


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


UpperCamelCase					:       Any			    =     logging.get_logger(__name__)

UpperCamelCase					:       Union[str, Any]			    =     {
    """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		'xglm'
  _lowercase									=		['past_key_values']

  _lowercase									=		{
      'num_attention_heads': 'attention_heads',
      'hidden_size': 'd_model',
      'num_hidden_layers': 'num_layers',
  }

  def __init__( self			:  List[str]				,				lowerCamelCase__			:  Optional[Any]=256_008				,				lowerCamelCase__			:  Dict=2_048				,				lowerCamelCase__			:  Optional[int]=1_024				,				lowerCamelCase__			:  Any=4_096				,				lowerCamelCase__			:  Any=24				,				lowerCamelCase__			:  Tuple=16				,				lowerCamelCase__			:  str="gelu"				,				lowerCamelCase__			:  List[str]=0.1				,				lowerCamelCase__			:  Optional[int]=0.1				,				lowerCamelCase__			:  int=0.0				,				lowerCamelCase__			:  List[str]=0.0				,				lowerCamelCase__			:  Optional[Any]=0.02				,				lowerCamelCase__			:  Tuple=True				,				lowerCamelCase__			:  Dict=True				,				lowerCamelCase__			:  List[str]=2				,				lowerCamelCase__			:  Dict=1				,				lowerCamelCase__			:  Optional[int]=0				,				lowerCamelCase__			:  int=2				,				**lowerCamelCase__			:  Optional[int]				,				):
        a__	:  int     =   vocab_size
        a__	:  int     =   max_position_embeddings
        a__	:  Union[str, Any]     =   d_model
        a__	:  List[Any]     =   ffn_dim
        a__	:  Optional[int]     =   num_layers
        a__	:  int     =   attention_heads
        a__	:  Any     =   activation_function
        a__	:  Union[str, Any]     =   dropout
        a__	:  List[Any]     =   attention_dropout
        a__	:  Union[str, Any]     =   activation_dropout
        a__	:  Union[str, Any]     =   layerdrop
        a__	:  Any     =   init_std
        a__	:  int     =   scale_embedding  # scale factor will be sqrt(d_model) if True
        a__	:  List[str]     =   use_cache

        super().__init__(
            pad_token_id=lowerCamelCase__				,				bos_token_id=lowerCamelCase__				,				eos_token_id=lowerCamelCase__				,				decoder_start_token_id=lowerCamelCase__				,				**lowerCamelCase__				,				)






from typing import TYPE_CHECKING

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


_lowercase    	=		{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MCTCTForCTC",
       "MCTCTModel",
       "MCTCTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
   from .feature_extraction_mctct import MCTCTFeatureExtractor
   from .processing_mctct import MCTCTProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
   import sys

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