


from typing import List, Optional, Union

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


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

lowerCAmelCase				:int        =      {
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class _lowerCamelCase	(						__SCREAMING_SNAKE_CASE							):



		'''simple docstring'''







		A_     :      Tuple				   =    'time_series_transformer'
		A_     :      Tuple				   =    {
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		    'num_hidden_layers': 'encoder_layers',
		}







		def __init__( self		:						int  ,					_A		:						int = None  ,					_A		:						Union[str, Any] = None  ,					_A		:						List[Any] = "student_t"  ,					_A		:						List[Any] = "nll"  ,					_A		:						Any = 1  ,					_A		:						Optional[int] = [1, 2, 3, 4, 5, 6, 7]  ,					_A		:						Optional[Any] = "mean"  ,					_A		:						List[str] = 0  ,					_A		:						List[Any] = 0  ,					_A		:						Dict = 0  ,					_A		:						List[Any] = 0  ,					_A		:						Tuple = None  ,					_A		:						List[Any] = None  ,					_A		:						Dict = 32  ,					_A		:						Any = 32  ,					_A		:						Tuple = 2  ,					_A		:						Union[str, Any] = 2  ,					_A		:						List[str] = 2  ,					_A		:						Optional[Any] = 2  ,					_A		:						Optional[Any] = True  ,					_A		:						List[Any] = "gelu"  ,					_A		:						Dict = 64  ,					_A		:						Union[str, Any] = 0.1  ,					_A		:						Optional[int] = 0.1  ,					_A		:						List[Any] = 0.1  ,					_A		:						Union[str, Any] = 0.1  ,					_A		:						Optional[Any] = 0.1  ,					_A		:						Dict = 100  ,					_A		:						int = 0.02  ,					_A		:						List[str]=True  ,					**_A		:						Any  ,					)  ->			List[Any]:
			# time series specific configuration
			__magic_name__	:   List[Any]					     =					prediction_length
			__magic_name__	:   Tuple					     =					context_length or prediction_length
			__magic_name__	:   List[Any]					     =					distribution_output
			__magic_name__	:   Optional[int]					     =					loss
			__magic_name__	:   str					     =					input_size
			__magic_name__	:   Optional[int]					     =					num_time_features
			__magic_name__	:   Union[str, Any]					     =					lags_sequence
			__magic_name__	:   Optional[Any]					     =					scaling
			__magic_name__	:   int					     =					num_dynamic_real_features
			__magic_name__	:   str					     =					num_static_real_features
			__magic_name__	:   str					     =					num_static_categorical_features
			if cardinality and num_static_categorical_features > 0:
				if len(_a      ) != num_static_categorical_features:
					raise ValueError(
					    'The cardinality should be a list of the same length as `num_static_categorical_features`'      )
				__magic_name__	:   Optional[Any]					     =					cardinality
			else:
				__magic_name__	:   List[Any]					     =					[0]
			if embedding_dimension and num_static_categorical_features > 0:
				if len(_a      ) != num_static_categorical_features:
					raise ValueError(
					    'The embedding dimension should be a list of the same length as `num_static_categorical_features`'      )
				__magic_name__	:   str					     =					embedding_dimension
			else:
				__magic_name__	:   Union[str, Any]					     =					[min(50  ,					(cat + 1) // 2      ) for cat in self.cardinality]
			__magic_name__	:   str					     =					num_parallel_samples

			# Transformer architecture configuration
			__magic_name__	:   int					     =					input_size * len(_a      ) + self._number_of_features
			__magic_name__	:   List[Any]					     =					d_model
			__magic_name__	:   List[str]					     =					encoder_attention_heads
			__magic_name__	:   Optional[int]					     =					decoder_attention_heads
			__magic_name__	:   int					     =					encoder_ffn_dim
			__magic_name__	:   Tuple					     =					decoder_ffn_dim
			__magic_name__	:   Union[str, Any]					     =					encoder_layers
			__magic_name__	:   Optional[Any]					     =					decoder_layers

			__magic_name__	:   str					     =					dropout
			__magic_name__	:   int					     =					attention_dropout
			__magic_name__	:   Union[str, Any]					     =					activation_dropout
			__magic_name__	:   Any					     =					encoder_layerdrop
			__magic_name__	:   int					     =					decoder_layerdrop

			__magic_name__	:   Union[str, Any]					     =					activation_function
			__magic_name__	:   Union[str, Any]					     =					init_std

			__magic_name__	:   str					     =					use_cache

			super().__init__(is_encoder_decoder=_a  ,					**_a      )







		@property
		def        __lowerCAmelCase		( self		:						str      )  ->			Dict:
			return (
			    sum(self.embedding_dimension      )
			    + self.num_dynamic_real_features
			    + self.num_time_features
			    + self.num_static_real_features
			    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
			)



'''simple docstring'''

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


lowerCAmelCase				:Any        =      logging.get_logger(__name__)

lowerCAmelCase				:int        =      {
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      List[str]				   =    """lxmert"""
		A_     :      int				   =    {}







		def __init__( self		:						int  ,					_A		:						List[Any]=30522  ,					_A		:						int=768  ,					_A		:						Any=12  ,					_A		:						Optional[Any]=9500  ,					_A		:						Any=1600  ,					_A		:						Tuple=400  ,					_A		:						str=3072  ,					_A		:						Tuple="gelu"  ,					_A		:						int=0.1  ,					_A		:						List[Any]=0.1  ,					_A		:						Dict=512  ,					_A		:						Dict=2  ,					_A		:						Tuple=0.02  ,					_A		:						List[Any]=1E-12  ,					_A		:						Optional[Any]=9  ,					_A		:						List[Any]=5  ,					_A		:						str=5  ,					_A		:						int=2048  ,					_A		:						Tuple=4  ,					_A		:						Tuple=6.67  ,					_A		:						Dict=True  ,					_A		:						str=True  ,					_A		:						str=True  ,					_A		:						Dict=True  ,					_A		:						str=True  ,					_A		:						int=True  ,					_A		:						Tuple=True  ,					**_A		:						int  ,					)  ->			str:
			__magic_name__	:   List[Any]					     =					vocab_size
			__magic_name__	:   List[Any]					     =					hidden_size
			__magic_name__	:   Optional[int]					     =					num_attention_heads
			__magic_name__	:   Dict					     =					hidden_act
			__magic_name__	:   Optional[Any]					     =					intermediate_size
			__magic_name__	:   str					     =					hidden_dropout_prob
			__magic_name__	:   List[str]					     =					attention_probs_dropout_prob
			__magic_name__	:   Tuple					     =					max_position_embeddings
			__magic_name__	:   Dict					     =					type_vocab_size
			__magic_name__	:   str					     =					initializer_range
			__magic_name__	:   str					     =					layer_norm_eps
			__magic_name__	:   Union[str, Any]					     =					num_qa_labels
			__magic_name__	:   str					     =					num_object_labels
			__magic_name__	:   List[str]					     =					num_attr_labels
			__magic_name__	:   Tuple					     =					l_layers
			__magic_name__	:   List[Any]					     =					x_layers
			__magic_name__	:   Optional[int]					     =					r_layers
			__magic_name__	:   Dict					     =					visual_feat_dim
			__magic_name__	:   Optional[int]					     =					visual_pos_dim
			__magic_name__	:   Optional[int]					     =					visual_loss_normalizer
			__magic_name__	:   int					     =					task_matched
			__magic_name__	:   Dict					     =					task_mask_lm
			__magic_name__	:   List[Any]					     =					task_obj_predict
			__magic_name__	:   str					     =					task_qa
			__magic_name__	:   List[Any]					     =					visual_obj_loss
			__magic_name__	:   Dict					     =					visual_attr_loss
			__magic_name__	:   int					     =					visual_feat_loss
			__magic_name__	:   Tuple					     =					{'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
			super().__init__(**_A      )