





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "donut-swin"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  **_lowerCAmelCase      ,  )     ->				Optional[Any]:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _lowerCAmelCase						      =   int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)









'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
    "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Any								=       "falcon"
 __lowerCamelCase    :				List[str]								=       ["past_key_values"]




 def __init__(						self      ,  _lowerCAmelCase=65024      ,  _lowerCAmelCase=4544      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=71      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=11      ,  _lowerCAmelCase=11      ,  **_lowerCAmelCase      ,  )     ->				Union[str, Any]:
     _lowerCAmelCase						      =   vocab_size
     # Backward compatibility with n_embed kwarg
     _lowerCAmelCase						      =   kwargs.pop("n_embed"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   hidden_size if n_embed is None else n_embed
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   layer_norm_epsilon
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   use_cache
     _lowerCAmelCase						      =   hidden_dropout
     _lowerCAmelCase						      =   attention_dropout

     _lowerCAmelCase						      =   bos_token_id
     _lowerCAmelCase						      =   eos_token_id
     _lowerCAmelCase						      =   num_attention_heads if num_kv_heads is None else num_kv_heads
     _lowerCAmelCase						      =   alibi
     _lowerCAmelCase						      =   new_decoder_architecture
     _lowerCAmelCase						      =   multi_query  # Ignored when new_decoder_architecture is True
     _lowerCAmelCase						      =   parallel_attn
     _lowerCAmelCase						      =   bias

     super().__init__(bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 @property
 def 				_snake_case (						self		)     ->				Optional[Any]:
     return self.hidden_size // self.num_attention_heads





 @property
 def 				_snake_case (						self		)     ->				Optional[Any]:
     return not self.alibi


