
import functools
import operator

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


UpperCAmelCase       =					logging.get_logger(__name__)

UpperCAmelCase       =					{
    '''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_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Union[str, Any]									=			"""sew-d"""





	def __init__(			self						,       snake_case=32						,       snake_case=768						,       snake_case=12						,       snake_case=12						,       snake_case=3072						,       snake_case=2						,       snake_case=512						,       snake_case=256						,       snake_case=True						,       snake_case=True						,       snake_case=("p2c", "c2p")						,       snake_case="layer_norm"						,       snake_case="gelu_python"						,       snake_case=0.1						,       snake_case=0.1						,       snake_case=0.1						,       snake_case=0.0						,       snake_case=0.1						,       snake_case=0.02						,       snake_case=1E-7						,       snake_case=1E-5						,       snake_case="group"						,       snake_case="gelu"						,       snake_case=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)						,       snake_case=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,       snake_case=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,       snake_case=False						,       snake_case=128						,       snake_case=16						,       snake_case=True						,       snake_case=0.05						,       snake_case=10						,       snake_case=2						,       snake_case=0.0						,       snake_case=10						,       snake_case=0						,       snake_case="mean"						,       snake_case=False						,       snake_case=False						,       snake_case=256						,       snake_case=0						,       snake_case=1						,       snake_case=2						,       **snake_case						,       ):
		super().__init__(**snake_case						,       pad_token_id=snake_case						,       bos_token_id=snake_case						,       eos_token_id=snake_case			)
		lowercase	    =       hidden_size
		lowercase	    =       feat_extract_norm
		lowercase	    =       feat_extract_activation
		lowercase	    =       list(snake_case			)
		lowercase	    =       list(snake_case			)
		lowercase	    =       list(snake_case			)
		lowercase	    =       conv_bias
		lowercase	    =       num_conv_pos_embeddings
		lowercase	    =       num_conv_pos_embedding_groups
		lowercase	    =       len(self.conv_dim			)
		lowercase	    =       num_hidden_layers
		lowercase	    =       intermediate_size
		lowercase	    =       squeeze_factor
		lowercase	    =       max_position_embeddings
		lowercase	    =       position_buckets
		lowercase	    =       share_att_key
		lowercase	    =       relative_attention
		lowercase	    =       norm_rel_ebd
		lowercase	    =       list(snake_case			)
		lowercase	    =       hidden_act
		lowercase	    =       num_attention_heads
		lowercase	    =       hidden_dropout
		lowercase	    =       attention_dropout
		lowercase	    =       activation_dropout
		lowercase	    =       feat_proj_dropout
		lowercase	    =       final_dropout
		lowercase	    =       layer_norm_eps
		lowercase	    =       feature_layer_norm_eps
		lowercase	    =       initializer_range
		lowercase	    =       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
		lowercase	    =       apply_spec_augment
		lowercase	    =       mask_time_prob
		lowercase	    =       mask_time_length
		lowercase	    =       mask_time_min_masks
		lowercase	    =       mask_feature_prob
		lowercase	    =       mask_feature_length
		lowercase	    =       mask_feature_min_masks

		# ctc loss
		lowercase	    =       ctc_loss_reduction
		lowercase	    =       ctc_zero_infinity

		# sequence classification
		lowercase	    =       use_weighted_layer_sum
		lowercase	    =       classifier_proj_size





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









from typing import TYPE_CHECKING

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


UpperCAmelCase__							  =						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase__							  =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


