
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()





class     lowerCamelCase      (       unittest.TestCase					):
	UpperCamelCase_  :					str    		=	StableDiffusionLDMaDPipeline
	UpperCamelCase_  :					int    		=	TEXT_TO_IMAGE_PARAMS
	UpperCamelCase_  :					Any    		=	TEXT_TO_IMAGE_BATCH_PARAMS
	UpperCamelCase_  :					str    		=	TEXT_TO_IMAGE_IMAGE_PARAMS







	def  snake_case__     (     self      :Optional[int]			)		->      str:







				"""simple docstring"""






				torch.manual_seed(0			)
				SCREAMING_SNAKE_CASE     						= UNetaDConditionModel(
				    block_out_channels=(3_2, 6_4)   ,     layers_per_block=2   ,     sample_size=3_2   ,     in_channels=4   ,     out_channels=4   ,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,     cross_attention_dim=3_2   ,     )
				SCREAMING_SNAKE_CASE     						= DDIMScheduler(
				    beta_start=0.0_00_85   ,     beta_end=0.0_12   ,     beta_schedule='''scaled_linear'''   ,     clip_sample=lowercase   ,     set_alpha_to_one=lowercase   ,     )
				torch.manual_seed(0			)
				SCREAMING_SNAKE_CASE     						= AutoencoderKL(
				    block_out_channels=[3_2, 6_4]   ,     in_channels=6   ,     out_channels=6   ,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,     latent_channels=4   ,     )
				torch.manual_seed(0			)
				SCREAMING_SNAKE_CASE     						= CLIPTextConfig(
				    bos_token_id=0   ,     eos_token_id=2   ,     hidden_size=3_2   ,     intermediate_size=3_7   ,     layer_norm_eps=1e-05   ,     num_attention_heads=4   ,     num_hidden_layers=5   ,     pad_token_id=1   ,     vocab_size=1_0_0_0   ,     )
				SCREAMING_SNAKE_CASE     						= CLIPTextModel(lowercase			)
				SCREAMING_SNAKE_CASE     						= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

				SCREAMING_SNAKE_CASE     						= {
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''safety_checker''': None,
				    '''feature_extractor''': None,
				}
				return components







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :List[Any]   ,     lowercase      :int=0			)		->      Optional[int]:







				"""simple docstring"""






				if str(lowercase			).startswith('''mps'''			):
							SCREAMING_SNAKE_CASE     						= torch.manual_seed(lowercase			)
				else:
							SCREAMING_SNAKE_CASE     						= torch.Generator(device=lowercase			).manual_seed(lowercase			)
				SCREAMING_SNAKE_CASE     						= {
				    '''prompt''': '''A painting of a squirrel eating a burger''',
				    '''generator''': generator,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				}
				return inputs







	def  snake_case__     (     self      :Dict			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''cpu'''  # ensure determinism for the device-dependent torch.Generator

				SCREAMING_SNAKE_CASE     						= self.get_dummy_components()
				SCREAMING_SNAKE_CASE     						= StableDiffusionLDMaDPipeline(**lowercase			)
				SCREAMING_SNAKE_CASE     						= ldmad_pipe.to(lowercase			)
				ldmad_pipe.set_progress_bar_config(disable=lowercase			)

				SCREAMING_SNAKE_CASE     						= self.get_dummy_inputs(lowercase			)
				SCREAMING_SNAKE_CASE     						= ldmad_pipe(**lowercase			)
				SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= output.rgb, output.depth

				SCREAMING_SNAKE_CASE     						= rgb[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE     						= depth[0, -3:, -1]

				assert rgb.shape == (1, 6_4, 6_4, 3)
				assert depth.shape == (1, 6_4, 6_4)

				SCREAMING_SNAKE_CASE     						= np.array(
				    [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62]			)
				SCREAMING_SNAKE_CASE     						= np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36]			)

				assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb			).max() < 1e-2
				assert np.abs(image_slice_depth.flatten() - expected_slice_depth			).max() < 1e-2







	def  snake_case__     (     self      :Union[str, Any]			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.get_dummy_components()
				SCREAMING_SNAKE_CASE     						= StableDiffusionLDMaDPipeline(**lowercase			)
				SCREAMING_SNAKE_CASE     						= ldmad_pipe.to(lowercase			)
				ldmad_pipe.set_progress_bar_config(disable=lowercase			)

				SCREAMING_SNAKE_CASE     						= self.get_dummy_inputs(lowercase			)
				SCREAMING_SNAKE_CASE     						= 3 * [inputs['''prompt''']]

				# forward
				SCREAMING_SNAKE_CASE     						= ldmad_pipe(**lowercase			)
				SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= output.rgb, output.depth
				SCREAMING_SNAKE_CASE     						= rgb_slice_a[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE     						= depth_slice_a[0, -3:, -1]

				SCREAMING_SNAKE_CASE     						= self.get_dummy_inputs(lowercase			)
				SCREAMING_SNAKE_CASE     						= 3 * [inputs.pop('''prompt'''			)]

				SCREAMING_SNAKE_CASE     						= ldmad_pipe.tokenizer(
				    lowercase   ,     padding='''max_length'''   ,     max_length=ldmad_pipe.tokenizer.model_max_length   ,     truncation=lowercase   ,     return_tensors='''pt'''   ,     )
				SCREAMING_SNAKE_CASE     						= text_inputs['''input_ids'''].to(lowercase			)

				SCREAMING_SNAKE_CASE     						= ldmad_pipe.text_encoder(lowercase			)[0]

				SCREAMING_SNAKE_CASE     						= prompt_embeds

				# forward
				SCREAMING_SNAKE_CASE     						= ldmad_pipe(**lowercase			)
				SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= output.rgb, output.depth
				SCREAMING_SNAKE_CASE     						= rgb_slice_a[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE     						= depth_slice_a[0, -3:, -1]

				assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()			).max() < 1e-4
				assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()			).max() < 1e-4







	def  snake_case__     (     self      :int			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE     						= self.get_dummy_components()
				SCREAMING_SNAKE_CASE     						= PNDMScheduler(skip_prk_steps=lowercase			)
				SCREAMING_SNAKE_CASE     						= StableDiffusionLDMaDPipeline(**lowercase			)
				SCREAMING_SNAKE_CASE     						= ldmad_pipe.to(lowercase			)
				ldmad_pipe.set_progress_bar_config(disable=lowercase			)

				SCREAMING_SNAKE_CASE     						= self.get_dummy_inputs(lowercase			)
				SCREAMING_SNAKE_CASE     						= '''french fries'''
				SCREAMING_SNAKE_CASE     						= ldmad_pipe(**lowercase   ,     negative_prompt=lowercase			)

				SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= output.rgb, output.depth
				SCREAMING_SNAKE_CASE     						= rgb[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE     						= depth[0, -3:, -1]

				assert rgb.shape == (1, 6_4, 6_4, 3)
				assert depth.shape == (1, 6_4, 6_4)

				SCREAMING_SNAKE_CASE     						= np.array(
				    [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17]			)
				SCREAMING_SNAKE_CASE     						= np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35]			)
				assert np.abs(rgb_slice.flatten() - expected_slice_rgb			).max() < 1e-2
				assert np.abs(depth_slice.flatten() - expected_slice_depth			).max() < 1e-2





@slow
@require_torch_gpu
class     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :Tuple			)		->      Optional[int]:







				"""simple docstring"""






				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







	def  snake_case__     (     self      :Any   ,     lowercase      :Any   ,     lowercase      :int="cpu"   ,     lowercase      :Union[str, Any]=torch.floataa   ,     lowercase      :Optional[int]=0			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= torch.Generator(device=lowercase			).manual_seed(lowercase			)
				SCREAMING_SNAKE_CASE     						= np.random.RandomState(lowercase			).standard_normal((1, 4, 6_4, 6_4)			)
				SCREAMING_SNAKE_CASE     						= torch.from_numpy(lowercase			).to(device=lowercase   ,     dtype=lowercase			)
				SCREAMING_SNAKE_CASE     						= {
				    '''prompt''': '''a photograph of an astronaut riding a horse''',
				    '''latents''': latents,
				    '''generator''': generator,
				    '''num_inference_steps''': 3,
				    '''guidance_scale''': 7.5,
				    '''output_type''': '''numpy''',
				}
				return inputs







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''			)
				SCREAMING_SNAKE_CASE     						= ldmad_pipe.to(lowercase			)
				ldmad_pipe.set_progress_bar_config(disable=lowercase			)

				SCREAMING_SNAKE_CASE     						= self.get_inputs(lowercase			)
				SCREAMING_SNAKE_CASE     						= ldmad_pipe(**lowercase			)
				SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= output.rgb, output.depth
				SCREAMING_SNAKE_CASE     						= rgb[0, -3:, -3:, -1].flatten()
				SCREAMING_SNAKE_CASE     						= rgb[0, -3:, -1].flatten()

				assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
				assert depth.shape == (1, 5_1_2, 5_1_2)

				SCREAMING_SNAKE_CASE     						= np.array(
				    [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06]			)
				SCREAMING_SNAKE_CASE     						= np.array(
				    [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06]			)
				assert np.abs(rgb_slice - expected_slice_rgb			).max() < 3e-3
				assert np.abs(depth_slice - expected_slice_depth			).max() < 3e-3





@nightly
@require_torch_gpu
class     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :List[Any]			)		->      Dict:







				"""simple docstring"""






				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







	def  snake_case__     (     self      :Any   ,     lowercase      :Any   ,     lowercase      :Optional[Any]="cpu"   ,     lowercase      :str=torch.floataa   ,     lowercase      :Optional[Any]=0			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= torch.Generator(device=lowercase			).manual_seed(lowercase			)
				SCREAMING_SNAKE_CASE     						= np.random.RandomState(lowercase			).standard_normal((1, 4, 6_4, 6_4)			)
				SCREAMING_SNAKE_CASE     						= torch.from_numpy(lowercase			).to(device=lowercase   ,     dtype=lowercase			)
				SCREAMING_SNAKE_CASE     						= {
				    '''prompt''': '''a photograph of an astronaut riding a horse''',
				    '''latents''': latents,
				    '''generator''': generator,
				    '''num_inference_steps''': 5_0,
				    '''guidance_scale''': 7.5,
				    '''output_type''': '''numpy''',
				}
				return inputs







	def  snake_case__     (     self      :Dict			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''			).to(lowercase			)
				ldmad_pipe.set_progress_bar_config(disable=lowercase			)

				SCREAMING_SNAKE_CASE     						= self.get_inputs(lowercase			)
				SCREAMING_SNAKE_CASE     						= ldmad_pipe(**lowercase			)
				SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= output.rgb, output.depth

				SCREAMING_SNAKE_CASE     						= 0.49_55_86
				SCREAMING_SNAKE_CASE     						= 0.33_79_55_15
				SCREAMING_SNAKE_CASE     						= 1_12.4_85_18
				SCREAMING_SNAKE_CASE     						= 98.48_97_46
				assert np.abs(expected_rgb_mean - rgb.mean()			) < 1e-3
				assert np.abs(expected_rgb_std - rgb.std()			) < 1e-3
				assert np.abs(expected_depth_mean - depth.mean()			) < 1e-3
				assert np.abs(expected_depth_std - depth.std()			) < 1e-3







	def  snake_case__     (     self      :Tuple			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''			).to(lowercase			)
				ldmad_pipe.set_progress_bar_config(disable=lowercase			)

				SCREAMING_SNAKE_CASE     						= self.get_inputs(lowercase			)
				SCREAMING_SNAKE_CASE     						= ldmad_pipe(**lowercase			)
				SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= output.rgb, output.depth

				SCREAMING_SNAKE_CASE     						= 0.4_19_41_27
				SCREAMING_SNAKE_CASE     						= 0.35_37_55_86
				SCREAMING_SNAKE_CASE     						= 0.5_63_85_02
				SCREAMING_SNAKE_CASE     						= 0.34_68_61_03

				assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
				assert depth.shape == (1, 5_1_2, 5_1_2, 1)
				assert np.abs(expected_rgb_mean - rgb.mean()			) < 1e-3
				assert np.abs(expected_rgb_std - rgb.std()			) < 1e-3
				assert np.abs(expected_depth_mean - depth.mean()			) < 1e-3
				assert np.abs(expected_depth_std - depth.std()			) < 1e-3

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      ( a	)     ->List[Any]:




			'''simple docstring'''


			if hor == 128:
						SCREAMING_SNAKE_CASE     						= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						SCREAMING_SNAKE_CASE     						= (32, 128, 256)
						SCREAMING_SNAKE_CASE     						= ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

			elif hor == 32:
						SCREAMING_SNAKE_CASE     						= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						SCREAMING_SNAKE_CASE     						= (32, 64, 128, 256)
						SCREAMING_SNAKE_CASE     						= ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
			SCREAMING_SNAKE_CASE     						= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""	)
			SCREAMING_SNAKE_CASE     						= model.state_dict()
			SCREAMING_SNAKE_CASE     						= {
			    '''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_5536,
			    '''mid_block_type''': '''MidResTemporalBlock1D''',
			    '''act_fn''': '''mish''',
			}
			SCREAMING_SNAKE_CASE     						= UNetaDModel(**a	)
			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     						= dict(zip(model.state_dict().keys()		,						hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						SCREAMING_SNAKE_CASE     						= state_dict.pop(a	)
			hf_value_function.load_state_dict(a	)

			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(a		,						a	)


def    a      ( )     ->Optional[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= {
			    '''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, 128, 256),
			    '''layers_per_block''': 1,
			    '''downsample_each_block''': True,
			    '''sample_size''': 6_5536,
			    '''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     						= torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''	)
			SCREAMING_SNAKE_CASE     						= model
			SCREAMING_SNAKE_CASE     						= UNetaDModel(**a	)
			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     						= dict(zip(state_dict.keys()		,						hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						SCREAMING_SNAKE_CASE     						= state_dict.pop(a	)

			hf_value_function.load_state_dict(a	)

			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(a		,						a	)


if __name__ == "__main__":
							unet(3_2)
							# unet(128)
							value_function()