


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)







from typing import List, Optional, Union

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


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

_lowercase       :      List[str]	=	{
    "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       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'informer'
				lowerCAmelCase 		= {
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				    'num_hidden_layers': 'encoder_layers',
				}

				def __init__(						self				,   a__ = None				,   a__ = None				,   a__ = "student_t"				,   a__ = "nll"				,   a__ = 1				,   a__ = None				,   a__ = "mean"				,   a__ = 0				,   a__ = 0				,   a__ = 0				,   a__ = 0				,   a__ = None				,   a__ = None				,   a__ = 64				,   a__ = 32				,   a__ = 32				,   a__ = 2				,   a__ = 2				,   a__ = 2				,   a__ = 2				,   a__ = True				,   a__ = "gelu"				,   a__ = 0.05				,   a__ = 0.1				,   a__ = 0.1				,   a__ = 0.1				,   a__ = 0.1				,   a__ = 100				,   a__ = 0.02				,   a__=True				,   a__ = "prob"				,   a__ = 5				,   a__ = True				,   **a__				,   )	->				Union[str, Any]:
					# time series specific configuration
					A								=				prediction_length
					A								=				context_length or prediction_length
					A								=				distribution_output
					A								=				loss
					A								=				input_size
					A								=				num_time_features
					A								=				lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
					A								=				scaling
					A								=				num_dynamic_real_features
					A								=				num_static_real_features
					A								=				num_static_categorical_features

					# set cardinality
					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`"""   )
						A								=				cardinality
					else:
						A								=				[0]

					# set embedding_dimension
					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`"""   )
						A								=				embedding_dimension
					else:
						A								=				[min(50				,   (cat + 1) // 2   ) for cat in self.cardinality]

					A								=				num_parallel_samples

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

					A								=				dropout
					A								=				attention_dropout
					A								=				activation_dropout
					A								=				encoder_layerdrop
					A								=				decoder_layerdrop

					A								=				activation_function
					A								=				init_std

					A								=				use_cache

					# Informer
					A								=				attention_type
					A								=				sampling_factor
					A								=				distil

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

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					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
					)



