import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import 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
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class UpperCAmelCase						( __SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = StableDiffusionSAGPipeline
				lowerCamelCase_    = TEXT_TO_IMAGE_PARAMS
				lowerCamelCase_    = TEXT_TO_IMAGE_BATCH_PARAMS
				lowerCamelCase_    = TEXT_TO_IMAGE_IMAGE_PARAMS
				lowerCamelCase_    = TEXT_TO_IMAGE_IMAGE_PARAMS
				lowerCamelCase_    = False
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    Union[str, Any]         =				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		,     )
						A_		:    Tuple         =				DDIMScheduler(
						    beta_start=0.0_0085		,     beta_end=0.012		,     beta_schedule='scaled_linear'		,     clip_sample=_snake_case		,     set_alpha_to_one=_snake_case		,     )
						torch.manual_seed(0 )
						A_		:    Any         =				AutoencoderKL(
						    block_out_channels=[3_2, 6_4]		,     in_channels=3		,     out_channels=3		,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,     latent_channels=4		,     )
						torch.manual_seed(0 )
						A_		:    Union[str, Any]         =				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		,     )
						A_		:    Union[str, Any]         =				CLIPTextModel(_snake_case )
						A_		:    Dict         =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

						A_		:    Optional[int]         =				{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=0 ):



						"""simple docstring"""





						if str(_snake_case ).startswith('mps' ):
								A_		:    Dict         =				torch.manual_seed(_snake_case )
						else:
								A_		:    List[Any]         =				torch.Generator(device=_snake_case ).manual_seed(_snake_case )
						A_		:    Dict         =				{
						    "prompt": ".",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 1.0,
						    "sag_scale": 1.0,
						    "output_type": "numpy",
						}
						return inputs







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						super().test_inference_batch_single_identical(expected_max_diff=3E-3 )





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


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' )
						A_		:    Tuple         =				sag_pipe.to(_snake_case )
						sag_pipe.set_progress_bar_config(disable=_snake_case )

						A_		:    Optional[int]         =				"."
						A_		:    Optional[int]         =				torch.manual_seed(0 )
						A_		:    List[str]         =				sag_pipe(
						    [prompt]		,     generator=_snake_case		,     guidance_scale=7.5		,     sag_scale=1.0		,     num_inference_steps=2_0		,     output_type='np' )

						A_		:    Tuple         =				output.images

						A_		:    int         =				image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						A_		:    List[Any]         =				np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )

						assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
						A_		:    Optional[Any]         =				sag_pipe.to(_snake_case )
						sag_pipe.set_progress_bar_config(disable=_snake_case )

						A_		:    Union[str, Any]         =				"."
						A_		:    Dict         =				torch.manual_seed(0 )
						A_		:    Union[str, Any]         =				sag_pipe(
						    [prompt]		,     generator=_snake_case		,     guidance_scale=7.5		,     sag_scale=1.0		,     num_inference_steps=2_0		,     output_type='np' )

						A_		:    Optional[Any]         =				output.images

						A_		:    Optional[int]         =				image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						A_		:    Union[str, Any]         =				np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )

						assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
						A_		:    str         =				sag_pipe.to(_snake_case )
						sag_pipe.set_progress_bar_config(disable=_snake_case )

						A_		:    str         =				"."
						A_		:    Union[str, Any]         =				torch.manual_seed(0 )
						A_		:    int         =				sag_pipe(
						    [prompt]		,     width=7_6_8		,     height=5_1_2		,     generator=_snake_case		,     guidance_scale=7.5		,     sag_scale=1.0		,     num_inference_steps=2_0		,     output_type='np'		,     )

						A_		:    Union[str, Any]         =				output.images

						assert image.shape == (1, 5_1_2, 7_6_8, 3)







import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_UpperCAmelCase            =		{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
	_UpperCAmelCase            =		"""hopper-medium-v2"""
	_UpperCAmelCase            =		gym.make(env_name)

	_UpperCAmelCase            =		ValueGuidedRLPipeline.from_pretrained(
	    """bglick13/hopper-medium-v2-value-function-hor32""",
	    env=env,
	)

	env.seed(0)
	_UpperCAmelCase            =		env.reset()
	_UpperCAmelCase            =		0
	_UpperCAmelCase            =		0
	_UpperCAmelCase            =		1000
	_UpperCAmelCase            =		[obs.copy()]
	try:
		for t in tqdm.tqdm(range(T)):
			# call the policy
			_UpperCAmelCase            =		pipeline(obs, planning_horizon=32)

			# execute action in environment
			_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase            =		env.step(denorm_actions)
			_UpperCAmelCase            =		env.get_normalized_score(total_reward)

			# update return
			total_reward += reward
			total_score += score
			print(
			    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
			    F""" {total_score}"""
			)

			# save observations for rendering
			rollout.append(next_observation.copy())

			_UpperCAmelCase            =		next_observation
	except KeyboardInterrupt:
		pass

	print(F"""Total reward: {total_reward}""")






