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


__lowerCAmelCase					:			Any			      =		logging.get_logger(__name__)

__lowerCAmelCase					:			str			      =		{
    "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__	:			Optional[Any]        =    '''xglm'''
      A__	:			Tuple        =    ['''past_key_values''']

      A__	:			List[Any]        =    {
          '''num_attention_heads''': '''attention_heads''',
          '''hidden_size''': '''d_model''',
          '''num_hidden_layers''': '''num_layers''',
      }




      def __init__(      self		:       Union[str, Any]				,       _snake_case		:       List[Any]=25_6008				,       _snake_case		:       Any=2048				,       _snake_case		:       Any=1024				,       _snake_case		:       Any=4096				,       _snake_case		:       Optional[Any]=24				,       _snake_case		:       str=16				,       _snake_case		:       Any="gelu"				,       _snake_case		:       Tuple=0.1				,       _snake_case		:       str=0.1				,       _snake_case		:       Union[str, Any]=0.0				,       _snake_case		:       Dict=0.0				,       _snake_case		:       List[Any]=0.02				,       _snake_case		:       Dict=True				,       _snake_case		:       List[Any]=True				,       _snake_case		:       List[str]=2				,       _snake_case		:       int=1				,       _snake_case		:       int=0				,       _snake_case		:       List[str]=2				,       **_snake_case		:       Tuple				,       ):
          __lowercase		:			Dict	    =       vocab_size
          __lowercase		:			Tuple	    =       max_position_embeddings
          __lowercase		:			str	    =       d_model
          __lowercase		:			Tuple	    =       ffn_dim
          __lowercase		:			Dict	    =       num_layers
          __lowercase		:			int	    =       attention_heads
          __lowercase		:			Union[str, Any]	    =       activation_function
          __lowercase		:			Any	    =       dropout
          __lowercase		:			Any	    =       attention_dropout
          __lowercase		:			int	    =       activation_dropout
          __lowercase		:			Optional[int]	    =       layerdrop
          __lowercase		:			List[str]	    =       init_std
          __lowercase		:			Any	    =       scale_embedding  # scale factor will be sqrt(d_model) if True
          __lowercase		:			Optional[int]	    =       use_cache

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




__lowerCAmelCase					:			Dict			      =		"\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase					:			Tuple			      =		[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase					:			List[Any]			      =		{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}


