

import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class    __A				(							lowerCAmelCase_						,						unittest.TestCase ):
    lowerCAmelCase_							:		List[Any]						=							KandinskyVaaPriorEmbaEmbPipeline
    lowerCAmelCase_							:		str						=							["""prompt""", """image"""]
    lowerCAmelCase_							:		List[str]						=							["""prompt""", """image"""]
    lowerCAmelCase_							:		Any						=							[
        """num_images_per_prompt""",
        """strength""",
        """generator""",
        """num_inference_steps""",
        """latents""",
        """negative_prompt""",
        """guidance_scale""",
        """output_type""",
        """return_dict""",
    ]
    lowerCAmelCase_							:		Dict						=							False






    @property
    def 	lowercase__ (	self			:    Optional[int]    ):
        return 32






    @property
    def 	lowercase__ (	self			:    Any    ):
        return 32






    @property
    def 	lowercase__ (	self			:    int    ):
        return self.time_input_dim






    @property
    def 	lowercase__ (	self			:    Optional[int]    ):
        return self.time_input_dim * 4






    @property
    def 	lowercase__ (	self			:    Any    ):
        return 100






    @property
    def 	lowercase__ (	self			:    int    ):
        lowerCAmelCase				:			Any    			=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
        return tokenizer






    @property
    def 	lowercase__ (	self			:    List[str]    ):
        torch.manual_seed(0    )
        lowerCAmelCase				:			Union[str, Any]    			=      CLIPTextConfig(
            bos_token_id=0    ,				eos_token_id=2    ,				hidden_size=self.text_embedder_hidden_size    ,				projection_dim=self.text_embedder_hidden_size    ,				intermediate_size=37    ,				layer_norm_eps=1E-05    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				pad_token_id=1    ,				vocab_size=1000    ,				)
        return CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE    )






    @property
    def 	lowercase__ (	self			:    Optional[Any]    ):
        torch.manual_seed(0    )

        lowerCAmelCase				:			List[str]    			=      {
            'num_attention_heads': 2,
            'attention_head_dim': 12,
            'embedding_dim': self.text_embedder_hidden_size,
            'num_layers': 1,
        }

        lowerCAmelCase				:			Union[str, Any]    			=      PriorTransformer(**__SCREAMING_SNAKE_CASE    )
        # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
        lowerCAmelCase				:			Optional[Any]    			=      nn.Parameter(torch.ones(model.clip_std.shape    )    )
        return model






    @property
    def 	lowercase__ (	self			:    Union[str, Any]    ):
        torch.manual_seed(0    )
        lowerCAmelCase				:			List[Any]    			=      CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size    ,				image_size=224    ,				projection_dim=self.text_embedder_hidden_size    ,				intermediate_size=37    ,				num_attention_heads=4    ,				num_channels=3    ,				num_hidden_layers=5    ,				patch_size=14    ,				)

        lowerCAmelCase				:			Optional[Any]    			=      CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE    )
        return model






    @property
    def 	lowercase__ (	self			:    Any    ):
        lowerCAmelCase				:			Optional[int]    			=      CLIPImageProcessor(
            crop_size=224    ,				do_center_crop=__SCREAMING_SNAKE_CASE    ,				do_normalize=__SCREAMING_SNAKE_CASE    ,				do_resize=__SCREAMING_SNAKE_CASE    ,				image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]    ,				image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]    ,				resample=3    ,				size=224    ,				)

        return image_processor






    def 	lowercase__ (	self			:    int    ):
        lowerCAmelCase				:			Any    			=      self.dummy_prior
        lowerCAmelCase				:			Dict    			=      self.dummy_image_encoder
        lowerCAmelCase				:			Optional[int]    			=      self.dummy_text_encoder
        lowerCAmelCase				:			Any    			=      self.dummy_tokenizer
        lowerCAmelCase				:			Optional[Any]    			=      self.dummy_image_processor

        lowerCAmelCase				:			Tuple    			=      UnCLIPScheduler(
            variance_type='fixed_small_log'    ,				prediction_type='sample'    ,				num_train_timesteps=1000    ,				clip_sample=__SCREAMING_SNAKE_CASE    ,				clip_sample_range=10.0    ,				)

        lowerCAmelCase				:			Dict    			=      {
            'prior': prior,
            'image_encoder': image_encoder,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'scheduler': scheduler,
            'image_processor': image_processor,
        }

        return components






    def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    List[Any]=0    ):

        if str(__SCREAMING_SNAKE_CASE    ).startswith('mps'    ):
            lowerCAmelCase				:			List[str]    			=      torch.manual_seed(__SCREAMING_SNAKE_CASE    )
        else:
            lowerCAmelCase				:			List[Any]    			=      torch.Generator(device=__SCREAMING_SNAKE_CASE    ).manual_seed(__SCREAMING_SNAKE_CASE    )

        lowerCAmelCase				:			Tuple    			=      floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(__SCREAMING_SNAKE_CASE    )    ).to(__SCREAMING_SNAKE_CASE    )
        lowerCAmelCase				:			int    			=      image.cpu().permute(0    ,				2    ,				3    ,				1    )[0]
        lowerCAmelCase				:			Dict    			=      Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE    )    ).convert('RGB'    ).resize((256, 256)    )

        lowerCAmelCase				:			Tuple    			=      {
            'prompt': 'horse',
            'image': init_image,
            'strength': 0.5,
            'generator': generator,
            'guidance_scale': 4.0,
            'num_inference_steps': 2,
            'output_type': 'np',
        }
        return inputs






    def 	lowercase__ (	self			:    Tuple    ):
        lowerCAmelCase				:			int    			=      'cpu'

        lowerCAmelCase				:			Union[str, Any]    			=      self.get_dummy_components()

        lowerCAmelCase				:			Tuple    			=      self.pipeline_class(**__SCREAMING_SNAKE_CASE    )
        lowerCAmelCase				:			int    			=      pipe.to(__SCREAMING_SNAKE_CASE    )

        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

        lowerCAmelCase				:			Union[str, Any]    			=      pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE    )    )
        lowerCAmelCase				:			Optional[Any]    			=      output.image_embeds

        lowerCAmelCase				:			Tuple    			=      pipe(
            **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE    )    ,				return_dict=__SCREAMING_SNAKE_CASE    ,				)[0]

        lowerCAmelCase				:			Any    			=      image[0, -10:]
        lowerCAmelCase				:			Optional[int]    			=      image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        lowerCAmelCase				:			str    			=      np.array(
            [
                0.1_07_12_84,
                1.3_33_02_71,
                0.61_26_02_23,
                -0.6_69_10_65,
                -0.3_84_68_52,
                -1.0_30_36_61,
                0.22_71_61_11,
                0.03_34_89_01,
                0.30_04_06_75,
                -0.24_80_50_29,
            ]    )

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






    @skip_mps
    def 	lowercase__ (	self			:    Any    ):
        lowerCAmelCase				:			List[Any]    			=      torch_device == 'cpu'
        lowerCAmelCase				:			Dict    			=      True
        lowerCAmelCase				:			Dict    			=      False

        self._test_inference_batch_single_identical(
            test_max_difference=__SCREAMING_SNAKE_CASE    ,				relax_max_difference=__SCREAMING_SNAKE_CASE    ,				test_mean_pixel_difference=__SCREAMING_SNAKE_CASE    ,				)






    @skip_mps
    def 	lowercase__ (	self			:    List[Any]    ):
        lowerCAmelCase				:			str    			=      torch_device == 'cpu'
        lowerCAmelCase				:			Optional[int]    			=      False

        self._test_attention_slicing_forward_pass(
            test_max_difference=__SCREAMING_SNAKE_CASE    ,				test_mean_pixel_difference=__SCREAMING_SNAKE_CASE    ,				)








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__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> str:



    '''simple docstring'''






    lowerCAmelCase				:			Optional[Any]    			=      states.reshape(1,      -1,      model.config.state_dim				)
    lowerCAmelCase				:			List[Any]    			=      actions.reshape(1,      -1,      model.config.act_dim				)
    lowerCAmelCase				:			List[Any]    			=      returns_to_go.reshape(1,      -1,      1				)
    lowerCAmelCase				:			Dict    			=      timesteps.reshape(1,      -1				)

    if model.config.max_length is not None:
        lowerCAmelCase				:			Optional[Any]    			=      states[:, -model.config.max_length :]
        lowerCAmelCase				:			Union[str, Any]    			=      actions[:, -model.config.max_length :]
        lowerCAmelCase				:			Dict    			=      returns_to_go[:, -model.config.max_length :]
        lowerCAmelCase				:			List[str]    			=      timesteps[:, -model.config.max_length :]

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

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

    return action_preds[0, -1]


# build the environment

__A   :     Dict      =			gym.make('''Hopper-v3''')
__A   :     Any      =			env.observation_space.shape[0]
__A   :     Optional[Any]      =			env.action_space.shape[0]
__A   :     Any      =			1000
__A   :     Tuple      =			'''cuda'''
__A   :     Optional[int]      =			10_00.0  # normalization for rewards/returns
__A   :     Optional[Any]      =			3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__A   :     str      =			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,
    ]
)
__A   :     Dict      =			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,
    ]
)
__A   :     int      =			torch.from_numpy(state_mean).to(device=device)
__A   :     Tuple      =			torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
__A   :     List[Any]      =			DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
__A   :     Any      =			model.to(device)
model.eval()

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

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

  __A   :     int      =			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),
  )
  __A   :     List[str]      =			action
  __A   :     List[str]      =			action.detach().cpu().numpy()

  __A     ,      __A     ,      __A     ,      __A   :     List[str]      =			env.step(action)

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

  __A   :     str      =			target_return[0, -1] - (reward / scale)
  __A   :     Dict      =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  __A   :     List[Any]      =			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





