





"""simple docstring"""





from typing import List, Optional, Union

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


lowercase__				:Optional[int]				   =       logging.get_logger(__name__)

lowercase__				:Union[str, Any]				   =       {
    '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 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       List[str]         =       'time_series_transformer'
      _A     :       Optional[int]         =       {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
          'num_hidden_layers': 'encoder_layers',
      }

      def __init__(							self       :      List[str]						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      str = "student_t"						,				__lowercase       :      str = "nll"						,				__lowercase       :      int = 1						,				__lowercase       :      List[int] = [1, 2, 3, 4, 5, 6, 7]						,				__lowercase       :      Optional[Union[str, bool]] = "mean"						,				__lowercase       :      int = 0						,				__lowercase       :      int = 0						,				__lowercase       :      int = 0						,				__lowercase       :      int = 0						,				__lowercase       :      Optional[List[int]] = None						,				__lowercase       :      Optional[List[int]] = None						,				__lowercase       :      int = 32						,				__lowercase       :      int = 32						,				__lowercase       :      int = 2						,				__lowercase       :      int = 2						,				__lowercase       :      int = 2						,				__lowercase       :      int = 2						,				__lowercase       :      bool = True						,				__lowercase       :      str = "gelu"						,				__lowercase       :      int = 64						,				__lowercase       :      float = 0.1						,				__lowercase       :      float = 0.1						,				__lowercase       :      float = 0.1						,				__lowercase       :      float = 0.1						,				__lowercase       :      float = 0.1						,				__lowercase       :      int = 100						,				__lowercase       :      float = 0.0_2						,				__lowercase       :      List[str]=True						,				**__lowercase       :      Dict						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						prediction_length
        __UpperCAmelCase    :						Union[str, Any]       =						context_length or prediction_length
        __UpperCAmelCase    :						Optional[int]       =						distribution_output
        __UpperCAmelCase    :						List[Any]       =						loss
        __UpperCAmelCase    :						Optional[Any]       =						input_size
        __UpperCAmelCase    :						int       =						num_time_features
        __UpperCAmelCase    :						List[Any]       =						lags_sequence
        __UpperCAmelCase    :						Dict       =						scaling
        __UpperCAmelCase    :						Union[str, Any]       =						num_dynamic_real_features
        __UpperCAmelCase    :						Any       =						num_static_real_features
        __UpperCAmelCase    :						List[str]       =						num_static_categorical_features
        if cardinality 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`''' )
          __UpperCAmelCase    :						Union[str, Any]       =						cardinality
        else:
          __UpperCAmelCase    :						Tuple       =						[0]
        if embedding_dimension 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`''' )
          __UpperCAmelCase    :						List[Any]       =						embedding_dimension
        else:
          __UpperCAmelCase    :						Optional[Any]       =						[min(50						,				(cat + 1) // 2 ) for cat in self.cardinality]
        __UpperCAmelCase    :						Optional[int]       =						num_parallel_samples

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

        __UpperCAmelCase    :						Union[str, Any]       =						dropout
        __UpperCAmelCase    :						List[Any]       =						attention_dropout
        __UpperCAmelCase    :						str       =						activation_dropout
        __UpperCAmelCase    :						Tuple       =						encoder_layerdrop
        __UpperCAmelCase    :						List[Any]       =						decoder_layerdrop

        __UpperCAmelCase    :						int       =						activation_function
        __UpperCAmelCase    :						Optional[int]       =						init_std

        __UpperCAmelCase    :						str       =						use_cache

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







      @property
      def       A_					(							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"""





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						OmegaConf.load(UpperCAmelCase_  )
  __UpperCAmelCase    :						Union[str, Any]       =						torch.load(UpperCAmelCase_  ,			map_location='''cpu'''  )['''model''']
  __UpperCAmelCase    :						List[Any]       =						list(state_dict.keys()  )

  # extract state_dict for VQVAE
  __UpperCAmelCase    :						Dict       =						{}
  __UpperCAmelCase    :						Tuple       =						'''first_stage_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						state_dict[key]

    # extract state_dict for UNetLDM
  __UpperCAmelCase    :						Union[str, Any]       =						{}
  __UpperCAmelCase    :						Any       =						'''model.diffusion_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						int       =						state_dict[key]

  __UpperCAmelCase    :						Any       =						config.model.params.first_stage_config.params
  __UpperCAmelCase    :						Dict       =						config.model.params.unet_config.params

  __UpperCAmelCase    :						Tuple       =						VQModel(**UpperCAmelCase_  ).eval()
  vqvae.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						UNetLDMModel(**UpperCAmelCase_  ).eval()
  unet.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						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=UpperCAmelCase_  ,			)

  __UpperCAmelCase    :						Tuple       =						LDMPipeline(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  pipeline.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:str				   =       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)
  lowercase__				:Dict				   =       parser.parse_args()

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