,    __a=False						,    __a=128						,    __a=16						,    __a=True						,    __a=0.05						,    __a=10						,    __a=2						,    __a=0.0						,    __a=10						,    __a=0						,    __a="mean"						,    __a=False						,    __a=False						,    __a=256						,    __a=0						,    __a=1						,    __a=2						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(**__a						,    pad_token_id=__a						,    bos_token_id=__a						,    eos_token_id=__a							)
						__a      :	Any    =  hidden_size
						__a      :	List[str]    =  feat_extract_norm
						__a      :	List[Any]    =  feat_extract_activation
						__a      :	Tuple    =  list(__a							)
						__a      :	Union[str, Any]    =  list(__a							)
						__a      :	List[Any]    =  list(__a							)
						__a      :	Dict    =  conv_bias
						__a      :	str    =  num_conv_pos_embeddings
						__a      :	Optional[Any]    =  num_conv_pos_embedding_groups
						__a      :	List[str]    =  len(self.conv_dim							)
						__a      :	str    =  num_hidden_layers
						__a      :	Any    =  intermediate_size
						__a      :	int    =  squeeze_factor
						__a      :	str    =  max_position_embeddings
						__a      :	int    =  position_buckets
						__a      :	Any    =  share_att_key
						__a      :	Dict    =  relative_attention
						__a      :	List[Any]    =  norm_rel_ebd
						__a      :	str    =  list(__a							)
						__a      :	Any    =  hidden_act
						__a      :	List[Any]    =  num_attention_heads
						__a      :	Dict    =  hidden_dropout
						__a      :	Optional[Any]    =  attention_dropout
						__a      :	List[str]    =  activation_dropout
						__a      :	Any    =  feat_proj_dropout
						__a      :	Dict    =  final_dropout
						__a      :	int    =  layer_norm_eps
						__a      :	Optional[Any]    =  feature_layer_norm_eps
						__a      :	Optional[int]    =  initializer_range
						__a      :	Optional[Any]    =  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      :	List[Any]    =  apply_spec_augment
						__a      :	int    =  mask_time_prob
						__a      :	int    =  mask_time_length
						__a      :	str    =  mask_time_min_masks
						__a      :	List[str]    =  mask_feature_prob
						__a      :	Optional[int]    =  mask_feature_length
						__a      :	Optional[int]    =  mask_feature_min_masks

						# ctc loss
						__a      :	Optional[Any]    =  ctc_loss_reduction
						__a      :	int    =  ctc_zero_infinity

						# sequence classification
						__a      :	int    =  use_weighted_layer_sum
						__a      :	Any    =  classifier_proj_size





	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowercase     :						Union[str, Any]	=     {
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	pass

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						List[str]	=     [
	    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RoCBertForCausalLM',
	    'RoCBertForMaskedLM',
	    'RoCBertForMultipleChoice',
	    'RoCBertForPreTraining',
	    'RoCBertForQuestionAnswering',
	    'RoCBertForSequenceClassification',
	    'RoCBertForTokenClassification',
	    'RoCBertLayer',
	    'RoCBertModel',
	    'RoCBertPreTrainedModel',
	    'load_tf_weights_in_roc_bert',
	]

if TYPE_CHECKING:
	from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
	from .tokenization_roc_bert import RoCBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		raise OptionalDependencyNotAvailable()

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roc_bert import (
		    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoCBertForCausalLM,
		    RoCBertForMaskedLM,
		    RoCBertForMultipleChoice,
		    RoCBertForPreTraining,
		    RoCBertForQuestionAnswering,
		    RoCBertForSequenceClassification,
		    RoCBertForTokenClassification,
		    RoCBertLayer,
		    RoCBertModel,
		    RoCBertPreTrainedModel,
		    load_tf_weights_in_roc_bert,
		)


else:
	import sys

	__lowercase     :						Any	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



