
from typing import List, Optional, Union

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


_SCREAMING_SNAKE_CASE    :      List[str]          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Dict          =			{
    '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  UpperCamelCase__		(    _snake_case   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				'time_series_transformer'
	_lowerCAmelCase							       =				{
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'encoder_attention_heads',
	    'num_hidden_layers': 'encoder_layers',
	}





	def __init__(			self				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = "student_t"				,       UpperCamelCase__ = "nll"				,       UpperCamelCase__ = 1				,       UpperCamelCase__ = [1, 2, 3, 4, 5, 6, 7]				,       UpperCamelCase__ = "mean"				,       UpperCamelCase__ = 0				,       UpperCamelCase__ = 0				,       UpperCamelCase__ = 0				,       UpperCamelCase__ = 0				,       UpperCamelCase__ = None				,       UpperCamelCase__ = None				,       UpperCamelCase__ = 32				,       UpperCamelCase__ = 32				,       UpperCamelCase__ = 2				,       UpperCamelCase__ = 2				,       UpperCamelCase__ = 2				,       UpperCamelCase__ = 2				,       UpperCamelCase__ = True				,       UpperCamelCase__ = "gelu"				,       UpperCamelCase__ = 64				,       UpperCamelCase__ = 0.1				,       UpperCamelCase__ = 0.1				,       UpperCamelCase__ = 0.1				,       UpperCamelCase__ = 0.1				,       UpperCamelCase__ = 0.1				,       UpperCamelCase__ = 100				,       UpperCamelCase__ = 0.0_2				,       UpperCamelCase__=True				,       **UpperCamelCase__				,       ):
					A__  :	str         =							prediction_length
					A__  :	Union[str, Any]         =							context_length or prediction_length
					A__  :	Tuple         =							distribution_output
					A__  :	Optional[Any]         =							loss
					A__  :	Dict         =							input_size
					A__  :	Dict         =							num_time_features
					A__  :	Dict         =							lags_sequence
					A__  :	int         =							scaling
					A__  :	int         =							num_dynamic_real_features
					A__  :	Tuple         =							num_static_real_features
					A__  :	int         =							num_static_categorical_features
					if cardinality and num_static_categorical_features > 0:
									if len(UpperCamelCase__		) != num_static_categorical_features:
													raise ValueError(
													    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''		)
									A__  :	Tuple         =							cardinality
					else:
									A__  :	Optional[int]         =							[0]
					if embedding_dimension and num_static_categorical_features > 0:
									if len(UpperCamelCase__		) != num_static_categorical_features:
													raise ValueError(
													    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''		)
									A__  :	List[str]         =							embedding_dimension
					else:
									A__  :	Optional[Any]         =							[min(50				,       (cat + 1) // 2		) for cat in self.cardinality]
					A__  :	Tuple         =							num_parallel_samples

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

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

					A__  :	int         =							activation_function
					A__  :	List[Any]         =							init_std

					A__  :	Tuple         =							use_cache

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





	@property
	def   __snake_case				(			self		):
					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
					)

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
					from .pipeline_kandinsky import KandinskyPipeline
					from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
					from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
					from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
					from .text_encoder import MultilingualCLIP