
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        A__					(						lowerCamelCase						)  ->	List[Any]:
				if hor == 1_28:
								UpperCamelCase_:							Dict	=						("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
								UpperCamelCase_:							int	=						(32, 1_28, 2_56)
								UpperCamelCase_:							List[Any]	=						("""UpResnetBlock1D""", """UpResnetBlock1D""")

				elif hor == 32:
								UpperCamelCase_:							str	=						("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
								UpperCamelCase_:							Any	=						(32, 64, 1_28, 2_56)
								UpperCamelCase_:							Any	=						("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
				UpperCamelCase_:							Tuple	=						torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''						)
				UpperCamelCase_:							int	=						model.state_dict()
				UpperCamelCase_:							Dict	=						{
				    """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""",
				}
				UpperCamelCase_:							Tuple	=						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()						)}'''						)
				UpperCamelCase_:							List[Any]	=						dict(zip(model.state_dict().keys()     ,    hf_value_function.state_dict().keys()						)						)
				for k, v in mapping.items():
								UpperCamelCase_:							Dict	=						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        A__					(						)  ->	Any:
				UpperCamelCase_:							Optional[Any]	=						{
				    """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""",
				}

				UpperCamelCase_:							Any	=						torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""						)
				UpperCamelCase_:							Tuple	=						model
				UpperCamelCase_:							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()						)}'''						)

				UpperCamelCase_:							str	=						dict(zip(state_dict.keys()     ,    hf_value_function.state_dict().keys()						)						)
				for k, v in mapping.items():
								UpperCamelCase_:							Union[str, Any]	=						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()








import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			@property
			def       lowerCAmelCase__	(       self :       int      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							Any	=						UNetaDModel(
							    block_out_channels=(32, 64)						,	layers_per_block=2						,	sample_size=32						,	in_channels=3						,	out_channels=3						,	down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,	up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,	)
							return model

			@property
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							Union[str, Any]	=						VQModel(
							    block_out_channels=[32, 64]						,	in_channels=3						,	out_channels=3						,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,	latent_channels=3						,	)
							return model

			@property
			def       lowerCAmelCase__	(       self :       Any      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							List[str]	=						CLIPTextConfig(
							    bos_token_id=0						,	eos_token_id=2						,	hidden_size=32						,	intermediate_size=37						,	layer_norm_eps=1e-05						,	num_attention_heads=4						,	num_hidden_layers=5						,	pad_token_id=1						,	vocab_size=1000						,	)
							return CLIPTextModel(snake_case_      )




			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Union[str, Any]	=						self.dummy_uncond_unet
							UpperCamelCase_:							Optional[Any]	=						DDIMScheduler()
							UpperCamelCase_:							List[str]	=						self.dummy_vq_model

							UpperCamelCase_:							List[Any]	=						LDMPipeline(unet=snake_case_						,	vqvae=snake_case_						,	scheduler=snake_case_      )
							ldm.to(snake_case_      )
							ldm.set_progress_bar_config(disable=snake_case_      )

							UpperCamelCase_:							str	=						torch.manual_seed(0      )
							UpperCamelCase_:							int	=						ldm(generator=snake_case_						,	num_inference_steps=2						,	output_type="""numpy"""      ).images

							UpperCamelCase_:							Dict	=						torch.manual_seed(0      )
							UpperCamelCase_:							str	=						ldm(generator=snake_case_						,	num_inference_steps=2						,	output_type="""numpy"""						,	return_dict=snake_case_      )[0]

							UpperCamelCase_:							List[Any]	=						image[0, -3:, -3:, -1]
							UpperCamelCase_:							Optional[int]	=						image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase_:							str	=						np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]      )
							UpperCamelCase_:							Optional[Any]	=						1e-2 if torch_device != """mps""" else 3e-2

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < tolerance
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < tolerance




@slow
@require_torch
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Dict	=						LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""      )
							ldm.to(snake_case_      )
							ldm.set_progress_bar_config(disable=snake_case_      )

							UpperCamelCase_:							List[str]	=						torch.manual_seed(0      )
							UpperCamelCase_:							Optional[int]	=						ldm(generator=snake_case_						,	num_inference_steps=5						,	output_type="""numpy"""      ).images

							UpperCamelCase_:							List[Any]	=						image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							UpperCamelCase_:							List[str]	=						np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]      )
							UpperCamelCase_:							Dict	=						1e-2 if torch_device != """mps""" else 3e-2

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < tolerance






