

'''simple docstring'''

from typing import List, Optional

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


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			"autoformer"
       lowerCAmelCase_												=			{
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
           "num_hidden_layers": "encoder_layers",
       }







       def __init__(				self      :			Any    ,   __lowercase      :			Optional[int] = None    ,   __lowercase      :			Optional[int] = None    ,   __lowercase      :			str = "student_t"    ,   __lowercase      :			str = "nll"    ,   __lowercase      :			int = 1    ,   __lowercase      :			List[int] = [1, 2, 3, 4, 5, 6, 7]    ,   __lowercase      :			bool = True    ,   __lowercase      :			int = 0    ,   __lowercase      :			int = 0    ,   __lowercase      :			int = 0    ,   __lowercase      :			int = 0    ,   __lowercase      :			Optional[List[int]] = None    ,   __lowercase      :			Optional[List[int]] = None    ,   __lowercase      :			int = 64    ,   __lowercase      :			int = 2    ,   __lowercase      :			int = 2    ,   __lowercase      :			int = 2    ,   __lowercase      :			int = 2    ,   __lowercase      :			int = 32    ,   __lowercase      :			int = 32    ,   __lowercase      :			str = "gelu"    ,   __lowercase      :			float = 0.1    ,   __lowercase      :			float = 0.1    ,   __lowercase      :			float = 0.1    ,   __lowercase      :			float = 0.1    ,   __lowercase      :			float = 0.1    ,   __lowercase      :			int = 100    ,   __lowercase      :			float = 0.0_2    ,   __lowercase      :			bool = True    ,   __lowercase      :			int=True    ,   __lowercase      :			int = 10    ,   __lowercase      :			int = 25    ,   __lowercase      :			int = 3    ,   **__lowercase      :			Dict    ,   ):







            """simple docstring"""







            __lowercase      		=prediction_length
            __lowercase      		=context_length if context_length is not None else prediction_length
            __lowercase      		=distribution_output
            __lowercase      		=loss
            __lowercase      		=input_size
            __lowercase      		=num_time_features
            __lowercase      		=lags_sequence
            __lowercase      		=scaling
            __lowercase      		=num_dynamic_real_features
            __lowercase      		=num_static_real_features
            __lowercase      		=num_static_categorical_features
            if cardinality is not None and num_static_categorical_features > 0:
                 if len(__lowercase   ) != num_static_categorical_features:
                      raise ValueError(
                          'The cardinality should be a list of the same length as `num_static_categorical_features`'   )
                 __lowercase      		=cardinality
            else:
                 __lowercase      		=[0]
            if embedding_dimension is not None and num_static_categorical_features > 0:
                 if len(__lowercase   ) != num_static_categorical_features:
                      raise ValueError(
                          'The embedding dimension should be a list of the same length as `num_static_categorical_features`'   )
                 __lowercase      		=embedding_dimension
            else:
                 __lowercase      		=[min(50    ,   (cat + 1) // 2   ) for cat in self.cardinality]
            __lowercase      		=num_parallel_samples

            # Transformer architecture configuration
            __lowercase      		=input_size * len(self.lags_sequence   ) + self._number_of_features
            __lowercase      		=d_model
            __lowercase      		=encoder_attention_heads
            __lowercase      		=decoder_attention_heads
            __lowercase      		=encoder_ffn_dim
            __lowercase      		=decoder_ffn_dim
            __lowercase      		=encoder_layers
            __lowercase      		=decoder_layers

            __lowercase      		=dropout
            __lowercase      		=attention_dropout
            __lowercase      		=activation_dropout
            __lowercase      		=encoder_layerdrop
            __lowercase      		=decoder_layerdrop

            __lowercase      		=activation_function
            __lowercase      		=init_std

            __lowercase      		=use_cache

            # Autoformer
            __lowercase      		=label_length
            __lowercase      		=moving_average
            __lowercase      		=autocorrelation_factor

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







       @property
       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}


class 	lowerCAmelCase  (   A ,      A ):
       lowerCAmelCase_												=			"dinat"

       lowerCAmelCase_												=			{
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }







       def __init__(				self      :			int    ,   __lowercase      :			str=4    ,   __lowercase      :			str=3    ,   __lowercase      :			int=64    ,   __lowercase      :			int=[3, 4, 6, 5]    ,   __lowercase      :			Union[str, Any]=[2, 4, 8, 16]    ,   __lowercase      :			Optional[int]=7    ,   __lowercase      :			str=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]    ,   __lowercase      :			Any=3.0    ,   __lowercase      :			Optional[Any]=True    ,   __lowercase      :			List[Any]=0.0    ,   __lowercase      :			Optional[Any]=0.0    ,   __lowercase      :			int=0.1    ,   __lowercase      :			Union[str, Any]="gelu"    ,   __lowercase      :			Any=0.0_2    ,   __lowercase      :			int=1E-5    ,   __lowercase      :			Optional[int]=0.0    ,   __lowercase      :			Optional[int]=None    ,   __lowercase      :			str=None    ,   **__lowercase      :			Any    ,   ):







            """simple docstring"""







            super().__init__(**__lowercase   )

            __lowercase      		=patch_size
            __lowercase      		=num_channels
            __lowercase      		=embed_dim
            __lowercase      		=depths
            __lowercase      		=len(__lowercase   )
            __lowercase      		=num_heads
            __lowercase      		=kernel_size
            __lowercase      		=dilations
            __lowercase      		=mlp_ratio
            __lowercase      		=qkv_bias
            __lowercase      		=hidden_dropout_prob
            __lowercase      		=attention_probs_dropout_prob
            __lowercase      		=drop_path_rate
            __lowercase      		=hidden_act
            __lowercase      		=layer_norm_eps
            __lowercase      		=initializer_range
            # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
            # this indicates the channel dimension after the last stage of the model
            __lowercase      		=int(embed_dim * 2 ** (len(__lowercase   ) - 1)   )
            __lowercase      		=layer_scale_init_value
            __lowercase      		=['stem'] + [f'''stage{idx}''' for idx in range(1    ,   len(__lowercase   ) + 1   )]
            __lowercase		,  __lowercase      		=get_aligned_output_features_output_indices(
                out_features=__lowercase    ,   out_indices=__lowercase    ,   stage_names=self.stage_names   )


