


import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class        lowerCamelCase      (     _UpperCamelCase   ):


	def __init__(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    ):
							super().__init__()
							__UpperCAmelCase :       List[Any]  						=  value_function
							__UpperCAmelCase :       List[Any]  						=  unet
							__UpperCAmelCase :       Optional[Any]  						=  scheduler
							__UpperCAmelCase :       int  						=  env
							__UpperCAmelCase :       Optional[int]  						=  env.get_dataset()
							__UpperCAmelCase :       List[Any]  						=  {}
							for key in self.data.keys():
													try:
																			__UpperCAmelCase :       Union[str, Any]  						=  self.data[key].mean()
													except:  # noqa: E722
																			pass
							__UpperCAmelCase :       Optional[int]  						=  {}
							for key in self.data.keys():
													try:
																			__UpperCAmelCase :       List[Any]  						=  self.data[key].std()
													except:  # noqa: E722
																			pass
							__UpperCAmelCase :       Tuple  						=  env.observation_space.shape[0]
							__UpperCAmelCase :       List[str]  						=  env.action_space.shape[0]


	def    A(						self							,    lowercase__							,    lowercase__):
							return (x_in - self.means[key]) / self.stds[key]


	def    A(						self							,    lowercase__							,    lowercase__):
							return x_in * self.stds[key] + self.means[key]


	def    A(						self							,    lowercase__):
							if type(lowercase__) is dict:
													return {k: self.to_torch(lowercase__) for k, v in x_in.items()}
							elif torch.is_tensor(lowercase__):
													return x_in.to(self.unet.device)
							return torch.tensor(lowercase__							,    device=self.unet.device)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__):
							for key, val in cond.items():
													__UpperCAmelCase :       Optional[int]  						=  val.clone()
							return x_in


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__):
							__UpperCAmelCase :       Optional[int]  						=  x.shape[0]
							__UpperCAmelCase :       Union[str, Any]  						=  None
							for i in tqdm.tqdm(self.scheduler.timesteps):
													# create batch of timesteps to pass into model
													__UpperCAmelCase :       Dict  						=  torch.full((batch_size,)							,    lowercase__							,    device=self.unet.device							,    dtype=torch.long)
													for _ in range(lowercase__):
																			with torch.enable_grad():
																									x.requires_grad_()

																									# permute to match dimension for pre-trained models
																									__UpperCAmelCase :       Optional[int]  						=  self.value_function(x.permute(0							,    2							,    1)							,    lowercase__).sample
																									__UpperCAmelCase :       Dict  						=  torch.autograd.grad([y.sum()]							,    [x])[0]

																									__UpperCAmelCase :       Union[str, Any]  						=  self.scheduler._get_variance(lowercase__)
																									__UpperCAmelCase :       str  						=  torch.exp(0.5 * posterior_variance)
																									__UpperCAmelCase :       Tuple  						=  model_std * grad

																			__UpperCAmelCase :       List[str]  						=  0
																			__UpperCAmelCase :       List[Any]  						=  x.detach()
																			__UpperCAmelCase :       Dict  						=  x + scale * grad
																			__UpperCAmelCase :       str  						=  self.reset_xa(lowercase__							,    lowercase__							,    self.action_dim)

													__UpperCAmelCase :       int  						=  self.unet(x.permute(0							,    2							,    1)							,    lowercase__).sample.permute(0							,    2							,    1)

													# TODO: verify deprecation of this kwarg
													__UpperCAmelCase :       List[Any]  						=  self.scheduler.step(lowercase__							,    lowercase__							,    lowercase__							,    predict_epsilon=lowercase__)['''prev_sample''']

													# apply conditions to the trajectory (set the initial state)
													__UpperCAmelCase :       List[Any]  						=  self.reset_xa(lowercase__							,    lowercase__							,    self.action_dim)
													__UpperCAmelCase :       Dict  						=  self.to_torch(lowercase__)
							return x, y


	def __call__(						self							,    lowercase__							,    lowercase__=6_4							,    lowercase__=3_2							,    lowercase__=2							,    lowercase__=0.1):
							# normalize the observations and create  batch dimension
							__UpperCAmelCase :       Any  						=  self.normalize(lowercase__							,    '''observations''')
							__UpperCAmelCase :       Any  						=  obs[None].repeat(lowercase__							,    axis=0)

							__UpperCAmelCase :       Optional[Any]  						=  {0: self.to_torch(lowercase__)}
							__UpperCAmelCase :       Tuple  						=  (batch_size, planning_horizon, self.state_dim + self.action_dim)

							# generate initial noise and apply our conditions (to make the trajectories start at current state)
							__UpperCAmelCase :       Any  						=  randn_tensor(lowercase__							,    device=self.unet.device)
							__UpperCAmelCase :       Any  						=  self.reset_xa(lowercase__							,    lowercase__							,    self.action_dim)
							__UpperCAmelCase :       List[Any]  						=  self.to_torch(lowercase__)

							# run the diffusion process
							__UpperCAmelCase						,			__UpperCAmelCase :       Optional[int]  						=  self.run_diffusion(lowercase__							,    lowercase__							,    lowercase__							,    lowercase__)

							# sort output trajectories by value
							__UpperCAmelCase :       Dict  						=  y.argsort(0							,    descending=lowercase__).squeeze()
							__UpperCAmelCase :       str  						=  x[sorted_idx]
							__UpperCAmelCase :       Dict  						=  sorted_values[:, :, : self.action_dim]
							__UpperCAmelCase :       int  						=  actions.detach().cpu().numpy()
							__UpperCAmelCase :       Tuple  						=  self.de_normalize(lowercase__							,    key='''actions''')

							# select the action with the highest value
							if y is not None:
													__UpperCAmelCase :       Tuple  						=  0
							else:
													# if we didn't run value guiding, select a random action
													__UpperCAmelCase :       str  						=  np.random.randint(0							,    lowercase__)

							__UpperCAmelCase :       Union[str, Any]  						=  denorm_actions[selected_index, 0]
							return denorm_actions





import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor




class        lowerCamelCase      (     unittest.TestCase   ):


	def __init__(						self							,    lowercase__							,    lowercase__=7							,    lowercase__=3							,    lowercase__=1_8							,    lowercase__=3_0							,    lowercase__=4_0_0							,    lowercase__=True							,    lowercase__=None							,    lowercase__=True							,    ):
							__UpperCAmelCase :       Union[str, Any]  						=  size if size is not None else {'''height''': 1_8, '''width''': 1_8}
							__UpperCAmelCase :       Any  						=  parent
							__UpperCAmelCase :       Dict  						=  batch_size
							__UpperCAmelCase :       List[str]  						=  num_channels
							__UpperCAmelCase :       int  						=  image_size
							__UpperCAmelCase :       Tuple  						=  min_resolution
							__UpperCAmelCase :       str  						=  max_resolution
							__UpperCAmelCase :       Optional[int]  						=  do_resize
							__UpperCAmelCase :       Tuple  						=  size
							__UpperCAmelCase :       Union[str, Any]  						=  do_normalize


	def    A(						self):
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
							            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
							        ]),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}






@require_torch
@require_vision
class        lowerCamelCase      (     _UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		Dict        =	ImageGPTImageProcessor if is_vision_available() else None


	def    A(						self):
							__UpperCAmelCase :       Optional[Any]  						=  ImageGPTImageProcessingTester(self)


	@property
	def    A(						self):
							return self.image_processor_tester.prepare_image_processor_dict()


	def    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class(**self.image_processor_dict)
							self.assertTrue(hasattr(lowercase__							,    '''clusters'''))
							self.assertTrue(hasattr(lowercase__							,    '''do_resize'''))
							self.assertTrue(hasattr(lowercase__							,    '''size'''))
							self.assertTrue(hasattr(lowercase__							,    '''do_normalize'''))


	def    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.image_processing_class.from_dict(self.image_processor_dict)
							self.assertEqual(image_processor.size							,    {'''height''': 1_8, '''width''': 1_8})

							__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class.from_dict(self.image_processor_dict							,    size=4_2)
							self.assertEqual(image_processor.size							,    {'''height''': 4_2, '''width''': 4_2})


	def    A(						self):
							__UpperCAmelCase :       Any  						=  self.image_processing_class(**self.image_processor_dict)
							__UpperCAmelCase :       Any  						=  json.loads(image_processor.to_json_string())
							for key, value in self.image_processor_dict.items():
													if key == "clusters":
																			self.assertTrue(np.array_equal(lowercase__							,    obj[key]))
													else:
																			self.assertEqual(obj[key]							,    lowercase__)


	def    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.image_processing_class(**self.image_processor_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase :       Dict  						=  os.path.join(lowercase__							,    '''image_processor.json''')
													image_processor_first.to_json_file(lowercase__)
													__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class.from_json_file(lowercase__).to_dict()

							__UpperCAmelCase :       Any  						=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
													if key == "clusters":
																			self.assertTrue(np.array_equal(lowercase__							,    image_processor_second[key]))
													else:
																			self.assertEqual(image_processor_first[key]							,    lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class(**self.image_processor_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													image_processor_first.save_pretrained(lowercase__)
													__UpperCAmelCase :       Dict  						=  self.image_processing_class.from_pretrained(lowercase__).to_dict()

							__UpperCAmelCase :       Optional[Any]  						=  image_processor_first.to_dict()
							for key, value in image_processor_first.items():
													if key == "clusters":
																			self.assertTrue(np.array_equal(lowercase__							,    image_processor_second[key]))
													else:
																			self.assertEqual(image_processor_first[key]							,    lowercase__)


	@unittest.skip('''ImageGPT requires clusters at initialization''')
	def    A(						self):
							pass






def 						__SCREAMING_SNAKE_CASE       (					)		->    int:




						'''simple docstring'''







						__UpperCAmelCase :       List[str]  						=  load_dataset('''hf-internal-testing/fixtures_image_utils'''			,					split='''test'''			)

						__UpperCAmelCase :       Optional[Any]  						=  Image.open(dataset[4]['''file''']			)
						__UpperCAmelCase :       Optional[int]  						=  Image.open(dataset[5]['''file''']			)

						__UpperCAmelCase :       int  						=  [imagea, imagea]

						return images





@require_vision
@require_torch
class        lowerCamelCase      (     unittest.TestCase   ):


	@slow
	def    A(						self):
							__UpperCAmelCase :       int  						=  ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small''')

							__UpperCAmelCase :       Any  						=  prepare_images()

							# test non-batched
							__UpperCAmelCase :       int  						=  image_processing(images[0]							,    return_tensors='''pt''')

							self.assertIsInstance(encoding.input_ids							,    torch.LongTensor)
							self.assertEqual(encoding.input_ids.shape							,    (1, 1_0_2_4))

							__UpperCAmelCase :       int  						=  [3_0_6, 1_9_1, 1_9_1]
							self.assertEqual(encoding.input_ids[0, :3].tolist()							,    lowercase__)

							# test batched
							__UpperCAmelCase :       int  						=  image_processing(lowercase__							,    return_tensors='''pt''')

							self.assertIsInstance(encoding.input_ids							,    torch.LongTensor)
							self.assertEqual(encoding.input_ids.shape							,    (2, 1_0_2_4))

							__UpperCAmelCase :       Any  						=  [3_0_3, 1_3, 1_3]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()							,    lowercase__)

