


'''simple docstring'''






import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
__A					:  int											=      logging.get_logger("transformers.models.speecht5")

__A					:  Optional[Any]											=      {
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
__A					:  List[str]											=      {
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
__A					:  Union[str, Any]											=      {
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
__A					:  Tuple											=      {
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
__A					:  Any											=      {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
__A					:  int											=      {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
__A					:  Tuple											=      {
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
__A					:  str											=      {
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
__A					:  Optional[int]											=      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__A					:  Union[str, Any]											=      {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__A					:  int											=      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__A					:  Any											=      []
__A					:  int											=      [
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
__A					:  Union[str, Any]											=      IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
__A					:  str											=      IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
__A					:  Union[str, Any]											=      IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]







def 				UpperCamelCase_			(    A__					:   Tuple							,    A__					:   Union[str, Any]							,    A__					:   List[Any]							,    A__					:   List[str]							,    A__					:   List[str]					):
  '''simple docstring'''





  for attribute in key.split("""."""					):
    lowerCAmelCase_			:      Optional[int]			=   getattr(__snake_case							,    __snake_case					)

  if weight_type is not None:
    lowerCAmelCase_			:      Tuple			=   getattr(__snake_case							,    __snake_case					).shape
  else:
    lowerCAmelCase_			:      Optional[int]			=   hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
        f' {value.shape} for {full_name}'					)

  if weight_type == "weight":
    lowerCAmelCase_			:      List[str]			=   value
  elif weight_type == "weight_g":
    lowerCAmelCase_			:      int			=   value
  elif weight_type == "weight_v":
    lowerCAmelCase_			:      str			=   value
  elif weight_type == "bias":
    lowerCAmelCase_			:      Dict			=   value
  elif weight_type == "running_mean":
    lowerCAmelCase_			:      List[str]			=   value
  elif weight_type == "running_var":
    lowerCAmelCase_			:      Optional[int]			=   value
  elif weight_type == "num_batches_tracked":
    lowerCAmelCase_			:      str			=   value
  else:
    lowerCAmelCase_			:      Optional[int]			=   value

  logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'					)







def 				UpperCamelCase_			(    A__					:   str							,    A__					:   Optional[Any]					):
  '''simple docstring'''





  for key in ignore_keys:
    if key.endswith(""".*"""					):
      if name.startswith(key[:-1]					):
        return True
    elif ".*." in key:
      lowerCAmelCase_,							lowerCAmelCase_			:      Tuple			=   key.split(""".*."""					)
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False







def 				UpperCamelCase_			(    A__					:   List[Any]							,    A__					:   Optional[Any]							,    A__					:   Optional[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Dict			=   []

  if task == "s2t":
    lowerCAmelCase_			:      List[Any]			=   hf_model.speechta.encoder.prenet.feature_encoder
    lowerCAmelCase_			:      List[str]			=   MAPPING_S2T
    lowerCAmelCase_			:      Optional[Any]			=   IGNORE_KEYS_S2T
  elif task == "t2s":
    lowerCAmelCase_			:      Tuple			=   None
    lowerCAmelCase_			:      Optional[int]			=   MAPPING_T2S
    lowerCAmelCase_			:      Optional[Any]			=   IGNORE_KEYS_T2S
  elif task == "s2s":
    lowerCAmelCase_			:      Union[str, Any]			=   hf_model.speechta.encoder.prenet.feature_encoder
    lowerCAmelCase_			:      Any			=   MAPPING_S2S
    lowerCAmelCase_			:      Optional[int]			=   IGNORE_KEYS_S2S
  else:
    raise ValueError(f'Unsupported task: {task}'					)

  for name, value in fairseq_dict.items():
    if should_ignore(__snake_case							,    __snake_case					):
      logger.info(f'{name} was ignored'					)
      continue

    lowerCAmelCase_			:      int			=   False
    if "conv_layers" in name:
      load_conv_layer(
          __snake_case							,    __snake_case							,    __snake_case							,    __snake_case							,    hf_model.config.feat_extract_norm == """group"""							,    )
      lowerCAmelCase_			:      Dict			=   True
    else:
      for key, mapped_key in MAPPING.items():
        # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

        if "*" in key:
          lowerCAmelCase_,							lowerCAmelCase_			:      Optional[Any]			=   key.split(""".*."""					)
          if prefix in name and suffix in name:
            lowerCAmelCase_			:      Tuple			=   suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          lowerCAmelCase_			:      Optional[int]			=   True
          if "*" in mapped_key:
            lowerCAmelCase_			:      Optional[int]			=   name.split(__snake_case					)[0].split("""."""					)[-2]
            lowerCAmelCase_			:      str			=   mapped_key.replace("""*"""							,    __snake_case					)
          if "weight_g" in name:
            lowerCAmelCase_			:      Tuple			=   """weight_g"""
          elif "weight_v" in name:
            lowerCAmelCase_			:      List[Any]			=   """weight_v"""
          elif "bias" in name:
            lowerCAmelCase_			:      Dict			=   """bias"""
          elif "weight" in name:
            lowerCAmelCase_			:      Tuple			=   """weight"""
          elif "running_mean" in name:
            lowerCAmelCase_			:      Union[str, Any]			=   """running_mean"""
          elif "running_var" in name:
            lowerCAmelCase_			:      Any			=   """running_var"""
          elif "num_batches_tracked" in name:
            lowerCAmelCase_			:      Union[str, Any]			=   """num_batches_tracked"""
          else:
            lowerCAmelCase_			:      Optional[Any]			=   None
          set_recursively(__snake_case							,    __snake_case							,    __snake_case							,    __snake_case							,    __snake_case					)
        continue
    if not is_used:
      unused_weights.append(__snake_case					)

  logger.warning(f'Unused weights: {unused_weights}'					)







def 				UpperCamelCase_			(    A__					:   List[str]							,    A__					:   str							,    A__					:   List[str]							,    A__					:   int							,    A__					:   Optional[Any]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Tuple			=   full_name.split("""conv_layers."""					)[-1]
  lowerCAmelCase_			:      Any			=   name.split("""."""					)
  lowerCAmelCase_			:      List[Any]			=   int(items[0]					)
  lowerCAmelCase_			:      Tuple			=   int(items[1]					)

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'					)
      lowerCAmelCase_			:      Optional[Any]			=   value
      logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'					)
      lowerCAmelCase_			:      List[str]			=   value
      logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'					)
      lowerCAmelCase_			:      List[str]			=   value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'					)
      lowerCAmelCase_			:      Dict			=   value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
  else:
    unused_weights.append(__snake_case					)







@torch.no_grad()
def 				UpperCamelCase_			(    A__					:   List[str]							,    A__					:   Optional[int]							,    A__					:   List[Any]							,    A__					:   str=None							,    A__					:   List[Any]=None							,    A__					:   List[str]=None							,    ):
  '''simple docstring'''





  if config_path is not None:
    lowerCAmelCase_			:      Tuple			=   SpeechTaConfig.from_pretrained(__snake_case					)
  else:
    lowerCAmelCase_			:      Optional[int]			=   SpeechTaConfig()

  if task == "s2t":
    lowerCAmelCase_			:      Optional[int]			=   config.max_text_positions
    lowerCAmelCase_			:      Any			=   SpeechTaForSpeechToText(__snake_case					)
  elif task == "t2s":
    lowerCAmelCase_			:      List[Any]			=   18_76
    lowerCAmelCase_			:      Union[str, Any]			=   6_00
    lowerCAmelCase_			:      Optional[Any]			=   config.max_speech_positions
    lowerCAmelCase_			:      Optional[int]			=   SpeechTaForTextToSpeech(__snake_case					)
  elif task == "s2s":
    lowerCAmelCase_			:      Optional[Any]			=   18_76
    lowerCAmelCase_			:      List[Any]			=   config.max_speech_positions
    lowerCAmelCase_			:      List[Any]			=   SpeechTaForSpeechToSpeech(__snake_case					)
  else:
    raise ValueError(f'Unknown task name: {task}'					)

  if vocab_path:
    lowerCAmelCase_			:      List[Any]			=   SpeechTaTokenizer(__snake_case							,    model_max_length=config.max_text_positions					)

    # Mask token behaves like a normal word, i.e. include the space before it
    lowerCAmelCase_			:      int			=   AddedToken("""<mask>"""							,    lstrip=__snake_case							,    rstrip=__snake_case					)
    lowerCAmelCase_			:      Optional[Any]			=   mask_token
    tokenizer.add_special_tokens({"""mask_token""": mask_token}					)
    tokenizer.add_tokens(["""<ctc_blank>"""]					)

  lowerCAmelCase_			:      Tuple			=   SpeechTaFeatureExtractor()
  lowerCAmelCase_			:      Optional[int]			=   SpeechTaProcessor(tokenizer=__snake_case							,    feature_extractor=__snake_case					)
  processor.save_pretrained(__snake_case					)

  lowerCAmelCase_			:      Any			=   torch.load(__snake_case					)
  recursively_load_weights(fairseq_checkpoint["""model"""]							,    __snake_case							,    __snake_case					)

  model.save_pretrained(__snake_case					)

  if repo_id:
    print("""Pushing to the hub..."""					)
    processor.push_to_hub(__snake_case					)
    model.push_to_hub(__snake_case					)


if __name__ == "__main__":
  __A					:  List[Any]											=      argparse.ArgumentParser()
  parser.add_argument(
      "--task",
      default="s2t",
      type=str,
      help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
  )
  parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
  parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
  )

  __A					:  Any											=      parser.parse_args()
  convert_speechta_checkpoint(
      args.task,
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.vocab_path,
      args.push_to_hub,
  )





"""simple docstring"""





import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler






class     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		__a				,		__a				,		__a				,		__a				,		)       ->       Optional[int]:






							'''simple docstring'''
							super().__init__()
							_UpperCamelCase							 =  value_function
							_UpperCamelCase							 =  unet
							_UpperCamelCase							 =  scheduler
							_UpperCamelCase							 =  env
							_UpperCamelCase							 =  env.get_dataset()
							_UpperCamelCase							 =  {}
							for key in self.data.keys():
												try:
																	_UpperCamelCase							 =  self.data[key].mean()
												except:  # noqa: E722
																	pass
							_UpperCamelCase							 =  {}
							for key in self.data.keys():
												try:
																	_UpperCamelCase							 =  self.data[key].std()
												except:  # noqa: E722
																	pass
							_UpperCamelCase							 =  env.observation_space.shape[0]
							_UpperCamelCase							 =  env.action_space.shape[0]



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       int:






							'''simple docstring'''
							return (x_in - self.means[key]) / self.stds[key]



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       List[str]:






							'''simple docstring'''
							return x_in * self.stds[key] + self.means[key]



		def 						UpperCAmelCase							(					self				,		__a)       ->       Union[str, Any]:






							'''simple docstring'''
							if type(__a) is dict:
												return {k: self.to_torch(__a) for k, v in x_in.items()}
							elif torch.is_tensor(__a):
												return x_in.to(self.unet.device)
							return torch.tensor(__a				,		device=self.unet.device)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       str:






							'''simple docstring'''
							for key, val in cond.items():
												_UpperCamelCase							 =  val.clone()
							return x_in



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  x.shape[0]
							_UpperCamelCase							 =  None
							for i in tqdm.tqdm(self.scheduler.timesteps):
												# create batch of timesteps to pass into model
												_UpperCamelCase							 =  torch.full((batch_size,)				,		__a				,		device=self.unet.device				,		dtype=torch.long)
												for _ in range(__a):
																	with torch.enable_grad():
																						x.requires_grad_()

																						# permute to match dimension for pre-trained models
																						_UpperCamelCase							 =  self.value_function(x.permute(0				,		2				,		1)				,		__a).sample
																						_UpperCamelCase							 =  torch.autograd.grad([y.sum()]				,		[x])[0]

																						_UpperCamelCase							 =  self.scheduler._get_variance(__a)
																						_UpperCamelCase							 =  torch.exp(0.5 * posterior_variance)
																						_UpperCamelCase							 =  model_std * grad

																	_UpperCamelCase							 =  0
																	_UpperCamelCase							 =  x.detach()
																	_UpperCamelCase							 =  x + scale * grad
																	_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)

												_UpperCamelCase							 =  self.unet(x.permute(0				,		2				,		1)				,		__a).sample.permute(0				,		2				,		1)

												# TODO: verify deprecation of this kwarg
												_UpperCamelCase							 =  self.scheduler.step(__a				,		__a				,		__a				,		predict_epsilon=__a)['''prev_sample''']

												# apply conditions to the trajectory (set the initial state)
												_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)
												_UpperCamelCase							 =  self.to_torch(__a)
							return x, y




		def __call__(					self				,		__a				,		__a=64				,		__a=32				,		__a=2				,		__a=0.1)       ->       Optional[Any]:






							'''simple docstring'''
							# normalize the observations and create  batch dimension
							_UpperCamelCase							 =  self.normalize(__a				,		'''observations''')
							_UpperCamelCase							 =  obs[None].repeat(__a				,		axis=0)

							_UpperCamelCase							 =  {0: self.to_torch(__a)}
							_UpperCamelCase							 =  (batch_size, planning_horizon, self.state_dim + self.action_dim)

							# generate initial noise and apply our conditions (to make the trajectories start at current state)
							_UpperCamelCase							 =  randn_tensor(__a				,		device=self.unet.device)
							_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)
							_UpperCamelCase							 =  self.to_torch(__a)

							# run the diffusion process
							_UpperCamelCase ,  _UpperCamelCase							 =  self.run_diffusion(__a				,		__a				,		__a				,		__a)

							# sort output trajectories by value
							_UpperCamelCase							 =  y.argsort(0				,		descending=__a).squeeze()
							_UpperCamelCase							 =  x[sorted_idx]
							_UpperCamelCase							 =  sorted_values[:, :, : self.action_dim]
							_UpperCamelCase							 =  actions.detach().cpu().numpy()
							_UpperCamelCase							 =  self.de_normalize(__a				,		key='''actions''')

							# select the action with the highest value
							if y is not None:
												_UpperCamelCase							 =  0
							else:
												# if we didn't run value guiding, select a random action
												_UpperCamelCase							 =  np.random.randint(0				,		__a)

							_UpperCamelCase							 =  denorm_actions[selected_index, 0]
							return denorm_actions






