






"""simple docstring"""







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


@skip_mps
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					StableDiffusionModelEditingPipeline
						__lowerCamelCase				       =					TEXT_TO_IMAGE_PARAMS
						__lowerCamelCase				       =					TEXT_TO_IMAGE_BATCH_PARAMS
						__lowerCamelCase				       =					TEXT_TO_IMAGE_IMAGE_PARAMS
						__lowerCamelCase				       =					TEXT_TO_IMAGE_IMAGE_PARAMS




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												torch.manual_seed(0			)
												lowercase__				:      Optional[int]=       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       ,  )
												lowercase__				:      Any=       DDIMScheduler()
												torch.manual_seed(0			)
												lowercase__				:      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       ,  )
												torch.manual_seed(0			)
												lowercase__				:      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       ,  )
												lowercase__				:      Optional[Any]=       CLIPTextModel(snake_case__			)
												lowercase__				:      int=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

												lowercase__				:      Union[str, Any]=       {
												    "unet": unet,
												    "scheduler": scheduler,
												    "vae": vae,
												    "text_encoder": text_encoder,
												    "tokenizer": tokenizer,
												    "safety_checker": None,
												    "feature_extractor": None,
												}
												return components




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__=0			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       torch.manual_seed(snake_case__			)
												lowercase__				:      Tuple=       {
												    "prompt": "A field of roses",
												    "generator": generator,
												    # Setting height and width to None to prevent OOMs on CPU.
												    "height": None,
												    "width": None,
												    "num_inference_steps": 2,
												    "guidance_scale": 6.0,
												    "output_type": "numpy",
												}
												return inputs




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Any=       "cpu"  # ensure determinism for the device-dependent torch.Generator
												lowercase__				:      Tuple=       self.get_dummy_components()
												lowercase__				:      List[str]=       StableDiffusionModelEditingPipeline(**snake_case__			)
												lowercase__				:      Dict=       sd_pipe.to(snake_case__			)
												sd_pipe.set_progress_bar_config(disable=snake_case__			)

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

												lowercase__				:      List[str]=       np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]			)

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




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       "cpu"  # ensure determinism for the device-dependent torch.Generator
												lowercase__				:      str=       self.get_dummy_components()
												lowercase__				:      Tuple=       StableDiffusionModelEditingPipeline(**snake_case__			)
												lowercase__				:      Union[str, Any]=       sd_pipe.to(snake_case__			)
												sd_pipe.set_progress_bar_config(disable=snake_case__			)

												lowercase__				:      Optional[int]=       self.get_dummy_inputs(snake_case__			)
												lowercase__				:      int=       "french fries"
												lowercase__				:      Optional[int]=       sd_pipe(**snake_case__       ,  negative_prompt=snake_case__			)
												lowercase__				:      Union[str, Any]=       output.images
												lowercase__				:      str=       image[0, -3:, -3:, -1]

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

												lowercase__				:      str=       np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]			)

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




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Dict=       "cpu"  # ensure determinism for the device-dependent torch.Generator
												lowercase__				:      Dict=       self.get_dummy_components()
												lowercase__				:      str=       EulerAncestralDiscreteScheduler(
												    beta_start=0.0_00_85       ,  beta_end=0.0_12       ,  beta_schedule="scaled_linear"			)
												lowercase__				:      int=       StableDiffusionModelEditingPipeline(**snake_case__			)
												lowercase__				:      List[Any]=       sd_pipe.to(snake_case__			)
												sd_pipe.set_progress_bar_config(disable=snake_case__			)

												lowercase__				:      Dict=       self.get_dummy_inputs(snake_case__			)
												lowercase__				:      Optional[Any]=       sd_pipe(**snake_case__			).images
												lowercase__				:      Any=       image[0, -3:, -3:, -1]

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

												lowercase__				:      Any=       np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]			)

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




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       "cpu"  # ensure determinism for the device-dependent torch.Generator
												lowercase__				:      Dict=       self.get_dummy_components()
												lowercase__				:      int=       PNDMScheduler()
												lowercase__				:      Optional[Any]=       StableDiffusionModelEditingPipeline(**snake_case__			)
												lowercase__				:      Tuple=       sd_pipe.to(snake_case__			)
												sd_pipe.set_progress_bar_config(disable=snake_case__			)

												lowercase__				:      List[Any]=       self.get_dummy_inputs(snake_case__			)
												# the pipeline does not expect pndm so test if it raises error.
												with self.assertRaises(snake_case__			):
																		lowercase__				:      List[str]=       sd_pipe(**snake_case__			).images




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												super().test_inference_batch_single_identical(expected_max_diff=5e-3			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												super().test_attention_slicing_forward_pass(expected_max_diff=5e-3			)


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




						"""simple docstring"""




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




						def 		UpperCAmelCase_       (   self       ,  snake_case__=0			):
												'''simple docstring'''
												lowercase__				:      Dict=       torch.manual_seed(snake_case__			)
												lowercase__				:      Union[str, Any]=       {
												    "prompt": "A field of roses",
												    "generator": generator,
												    "num_inference_steps": 3,
												    "guidance_scale": 7.5,
												    "output_type": "numpy",
												}
												return inputs




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       "CompVis/stable-diffusion-v1-4"
												lowercase__				:      int=       StableDiffusionModelEditingPipeline.from_pretrained(snake_case__       ,  safety_checker=snake_case__			)
												pipe.to(snake_case__			)
												pipe.set_progress_bar_config(disable=snake_case__			)
												pipe.enable_attention_slicing()

												lowercase__				:      List[Any]=       self.get_inputs()
												lowercase__				:      int=       pipe(**snake_case__			).images
												lowercase__				:      Optional[Any]=       image[0, -3:, -3:, -1].flatten()

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

												lowercase__				:      Tuple=       np.array(
												    [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]			)

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

												# make sure image changes after editing
												pipe.edit_model("A pack of roses"       ,  "A pack of blue roses"			)

												lowercase__				:      int=       pipe(**snake_case__			).images
												lowercase__				:      int=       image[0, -3:, -3:, -1].flatten()

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

												assert np.abs(expected_slice - image_slice			).max() > 1e-1




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												lowercase__				:      Optional[Any]=       "CompVis/stable-diffusion-v1-4"
												lowercase__				:      str=       DDIMScheduler.from_pretrained(snake_case__       ,  subfolder="scheduler"			)
												lowercase__				:      List[str]=       StableDiffusionModelEditingPipeline.from_pretrained(
												    snake_case__       ,  scheduler=snake_case__       ,  safety_checker=snake_case__			)
												lowercase__				:      int=       pipe.to(snake_case__			)
												pipe.set_progress_bar_config(disable=snake_case__			)
												pipe.enable_attention_slicing(1			)
												pipe.enable_sequential_cpu_offload()

												lowercase__				:      List[str]=       self.get_inputs()
												lowercase__				:      Optional[int]=       pipe(**snake_case__			)

												lowercase__				:      int=       torch.cuda.max_memory_allocated()
												# make sure that less than 4.4 GB is allocated
												assert mem_bytes < 4.4 * 10**9














"""simple docstring"""



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      SCREAMING_SNAKE_CASE__    (   snake_case    :      Any       , snake_case    :      Optional[int]       , snake_case    :      Tuple       , snake_case    :      Union[str, Any]       , snake_case    :      Dict       , snake_case    :      str							)->						Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				states.reshape(1       , -1       , model.config.state_dim							)
     UpperCAmelCase__		:  str				=				actions.reshape(1       , -1       , model.config.act_dim							)
     UpperCAmelCase__		:  Any				=				returns_to_go.reshape(1       , -1       , 1							)
     UpperCAmelCase__		:  Union[str, Any]				=				timesteps.reshape(1       , -1							)

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

          # pad all tokens to sequence length
          UpperCAmelCase__		:  Optional[Any]				=				torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]							), torch.ones(states.shape[1]							)]							)
          UpperCAmelCase__		:  Optional[Any]				=				attention_mask.to(dtype=torch.long       , device=states.device							).reshape(1       , -1							)
          UpperCAmelCase__		:  Optional[int]				=				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							)
          UpperCAmelCase__		:  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							)
          UpperCAmelCase__		:  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							)
          UpperCAmelCase__		:  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:
          UpperCAmelCase__		:  Any				=				None

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

     return action_preds[0, -1]


# build the environment

_lowerCAmelCase     :     Dict            =	gym.make("""Hopper-v3""")
_lowerCAmelCase     :     Any            =	env.observation_space.shape[0]
_lowerCAmelCase     :     List[Any]            =	env.action_space.shape[0]
_lowerCAmelCase     :     Any            =	1_000
_lowerCAmelCase     :     Union[str, Any]            =	"""cuda"""
_lowerCAmelCase     :     Optional[Any]            =	10_00.0  # normalization for rewards/returns
_lowerCAmelCase     :     Optional[int]            =	3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase     :     Any            =	np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
_lowerCAmelCase     :     Optional[Any]            =	np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
_lowerCAmelCase     :     Any            =	torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase     :     List[Any]            =	torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_lowerCAmelCase     :     List[str]            =	DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
_lowerCAmelCase     :     Optional[int]            =	model.to(device)
model.eval()

for ep in range(10):
 _lowerCAmelCase       ,   _lowerCAmelCase     :     Dict            =	0, 0
 _lowerCAmelCase     :     Dict            =	env.reset()
 _lowerCAmelCase     :     str            =	torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 _lowerCAmelCase     :     Union[str, Any]            =	torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 _lowerCAmelCase     :     List[Any]            =	torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 _lowerCAmelCase     :     Dict            =	torch.zeros(0, device=device, dtype=torch.floataa)

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

  _lowerCAmelCase     :     Tuple            =	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     :     List[str]            =	action
  _lowerCAmelCase     :     Optional[int]            =	action.detach().cpu().numpy()

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

  _lowerCAmelCase     :     Union[str, Any]            =	torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  _lowerCAmelCase     :     Optional[int]            =	torch.cat([states, cur_state], dim=0)
  _lowerCAmelCase     :     List[Any]            =	reward

  _lowerCAmelCase     :     List[str]            =	target_return[0, -1] - (reward / scale)
  _lowerCAmelCase     :     Optional[int]            =	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  _lowerCAmelCase     :     Tuple            =	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


