




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    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 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase  ,	lowerCamelCase  ,	unittest.TestCase    ):
					_lowerCamelCase						    =					StableDiffusionParadigmsPipeline
					_lowerCamelCase						    =					TEXT_TO_IMAGE_PARAMS
					_lowerCamelCase						    =					TEXT_TO_IMAGE_BATCH_PARAMS
					_lowerCamelCase						    =					TEXT_TO_IMAGE_IMAGE_PARAMS
					_lowerCamelCase						    =					TEXT_TO_IMAGE_IMAGE_PARAMS

					def       lowercase_				(							self   ):







						'''simple docstring'''




						torch.manual_seed(0   )
						lowerCamelCase_				:						List[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					,			attention_head_dim=(2, 4)					,			use_linear_projection=__lowercase					,			)
						lowerCamelCase_				:						Any				  =				DDIMParallelScheduler(
						    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   )
						lowerCamelCase_				:						Optional[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					,			sample_size=128					,			)
						torch.manual_seed(0   )
						lowerCamelCase_				:						int				  =				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=1_000					,			hidden_act='gelu'					,			projection_dim=512					,			)
						lowerCamelCase_				:						int				  =				CLIPTextModel(__lowercase   )
						lowerCamelCase_				:						Optional[Any]				  =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

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

					def       lowercase_				(							self					,			__lowercase					,			__lowercase=0   ):







						'''simple docstring'''




						if str(__lowercase   ).startswith('mps'   ):
							lowerCamelCase_				:						Any				  =				torch.manual_seed(__lowercase   )
						else:
							lowerCamelCase_				:						str				  =				torch.Generator(device=__lowercase   ).manual_seed(__lowercase   )
						lowerCamelCase_				:						Optional[Any]				  =				{
						    'prompt': 'a photograph of an astronaut riding a horse',
						    'generator': generator,
						    'num_inference_steps': 10,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						    'parallel': 3,
						    'debug': True,
						}
						return inputs

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				'cpu'  # ensure determinism for the device-dependent torch.Generator
						lowerCamelCase_				:						str				  =				self.get_dummy_components()
						lowerCamelCase_				:						int				  =				StableDiffusionParadigmsPipeline(**__lowercase   )
						lowerCamelCase_				:						int				  =				sd_pipe.to(__lowercase   )
						sd_pipe.set_progress_bar_config(disable=__lowercase   )

						lowerCamelCase_				:						List[str]				  =				self.get_dummy_inputs(__lowercase   )
						lowerCamelCase_				:						Tuple				  =				sd_pipe(**__lowercase   ).images
						lowerCamelCase_				:						List[Any]				  =				image[0, -3:, -3:, -1]
						assert image.shape == (1, 64, 64, 3)

						lowerCamelCase_				:						List[Any]				  =				np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]   )

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

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				'cpu'  # ensure determinism for the device-dependent torch.Generator
						lowerCamelCase_				:						Any				  =				self.get_dummy_components()
						torch.manual_seed(0   )
						lowerCamelCase_				:						str				  =				DDPMParallelScheduler()
						torch.manual_seed(0   )
						lowerCamelCase_				:						int				  =				StableDiffusionParadigmsPipeline(**__lowercase   )
						lowerCamelCase_				:						Any				  =				sd_pipe.to(__lowercase   )
						sd_pipe.set_progress_bar_config(disable=__lowercase   )

						lowerCamelCase_				:						List[Any]				  =				self.get_dummy_inputs(__lowercase   )
						lowerCamelCase_				:						Union[str, Any]				  =				sd_pipe(**__lowercase   ).images
						lowerCamelCase_				:						int				  =				image[0, -3:, -3:, -1]
						assert image.shape == (1, 64, 64, 3)

						lowerCamelCase_				:						List[str]				  =				np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]   )

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

					def       lowercase_				(							self   ):







						'''simple docstring'''




						super().test_inference_batch_consistent(batch_sizes=[1, 2]   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						super().test_inference_batch_single_identical(batch_size=2					,			expected_max_diff=3E-3   )





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				'cpu'  # ensure determinism for the device-dependent torch.Generator
						lowerCamelCase_				:						Optional[Any]				  =				self.get_dummy_components()
						lowerCamelCase_				:						List[str]				  =				StableDiffusionParadigmsPipeline(**__lowercase   )
						lowerCamelCase_				:						str				  =				sd_pipe.to(__lowercase   )
						sd_pipe.set_progress_bar_config(disable=__lowercase   )

						lowerCamelCase_				:						Tuple				  =				self.get_dummy_inputs(__lowercase   )
						lowerCamelCase_				:						Any				  =				'french fries'
						lowerCamelCase_				:						List[str]				  =				sd_pipe(**__lowercase					,			negative_prompt=__lowercase   )
						lowerCamelCase_				:						Optional[int]				  =				output.images
						lowerCamelCase_				:						int				  =				image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						lowerCamelCase_				:						List[str]				  =				np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]   )

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


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

					def       lowercase_				(							self   ):







						'''simple docstring'''




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

					def       lowercase_				(							self					,			__lowercase=0   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				torch.Generator(device=__lowercase   ).manual_seed(__lowercase   )
						lowerCamelCase_				:						int				  =				{
						    'prompt': 'a photograph of an astronaut riding a horse',
						    'generator': generator,
						    'num_inference_steps': 10,
						    'guidance_scale': 7.5,
						    'output_type': 'numpy',
						    'parallel': 3,
						    'debug': True,
						}
						return inputs





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[Any]				  =				'stabilityai/stable-diffusion-2-base'
						lowerCamelCase_				:						List[Any]				  =				DDIMParallelScheduler.from_pretrained(__lowercase					,			subfolder='scheduler'   )
						lowerCamelCase_				:						Optional[Any]				  =				StableDiffusionParadigmsPipeline.from_pretrained(__lowercase					,			scheduler=__lowercase					,			safety_checker=__lowercase   )
						pipe.to(__lowercase   )
						pipe.set_progress_bar_config(disable=__lowercase   )
						pipe.enable_attention_slicing()

						lowerCamelCase_				:						int				  =				self.get_inputs()
						lowerCamelCase_				:						Optional[int]				  =				pipe(**__lowercase   ).images
						lowerCamelCase_				:						int				  =				image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)

						lowerCamelCase_				:						int				  =				np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]   )

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

import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.


def       a__      (					a  ,   a  ,   a  ,   a  ,   a  ,   a							)		->	int:
       # we don't care about the past rewards in this model

       A_							:       Dict	      =				states.reshape(1  ,   -1  ,   model.config.state_dim							)
       A_							:       Dict	      =				actions.reshape(1  ,   -1  ,   model.config.act_dim							)
       A_							:       Dict	      =				returns_to_go.reshape(1  ,   -1  ,   1							)
       A_							:       Tuple	      =				timesteps.reshape(1  ,   -1							)

       if model.config.max_length is not None:
              A_							:       int	      =				states[:, -model.config.max_length :]
              A_							:       Optional[int]	      =				actions[:, -model.config.max_length :]
              A_							:       Any	      =				returns_to_go[:, -model.config.max_length :]
              A_							:       Tuple	      =				timesteps[:, -model.config.max_length :]

              # pad all tokens to sequence length
              A_							:       Optional[Any]	      =				torch.cat(
                  [torch.zeros(model.config.max_length - states.shape[1]							), torch.ones(states.shape[1]							)]							)
              A_							:       Optional[Any]	      =				attention_mask.to(dtype=torch.long  ,   device=states.device							).reshape(1  ,   -1							)
              A_							:       Union[str, Any]	      =				torch.cat(
                  [
                      torch.zeros(
                          (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)  ,   device=states.device  ,   ),
                      states,
                  ]  ,   dim=1  ,   ).to(dtype=torch.floataa							)
              A_							:       Optional[Any]	      =				torch.cat(
                  [
                      torch.zeros(
                          (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)  ,   device=actions.device  ,   ),
                      actions,
                  ]  ,   dim=1  ,   ).to(dtype=torch.floataa							)
              A_							:       Optional[Any]	      =				torch.cat(
                  [
                      torch.zeros(
                          (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)  ,   device=returns_to_go.device  ,   ),
                      returns_to_go,
                  ]  ,   dim=1  ,   ).to(dtype=torch.floataa							)
              A_							:       List[str]	      =				torch.cat(
                  [
                      torch.zeros(
                          (timesteps.shape[0], model.config.max_length - timesteps.shape[1])  ,   device=timesteps.device							),
                      timesteps,
                  ]  ,   dim=1  ,   ).to(dtype=torch.long							)
       else:
              A_							:       str	      =				None

       A_      ,							A_      ,							A_							:       List[str]	      =				model(
           states=a  ,   actions=a  ,   rewards=a  ,   returns_to_go=a  ,   timesteps=a  ,   attention_mask=a  ,   return_dict=a  ,   )

       return action_preds[0, -1]


# build the environment

_lowerCAmelCase        =       gym.make('Hopper-v3')
_lowerCAmelCase        =       env.observation_space.shape[0]
_lowerCAmelCase        =       env.action_space.shape[0]
_lowerCAmelCase        =       1_0_0_0
_lowerCAmelCase        =       'cuda'
_lowerCAmelCase        =       1000.0  # normalization for rewards/returns
_lowerCAmelCase        =       3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase        =       np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
_lowerCAmelCase        =       np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
_lowerCAmelCase        =       torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase        =       torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_lowerCAmelCase        =       DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
_lowerCAmelCase        =       model.to(device)
model.eval()

for ep in range(1_0):
    _lowerCAmelCase  ,			_lowerCAmelCase        =       0, 0
    _lowerCAmelCase        =       env.reset()
    _lowerCAmelCase        =       torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
    _lowerCAmelCase        =       torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
    _lowerCAmelCase        =       torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
    _lowerCAmelCase        =       torch.zeros(0, device=device, dtype=torch.floataa)

    _lowerCAmelCase        =       torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
    for t in range(max_ep_len):
        env.render()
        # add padding
        _lowerCAmelCase        =       torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
        _lowerCAmelCase        =       torch.cat([rewards, torch.zeros(1, device=device)])

        _lowerCAmelCase        =       get_action(
            model,
            (states.to(dtype=torch.floataa) - state_mean) / state_std,
            actions.to(dtype=torch.floataa),
            rewards.to(dtype=torch.floataa),
            target_return.to(dtype=torch.floataa),
            timesteps.to(dtype=torch.long),
        )
        _lowerCAmelCase        =       action
        _lowerCAmelCase        =       action.detach().cpu().numpy()

        _lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase        =       env.step(action)

        _lowerCAmelCase        =       torch.from_numpy(state).to(device=device).reshape(1, state_dim)
        _lowerCAmelCase        =       torch.cat([states, cur_state], dim=0)
        _lowerCAmelCase        =       reward

        _lowerCAmelCase        =       target_return[0, -1] - (reward / scale)
        _lowerCAmelCase        =       torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
        _lowerCAmelCase        =       torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

        episode_return += reward
        episode_length += 1

        if done:
            break


