






from typing import TYPE_CHECKING

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


A_		:		int		     =			{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		Dict		     =			[
       'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'GPTBigCodeForSequenceClassification',
       'GPTBigCodeForTokenClassification',
       'GPTBigCodeForCausalLM',
       'GPTBigCodeModel',
       'GPTBigCodePreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpt_bigcode import (
          GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTBigCodeForCausalLM,
          GPTBigCodeForSequenceClassification,
          GPTBigCodeForTokenClassification,
          GPTBigCodeModel,
          GPTBigCodePreTrainedModel,
      )


else:
   import sys

   A_		:		str		     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







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


A_		:		Tuple		     =			logging.get_logger(__name__)

A_		:		Dict		     =			{
    '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     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									='''xglm'''
       a    :		List[Any]									=['''past_key_values''']

       a    :		Union[str, Any]									={
           '''num_attention_heads''': '''attention_heads''',
           '''hidden_size''': '''d_model''',
           '''num_hidden_layers''': '''num_layers''',
       }





       def __init__( self  ,      _lowerCamelCase=2_5_6_0_0_8  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=4_0_9_6  ,      _lowerCamelCase=2_4  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=2  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    vocab_size
         UpperCamelCase_: Optional[int]   				=    max_position_embeddings
         UpperCamelCase_: List[str]   				=    d_model
         UpperCamelCase_: List[Any]   				=    ffn_dim
         UpperCamelCase_: List[Any]   				=    num_layers
         UpperCamelCase_: List[Any]   				=    attention_heads
         UpperCamelCase_: Tuple   				=    activation_function
         UpperCamelCase_: Tuple   				=    dropout
         UpperCamelCase_: Tuple   				=    attention_dropout
         UpperCamelCase_: Optional[Any]   				=    activation_dropout
         UpperCamelCase_: List[str]   				=    layerdrop
         UpperCamelCase_: Any   				=    init_std
         UpperCamelCase_: Any   				=    scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCamelCase_: Union[str, Any]   				=    use_cache

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