


"""simple docstring"""




import warnings

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


__magic_name__						: Optional[Any]    =				logging.get_logger(__name__)

__magic_name__						: Optional[int]    =				{
    '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 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''xlnet'''
							__a					 =       ['''mems''']
							__a					 =       {
							    '''n_token''': '''vocab_size''',  # Backward compatibility
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''n_head''',
							    '''num_hidden_layers''': '''n_layer''',
							}




							def __init__(     self:    List[Any]     ,							A_:    Optional[Any]=3_20_00     ,							A_:    List[Any]=10_24     ,							A_:    int=24     ,							A_:    Union[str, Any]=16     ,							A_:    str=40_96     ,							A_:    List[Any]="gelu"     ,							A_:    List[str]=True     ,							A_:    Optional[int]="bi"     ,							A_:    Any=0.02     ,							A_:    List[Any]=1E-12     ,							A_:    Any=0.1     ,							A_:    List[str]=5_12     ,							A_:    str=None     ,							A_:    Union[str, Any]=True     ,							A_:    str=False     ,							A_:    str=False     ,							A_:    List[Any]=-1     ,							A_:    Dict=False     ,							A_:    Union[str, Any]="last"     ,							A_:    str=True     ,							A_:    Optional[int]="tanh"     ,							A_:    List[Any]=0.1     ,							A_:    int=5     ,							A_:    int=5     ,							A_:    int=5     ,							A_:    List[Any]=1     ,							A_:    Any=2     ,							**A_:    int     ,							):
										__lowerCamelCase          =      vocab_size
										__lowerCamelCase          =      d_model
										__lowerCamelCase          =      n_layer
										__lowerCamelCase          =      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          =      d_model // n_head
										__lowerCamelCase          =      ff_activation
										__lowerCamelCase          =      d_inner
										__lowerCamelCase          =      untie_r
										__lowerCamelCase          =      attn_type

										__lowerCamelCase          =      initializer_range
										__lowerCamelCase          =      layer_norm_eps

										__lowerCamelCase          =      dropout
										__lowerCamelCase          =      mem_len
										__lowerCamelCase          =      reuse_len
										__lowerCamelCase          =      bi_data
										__lowerCamelCase          =      clamp_len
										__lowerCamelCase          =      same_length

										__lowerCamelCase          =      summary_type
										__lowerCamelCase          =      summary_use_proj
										__lowerCamelCase          =      summary_activation
										__lowerCamelCase          =      summary_last_dropout
										__lowerCamelCase          =      start_n_top
										__lowerCamelCase          =      end_n_top

										__lowerCamelCase          =      bos_token_id
										__lowerCamelCase          =      pad_token_id
										__lowerCamelCase          =      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          =      kwargs["""use_cache"""]

										__lowerCamelCase          =      use_mems_eval
										__lowerCamelCase          =      use_mems_train
										super().__init__(pad_token_id=A_     ,							bos_token_id=A_     ,							eos_token_id=A_     ,							**A_   )




							@property
							def   __a   (     self:    List[str]   ):
										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   __a   (     self:    int     ,							A_:    Tuple   ):
										# Message copied from Transformer-XL documentation
										raise NotImplementedError(
										    f'The model {self.model_type} is one of the few models that has no sequence length limit.'   )








"""simple docstring"""




from typing import TYPE_CHECKING

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


__magic_name__						: Any    =				{'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Dict    =				['GLPNFeatureExtractor']
					__magic_name__						: Union[str, Any]    =				['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: int    =				[
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__magic_name__						: Optional[int]    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




