



import warnings

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


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'xlnet'
							lowerCamelCase__         =	['mems']
							lowerCamelCase__         =	{
							    'n_token': 'vocab_size',  # Backward compatibility
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}







							def __init__(     self,    __a=3_2000,    __a=1024,    __a=24,    __a=16,    __a=4096,    __a="gelu",    __a=True,    __a="bi",    __a=0.02,    __a=1E-12,    __a=0.1,    __a=512,    __a=None,    __a=True,    __a=False,    __a=False,    __a=-1,    __a=False,    __a="last",    __a=True,    __a="tanh",    __a=0.1,    __a=5,    __a=5,    __a=5,    __a=1,    __a=2,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					vocab_size
														_lowerCAmelCase  :							Optional[int]				     =					d_model
														_lowerCAmelCase  :							Tuple				     =					n_layer
														_lowerCAmelCase  :							List[Any]				     =					n_head
														if d_model % n_head != 0:
																					raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0")
														if "d_head" in kwargs:
																					if kwargs["d_head"] != d_model // n_head:
																												raise ValueError(
																												    f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})")
														_lowerCAmelCase  :							Optional[int]				     =					d_model // n_head
														_lowerCAmelCase  :							List[str]				     =					ff_activation
														_lowerCAmelCase  :							Tuple				     =					d_inner
														_lowerCAmelCase  :							List[Any]				     =					untie_r
														_lowerCAmelCase  :							List[str]				     =					attn_type

														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							Any				     =					layer_norm_eps

														_lowerCAmelCase  :							List[Any]				     =					dropout
														_lowerCAmelCase  :							Optional[int]				     =					mem_len
														_lowerCAmelCase  :							Union[str, Any]				     =					reuse_len
														_lowerCAmelCase  :							List[str]				     =					bi_data
														_lowerCAmelCase  :							List[str]				     =					clamp_len
														_lowerCAmelCase  :							Any				     =					same_length

														_lowerCAmelCase  :							List[str]				     =					summary_type
														_lowerCAmelCase  :							int				     =					summary_use_proj
														_lowerCAmelCase  :							Optional[Any]				     =					summary_activation
														_lowerCAmelCase  :							Tuple				     =					summary_last_dropout
														_lowerCAmelCase  :							Union[str, Any]				     =					start_n_top
														_lowerCAmelCase  :							Optional[int]				     =					end_n_top

														_lowerCAmelCase  :							Tuple				     =					bos_token_id
														_lowerCAmelCase  :							List[Any]				     =					pad_token_id
														_lowerCAmelCase  :							Dict				     =					eos_token_id

														if "use_cache" in kwargs:
																					warnings.warn(
																					    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
																					    " instead.",    __a,    )
																					_lowerCAmelCase  :							Union[str, Any]				     =					kwargs["use_cache"]

														_lowerCAmelCase  :							Union[str, Any]				     =					use_mems_eval
														_lowerCAmelCase  :							Any				     =					use_mems_train
														super().__init__(pad_token_id=__a,    bos_token_id=__a,    eos_token_id=__a,    **__a)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.")
														return -1







							@max_position_embeddings.setter
							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														raise NotImplementedError(
														    f"The model {self.model_type} is one of the few models that has no sequence length limit.")





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a     ,      a):
							lowerCamelCase__         =	'focalnet'







							def __init__(     self,    __a=224,    __a=4,    __a=3,    __a=96,    __a=False,    __a=[192, 384, 768, 768],    __a=[2, 2, 6, 2],    __a=[2, 2, 2, 2],    __a=[3, 3, 3, 3],    __a="gelu",    __a=4.0,    __a=0.0,    __a=0.1,    __a=False,    __a=1E-4,    __a=False,    __a=False,    __a=False,    __a=0.02,    __a=1E-5,    __a=32,    __a=None,    __a=None,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase  :							str				     =					image_size
														_lowerCAmelCase  :							List[str]				     =					patch_size
														_lowerCAmelCase  :							List[Any]				     =					num_channels
														_lowerCAmelCase  :							Tuple				     =					embed_dim
														_lowerCAmelCase  :							List[Any]				     =					use_conv_embed
														_lowerCAmelCase  :							Any				     =					hidden_sizes
														_lowerCAmelCase  :							Tuple				     =					depths
														_lowerCAmelCase  :							Dict				     =					focal_levels
														_lowerCAmelCase  :							Optional[Any]				     =					focal_windows
														_lowerCAmelCase  :							str				     =					hidden_act
														_lowerCAmelCase  :							Union[str, Any]				     =					mlp_ratio
														_lowerCAmelCase  :							Any				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Dict				     =					drop_path_rate
														_lowerCAmelCase  :							str				     =					use_layerscale
														_lowerCAmelCase  :							str				     =					layerscale_value
														_lowerCAmelCase  :							Union[str, Any]				     =					use_post_layernorm
														_lowerCAmelCase  :							Optional[int]				     =					use_post_layernorm_in_modulation
														_lowerCAmelCase  :							str				     =					normalize_modulator
														_lowerCAmelCase  :							Any				     =					initializer_range
														_lowerCAmelCase  :							Union[str, Any]				     =					layer_norm_eps
														_lowerCAmelCase  :							Any				     =					encoder_stride
														_lowerCAmelCase  :							List[str]				     =					["stem"] + [f"stage{idx}" for idx in range(1,    len(self.depths) + 1)]
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					get_aligned_output_features_output_indices(
														    out_features=__a,    out_indices=__a,    stage_names=self.stage_names)
