




"""simple docstring"""





import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class  __lowercase    (     utils.Parser		):


				'''simple docstring'''


				_lowerCAmelCase		     ="halfcheetah-medium-expert-v2"
				_lowerCAmelCase		     ="config.offline"







def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__     ,		snake_case__    :   int							     =   utils.load_model(lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     epoch=lowerCamelCase ,     device=lowerCamelCase						)
						snake_case__    :   str							     =   TrajectoryTransformerModel(gpt.config						)

						trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()						)
						snake_case__    :   Any							     =   gpt.pos_emb
						trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()						)
						trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()						)
						trajectory_transformer.head.load_state_dict(gpt.head.state_dict()						)

						for i, block in enumerate(gpt.blocks						):
												trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()						)
												trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()						)
												trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()						)

												trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()						)
												trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()						)
												trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()						)
												trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()						)

						torch.save(trajectory_transformer.state_dict() ,     '''pytorch_model.bin'''						)


if __name__ == "__main__":
							_lowercase		 =      Parser().parse_args("""plan""")
							convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
							    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
							)








"""simple docstring"""





import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


_lowercase		 =      logging.get_logger(__name__)  # pylint: disable=invalid-name

def 						lowerCAmelCase							(	self ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Optional[Any]							     =   image.to(device=lowerCamelCase						)
						snake_case__    :   List[Any]							     =   image  # take image as image_embeddings
						snake_case__    :   Tuple							     =   image_embeddings.unsqueeze(1						)

						# duplicate image embeddings for each generation per prompt, using mps friendly method
						snake_case__     ,		snake_case__     ,		snake_case__    :   int							     =   image_embeddings.shape
						snake_case__    :   List[Any]							     =   image_embeddings.repeat(1 ,     lowerCamelCase ,     1						)
						snake_case__    :   int							     =   image_embeddings.view(bs_embed * num_images_per_prompt ,     lowerCamelCase ,     -1						)

						if do_classifier_free_guidance:
												snake_case__    :   Tuple							     =   torch.zeros_like(lowerCamelCase						)

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												snake_case__    :   Any							     =   torch.cat([uncond_embeddings, image_embeddings]						)

						return image_embeddings

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				def __init__(				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       PriorTransformer ,SCREAMING_SNAKE_CASE_				:       CLIPTokenizer ,SCREAMING_SNAKE_CASE_				:       CLIPTextModelWithProjection ,SCREAMING_SNAKE_CASE_				:       UnCLIPScheduler ,SCREAMING_SNAKE_CASE_				:       Optional[dict] = None ,):
										super().__init__()

										snake_case__    :   Optional[Any]							     =   {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

										snake_case__    :   List[Any]							     =   decoder_pipe_kwargs.get('''torch_dtype''' ,SCREAMING_SNAKE_CASE_ ) or prior.dtype

										snake_case__    :   str							     =   StableDiffusionImageVariationPipeline.from_pretrained(
										    '''lambdalabs/sd-image-variations-diffusers''' ,**SCREAMING_SNAKE_CASE_ )

										# replace `_encode_image` method
										snake_case__    :   Tuple							     =   types.MethodType(_encode_image ,self.decoder_pipe )

										self.register_modules(
										    prior=SCREAMING_SNAKE_CASE_ ,tokenizer=SCREAMING_SNAKE_CASE_ ,text_encoder=SCREAMING_SNAKE_CASE_ ,prior_scheduler=SCREAMING_SNAKE_CASE_ ,)

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Optional[Union[CLIPTextModelOutput, Tuple]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Tensor] = None ,):
										if text_model_output is None:
																snake_case__    :   str							     =   len(SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else 1
																# get prompt text embeddings
																snake_case__    :   Dict							     =   self.tokenizer(
																    SCREAMING_SNAKE_CASE_ ,padding='''max_length''' ,max_length=self.tokenizer.model_max_length ,return_tensors='''pt''' ,)
																snake_case__    :   List[Any]							     =   text_inputs.input_ids
																snake_case__    :   List[str]							     =   text_inputs.attention_mask.bool().to(SCREAMING_SNAKE_CASE_ )

																if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																						snake_case__    :   Optional[int]							     =   self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
																						logger.warning(
																						    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																						    F''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
																						snake_case__    :   str							     =   text_input_ids[:, : self.tokenizer.model_max_length]

																snake_case__    :   Tuple							     =   self.text_encoder(text_input_ids.to(SCREAMING_SNAKE_CASE_ ) )

																snake_case__    :   Any							     =   text_encoder_output.text_embeds
																snake_case__    :   List[Any]							     =   text_encoder_output.last_hidden_state

										else:
																snake_case__    :   List[Any]							     =   text_model_output[0].shape[0]
																snake_case__     ,		snake_case__    :   List[Any]							     =   text_model_output[0], text_model_output[1]
																snake_case__    :   Tuple							     =   text_attention_mask

										snake_case__    :   Tuple							     =   text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )
										snake_case__    :   Optional[int]							     =   text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )
										snake_case__    :   Optional[int]							     =   text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )

										if do_classifier_free_guidance:
																snake_case__    :   Optional[Any]							     =   [''''''] * batch_size

																snake_case__    :   str							     =   self.tokenizer(
																    SCREAMING_SNAKE_CASE_ ,padding='''max_length''' ,max_length=self.tokenizer.model_max_length ,truncation=SCREAMING_SNAKE_CASE_ ,return_tensors='''pt''' ,)
																snake_case__    :   Union[str, Any]							     =   uncond_input.attention_mask.bool().to(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Tuple							     =   self.text_encoder(uncond_input.input_ids.to(SCREAMING_SNAKE_CASE_ ) )

																snake_case__    :   Optional[Any]							     =   uncond_embeddings_text_encoder_output.text_embeds
																snake_case__    :   Optional[int]							     =   uncond_embeddings_text_encoder_output.last_hidden_state

																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

																snake_case__    :   Tuple							     =   uncond_embeddings.shape[1]
																snake_case__    :   int							     =   uncond_embeddings.repeat(1 ,SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Optional[int]							     =   uncond_embeddings.view(batch_size * num_images_per_prompt ,SCREAMING_SNAKE_CASE_ )

																snake_case__    :   Tuple							     =   uncond_text_encoder_hidden_states.shape[1]
																snake_case__    :   int							     =   uncond_text_encoder_hidden_states.repeat(1 ,SCREAMING_SNAKE_CASE_ ,1 )
																snake_case__    :   Optional[Any]							     =   uncond_text_encoder_hidden_states.view(
																    batch_size * num_images_per_prompt ,SCREAMING_SNAKE_CASE_ ,-1 )
																snake_case__    :   int							     =   uncond_text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )

																# done duplicates

																# For classifier free guidance, we need to do two forward passes.
																# Here we concatenate the unconditional and text embeddings into a single batch
																# to avoid doing two forward passes
																snake_case__    :   Dict							     =   torch.cat([uncond_embeddings, text_embeddings] )
																snake_case__    :   Dict							     =   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )

																snake_case__    :   Optional[int]							     =   torch.cat([uncond_text_mask, text_mask] )

										return text_embeddings, text_encoder_hidden_states, text_mask

				@property
				def  _lowerCamelCase   (				self				:       Dict ):
										if self.device != torch.device('''meta''' ) or not hasattr(self.prior ,'''_hf_hook''' ):
																return self.device
										for module in self.prior.modules():
																if (
																    hasattr(SCREAMING_SNAKE_CASE_ ,'''_hf_hook''' )
																    and hasattr(module._hf_hook ,'''execution_device''' )
																    and module._hf_hook.execution_device is not None
																):
																						return torch.device(module._hf_hook.execution_device )
										return self.device

				def  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Dict ):
										if latents is None:
																snake_case__    :   str							     =   randn_tensor(SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,device=SCREAMING_SNAKE_CASE_ ,dtype=SCREAMING_SNAKE_CASE_ )
										else:
																if latents.shape != shape:
																						raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
																snake_case__    :   Any							     =   latents.to(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Optional[Any]							     =   latents * scheduler.init_noise_sigma
										return latents

				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       Optional[Union[str, torch.device]] = None ):
										self.decoder_pipe.to(SCREAMING_SNAKE_CASE_ )
										super().to(SCREAMING_SNAKE_CASE_ )





				@torch.no_grad()
				def __call__(				self				:       str ,SCREAMING_SNAKE_CASE_				:       Optional[Union[str, List[str]]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,SCREAMING_SNAKE_CASE_				:       int = 1 ,SCREAMING_SNAKE_CASE_				:       int = 2_5 ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Generator] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.FloatTensor] = None ,SCREAMING_SNAKE_CASE_				:       Optional[Union[CLIPTextModelOutput, Tuple]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Tensor] = None ,SCREAMING_SNAKE_CASE_				:       float = 4.0 ,SCREAMING_SNAKE_CASE_				:       float = 8.0 ,SCREAMING_SNAKE_CASE_				:       int = 5_0 ,SCREAMING_SNAKE_CASE_				:       Optional[int] = 1 ,SCREAMING_SNAKE_CASE_				:       float = 0.0 ,SCREAMING_SNAKE_CASE_				:       Optional[str] = "pil" ,SCREAMING_SNAKE_CASE_				:       bool = True ,):
										if prompt is not None:
																if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																						snake_case__    :   Dict							     =   1
																elif isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																						snake_case__    :   List[Any]							     =   len(SCREAMING_SNAKE_CASE_ )
																else:
																						raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_ )}''' )
										else:
																snake_case__    :   Tuple							     =   text_model_output[0].shape[0]

										snake_case__    :   List[str]							     =   self._execution_device

										snake_case__    :   str							     =   batch_size * num_images_per_prompt

										snake_case__    :   List[str]							     =   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

										snake_case__     ,		snake_case__     ,		snake_case__    :   Tuple							     =   self._encode_prompt(
										    SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )

										# prior

										self.prior_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ,device=SCREAMING_SNAKE_CASE_ )
										snake_case__    :   str							     =   self.prior_scheduler.timesteps

										snake_case__    :   str							     =   self.prior.config.embedding_dim

										snake_case__    :   Optional[int]							     =   self.prepare_latents(
										    (batch_size, embedding_dim) ,text_embeddings.dtype ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,self.prior_scheduler ,)

										for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
																# expand the latents if we are doing classifier free guidance
																snake_case__    :   Tuple							     =   torch.cat([prior_latents] * 2 ) if do_classifier_free_guidance else prior_latents

																snake_case__    :   Optional[int]							     =   self.prior(
																    SCREAMING_SNAKE_CASE_ ,timestep=SCREAMING_SNAKE_CASE_ ,proj_embedding=SCREAMING_SNAKE_CASE_ ,encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,attention_mask=SCREAMING_SNAKE_CASE_ ,).predicted_image_embedding

																if do_classifier_free_guidance:
																						snake_case__     ,		snake_case__    :   Dict							     =   predicted_image_embedding.chunk(2 )
																						snake_case__    :   Any							     =   predicted_image_embedding_uncond + prior_guidance_scale * (
																						    predicted_image_embedding_text - predicted_image_embedding_uncond
																						)

																if i + 1 == prior_timesteps_tensor.shape[0]:
																						snake_case__    :   str							     =   None
																else:
																						snake_case__    :   Tuple							     =   prior_timesteps_tensor[i + 1]

																snake_case__    :   List[Any]							     =   self.prior_scheduler.step(
																    SCREAMING_SNAKE_CASE_ ,timestep=SCREAMING_SNAKE_CASE_ ,sample=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,prev_timestep=SCREAMING_SNAKE_CASE_ ,).prev_sample

										snake_case__    :   Union[str, Any]							     =   self.prior.post_process_latents(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Any							     =   prior_latents

										snake_case__    :   Dict							     =   self.decoder_pipe(
										    image=SCREAMING_SNAKE_CASE_ ,height=SCREAMING_SNAKE_CASE_ ,width=SCREAMING_SNAKE_CASE_ ,num_inference_steps=SCREAMING_SNAKE_CASE_ ,guidance_scale=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,output_type=SCREAMING_SNAKE_CASE_ ,return_dict=SCREAMING_SNAKE_CASE_ ,num_images_per_prompt=SCREAMING_SNAKE_CASE_ ,eta=SCREAMING_SNAKE_CASE_ ,)
										return output


