'''simple docstring'''





import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 BridgeTowerImageProcessor






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__ = True    ,							lowercase__ = None    ,							lowercase__ = 32    ,							lowercase__ = True    ,							lowercase__ = 1 / 255    ,							lowercase__ = True    ,							lowercase__ = True    ,							lowercase__ = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,							lowercase__ = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,							lowercase__ = True    ,							lowercase__=7    ,							lowercase__=30    ,							lowercase__=400    ,							lowercase__=3    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			int    	=       parent
         SCREAMING_SNAKE_CASE					:			Dict    	=       do_resize
         SCREAMING_SNAKE_CASE					:			List[str]    	=       size if size is not None else {"shortest_edge": 288}
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       size_divisor
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       do_rescale
         SCREAMING_SNAKE_CASE					:			Tuple    	=       rescale_factor
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       do_normalize
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       do_center_crop
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image_mean
         SCREAMING_SNAKE_CASE					:			Dict    	=       image_std
         SCREAMING_SNAKE_CASE					:			List[str]    	=       do_pad
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       batch_size
         SCREAMING_SNAKE_CASE					:			int    	=       num_channels
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       min_resolution
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       max_resolution






    def       _UpperCamelCase    (       self )							-> Tuple:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=False )							-> Optional[Any]:
         if not batched:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.size["shortest_edge"]
              SCREAMING_SNAKE_CASE					:			Dict    	=       image_inputs[0]
              if isinstance(lowercase__    ,							Image.Image ):
                   SCREAMING_SNAKE_CASE					:			Dict    	=       image.size
              else:
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       image.shape[1], image.shape[2]
              SCREAMING_SNAKE_CASE					:			Any    	=       size / min(lowercase__    ,							lowercase__ )
              if h < w:
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       size, scale * w
              else:
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       scale * h, size

              SCREAMING_SNAKE_CASE					:			List[Any]    	=       int((1_333 / 800) * size )
              if max(lowercase__    ,							lowercase__ ) > max_size:
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       max_size / max(lowercase__    ,							lowercase__ )
                   SCREAMING_SNAKE_CASE					:			int    	=       newh * scale
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       neww * scale

              SCREAMING_SNAKE_CASE					:			Any    	=       int(newh + 0.5 ), int(neww + 0.5 )
              SCREAMING_SNAKE_CASE					:			Any    	=       (
                  newh // self.size_divisor * self.size_divisor,
                  neww // self.size_divisor * self.size_divisor,
              )

         else:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       []
              for image in image_inputs:
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_expected_values([image] )
                   expected_values.append((expected_height, expected_width) )
              SCREAMING_SNAKE_CASE					:			Tuple    	=       max(lowercase__    ,							key=lambda lowercase__ : item[0] )[0]
              SCREAMING_SNAKE_CASE					:			Tuple    	=       max(lowercase__    ,							key=lambda lowercase__ : item[1] )[1]

         return expected_height, expected_width






@require_torch
@require_vision
class 				UpperCAmelCase (       lowercase__   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   Union[str, Any]							=						BridgeTowerImageProcessor if is_vision_available() else None






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       BridgeTowerImageProcessingTester(self )






    @property
    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			int    	=       self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(lowercase__    ,							'image_mean' ) )
         self.assertTrue(hasattr(lowercase__    ,							'image_std' ) )
         self.assertTrue(hasattr(lowercase__    ,							'do_normalize' ) )
         self.assertTrue(hasattr(lowercase__    ,							'do_resize' ) )
         self.assertTrue(hasattr(lowercase__    ,							'size' ) )
         self.assertTrue(hasattr(lowercase__    ,							'size_divisor' ) )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         pass






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         # Initialize image processor
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=lowercase__ )
         for image in image_inputs:
              self.assertIsInstance(lowercase__    ,							Image.Image )

         # Test not batched input
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       image_processing(image_inputs[0]    ,							return_tensors='pt' ).pixel_values

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.image_processor_tester.get_expected_values(lowercase__ )
         self.assertEqual(
             encoded_images.shape    ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,							)

         # Test batched
         SCREAMING_SNAKE_CASE					:			int    	=       image_processing(lowercase__    ,							return_tensors='pt' ).pixel_values

         SCREAMING_SNAKE_CASE					:			Any    	=       self.image_processor_tester.get_expected_values(lowercase__    ,							batched=lowercase__ )
         self.assertEqual(
             encoded_images.shape    ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,							)






    def       _UpperCamelCase    (       self )							-> Any:
         # Initialize image processor
         SCREAMING_SNAKE_CASE					:			str    	=       self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         SCREAMING_SNAKE_CASE					:			str    	=       prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=lowercase__    ,							numpify=lowercase__ )
         for image in image_inputs:
              self.assertIsInstance(lowercase__    ,							np.ndarray )

         # Test not batched input
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       image_processing(image_inputs[0]    ,							return_tensors='pt' ).pixel_values

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.image_processor_tester.get_expected_values(lowercase__ )
         self.assertEqual(
             encoded_images.shape    ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,							)

         # Test batched
         SCREAMING_SNAKE_CASE					:			Tuple    	=       image_processing(lowercase__    ,							return_tensors='pt' ).pixel_values

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.image_processor_tester.get_expected_values(lowercase__    ,							batched=lowercase__ )
         self.assertEqual(
             encoded_images.shape    ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,							)






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         # Initialize image processor
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=lowercase__    ,							torchify=lowercase__ )
         for image in image_inputs:
              self.assertIsInstance(lowercase__    ,							torch.Tensor )

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

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.image_processor_tester.get_expected_values(lowercase__ )
         self.assertEqual(
             encoded_images.shape    ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,							)

         # Test batched
         SCREAMING_SNAKE_CASE					:			Any    	=       image_processing(lowercase__    ,							return_tensors='pt' ).pixel_values

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.image_processor_tester.get_expected_values(lowercase__    ,							batched=lowercase__ )
         self.assertEqual(
             encoded_images.shape    ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,							)


'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						(DDPMParallelScheduler,)






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {
             'num_train_timesteps': 1_000,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**lowercase__ )
         return config






    def       _UpperCamelCase    (       self )							-> int:
         for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,							[0.0_0_2, 0.0_2, 0.2, 2] ):
              self.check_over_configs(beta_start=lowercase__    ,							beta_end=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         self.check_over_configs(thresholding=lowercase__ )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=lowercase__    ,							prediction_type=lowercase__    ,							sample_max_value=lowercase__    ,							)






    def       _UpperCamelCase    (       self )							-> Dict:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for t in [0, 500, 999]:
              self.check_over_forward(time_step=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1E-5






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			str    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.dummy_sample_deter + 0.1
         SCREAMING_SNAKE_CASE					:			int    	=       self.dummy_sample_deter - 0.1

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       samplea.shape[0]
         SCREAMING_SNAKE_CASE					:			str    	=       torch.stack([samplea, samplea, samplea]    ,							dim=0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.arange(lowercase__ )[0:3, None].repeat(1    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(samples.flatten(0    ,							1 )    ,							timesteps.flatten(0    ,							1 ) )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler.batch_step_no_noise(lowercase__    ,							timesteps.flatten(0    ,							1 )    ,							samples.flatten(0    ,							1 ) )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1E-2
         assert abs(result_mean.item() - 0.5_0_0_5 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			int    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Dict    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[str]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Dict    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1E-2
         assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_scheduler_config(prediction_type='v_prediction' )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			int    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[Any]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			str    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1E-2
         assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       scheduler.timesteps

         for i, timestep in enumerate(lowercase__ ):
              if i == len(lowercase__ ) - 1:
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       -1
              else:
                   SCREAMING_SNAKE_CASE					:			Any    	=       timesteps[i + 1]

              SCREAMING_SNAKE_CASE					:			str    	=       scheduler.previous_timestep(lowercase__ )
              SCREAMING_SNAKE_CASE					:			str    	=       prev_t.item()

              self.assertEqual(lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       [100, 87, 50, 51, 0]

         with self.assertRaises(lowercase__    ,							msg='`custom_timesteps` must be in descending order.' ):
              scheduler.set_timesteps(timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       len(lowercase__ )

         with self.assertRaises(lowercase__    ,							msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
              scheduler.set_timesteps(num_inference_steps=lowercase__    ,							timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             lowercase__    ,							msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'    ,							):
              scheduler.set_timesteps(timesteps=lowercase__ )



