






"""simple docstring"""
from typing import List, Optional, Union

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


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "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      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				"""time_series_transformer"""
	_A       :				Union[str, Any]    =				{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	    """num_hidden_layers""": """encoder_layers""",
	}





	def __init__(self     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = "student_t"     ,     lowerCAmelCase_ = "nll"     ,     lowerCAmelCase_ = 1     ,     lowerCAmelCase_ = [1, 2, 3, 4, 5, 6, 7]     ,     lowerCAmelCase_ = "mean"     ,     lowerCAmelCase_ = 0     ,     lowerCAmelCase_ = 0     ,     lowerCAmelCase_ = 0     ,     lowerCAmelCase_ = 0     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = 32     ,     lowerCAmelCase_ = 32     ,     lowerCAmelCase_ = 2     ,     lowerCAmelCase_ = 2     ,     lowerCAmelCase_ = 2     ,     lowerCAmelCase_ = 2     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = "gelu"     ,     lowerCAmelCase_ = 64     ,     lowerCAmelCase_ = 0.1     ,     lowerCAmelCase_ = 0.1     ,     lowerCAmelCase_ = 0.1     ,     lowerCAmelCase_ = 0.1     ,     lowerCAmelCase_ = 0.1     ,     lowerCAmelCase_ = 100     ,     lowerCAmelCase_ = 0.02     ,     lowerCAmelCase_=True     ,     **lowerCAmelCase_     ,     ):
		# time series specific configuration
		A_		:						Tuple	    =   prediction_length
		A_		:						Union[str, Any]	    =   context_length or prediction_length
		A_		:						Tuple	    =   distribution_output
		A_		:						int	    =   loss
		A_		:						Optional[int]	    =   input_size
		A_		:						Dict	    =   num_time_features
		A_		:						Optional[Any]	    =   lags_sequence
		A_		:						List[str]	    =   scaling
		A_		:						int	    =   num_dynamic_real_features
		A_		:						Union[str, Any]	    =   num_static_real_features
		A_		:						Tuple	    =   num_static_categorical_features
		if cardinality and num_static_categorical_features > 0:
			if len(lowerCAmelCase_							) != num_static_categorical_features:
				raise ValueError(
				    """The cardinality should be a list of the same length as `num_static_categorical_features`"""							)
			A_		:						List[str]	    =   cardinality
		else:
			A_		:						List[Any]	    =   [0]
		if embedding_dimension and num_static_categorical_features > 0:
			if len(lowerCAmelCase_							) != num_static_categorical_features:
				raise ValueError(
				    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""							)
			A_		:						Tuple	    =   embedding_dimension
		else:
			A_		:						int	    =   [min(50     ,     (cat + 1) // 2							) for cat in self.cardinality]
		A_		:						Optional[int]	    =   num_parallel_samples

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

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

		A_		:						str	    =   activation_function
		A_		:						Any	    =   init_std

		A_		:						Optional[Any]	    =   use_cache

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





	@property
	def    lowerCamelCase(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
		)









"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   OmegaConf.load(snake_case__					)
	A_		:						List[str]	    =   torch.load(snake_case__   ,   map_location="""cpu"""					)["""model"""]
	A_		:						Any	    =   list(state_dict.keys()					)

	# extract state_dict for VQVAE
	A_		:						Any	    =   {}
	A_		:						Union[str, Any]	    =   """first_stage_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						List[Any]	    =   state_dict[key]

    # extract state_dict for UNetLDM
	A_		:						Tuple	    =   {}
	A_		:						Dict	    =   """model.diffusion_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						Optional[Any]	    =   state_dict[key]

	A_		:						List[Any]	    =   config.model.params.first_stage_config.params
	A_		:						Tuple	    =   config.model.params.unet_config.params

	A_		:						Any	    =   VQModel(**snake_case__					).eval()
	vqvae.load_state_dict(snake_case__					)

	A_		:						Optional[int]	    =   UNetLDMModel(**snake_case__					).eval()
	unet.load_state_dict(snake_case__					)

	A_		:						List[str]	    =   DDIMScheduler(
	    timesteps=config.model.params.timesteps   ,   beta_schedule="""scaled_linear"""   ,   beta_start=config.model.params.linear_start   ,   beta_end=config.model.params.linear_end   ,   clip_sample=snake_case__   ,   )

	A_		:						str	    =   LDMPipeline(snake_case__   ,   snake_case__   ,   snake_case__					)
	pipeline.save_pretrained(snake_case__					)


if __name__ == "__main__":
							_lowerCAmelCase         =							argparse.ArgumentParser()
							parser.add_argument("--checkpoint_path", type=str, required=True)
							parser.add_argument("--config_path", type=str, required=True)
							parser.add_argument("--output_path", type=str, required=True)
							_lowerCAmelCase         =							parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)

