"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

a							='\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase=8    )							-> int:




						'''simple docstring'''



						lowerCamelCase__ =height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						lowerCamelCase__ =width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase=512       ,   __lowerCAmelCase=512    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =pil_image.resize((w, h)       ,   resample=Image.BICUBIC       ,   reducing_gap=1    )
						lowerCamelCase__ =np.array(pil_image.convert("RGB"    )    )
						lowerCamelCase__ =arr.astype(np.floataa    ) / 127.5 - 1
						lowerCamelCase__ =np.transpose(__lowerCAmelCase       ,   [2, 0, 1]    )
						lowerCamelCase__ =torch.from_numpy(__lowerCAmelCase    ).unsqueeze(0    )
						return image






class        __UpperCAmelCase						(     __lowerCAmelCase     ):

				def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
										super().__init__()

										self.register_modules(
										    unet=_lowerCamelCase     ,			scheduler=_lowerCamelCase     ,			movq=_lowerCamelCase     ,			)
										lowerCamelCase__ =2 ** (len(self.movq.config.block_out_channels				) - 1)

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										# get the original timestep using init_timestep
										lowerCamelCase__ =min(int(num_inference_steps * strength				)     ,			_lowerCamelCase				)

										lowerCamelCase__ =max(num_inference_steps - init_timestep     ,			0				)
										lowerCamelCase__ =self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=None				):
										if not isinstance(_lowerCamelCase     ,			(torch.Tensor, PIL.Image.Image, list)				):
																raise ValueError(
																    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCamelCase				)}'''				)

										lowerCamelCase__ =image.to(device=_lowerCamelCase     ,			dtype=_lowerCamelCase				)

										lowerCamelCase__ =batch_size * num_images_per_prompt

										if image.shape[1] == 4:
																lowerCamelCase__ =image

										else:
																if isinstance(_lowerCamelCase     ,			_lowerCamelCase				) and len(_lowerCamelCase				) != batch_size:
																						raise ValueError(
																						    F'''You have passed a list of generators of length {len(_lowerCamelCase				)}, but requested an effective batch'''
																						    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''				)

																elif isinstance(_lowerCamelCase     ,			_lowerCamelCase				):
																						lowerCamelCase__ =[
																						    self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(_lowerCamelCase				)
																						]
																						lowerCamelCase__ =torch.cat(_lowerCamelCase     ,			dim=0				)
																else:
																						lowerCamelCase__ =self.movq.encode(_lowerCamelCase				).latent_dist.sample(_lowerCamelCase				)

																lowerCamelCase__ =self.movq.config.scaling_factor * init_latents

										lowerCamelCase__ =torch.cat([init_latents]     ,			dim=0				)

										lowerCamelCase__ =init_latents.shape
										lowerCamelCase__ =randn_tensor(_lowerCamelCase     ,			generator=_lowerCamelCase     ,			device=_lowerCamelCase     ,			dtype=_lowerCamelCase				)

										# get latents
										lowerCamelCase__ =self.scheduler.add_noise(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				)

										lowerCamelCase__ =init_latents

										return latents

				def 		_a     (				self     ,			_lowerCamelCase=0				):
										if is_accelerate_available():
																from accelerate import cpu_offload
										else:
																raise ImportError("Please install accelerate via `pip install accelerate`"				)

										lowerCamelCase__ =torch.device(F'''cuda:{gpu_id}'''				)

										lowerCamelCase__ =[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																						cpu_offload(_lowerCamelCase     ,			_lowerCamelCase				)

				def 		_a     (				self     ,			_lowerCamelCase=0				):
										if is_accelerate_available() and is_accelerate_version(">="     ,			"0.17.0.dev0"				):
																from accelerate import cpu_offload_with_hook
										else:
																raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."				)

										lowerCamelCase__ =torch.device(F'''cuda:{gpu_id}'''				)

										if self.device.type != "cpu":
																self.to("cpu"     ,			silence_dtype_warnings=_lowerCamelCase				)
																torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										lowerCamelCase__ =None
										for cpu_offloaded_model in [self.unet, self.movq]:
																lowerCamelCase__ ,				lowerCamelCase__ =cpu_offload_with_hook(_lowerCamelCase     ,			_lowerCamelCase     ,			prev_module_hook=_lowerCamelCase				)

										# We'll offload the last model manually.
										lowerCamelCase__ =hook

				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 		_a     (				self				):
										if not hasattr(self.unet     ,			"_hf_hook"				):
																return self.device
										for module in self.unet.modules():
																if (
																    hasattr(_lowerCamelCase     ,			"_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






				@torch.no_grad()
				@replace_example_docstring(_lowerCamelCase				)
				def __call__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = 512     ,			_lowerCamelCase = 512     ,			_lowerCamelCase = 100     ,			_lowerCamelCase = 4.0     ,			_lowerCamelCase = 0.3     ,			_lowerCamelCase = 1     ,			_lowerCamelCase = None     ,			_lowerCamelCase = "pil"     ,			_lowerCamelCase = True     ,			):
										lowerCamelCase__ =self._execution_device

										lowerCamelCase__ =guidance_scale > 1.0

										if isinstance(_lowerCamelCase     ,			_lowerCamelCase				):
																lowerCamelCase__ =torch.cat(_lowerCamelCase     ,			dim=0				)
										if isinstance(_lowerCamelCase     ,			_lowerCamelCase				):
																lowerCamelCase__ =torch.cat(_lowerCamelCase     ,			dim=0				)
										if isinstance(_lowerCamelCase     ,			_lowerCamelCase				):
																lowerCamelCase__ =torch.cat(_lowerCamelCase     ,			dim=0				)

										lowerCamelCase__ =image_embeds.shape[0]

										if do_classifier_free_guidance:
																lowerCamelCase__ =image_embeds.repeat_interleave(_lowerCamelCase     ,			dim=0				)
																lowerCamelCase__ =negative_image_embeds.repeat_interleave(_lowerCamelCase     ,			dim=0				)
																lowerCamelCase__ =hint.repeat_interleave(_lowerCamelCase     ,			dim=0				)

										lowerCamelCase__ =torch.cat([negative_image_embeds, image_embeds]     ,			dim=0				).to(dtype=self.unet.dtype     ,			device=_lowerCamelCase				)
										lowerCamelCase__ =torch.cat([hint, hint]     ,			dim=0				).to(dtype=self.unet.dtype     ,			device=_lowerCamelCase				)

										if not isinstance(_lowerCamelCase     ,			_lowerCamelCase				):
																lowerCamelCase__ =[image]
										if not all(isinstance(_lowerCamelCase     ,			(PIL.Image.Image, torch.Tensor)				) for i in image				):
																raise ValueError(
																    F'''Input is in incorrect format: {[type(_lowerCamelCase				) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''				)

										lowerCamelCase__ =torch.cat([prepare_image(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				) for i in image]     ,			dim=0				)
										lowerCamelCase__ =image.to(dtype=image_embeds.dtype     ,			device=_lowerCamelCase				)

										lowerCamelCase__ =self.movq.encode(_lowerCamelCase				)["latents"]
										lowerCamelCase__ =latents.repeat_interleave(_lowerCamelCase     ,			dim=0				)
										self.scheduler.set_timesteps(_lowerCamelCase     ,			device=_lowerCamelCase				)
										lowerCamelCase__ ,				lowerCamelCase__ =self.get_timesteps(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				)
										lowerCamelCase__ =timesteps[:1].repeat(batch_size * num_images_per_prompt				)
										lowerCamelCase__ ,				lowerCamelCase__ =downscale_height_and_width(_lowerCamelCase     ,			_lowerCamelCase     ,			self.movq_scale_factor				)
										lowerCamelCase__ =self.prepare_latents(
										    _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			image_embeds.dtype     ,			_lowerCamelCase     ,			_lowerCamelCase				)
										for i, t in enumerate(self.progress_bar(_lowerCamelCase				)				):
																# expand the latents if we are doing classifier free guidance
																lowerCamelCase__ =torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

																lowerCamelCase__ ={"image_embeds": image_embeds, "hint": hint}
																lowerCamelCase__ =self.unet(
																    sample=_lowerCamelCase     ,			timestep=_lowerCamelCase     ,			encoder_hidden_states=_lowerCamelCase     ,			added_cond_kwargs=_lowerCamelCase     ,			return_dict=_lowerCamelCase     ,			)[0]

																if do_classifier_free_guidance:
																						lowerCamelCase__ ,				lowerCamelCase__ =noise_pred.split(latents.shape[1]     ,			dim=1				)
																						lowerCamelCase__ ,				lowerCamelCase__ =noise_pred.chunk(2				)
																						lowerCamelCase__ ,				lowerCamelCase__ =variance_pred.chunk(2				)
																						lowerCamelCase__ =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						lowerCamelCase__ =torch.cat([noise_pred, variance_pred_text]     ,			dim=1				)

																if not (
																    hasattr(self.scheduler.config     ,			"variance_type"				)
																    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																):
																						lowerCamelCase__ ,				lowerCamelCase__ =noise_pred.split(latents.shape[1]     ,			dim=1				)

																# compute the previous noisy sample x_t -> x_t-1

																lowerCamelCase__ =self.scheduler.step(
																    _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			generator=_lowerCamelCase     ,			)[0]

										# post-processing
										lowerCamelCase__ =self.movq.decode(_lowerCamelCase     ,			force_not_quantize=_lowerCamelCase				)["sample"]

										if output_type not in ["pt", "np", "pil"]:
																raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''				)

										if output_type in ["np", "pil"]:
																lowerCamelCase__ =image * 0.5 + 0.5
																lowerCamelCase__ =image.clamp(0     ,			1				)
																lowerCamelCase__ =image.cpu().permute(0     ,			2     ,			3     ,			1				).float().numpy()

										if output_type == "pil":
																lowerCamelCase__ =self.numpy_to_pil(_lowerCamelCase				)

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=_lowerCamelCase				)



"""simple docstring"""


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel






class        __UpperCAmelCase						(     utils.Parser     ):
				A__				:       str  							=	"halfcheetah-medium-expert-v2"
				A__				:       str  							=	"config.offline"





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						lowerCamelCase__ ,				lowerCamelCase__ =utils.load_model(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   epoch=__lowerCAmelCase       ,   device=__lowerCAmelCase    )
						lowerCamelCase__ =TrajectoryTransformerModel(gpt.config    )

						trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()    )
						lowerCamelCase__ =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__":
	a							=Parser().parse_args('plan')
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)


