






"""simple docstring"""



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        lowerCAmelCase__       (  __magic_name__				,		unittest.TestCase					):
   SCREAMING_SNAKE_CASE_					      =KandinskyVaaPriorEmbaEmbPipeline
   SCREAMING_SNAKE_CASE_					      =['''prompt''', '''image''']
   SCREAMING_SNAKE_CASE_					      =['''prompt''', '''image''']
   SCREAMING_SNAKE_CASE_					      =[
       '''num_images_per_prompt''',
       '''strength''',
       '''generator''',
       '''num_inference_steps''',
       '''latents''',
       '''negative_prompt''',
       '''guidance_scale''',
       '''output_type''',
       '''return_dict''',
   ]
   SCREAMING_SNAKE_CASE_					      =False
   @property
   def 	__a				(						self	:     Tuple				):


        '''simple docstring'''
        return 3_2
   @property
   def 	__a				(						self	:     Tuple				):


        '''simple docstring'''
        return 3_2
   @property
   def 	__a				(						self	:     Any				):


        '''simple docstring'''
        return self.time_input_dim
   @property
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        return self.time_input_dim * 4
   @property
   def 	__a				(						self	:     Any				):


        '''simple docstring'''
        return 1_0_0
   @property
   def 	__a				(						self	:     int				):


        '''simple docstring'''
        UpperCAmelCase__		:  Tuple				=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)
        return tokenizer
   @property
   def 	__a				(						self	:     List[str]				):


        '''simple docstring'''
        torch.manual_seed(0				)
        UpperCAmelCase__		:  Dict				=				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=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				,       )
        return CLIPTextModelWithProjection(snake_case__				)
   @property
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        torch.manual_seed(0				)

        UpperCAmelCase__		:  int				=				{
            "num_attention_heads": 2,
            "attention_head_dim": 1_2,
            "embedding_dim": self.text_embedder_hidden_size,
            "num_layers": 1,
        }

        UpperCAmelCase__		:  Optional[int]				=				PriorTransformer(**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
        UpperCAmelCase__		:  List[str]				=				nn.Parameter(torch.ones(model.clip_std.shape				)				)
        return model
   @property
   def 	__a				(						self	:     List[str]				):


        '''simple docstring'''
        torch.manual_seed(0				)
        UpperCAmelCase__		:  Optional[int]				=				CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size				,       image_size=2_2_4				,       projection_dim=self.text_embedder_hidden_size				,       intermediate_size=3_7				,       num_attention_heads=4				,       num_channels=3				,       num_hidden_layers=5				,       patch_size=1_4				,       )

        UpperCAmelCase__		:  Any				=				CLIPVisionModelWithProjection(snake_case__				)
        return model
   @property
   def 	__a				(						self	:     Tuple				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				CLIPImageProcessor(
            crop_size=2_2_4				,       do_center_crop=snake_case__				,       do_normalize=snake_case__				,       do_resize=snake_case__				,       image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]				,       image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]				,       resample=3				,       size=2_2_4				,       )

        return image_processor
   def 	__a				(						self	:     Optional[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  str				=				self.dummy_prior
        UpperCAmelCase__		:  List[Any]				=				self.dummy_image_encoder
        UpperCAmelCase__		:  List[str]				=				self.dummy_text_encoder
        UpperCAmelCase__		:  int				=				self.dummy_tokenizer
        UpperCAmelCase__		:  Union[str, Any]				=				self.dummy_image_processor

        UpperCAmelCase__		:  List[Any]				=				UnCLIPScheduler(
            variance_type="fixed_small_log"				,       prediction_type="sample"				,       num_train_timesteps=1_0_0_0				,       clip_sample=snake_case__				,       clip_sample_range=10.0				,       )

        UpperCAmelCase__		:  List[str]				=				{
            "prior": prior,
            "image_encoder": image_encoder,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "scheduler": scheduler,
            "image_processor": image_processor,
        }

        return components
   def 	__a				(						self	:     str				,       snake_case__	:     List[str]				,       snake_case__	:     Tuple=0				):


        '''simple docstring'''
        if str(snake_case__				).startswith("mps"				):
             UpperCAmelCase__		:  List[Any]				=				torch.manual_seed(snake_case__				)
        else:
             UpperCAmelCase__		:  int				=				torch.Generator(device=snake_case__				).manual_seed(snake_case__				)

        UpperCAmelCase__		:  Any				=				floats_tensor((1, 3, 6_4, 6_4)				,       rng=random.Random(snake_case__				)				).to(snake_case__				)
        UpperCAmelCase__		:  Any				=				image.cpu().permute(0				,       2				,       3				,       1				)[0]
        UpperCAmelCase__		:  Any				=				Image.fromarray(np.uinta(snake_case__				)				).convert("RGB"				).resize((2_5_6, 2_5_6)				)

        UpperCAmelCase__		:  Optional[int]				=				{
            "prompt": "horse",
            "image": init_image,
            "strength": 0.5,
            "generator": generator,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs
   def 	__a				(						self	:     Optional[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  str				=				"cpu"

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

        UpperCAmelCase__		:  List[str]				=				self.pipeline_class(**snake_case__				)
        UpperCAmelCase__		:  int				=				pipe.to(snake_case__				)

        pipe.set_progress_bar_config(disable=snake_case__				)

        UpperCAmelCase__		:  Optional[int]				=				pipe(**self.get_dummy_inputs(snake_case__				)				)
        UpperCAmelCase__		:  Union[str, Any]				=				output.image_embeds

        UpperCAmelCase__		:  str				=				pipe(
            **self.get_dummy_inputs(snake_case__				)				,       return_dict=snake_case__				,       )[0]

        UpperCAmelCase__		:  Tuple				=				image[0, -1_0:]
        UpperCAmelCase__		:  Any				=				image_from_tuple[0, -1_0:]

        assert image.shape == (1, 3_2)

        UpperCAmelCase__		:  Optional[int]				=				np.array(
            [
                0.107_1284,
                1.333_0271,
                0.6126_0223,
                -0.669_1065,
                -0.384_6852,
                -1.030_3661,
                0.2271_6111,
                0.0334_8901,
                0.3004_0675,
                -0.2480_5029,
            ]				)

        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 	__a				(						self	:     int				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				torch_device == "cpu"
        UpperCAmelCase__		:  int				=				True
        UpperCAmelCase__		:  Dict				=				False

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




   @skip_mps
   def 	__a				(						self	:     Any				):


        '''simple docstring'''
        UpperCAmelCase__		:  Any				=				torch_device == "cpu"
        UpperCAmelCase__		:  Union[str, Any]				=				False

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










"""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


