
"""simple docstring"""



import functools
import operator

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


UpperCamelCase__          =	logging.get_logger(__name__)

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





class 				a			(					SCREAMING_SNAKE_CASE__      ):
      UpperCamelCase							:   List[str]           =     """sew"""



      def __init__(   self     ,	UpperCamelCase_=32     ,	UpperCamelCase_=768     ,	UpperCamelCase_=12     ,	UpperCamelCase_=12     ,	UpperCamelCase_=3_072     ,	UpperCamelCase_=2     ,	UpperCamelCase_="gelu"     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=0.0     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=0.1     ,	UpperCamelCase_=0.02     ,	UpperCamelCase_=1E-5     ,	UpperCamelCase_="group"     ,	UpperCamelCase_="gelu"     ,	UpperCamelCase_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)     ,	UpperCamelCase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     ,	UpperCamelCase_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     ,	UpperCamelCase_=False     ,	UpperCamelCase_=128     ,	UpperCamelCase_=16     ,	UpperCamelCase_=True     ,	UpperCamelCase_=0.05     ,	UpperCamelCase_=10     ,	UpperCamelCase_=2     ,	UpperCamelCase_=0.0     ,	UpperCamelCase_=10     ,	UpperCamelCase_=0     ,	UpperCamelCase_="mean"     ,	UpperCamelCase_=False     ,	UpperCamelCase_=False     ,	UpperCamelCase_=256     ,	UpperCamelCase_=0     ,	UpperCamelCase_=1     ,	UpperCamelCase_=2     ,	**UpperCamelCase_     ,	):
          super().__init__(**A_     ,	pad_token_id=A_     ,	bos_token_id=A_     ,	eos_token_id=A_      )
          UpperCAmelCase__	:      int     							=					hidden_size
          UpperCAmelCase__	:      Union[str, Any]     							=					feat_extract_norm
          UpperCAmelCase__	:      Optional[Any]     							=					feat_extract_activation
          UpperCAmelCase__	:      Optional[int]     							=					list(A_      )
          UpperCAmelCase__	:      Dict     							=					list(A_      )
          UpperCAmelCase__	:      Any     							=					list(A_      )
          UpperCAmelCase__	:      Optional[int]     							=					conv_bias
          UpperCAmelCase__	:      List[str]     							=					num_conv_pos_embeddings
          UpperCAmelCase__	:      Tuple     							=					num_conv_pos_embedding_groups
          UpperCAmelCase__	:      List[Any]     							=					len(self.conv_dim      )
          UpperCAmelCase__	:      Any     							=					num_hidden_layers
          UpperCAmelCase__	:      List[Any]     							=					intermediate_size
          UpperCAmelCase__	:      Dict     							=					squeeze_factor
          UpperCAmelCase__	:      str     							=					hidden_act
          UpperCAmelCase__	:      List[Any]     							=					num_attention_heads
          UpperCAmelCase__	:      List[str]     							=					hidden_dropout
          UpperCAmelCase__	:      Dict     							=					attention_dropout
          UpperCAmelCase__	:      Union[str, Any]     							=					activation_dropout
          UpperCAmelCase__	:      int     							=					feat_proj_dropout
          UpperCAmelCase__	:      List[Any]     							=					final_dropout
          UpperCAmelCase__	:      Union[str, Any]     							=					layerdrop
          UpperCAmelCase__	:      str     							=					layer_norm_eps
          UpperCAmelCase__	:      Union[str, Any]     							=					initializer_range
          UpperCAmelCase__	:      List[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
          UpperCAmelCase__	:      int     							=					apply_spec_augment
          UpperCAmelCase__	:      Union[str, Any]     							=					mask_time_prob
          UpperCAmelCase__	:      Union[str, Any]     							=					mask_time_length
          UpperCAmelCase__	:      Any     							=					mask_time_min_masks
          UpperCAmelCase__	:      str     							=					mask_feature_prob
          UpperCAmelCase__	:      Dict     							=					mask_feature_length
          UpperCAmelCase__	:      Union[str, Any]     							=					mask_feature_min_masks

          # ctc loss
          UpperCAmelCase__	:      Any     							=					ctc_loss_reduction
          UpperCAmelCase__	:      List[str]     							=					ctc_zero_infinity

          # sequence classification
          UpperCAmelCase__	:      int     							=					use_weighted_layer_sum
          UpperCAmelCase__	:      Optional[int]     							=					classifier_proj_size



      @property
      def  __snake_case							(   self      ):
          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


A__ :       List[str]				= {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      '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

  A__ :       int				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
