



'''simple docstring'''




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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


def        lowerCAmelCase_     (			_lowerCamelCase:     Any      ):
		__SCREAMING_SNAKE_CASE :	str					 =	image.size
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		__SCREAMING_SNAKE_CASE :	Any					 =	image.resize((w, h) ,   resample=PIL_INTERPOLATION["""lanczos"""]      )
		__SCREAMING_SNAKE_CASE :	int					 =	np.array(_lowerCamelCase      ).astype(np.floataa      ) / 2_55.0
		__SCREAMING_SNAKE_CASE :	int					 =	image[None].transpose(0 ,   3 ,   1 ,   2      )
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	torch.from_numpy(_lowerCamelCase      )
		return 2.0 * image - 1.0






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Optional[Any]							,  lowerCAmelCase__  :				VQModel							,  lowerCAmelCase__  :				UNetaDModel							,  lowerCAmelCase__  :				Union[
			        DDIMScheduler,
			        PNDMScheduler,
			        LMSDiscreteScheduler,
			        EulerDiscreteScheduler,
			        EulerAncestralDiscreteScheduler,
			        DPMSolverMultistepScheduler,
			    ]							,  ):





					"""simple docstring"""






					super().__init__()
					self.register_modules(vqvae=lowerCAmelCase__							,  unet=lowerCAmelCase__							,  scheduler=lowerCAmelCase__		)



			@torch.no_grad()
			def __call__(							self  :				Optional[Any]							,  lowerCAmelCase__  :				Union[torch.Tensor, PIL.Image.Image] = None							,  lowerCAmelCase__  :				Optional[int] = 1							,  lowerCAmelCase__  :				Optional[int] = 1_0_0							,  lowerCAmelCase__  :				Optional[float] = 0.0							,  lowerCAmelCase__  :				Optional[Union[torch.Generator, List[torch.Generator]]] = None							,  lowerCAmelCase__  :				Optional[str] = "pil"							,  lowerCAmelCase__  :				bool = True							,  ):





					"""simple docstring"""






					if isinstance(lowerCAmelCase__							,  PIL.Image.Image		):
							__SCREAMING_SNAKE_CASE :	Dict					 =	1
					elif isinstance(lowerCAmelCase__							,  torch.Tensor		):
							__SCREAMING_SNAKE_CASE :	Tuple					 =	image.shape[0]
					else:
							raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCAmelCase__		)}"		)

					if isinstance(lowerCAmelCase__							,  PIL.Image.Image		):
							__SCREAMING_SNAKE_CASE :	int					 =	preprocess(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	image.shape[-2:]

					# in_channels should be 6: 3 for latents, 3 for low resolution image
					__SCREAMING_SNAKE_CASE :	Tuple					 =	(batch_size, self.unet.config.in_channels // 2, height, width)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	next(self.unet.parameters()		).dtype

					__SCREAMING_SNAKE_CASE :	int					 =	randn_tensor(lowerCAmelCase__							,  generator=lowerCAmelCase__							,  device=self.device							,  dtype=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	image.to(device=self.device							,  dtype=lowerCAmelCase__		)

					# set timesteps and move to the correct device
					self.scheduler.set_timesteps(lowerCAmelCase__							,  device=self.device		)
					__SCREAMING_SNAKE_CASE :	Any					 =	self.scheduler.timesteps

					# scale the initial noise by the standard deviation required by the scheduler
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	latents * self.scheduler.init_noise_sigma

					# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
					# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
					# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
					# and should be between [0, 1]
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	"""eta""" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
					__SCREAMING_SNAKE_CASE :	int					 =	{}
					if accepts_eta:
							__SCREAMING_SNAKE_CASE :	str					 =	eta

					for t in self.progress_bar(lowerCAmelCase__		):
							# concat latents and low resolution image in the channel dimension.
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.cat([latents, image]							,  dim=1		)
							__SCREAMING_SNAKE_CASE :	Any					 =	self.scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)
							# predict the noise residual
							__SCREAMING_SNAKE_CASE :	Any					 =	self.unet(lowerCAmelCase__							,  lowerCAmelCase__		).sample
							# compute the previous noisy sample x_t -> x_t-1
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  **lowerCAmelCase__		).prev_sample

					# decode the image latents with the VQVAE
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.vqvae.decode(lowerCAmelCase__		).sample
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.clamp(lowerCAmelCase__							,  -1.0							,  1.0		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	image / 2 + 0.5
					__SCREAMING_SNAKE_CASE :	int					 =	image.cpu().permute(0							,  2							,  3							,  1		).numpy()

					if output_type == "pil":
							__SCREAMING_SNAKE_CASE :	Tuple					 =	self.numpy_to_pil(lowerCAmelCase__		)

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=lowerCAmelCase__		)




'''simple docstring'''




import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)

os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		if hor == 1_28:
				__SCREAMING_SNAKE_CASE :	Any					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	(32, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	str					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""")

		elif hor == 32:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	str					 =	(32, 64, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	Tuple					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"      )
		__SCREAMING_SNAKE_CASE :	Any					 =	model.state_dict()
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{
		    """down_block_types""": down_block_types,
		    """block_out_channels""": block_out_channels,
		    """up_block_types""": up_block_types,
		    """layers_per_block""": 1,
		    """use_timestep_embedding""": True,
		    """out_block_type""": """OutConv1DBlock""",
		    """norm_num_groups""": 8,
		    """downsample_each_block""": False,
		    """in_channels""": 14,
		    """out_channels""": 14,
		    """extra_in_channels""": 0,
		    """time_embedding_type""": """positional""",
		    """flip_sin_to_cos""": False,
		    """freq_shift""": 1,
		    """sample_size""": 6_55_36,
		    """mid_block_type""": """MidResTemporalBlock1D""",
		    """act_fn""": """mish""",
		}
		__SCREAMING_SNAKE_CASE :	int					 =	UNetaDModel(**_lowerCamelCase      )
		print(F"length of state dict: {len(state_dict.keys()      )}"      )
		print(F"length of value function dict: {len(hf_value_function.state_dict().keys()      )}"      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	dict(zip(model.state_dict().keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	state_dict.pop(_lowerCamelCase      )
		hf_value_function.load_state_dict(_lowerCamelCase      )

		torch.save(hf_value_function.state_dict() ,   F"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"      )
		with open(F"hub/hopper-medium-v2/unet/hor{hor}/config.json" ,   """w"""      ) as f:
				json.dump(_lowerCamelCase ,   _lowerCamelCase      )


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Dict					 =	{
		    """in_channels""": 14,
		    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
		    """up_block_types""": (),
		    """out_block_type""": """ValueFunction""",
		    """mid_block_type""": """ValueFunctionMidBlock1D""",
		    """block_out_channels""": (32, 64, 1_28, 2_56),
		    """layers_per_block""": 1,
		    """downsample_each_block""": True,
		    """sample_size""": 6_55_36,
		    """out_channels""": 14,
		    """extra_in_channels""": 0,
		    """time_embedding_type""": """positional""",
		    """use_timestep_embedding""": True,
		    """flip_sin_to_cos""": False,
		    """freq_shift""": 1,
		    """norm_num_groups""": 8,
		    """act_fn""": """mish""",
		}

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	model
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	UNetaDModel(**_lowerCamelCase      )
		print(F"length of state dict: {len(state_dict.keys()      )}"      )
		print(F"length of value function dict: {len(hf_value_function.state_dict().keys()      )}"      )

		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	dict(zip(state_dict.keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	str					 =	state_dict.pop(_lowerCamelCase      )

		hf_value_function.load_state_dict(_lowerCamelCase      )

		torch.save(hf_value_function.state_dict() ,   """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"""      )
		with open("""hub/hopper-medium-v2/value_function/config.json""" ,   """w"""      ) as f:
				json.dump(_lowerCamelCase ,   _lowerCamelCase      )


if __name__ == "__main__":
						unet(32)
						# unet(128)
						value_function()