



"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class     A__     (						unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> List[str]:

				"""simple docstring"""

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

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= 1
				__lowerCAmelCase   :						Union[str, Any]				= 3
				__lowerCAmelCase   :						int				= (32, 32)

				__lowerCAmelCase   :						Any				= floats_tensor((batch_size, num_channels) + sizes ,    rng=random.Random(0)).to(_SCREAMING_SNAKE_CASE)
				return image

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Any:

				"""simple docstring"""

				torch.manual_seed(0)
				__lowerCAmelCase   :						Any				= UNetaDConditionModel(
				    block_out_channels=(32, 64) ,    layers_per_block=2 ,    sample_size=32 ,    in_channels=4 ,    out_channels=4 ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,    cross_attention_dim=32 ,    )
				return model

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Optional[int]:

				"""simple docstring"""

				torch.manual_seed(0)
				__lowerCAmelCase   :						Any				= AutoencoderKL(
				    block_out_channels=[32, 64] ,    in_channels=3 ,    out_channels=3 ,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,    latent_channels=4 ,    )
				return model

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> int:

				"""simple docstring"""

				torch.manual_seed(0)
				__lowerCAmelCase   :						Optional[Any]				= RobertaSeriesConfig(
				    hidden_size=32 ,    project_dim=32 ,    intermediate_size=37 ,    layer_norm_eps=1e-05 ,    num_attention_heads=4 ,    num_hidden_layers=5 ,    pad_token_id=1 ,    vocab_size=5006 ,    )
				return RobertaSeriesModelWithTransformation(_SCREAMING_SNAKE_CASE)

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Dict:

				"""simple docstring"""

				def extract(*_SCREAMING_SNAKE_CASE:	List[Any] ,    **_SCREAMING_SNAKE_CASE:	str):



							class     A__     :




								'''simple docstring'''





								def __init__(  self:	Dict)       -> Tuple:

											"""simple docstring"""

											__lowerCAmelCase   :						Any				= torch.ones([0])







								def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	List[Any])       -> Optional[int]:

											"""simple docstring"""

											self.pixel_values.to(_SCREAMING_SNAKE_CASE)
											return self






							return Out()

				return extract

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= "cpu"  # ensure determinism for the device-dependent torch.Generator
				__lowerCAmelCase   :						Tuple				= self.dummy_cond_unet
				__lowerCAmelCase   :						List[Any]				= PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Dict				= self.dummy_vae
				__lowerCAmelCase   :						Dict				= self.dummy_text_encoder
				__lowerCAmelCase   :						Tuple				= XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
				__lowerCAmelCase   :						List[str]				= 77

				__lowerCAmelCase   :						List[Any]				= self.dummy_image.to(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Dict				= init_image / 2 + 0.5

				# make sure here that pndm scheduler skips prk
				__lowerCAmelCase   :						Optional[Any]				= AltDiffusionImgaImgPipeline(
				    unet=_SCREAMING_SNAKE_CASE ,    scheduler=_SCREAMING_SNAKE_CASE ,    vae=_SCREAMING_SNAKE_CASE ,    text_encoder=_SCREAMING_SNAKE_CASE ,    tokenizer=_SCREAMING_SNAKE_CASE ,    safety_checker=_SCREAMING_SNAKE_CASE ,    feature_extractor=self.dummy_extractor ,    )
				__lowerCAmelCase   :						List[str]				= VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor ,    do_normalize=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[str]				= alt_pipe.to(_SCREAMING_SNAKE_CASE)
				alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Any				= "A painting of a squirrel eating a burger"
				__lowerCAmelCase   :						Optional[int]				= torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(0)
				__lowerCAmelCase   :						str				= alt_pipe(
				    [prompt] ,    generator=_SCREAMING_SNAKE_CASE ,    guidance_scale=6.0 ,    num_inference_steps=2 ,    output_type="np" ,    image=_SCREAMING_SNAKE_CASE ,    )

				__lowerCAmelCase   :						int				= output.images

				__lowerCAmelCase   :						Optional[Any]				= torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(0)
				__lowerCAmelCase   :						int				= alt_pipe(
				    [prompt] ,    generator=_SCREAMING_SNAKE_CASE ,    guidance_scale=6.0 ,    num_inference_steps=2 ,    output_type="np" ,    image=_SCREAMING_SNAKE_CASE ,    return_dict=_SCREAMING_SNAKE_CASE ,    )[0]

				__lowerCAmelCase   :						Any				= image[0, -3:, -3:, -1]
				__lowerCAmelCase   :						List[str]				= image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				__lowerCAmelCase   :						Optional[int]				= np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499])

				assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5e-3

	@unittest.skipIf(torch_device != "cuda" ,    "This test requires a GPU")
	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= self.dummy_cond_unet
				__lowerCAmelCase   :						Optional[Any]				= PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= self.dummy_vae
				__lowerCAmelCase   :						List[Any]				= self.dummy_text_encoder
				__lowerCAmelCase   :						List[str]				= XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
				__lowerCAmelCase   :						int				= 77

				__lowerCAmelCase   :						List[Any]				= self.dummy_image.to(_SCREAMING_SNAKE_CASE)

				# put models in fp16
				__lowerCAmelCase   :						Union[str, Any]				= unet.half()
				__lowerCAmelCase   :						List[Any]				= vae.half()
				__lowerCAmelCase   :						Optional[int]				= bert.half()

				# make sure here that pndm scheduler skips prk
				__lowerCAmelCase   :						List[str]				= AltDiffusionImgaImgPipeline(
				    unet=_SCREAMING_SNAKE_CASE ,    scheduler=_SCREAMING_SNAKE_CASE ,    vae=_SCREAMING_SNAKE_CASE ,    text_encoder=_SCREAMING_SNAKE_CASE ,    tokenizer=_SCREAMING_SNAKE_CASE ,    safety_checker=_SCREAMING_SNAKE_CASE ,    feature_extractor=self.dummy_extractor ,    )
				__lowerCAmelCase   :						Any				= VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor ,    do_normalize=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Tuple				= alt_pipe.to(_SCREAMING_SNAKE_CASE)
				alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						str				= "A painting of a squirrel eating a burger"
				__lowerCAmelCase   :						Tuple				= torch.manual_seed(0)
				__lowerCAmelCase   :						Any				= alt_pipe(
				    [prompt] ,    generator=_SCREAMING_SNAKE_CASE ,    num_inference_steps=2 ,    output_type="np" ,    image=_SCREAMING_SNAKE_CASE ,    ).images

				assert image.shape == (1, 32, 32, 3)







	@unittest.skipIf(torch_device != "cuda" ,    "This test requires a GPU")
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/img2img/sketch-mountains-input.jpg")
				# resize to resolution that is divisible by 8 but not 16 or 32
				__lowerCAmelCase   :						Tuple				= init_image.resize((760, 504))

				__lowerCAmelCase   :						Union[str, Any]				= "BAAI/AltDiffusion"
				__lowerCAmelCase   :						List[Any]				= AltDiffusionImgaImgPipeline.from_pretrained(
				    _SCREAMING_SNAKE_CASE ,    safety_checker=_SCREAMING_SNAKE_CASE ,    )
				pipe.to(_SCREAMING_SNAKE_CASE)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
				pipe.enable_attention_slicing()

				__lowerCAmelCase   :						Dict				= "A fantasy landscape, trending on artstation"

				__lowerCAmelCase   :						List[str]				= torch.manual_seed(0)
				__lowerCAmelCase   :						int				= pipe(
				    prompt=_SCREAMING_SNAKE_CASE ,    image=_SCREAMING_SNAKE_CASE ,    strength=0.75 ,    guidance_scale=7.5 ,    generator=_SCREAMING_SNAKE_CASE ,    output_type="np" ,    )
				__lowerCAmelCase   :						Optional[Any]				= output.images[0]

				__lowerCAmelCase   :						Tuple				= image[255:258, 383:386, -1]

				assert image.shape == (504, 760, 3)
				__lowerCAmelCase   :						int				= np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000])

				assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2






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




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> List[str]:

				"""simple docstring"""

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







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

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/img2img/sketch-mountains-input.jpg")
				__lowerCAmelCase   :						List[str]				= init_image.resize((768, 512))
				__lowerCAmelCase   :						Dict				= load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy")

				__lowerCAmelCase   :						List[Any]				= "BAAI/AltDiffusion"
				__lowerCAmelCase   :						Optional[int]				= AltDiffusionImgaImgPipeline.from_pretrained(
				    _SCREAMING_SNAKE_CASE ,    safety_checker=_SCREAMING_SNAKE_CASE ,    )
				pipe.to(_SCREAMING_SNAKE_CASE)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
				pipe.enable_attention_slicing()

				__lowerCAmelCase   :						Optional[int]				= "A fantasy landscape, trending on artstation"

				__lowerCAmelCase   :						int				= torch.manual_seed(0)
				__lowerCAmelCase   :						Optional[Any]				= pipe(
				    prompt=_SCREAMING_SNAKE_CASE ,    image=_SCREAMING_SNAKE_CASE ,    strength=0.75 ,    guidance_scale=7.5 ,    generator=_SCREAMING_SNAKE_CASE ,    output_type="np" ,    )
				__lowerCAmelCase   :						List[Any]				= output.images[0]

				assert image.shape == (512, 768, 3)
				# img2img is flaky across GPUs even in fp32, so using MAE here
				assert np.abs(expected_image - image).max() < 1e-2




"""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     _lowercase       (						__snake_case			)		->     int:
			if hor == 128:
						__lowerCAmelCase   :						str				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						int				= (32, 128, 256)
						__lowerCAmelCase   :						Optional[Any]				= ("UpResnetBlock1D", "UpResnetBlock1D")

			elif hor == 32:
						__lowerCAmelCase   :						List[str]				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						Optional[Any]				= (32, 64, 128, 256)
						__lowerCAmelCase   :						Any				= ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
			__lowerCAmelCase   :						Union[str, Any]				= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""			)
			__lowerCAmelCase   :						List[Any]				= model.state_dict()
			__lowerCAmelCase   :						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": 65_536,
			    "mid_block_type": "MidResTemporalBlock1D",
			    "act_fn": "mish",
			}
			__lowerCAmelCase   :						Dict				= UNetaDModel(**__snake_case			)
			print(F"""length of state dict: {len(state_dict.keys()			)}"""			)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()			)}"""			)
			__lowerCAmelCase   :						Dict				= dict(zip(model.state_dict().keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						int				= state_dict.pop(__snake_case			)
			hf_value_function.load_state_dict(__snake_case			)

			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(__snake_case      ,__snake_case			)


def     _lowercase       (						)		->     List[str]:
			__lowerCAmelCase   :						Union[str, 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, 128, 256),
			    "layers_per_block": 1,
			    "downsample_each_block": True,
			    "sample_size": 65_536,
			    "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",
			}

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

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

			hf_value_function.load_state_dict(__snake_case			)

			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(__snake_case      ,__snake_case			)


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