

from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


a		=logging.get_logger(__name__)

# General docstring
a		="""ResNetConfig"""

# Base docstring
a		="""microsoft/resnet-50"""
a		=[1, 2048, 7, 7]

# Image classification docstring
a		="""microsoft/resnet-50"""
a		="""tiger cat"""

a		=[
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]






class        A_		(       nn.Module				):
							def __init__(		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int = 3   ,SCREAMING_SNAKE_CASE__		:			int = 1   ,SCREAMING_SNAKE_CASE__		:			str = "relu"):
								super().__init__()
								__lowerCamelCase				:				str					       = nn.Convad(
								    SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,kernel_size=SCREAMING_SNAKE_CASE__   ,stride=SCREAMING_SNAKE_CASE__   ,padding=kernel_size // 2   ,bias=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[int]					       = nn.BatchNormad(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Any					       = ACTaFN[activation] if activation is not None else nn.Identity()


							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Tensor):
								__lowerCamelCase				:				Any					       = self.convolution(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[int]					       = self.normalization(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[int]					       = self.activation(SCREAMING_SNAKE_CASE__)
								return hidden_state






class        A_		(       nn.Module				):
							def __init__(		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			ResNetConfig):
								super().__init__()
								__lowerCamelCase				:				Optional[int]					       = ResNetConvLayer(
								    config.num_channels   ,config.embedding_size   ,kernel_size=7   ,stride=2   ,activation=config.hidden_act)
								__lowerCamelCase				:				Dict					       = nn.MaxPoolad(kernel_size=3   ,stride=2   ,padding=1)
								__lowerCamelCase				:				List[Any]					       = config.num_channels


							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			Tensor):
								__lowerCamelCase				:				int					       = pixel_values.shape[1]
								if num_channels != self.num_channels:
									raise ValueError(
									    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.')
								__lowerCamelCase				:				List[str]					       = self.embedder(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Tuple					       = self.pooler(SCREAMING_SNAKE_CASE__)
								return embedding






class        A_		(       nn.Module				):
							def __init__(		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int = 2):
								super().__init__()
								__lowerCamelCase				:				str					       = nn.Convad(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,kernel_size=1   ,stride=SCREAMING_SNAKE_CASE__   ,bias=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Any					       = nn.BatchNormad(SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			Tensor):
								__lowerCamelCase				:				Dict					       = self.convolution(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[Any]					       = self.normalization(SCREAMING_SNAKE_CASE__)
								return hidden_state






class        A_		(       nn.Module				):
							def __init__(		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int = 1   ,SCREAMING_SNAKE_CASE__		:			str = "relu"):
								super().__init__()
								__lowerCamelCase				:				List[str]					       = in_channels != out_channels or stride != 1
								__lowerCamelCase				:				Union[str, Any]					       = (
								    ResNetShortCut(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,stride=SCREAMING_SNAKE_CASE__) if should_apply_shortcut else nn.Identity()
								)
								__lowerCamelCase				:				int					       = nn.Sequential(
								    ResNetConvLayer(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,stride=SCREAMING_SNAKE_CASE__)   ,ResNetConvLayer(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,activation=SCREAMING_SNAKE_CASE__)   ,)
								__lowerCamelCase				:				str					       = ACTaFN[activation]


							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			str):
								__lowerCamelCase				:				List[Any]					       = hidden_state
								__lowerCamelCase				:				Dict					       = self.layer(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[int]					       = self.shortcut(SCREAMING_SNAKE_CASE__)
								hidden_state += residual
								__lowerCamelCase				:				List[str]					       = self.activation(SCREAMING_SNAKE_CASE__)
								return hidden_state






class        A_		(       nn.Module				):
							def __init__(		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int = 1   ,SCREAMING_SNAKE_CASE__		:			str = "relu"   ,SCREAMING_SNAKE_CASE__		:			int = 4):
								super().__init__()
								__lowerCamelCase				:				List[str]					       = in_channels != out_channels or stride != 1
								__lowerCamelCase				:				Dict					       = out_channels // reduction
								__lowerCamelCase				:				Any					       = (
								    ResNetShortCut(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,stride=SCREAMING_SNAKE_CASE__) if should_apply_shortcut else nn.Identity()
								)
								__lowerCamelCase				:				Dict					       = nn.Sequential(
								    ResNetConvLayer(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,kernel_size=1)   ,ResNetConvLayer(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,stride=SCREAMING_SNAKE_CASE__)   ,ResNetConvLayer(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,kernel_size=1   ,activation=SCREAMING_SNAKE_CASE__)   ,)
								__lowerCamelCase				:				str					       = ACTaFN[activation]


							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			Optional[int]):
								__lowerCamelCase				:				List[Any]					       = hidden_state
								__lowerCamelCase				:				List[Any]					       = self.layer(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				int					       = self.shortcut(SCREAMING_SNAKE_CASE__)
								hidden_state += residual
								__lowerCamelCase				:				List[Any]					       = self.activation(SCREAMING_SNAKE_CASE__)
								return hidden_state






class        A_		(       nn.Module				):
							def __init__(		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			ResNetConfig   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int = 2   ,SCREAMING_SNAKE_CASE__		:			int = 2   ,):
								super().__init__()

								__lowerCamelCase				:				List[str]					       = ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

								__lowerCamelCase				:				Optional[Any]					       = nn.Sequential(
								    # downsampling is done in the first layer with stride of 2
								    layer(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,stride=SCREAMING_SNAKE_CASE__   ,activation=config.hidden_act)   ,*[layer(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,activation=config.hidden_act) for _ in range(depth - 1)]   ,)


							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			Tensor):
								__lowerCamelCase				:				Optional[int]					       = input
								for layer in self.layers:
									__lowerCamelCase				:				Any					       = layer(SCREAMING_SNAKE_CASE__)
								return hidden_state






class        A_		(       nn.Module				):
							def __init__(		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			ResNetConfig):
								super().__init__()
								__lowerCamelCase				:				Tuple					       = nn.ModuleList([])
								# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
								self.stages.append(
								    ResNetStage(
								        SCREAMING_SNAKE_CASE__   ,config.embedding_size   ,config.hidden_sizes[0]   ,stride=2 if config.downsample_in_first_stage else 1   ,depth=config.depths[0]   ,))
								__lowerCamelCase				:				Union[str, Any]					       = zip(config.hidden_sizes   ,config.hidden_sizes[1:])
								for (in_channels, out_channels), depth in zip(SCREAMING_SNAKE_CASE__   ,config.depths[1:]):
									self.stages.append(ResNetStage(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,depth=SCREAMING_SNAKE_CASE__))


							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			Tensor   ,SCREAMING_SNAKE_CASE__		:			bool = False   ,SCREAMING_SNAKE_CASE__		:			bool = True):
								__lowerCamelCase				:				str					       = () if output_hidden_states else None

								for stage_module in self.stages:
									if output_hidden_states:
										__lowerCamelCase				:				Dict					       = hidden_states + (hidden_state,)

									__lowerCamelCase				:				str					       = stage_module(SCREAMING_SNAKE_CASE__)

								if output_hidden_states:
									__lowerCamelCase				:				List[str]					       = hidden_states + (hidden_state,)

								if not return_dict:
									return tuple(v for v in [hidden_state, hidden_states] if v is not None)

								return BaseModelOutputWithNoAttention(
								    last_hidden_state=SCREAMING_SNAKE_CASE__   ,hidden_states=SCREAMING_SNAKE_CASE__   ,)






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  int						      =      ResNetConfig
							_UpperCAmelCase    :  Union[str, Any]						      =      '''resnet'''
							_UpperCAmelCase    :  List[str]						      =      '''pixel_values'''
							_UpperCAmelCase    :  Optional[int]						      =      True
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]):
								if isinstance(SCREAMING_SNAKE_CASE__   ,nn.Convad):
									nn.init.kaiming_normal_(module.weight   ,mode='fan_out'   ,nonlinearity='relu')
								elif isinstance(SCREAMING_SNAKE_CASE__   ,(nn.BatchNormad, nn.GroupNorm)):
									nn.init.constant_(module.weight   ,1)
									nn.init.constant_(module.bias   ,0)


							def     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			int=False):
								if isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__):
									__lowerCamelCase				:				Tuple					       = value
a		=r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

a		=r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""






@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''			,							SCREAMING_SNAKE_CASE			,							)
class        A_		(       SCREAMING_SNAKE_CASE				):
							def __init__(		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			int):
								super().__init__(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Tuple					       = config
								__lowerCamelCase				:				Tuple					       = ResNetEmbeddings(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[str]					       = ResNetEncoder(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Dict					       = nn.AdaptiveAvgPoolad((1, 1))
								# Initialize weights and apply final processing
								self.post_init()


							@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC   ,output_type=SCREAMING_SNAKE_CASE__   ,config_class=_CONFIG_FOR_DOC   ,modality='vision'   ,expected_output=_EXPECTED_OUTPUT_SHAPE   ,)
							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			Tensor   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None):
								__lowerCamelCase				:				List[str]					       = (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								__lowerCamelCase				:				Tuple					       = return_dict if return_dict is not None else self.config.use_return_dict

								__lowerCamelCase				:				List[Any]					       = self.embedder(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				int					       = self.encoder(
								    SCREAMING_SNAKE_CASE__   ,output_hidden_states=SCREAMING_SNAKE_CASE__   ,return_dict=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Dict					       = encoder_outputs[0]

								__lowerCamelCase				:				Dict					       = self.pooler(SCREAMING_SNAKE_CASE__)

								if not return_dict:
									return (last_hidden_state, pooled_output) + encoder_outputs[1:]

								return BaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=SCREAMING_SNAKE_CASE__   ,pooler_output=SCREAMING_SNAKE_CASE__   ,hidden_states=encoder_outputs.hidden_states   ,)






@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''			,							SCREAMING_SNAKE_CASE			,							)
class        A_		(       SCREAMING_SNAKE_CASE				):
							def __init__(		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			int):
								super().__init__(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				int					       = config.num_labels
								__lowerCamelCase				:				Optional[int]					       = ResNetModel(SCREAMING_SNAKE_CASE__)
								# classification head
								__lowerCamelCase				:				Optional[int]					       = nn.Sequential(
								    nn.Flatten()   ,nn.Linear(config.hidden_sizes[-1]   ,config.num_labels) if config.num_labels > 0 else nn.Identity()   ,)
								# initialize weights and apply final processing
								self.post_init()


							@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT   ,output_type=SCREAMING_SNAKE_CASE__   ,config_class=_CONFIG_FOR_DOC   ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,)
							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[torch.FloatTensor] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[torch.LongTensor] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,):
								__lowerCamelCase				:				List[str]					       = return_dict if return_dict is not None else self.config.use_return_dict

								__lowerCamelCase				:				Any					       = self.resnet(SCREAMING_SNAKE_CASE__   ,output_hidden_states=SCREAMING_SNAKE_CASE__   ,return_dict=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				int					       = outputs.pooler_output if return_dict else outputs[1]

								__lowerCamelCase				:				List[Any]					       = self.classifier(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Tuple					       = None

								if labels is not None:
									if self.config.problem_type is None:
										if self.num_labels == 1:
											__lowerCamelCase				:				Optional[int]					       = 'regression'
										elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
											__lowerCamelCase				:				Tuple					       = 'single_label_classification'
										else:
											__lowerCamelCase				:				Any					       = 'multi_label_classification'
									if self.config.problem_type == "regression":
										__lowerCamelCase				:				List[str]					       = MSELoss()
										if self.num_labels == 1:
											__lowerCamelCase				:				Tuple					       = loss_fct(logits.squeeze()   ,labels.squeeze())
										else:
											__lowerCamelCase				:				str					       = loss_fct(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
									elif self.config.problem_type == "single_label_classification":
										__lowerCamelCase				:				Tuple					       = CrossEntropyLoss()
										__lowerCamelCase				:				List[Any]					       = loss_fct(logits.view(-1   ,self.num_labels)   ,labels.view(-1))
									elif self.config.problem_type == "multi_label_classification":
										__lowerCamelCase				:				str					       = BCEWithLogitsLoss()
										__lowerCamelCase				:				Optional[Any]					       = loss_fct(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)

								if not return_dict:
									__lowerCamelCase				:				Optional[Any]					       = (logits,) + outputs[2:]
									return (loss,) + output if loss is not None else output

								return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE__   ,logits=SCREAMING_SNAKE_CASE__   ,hidden_states=outputs.hidden_states)






@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''			,							SCREAMING_SNAKE_CASE			,							)
class        A_		(       SCREAMING_SNAKE_CASE			,							SCREAMING_SNAKE_CASE				):
							def __init__(		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Any):
								super().__init__(SCREAMING_SNAKE_CASE__)
								super()._init_backbone(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				int					       = [config.embedding_size] + config.hidden_sizes
								__lowerCamelCase				:				List[str]					       = ResNetEmbeddings(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[str]					       = ResNetEncoder(SCREAMING_SNAKE_CASE__)

								# initialize weights and apply final processing
								self.post_init()


							@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__)
							@replace_return_docstrings(output_type=SCREAMING_SNAKE_CASE__   ,config_class=_CONFIG_FOR_DOC)
							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Tensor   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None):
								__lowerCamelCase				:				Tuple					       = return_dict if return_dict is not None else self.config.use_return_dict
								__lowerCamelCase				:				Union[str, Any]					       = (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)

								__lowerCamelCase				:				int					       = self.embedder(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				str					       = self.encoder(SCREAMING_SNAKE_CASE__   ,output_hidden_states=SCREAMING_SNAKE_CASE__   ,return_dict=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Tuple					       = outputs.hidden_states

								__lowerCamelCase				:				Optional[int]					       = ()
								for idx, stage in enumerate(self.stage_names):
									if stage in self.out_features:
										feature_maps += (hidden_states[idx],)

								if not return_dict:
									__lowerCamelCase				:				Tuple					       = (feature_maps,)
									if output_hidden_states:
										output += (outputs.hidden_states,)
									return output

								return BackboneOutput(
								    feature_maps=SCREAMING_SNAKE_CASE__   ,hidden_states=outputs.hidden_states if output_hidden_states else None   ,attentions=SCREAMING_SNAKE_CASE__   ,)











from typing import List, Optional, Union

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


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    'huggingface/informer-tourism-monthly': (
        'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Optional[int]   		=		"informer"
   a						:							Tuple   		=		{
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
       "num_hidden_layers": "encoder_layers",
   }




   def __init__(    self,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = "student_t",		__magic_name__ = "nll",		__magic_name__ = 1,		__magic_name__ = None,		__magic_name__ = "mean",		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = 64,		__magic_name__ = 32,		__magic_name__ = 32,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = True,		__magic_name__ = "gelu",		__magic_name__ = 0.05,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 100,		__magic_name__ = 0.02,		__magic_name__=True,		__magic_name__ = "prob",		__magic_name__ = 5,		__magic_name__ = True,		**__magic_name__,		) ->      Optional[int]:






       """simple docstring"""
       # time series specific configuration
       UpperCamelCase__							:				List[Any]          =      prediction_length
       UpperCamelCase__							:				Any          =      context_length or prediction_length
       UpperCamelCase__							:				Optional[int]          =      distribution_output
       UpperCamelCase__							:				Union[str, Any]          =      loss
       UpperCamelCase__							:				Optional[Any]          =      input_size
       UpperCamelCase__							:				Dict          =      num_time_features
       UpperCamelCase__							:				Dict          =      lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
       UpperCamelCase__							:				Optional[int]          =      scaling
       UpperCamelCase__							:				Any          =      num_dynamic_real_features
       UpperCamelCase__							:				Optional[int]          =      num_static_real_features
       UpperCamelCase__							:				Optional[Any]          =      num_static_categorical_features

       # set cardinality
       if cardinality and num_static_categorical_features > 0:
           if len(__magic_name__							) != num_static_categorical_features:
               raise ValueError(
                   '''The cardinality should be a list of the same length as `num_static_categorical_features`'''							)
           UpperCamelCase__							:				str          =      cardinality
       else:
           UpperCamelCase__							:				int          =      [0]

       # set embedding_dimension
       if embedding_dimension and num_static_categorical_features > 0:
           if len(__magic_name__							) != num_static_categorical_features:
               raise ValueError(
                   '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''							)
           UpperCamelCase__							:				Any          =      embedding_dimension
       else:
           UpperCamelCase__							:				Union[str, Any]          =      [min(50,		(cat + 1) // 2							) for cat in self.cardinality]

       UpperCamelCase__							:				Optional[Any]          =      num_parallel_samples

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

       UpperCamelCase__							:				Optional[Any]          =      dropout
       UpperCamelCase__							:				List[Any]          =      attention_dropout
       UpperCamelCase__							:				Any          =      activation_dropout
       UpperCamelCase__							:				Optional[int]          =      encoder_layerdrop
       UpperCamelCase__							:				Union[str, Any]          =      decoder_layerdrop

       UpperCamelCase__							:				Tuple          =      activation_function
       UpperCamelCase__							:				List[str]          =      init_std

       UpperCamelCase__							:				int          =      use_cache

       # Informer
       UpperCamelCase__							:				Optional[int]          =      attention_type
       UpperCamelCase__							:				Optional[int]          =      sampling_factor
       UpperCamelCase__							:				Dict          =      distil

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




   @property
   def       UpperCamelCase__  (    self							) ->      int:






       """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
       )

