


import json
import pathlib
import unittest

import numpy as np

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, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DetaImageProcessor







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=7		,				_UpperCAmelCase=3		,				_UpperCAmelCase=30		,				_UpperCAmelCase=4_00		,				_UpperCAmelCase=True		,				_UpperCAmelCase=None		,				_UpperCAmelCase=True		,				_UpperCAmelCase=[0.5, 0.5, 0.5]		,				_UpperCAmelCase=[0.5, 0.5, 0.5]		,				_UpperCAmelCase=True		,				_UpperCAmelCase=1 / 2_55		,				_UpperCAmelCase=True		,				):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       snake_case_	=				size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				num_channels
       snake_case_	=				min_resolution
       snake_case_	=				max_resolution
       snake_case_	=				do_resize
       snake_case_	=				size
       snake_case_	=				do_normalize
       snake_case_	=				image_mean
       snake_case_	=				image_std
       snake_case_	=				do_rescale
       snake_case_	=				rescale_factor
       snake_case_	=				do_pad



 def 						UpperCamelCase__					(     self ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=False ):
       if not batched:
             snake_case_	=				image_inputs[0]
             if isinstance(_UpperCAmelCase		,				Image.Image ):
                   snake_case_	,   snake_case_	=				image.size
             else:
                   snake_case_	,   snake_case_	=				image.shape[1], image.shape[2]
             if w < h:
                   snake_case_	=				int(self.size['''shortest_edge'''] * h / w )
                   snake_case_	=				self.size['''shortest_edge''']
             elif w > h:
                   snake_case_	=				self.size['''shortest_edge''']
                   snake_case_	=				int(self.size['''shortest_edge'''] * w / h )
             else:
                   snake_case_	=				self.size['''shortest_edge''']
                   snake_case_	=				self.size['''shortest_edge''']

       else:
             snake_case_	=				[]
             for image in image_inputs:
                   snake_case_	,   snake_case_	=				self.get_expected_values([image] )
                   expected_values.append((expected_height, expected_width) )
             snake_case_	=				max(_UpperCAmelCase		,				key=lambda _UpperCAmelCase : item[0] )[0]
             snake_case_	=				max(_UpperCAmelCase		,				key=lambda _UpperCAmelCase : item[1] )[1]

       return expected_height, expected_width







@require_torch
@require_vision
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  DetaImageProcessor if is_vision_available() else None



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				DetaImageProcessingTester(self )



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



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''image_mean''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''image_std''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''do_normalize''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''do_resize''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''do_rescale''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''do_pad''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''size''' ) )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size		,				{'''shortest_edge''': 18, '''longest_edge''': 13_33} )
       self.assertEqual(image_processor.do_pad		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       pass



 def 						UpperCamelCase__					(     self ):
       # Initialize image_processing
       snake_case_	=				self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       snake_case_	=				prepare_image_inputs(self.image_processor_tester		,				equal_resolution=_UpperCAmelCase )
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase		,				Image.Image )

       # Test not batched input
       snake_case_	=				image_processing(image_inputs[0]		,				return_tensors='''pt''' ).pixel_values

       snake_case_	,   snake_case_	=				self.image_processor_tester.get_expected_values(_UpperCAmelCase )

       self.assertEqual(
           encoded_images.shape		,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,				)

       # Test batched
       snake_case_	,   snake_case_	=				self.image_processor_tester.get_expected_values(_UpperCAmelCase		,				batched=_UpperCAmelCase )

       snake_case_	=				image_processing(_UpperCAmelCase		,				return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape		,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )		,				)



 def 						UpperCamelCase__					(     self ):
       # Initialize image_processing
       snake_case_	=				self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       snake_case_	=				prepare_image_inputs(self.image_processor_tester		,				equal_resolution=_UpperCAmelCase		,				numpify=_UpperCAmelCase )
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase		,				np.ndarray )

       # Test not batched input
       snake_case_	=				image_processing(image_inputs[0]		,				return_tensors='''pt''' ).pixel_values

       snake_case_	,   snake_case_	=				self.image_processor_tester.get_expected_values(_UpperCAmelCase )

       self.assertEqual(
           encoded_images.shape		,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,				)

       # Test batched
       snake_case_	=				image_processing(_UpperCAmelCase		,				return_tensors='''pt''' ).pixel_values

       snake_case_	,   snake_case_	=				self.image_processor_tester.get_expected_values(_UpperCAmelCase		,				batched=_UpperCAmelCase )

       self.assertEqual(
           encoded_images.shape		,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )		,				)



 def 						UpperCamelCase__					(     self ):
       # Initialize image_processing
       snake_case_	=				self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       snake_case_	=				prepare_image_inputs(self.image_processor_tester		,				equal_resolution=_UpperCAmelCase		,				torchify=_UpperCAmelCase )
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase		,				torch.Tensor )

       # Test not batched input
       snake_case_	=				image_processing(image_inputs[0]		,				return_tensors='''pt''' ).pixel_values

       snake_case_	,   snake_case_	=				self.image_processor_tester.get_expected_values(_UpperCAmelCase )

       self.assertEqual(
           encoded_images.shape		,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,				)

       # Test batched
       snake_case_	=				image_processing(_UpperCAmelCase		,				return_tensors='''pt''' ).pixel_values

       snake_case_	,   snake_case_	=				self.image_processor_tester.get_expected_values(_UpperCAmelCase		,				batched=_UpperCAmelCase )

       self.assertEqual(
           encoded_images.shape		,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )		,				)



 @slow
 def 						UpperCamelCase__					(     self ):
       # prepare image and target
       snake_case_	=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		,				'''r''' ) as f:
             snake_case_	=				json.loads(f.read() )

       snake_case_	=				{'''image_id''': 3_97_69, '''annotations''': target}

       # encode them
       snake_case_	=				DetaImageProcessor()
       snake_case_	=				image_processing(images=_UpperCAmelCase		,				annotations=_UpperCAmelCase		,				return_tensors='''pt''' )

       # verify pixel values
       snake_case_	=				torch.Size([1, 3, 8_00, 10_66] )
       self.assertEqual(encoding['''pixel_values'''].shape		,				_UpperCAmelCase )

       snake_case_	=				torch.tensor([0.2_796, 0.3_138, 0.3_481] )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		,				_UpperCAmelCase		,				atol=1E-4 ) )

       # verify area
       snake_case_	=				torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		,				_UpperCAmelCase ) )
       # verify boxes
       snake_case_	=				torch.Size([6, 4] )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,				_UpperCAmelCase )
       snake_case_	=				torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,				_UpperCAmelCase		,				atol=1E-3 ) )
       # verify image_id
       snake_case_	=				torch.tensor([3_97_69] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,				_UpperCAmelCase ) )
       # verify is_crowd
       snake_case_	=				torch.tensor([0, 0, 0, 0, 0, 0] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,				_UpperCAmelCase ) )
       # verify class_labels
       snake_case_	=				torch.tensor([75, 75, 63, 65, 17, 17] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,				_UpperCAmelCase ) )
       # verify orig_size
       snake_case_	=				torch.tensor([4_80, 6_40] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,				_UpperCAmelCase ) )
       # verify size
       snake_case_	=				torch.tensor([8_00, 10_66] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,				_UpperCAmelCase ) )





 @slow
 def 						UpperCamelCase__					(     self ):
       # prepare image, target and masks_path
       snake_case_	=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''		,				'''r''' ) as f:
             snake_case_	=				json.loads(f.read() )

       snake_case_	=				{'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

       snake_case_	=				pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )

       # encode them
       snake_case_	=				DetaImageProcessor(format='''coco_panoptic''' )
       snake_case_	=				image_processing(images=_UpperCAmelCase		,				annotations=_UpperCAmelCase		,				masks_path=_UpperCAmelCase		,				return_tensors='''pt''' )

       # verify pixel values
       snake_case_	=				torch.Size([1, 3, 8_00, 10_66] )
       self.assertEqual(encoding['''pixel_values'''].shape		,				_UpperCAmelCase )

       snake_case_	=				torch.tensor([0.2_796, 0.3_138, 0.3_481] )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		,				_UpperCAmelCase		,				atol=1E-4 ) )

       # verify area
       snake_case_	=				torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		,				_UpperCAmelCase ) )
       # verify boxes
       snake_case_	=				torch.Size([6, 4] )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,				_UpperCAmelCase )
       snake_case_	=				torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,				_UpperCAmelCase		,				atol=1E-3 ) )
       # verify image_id
       snake_case_	=				torch.tensor([3_97_69] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,				_UpperCAmelCase ) )
       # verify is_crowd
       snake_case_	=				torch.tensor([0, 0, 0, 0, 0, 0] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,				_UpperCAmelCase ) )
       # verify class_labels
       snake_case_	=				torch.tensor([17, 17, 63, 75, 75, 93] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,				_UpperCAmelCase ) )
       # verify masks
       snake_case_	=				82_28_73
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		,				_UpperCAmelCase )
       # verify orig_size
       snake_case_	=				torch.tensor([4_80, 6_40] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,				_UpperCAmelCase ) )
       # verify size
       snake_case_	=				torch.tensor([8_00, 10_66] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,				_UpperCAmelCase ) )



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_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       super().__init__()
       snake_case_	=				value_function
       snake_case_	=				unet
       snake_case_	=				scheduler
       snake_case_	=				env
       snake_case_	=				env.get_dataset()
       snake_case_	=				{}
       for key in self.data.keys():
             try:
                   snake_case_	=				self.data[key].mean()
             except:  # noqa: E722
                   pass
       snake_case_	=				{}
       for key in self.data.keys():
             try:
                   snake_case_	=				self.data[key].std()
             except:  # noqa: E722
                   pass
       snake_case_	=				env.observation_space.shape[0]
       snake_case_	=				env.action_space.shape[0]



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



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



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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       for key, val in cond.items():
             snake_case_	=				val.clone()
       return x_in



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				x.shape[0]
       snake_case_	=				None
       for i in tqdm.tqdm(self.scheduler.timesteps ):
             # create batch of timesteps to pass into model
             snake_case_	=				torch.full((batch_size,)		,				_UpperCAmelCase		,				device=self.unet.device		,				dtype=torch.long )
             for _ in range(_UpperCAmelCase ):
                   with torch.enable_grad():
                         x.requires_grad_()

                         # permute to match dimension for pre-trained models
                         snake_case_	=				self.value_function(x.permute(0		,				2		,				1 )		,				_UpperCAmelCase ).sample
                         snake_case_	=				torch.autograd.grad([y.sum()]		,				[x] )[0]

                         snake_case_	=				self.scheduler._get_variance(_UpperCAmelCase )
                         snake_case_	=				torch.exp(0.5 * posterior_variance )
                         snake_case_	=				model_std * grad

                   snake_case_	=				0
                   snake_case_	=				x.detach()
                   snake_case_	=				x + scale * grad
                   snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )

             snake_case_	=				self.unet(x.permute(0		,				2		,				1 )		,				_UpperCAmelCase ).sample.permute(0		,				2		,				1 )

             # TODO: verify deprecation of this kwarg
             snake_case_	=				self.scheduler.step(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				predict_epsilon=_UpperCAmelCase )['''prev_sample''']

             # apply conditions to the trajectory (set the initial state)
             snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )
             snake_case_	=				self.to_torch(_UpperCAmelCase )
       return x, y





 def __call__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=64		,				_UpperCAmelCase=32		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1 ):
       # normalize the observations and create  batch dimension
       snake_case_	=				self.normalize(_UpperCAmelCase		,				'''observations''' )
       snake_case_	=				obs[None].repeat(_UpperCAmelCase		,				axis=0 )

       snake_case_	=				{0: self.to_torch(_UpperCAmelCase )}
       snake_case_	=				(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)
       snake_case_	=				randn_tensor(_UpperCAmelCase		,				device=self.unet.device )
       snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )
       snake_case_	=				self.to_torch(_UpperCAmelCase )

       # run the diffusion process
       snake_case_	,   snake_case_	=				self.run_diffusion(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

       # sort output trajectories by value
       snake_case_	=				y.argsort(0		,				descending=_UpperCAmelCase ).squeeze()
       snake_case_	=				x[sorted_idx]
       snake_case_	=				sorted_values[:, :, : self.action_dim]
       snake_case_	=				actions.detach().cpu().numpy()
       snake_case_	=				self.de_normalize(_UpperCAmelCase		,				key='''actions''' )

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

       snake_case_	=				denorm_actions[selected_index, 0]
       return denorm_actions