
"""simple docstring"""




import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger




class 					UpperCamelCase     (	snake_case__				):

				"""simple docstring"""



				UpperCAmelCase_    :				str						  =    '''config.json'''


				@register_to_config
				def __init__(    self		,     lowercase__=2		,     lowercase__=5		,     lowercase__=(2, 5)		,     lowercase__="for diffusion"		,     lowercase__=[1, 3]		,     )   ->   List[Any]:
									"""simple docstring"""







									pass






class 					UpperCamelCase     (	snake_case__				):

				"""simple docstring"""



				UpperCAmelCase_    :				Tuple						  =    '''config.json'''


				@register_to_config
				def __init__(    self		,     lowercase__=2		,     lowercase__=5		,     lowercase__=(2, 5)		,     lowercase__="for diffusion"		,     lowercase__=[1, 3]		,     )   ->   Union[str, Any]:
									"""simple docstring"""







									pass






class 					UpperCamelCase     (	snake_case__				):

				"""simple docstring"""



				UpperCAmelCase_    :				List[Any]						  =    '''config.json'''


				@register_to_config
				def __init__(    self		,     lowercase__=2		,     lowercase__=5		,     lowercase__=(2, 5)		,     lowercase__="for diffusion"		,     lowercase__=[1, 3]		,     lowercase__=[1, 3]		,     )   ->   Union[str, Any]:
									"""simple docstring"""







									pass






class 					UpperCamelCase     (	snake_case__				):

				"""simple docstring"""



				UpperCAmelCase_    :				Dict						  =    '''config.json'''


				@register_to_config
				def __init__(    self		,     lowercase__=2		,     lowercase__=5		,     lowercase__=(2, 5)		,     lowercase__="for diffusion"		,     lowercase__=[1, 5]		,     lowercase__=[5, 4]		,     )   ->   Optional[int]:
									"""simple docstring"""







									pass






class 					UpperCamelCase     (	unittest.TestCase				):

				"""simple docstring"""



				def 							A (    self      )   ->   str:
									"""simple docstring"""







									with self.assertRaises(_A      ):
														ConfigMixin.load_config('dummy_path'      )


				def 							A (    self      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     SampleObject()
									SCREAMING_SNAKE_CASE           =     obj.config
									assert config["a"] == 2
									assert config["b"] == 5
									assert config["c"] == (2, 5)
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]

									# init ignore private arguments
									SCREAMING_SNAKE_CASE           =     SampleObject(_name_or_path='lalala'      )
									SCREAMING_SNAKE_CASE           =     obj.config
									assert config["a"] == 2
									assert config["b"] == 5
									assert config["c"] == (2, 5)
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]

									# can override default
									SCREAMING_SNAKE_CASE           =     SampleObject(c=6      )
									SCREAMING_SNAKE_CASE           =     obj.config
									assert config["a"] == 2
									assert config["b"] == 5
									assert config["c"] == 6
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]

									# can use positional arguments.
									SCREAMING_SNAKE_CASE           =     SampleObject(1		,     c=6      )
									SCREAMING_SNAKE_CASE           =     obj.config
									assert config["a"] == 1
									assert config["b"] == 5
									assert config["c"] == 6
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]


				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     SampleObject()
									SCREAMING_SNAKE_CASE           =     obj.config

									assert config["a"] == 2
									assert config["b"] == 5
									assert config["c"] == (2, 5)
									assert config["d"] == "for diffusion"
									assert config["e"] == [1, 3]

									with tempfile.TemporaryDirectory() as tmpdirname:
														obj.save_config(_A      )
														SCREAMING_SNAKE_CASE           =     SampleObject.from_config(SampleObject.load_config(_A      )      )
														SCREAMING_SNAKE_CASE           =     new_obj.config

									# unfreeze configs
									SCREAMING_SNAKE_CASE           =     dict(_A      )
									SCREAMING_SNAKE_CASE           =     dict(_A      )

									assert config.pop('c'      ) == (2, 5)  # instantiated as tuple
									assert new_config.pop('c'      ) == [2, 5]  # saved & loaded as list because of json
									config.pop('_use_default_values'      )
									assert config == new_config


				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     logging.get_logger('diffusers.configuration_utils'      )
									# 30 for warning
									logger.setLevel(30      )

									with CaptureLogger(_A      ) as cap_logger:
														SCREAMING_SNAKE_CASE           =     DDIMScheduler.from_pretrained(
														    'hf-internal-testing/tiny-stable-diffusion-torch'		,     subfolder='scheduler'      )

									assert ddim.__class__ == DDIMScheduler
									# no warning should be thrown
									assert cap_logger.out == ""


				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     logging.get_logger('diffusers.configuration_utils'      )
									# 30 for warning
									logger.setLevel(30      )

									with CaptureLogger(_A      ) as cap_logger:
														SCREAMING_SNAKE_CASE           =     EulerDiscreteScheduler.from_pretrained(
														    'hf-internal-testing/tiny-stable-diffusion-torch'		,     subfolder='scheduler'      )

									assert euler.__class__ == EulerDiscreteScheduler
									# no warning should be thrown
									assert cap_logger.out == ""


				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     logging.get_logger('diffusers.configuration_utils'      )
									# 30 for warning
									logger.setLevel(30      )

									with CaptureLogger(_A      ) as cap_logger:
														SCREAMING_SNAKE_CASE           =     EulerAncestralDiscreteScheduler.from_pretrained(
														    'hf-internal-testing/tiny-stable-diffusion-torch'		,     subfolder='scheduler'      )

									assert euler.__class__ == EulerAncestralDiscreteScheduler
									# no warning should be thrown
									assert cap_logger.out == ""


				def 							A (    self      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     logging.get_logger('diffusers.configuration_utils'      )
									# 30 for warning
									logger.setLevel(30      )

									with CaptureLogger(_A      ) as cap_logger:
														SCREAMING_SNAKE_CASE           =     PNDMScheduler.from_pretrained(
														    'hf-internal-testing/tiny-stable-diffusion-torch'		,     subfolder='scheduler'      )

									assert pndm.__class__ == PNDMScheduler
									# no warning should be thrown
									assert cap_logger.out == ""


				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     logging.get_logger('diffusers.configuration_utils'      )
									# 30 for warning
									logger.setLevel(30      )

									with CaptureLogger(_A      ) as cap_logger:
														SCREAMING_SNAKE_CASE           =     DDPMScheduler.from_pretrained(
														    'hf-internal-testing/tiny-stable-diffusion-torch'		,     subfolder='scheduler'		,     prediction_type='sample'		,     beta_end=8		,     )

									with CaptureLogger(_A      ) as cap_logger_a:
														SCREAMING_SNAKE_CASE           =     DDPMScheduler.from_pretrained('google/ddpm-celebahq-256'		,     beta_start=88      )

									assert ddpm.__class__ == DDPMScheduler
									assert ddpm.config.prediction_type == "sample"
									assert ddpm.config.beta_end == 8
									assert ddpm_a.config.beta_start == 88

									# no warning should be thrown
									assert cap_logger.out == ""
									assert cap_logger_a.out == ""


				def 							A (    self      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     logging.get_logger('diffusers.configuration_utils'      )
									# 30 for warning
									logger.setLevel(30      )

									with CaptureLogger(_A      ) as cap_logger:
														SCREAMING_SNAKE_CASE           =     DPMSolverMultistepScheduler.from_pretrained(
														    'hf-internal-testing/tiny-stable-diffusion-torch'		,     subfolder='scheduler'      )

									assert dpm.__class__ == DPMSolverMultistepScheduler
									# no warning should be thrown
									assert cap_logger.out == ""




				def 							A (    self      )   ->   Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     SampleObject()

									SCREAMING_SNAKE_CASE           =     {k: v for k, v in config.config.items() if not k.startswith('_'      )}

									# make sure that default config has all keys in `_use_default_values`
									assert set(config_dict.keys()      ) == set(config.config._use_default_values      )

									with tempfile.TemporaryDirectory() as tmpdirname:
														config.save_config(_A      )

														# now loading it with SampleObject2 should put f into `_use_default_values`
														SCREAMING_SNAKE_CASE           =     SampleObjecta.from_config(_A      )

														assert "f" in config._use_default_values
														assert config.f == [1, 3]

									# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
									# **BECAUSE** it is part of `config._use_default_values`
									SCREAMING_SNAKE_CASE           =     SampleObjecta.from_config(config.config      )
									assert new_config.f == [5, 4]

									config.config._use_default_values.pop()
									SCREAMING_SNAKE_CASE           =     SampleObjecta.from_config(config.config      )
									assert new_config_a.f == [1, 3]

									# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
									assert new_config_a.e == [1, 3]


"""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   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					# we don't care about the past rewards in this model

					SCREAMING_SNAKE_CASE           =     states.reshape(1,					-1,					model.config.state_dim      )
					SCREAMING_SNAKE_CASE           =     actions.reshape(1,					-1,					model.config.act_dim      )
					SCREAMING_SNAKE_CASE           =     returns_to_go.reshape(1,					-1,					1      )
					SCREAMING_SNAKE_CASE           =     timesteps.reshape(1,					-1      )

					if model.config.max_length is not None:
										SCREAMING_SNAKE_CASE           =     states[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =     actions[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =     returns_to_go[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =     timesteps[:, -model.config.max_length :]

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

					SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     model(
					    states=SCREAMING_SNAKE_CASE_,					actions=SCREAMING_SNAKE_CASE_,					rewards=SCREAMING_SNAKE_CASE_,					returns_to_go=SCREAMING_SNAKE_CASE_,					timesteps=SCREAMING_SNAKE_CASE_,					attention_mask=SCREAMING_SNAKE_CASE_,					return_dict=SCREAMING_SNAKE_CASE_,					)

					return action_preds[0, -1]


# build the environment

snake_case			       =		gym.make('Hopper-v3')
snake_case			       =		env.observation_space.shape[0]
snake_case			       =		env.action_space.shape[0]
snake_case			       =		1_0_0_0
snake_case			       =		'cuda'
snake_case			       =		1_0_0_0.0  # normalization for rewards/returns
snake_case			       =		3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case			       =		np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
snake_case			       =		np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
snake_case			       =		torch.from_numpy(state_mean).to(device=device)
snake_case			       =		torch.from_numpy(state_std).to(device=device)

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

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

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

		snake_case			       =		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),
		)
		snake_case			       =		action
		snake_case			       =		action.detach().cpu().numpy()

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

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

		snake_case			       =		target_return[0, -1] - (reward / scale)
		snake_case			       =		torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
		snake_case			       =		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
