

from typing import List, Optional

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


lowerCAmelCase__		      =       logging.get_logger(__name__)

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

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				'autoformer'
			__lowerCamelCase       				=				{
			    'hidden_size': 'd_model',
			    'num_attention_heads': 'encoder_attention_heads',
			    'num_hidden_layers': 'encoder_layers',
			}


			def __init__(			self					,					lowercase = None					,					lowercase = None					,					lowercase = "student_t"					,					lowercase = "nll"					,					lowercase = 1					,					lowercase = [1, 2, 3, 4, 5, 6, 7]					,					lowercase = True					,					lowercase = 0					,					lowercase = 0					,					lowercase = 0					,					lowercase = 0					,					lowercase = None					,					lowercase = None					,					lowercase = 64					,					lowercase = 2					,					lowercase = 2					,					lowercase = 2					,					lowercase = 2					,					lowercase = 32					,					lowercase = 32					,					lowercase = "gelu"					,					lowercase = 0.1					,					lowercase = 0.1					,					lowercase = 0.1					,					lowercase = 0.1					,					lowercase = 0.1					,					lowercase = 100					,					lowercase = 0.02					,					lowercase = True					,					lowercase=True					,					lowercase = 10					,					lowercase = 25					,					lowercase = 3					,					**lowercase					,					)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	prediction_length
							A__						=	context_length if context_length is not None else prediction_length
							A__						=	distribution_output
							A__						=	loss
							A__						=	input_size
							A__						=	num_time_features
							A__						=	lags_sequence
							A__						=	scaling
							A__						=	num_dynamic_real_features
							A__						=	num_static_real_features
							A__						=	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`"				)
											A__						=	cardinality
							else:
											A__						=	[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`"				)
											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

							# Autoformer
							A__						=	label_length
							A__						=	moving_average
							A__						=	autocorrelation_factor

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


			@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
							)





"""simple docstring"""




__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(['''image'''])

__UpperCamelCase						=      frozenset(
    [
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''image'''])

__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''image''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset(
    [
        # Text guided image variation with an image mask
        '''prompt''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''image''', '''mask_image''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset(
    [
        # image variation with an image mask
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''image''', '''mask_image'''])

__UpperCamelCase						=      frozenset(
    [
        '''example_image''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''example_image''', '''image''', '''mask_image'''])

__UpperCamelCase						=      frozenset(['''class_labels'''])

__UpperCamelCase						=      frozenset(['''class_labels'''])

__UpperCamelCase						=      frozenset(['''batch_size'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(['''batch_size'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''audio_length_in_s''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''negative_prompt'''])
__UpperCamelCase						=      frozenset(['''input_tokens'''])

__UpperCamelCase						=      frozenset(['''input_tokens'''])


