

'''simple docstring'''

import unittest

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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor




class 			A_					(							unittest.TestCase				):






     '''simple docstring'''



     def __init__(						self					:	Optional[int]	,     lowercase_					:	Dict	,     lowercase_					:	int=7	,     lowercase_					:	Optional[Any]=3	,     lowercase_					:	Tuple=18	,     lowercase_					:	Dict=30	,     lowercase_					:	Tuple=400	,     lowercase_					:	List[Any]=True	,     lowercase_					:	Any=None	,     lowercase_					:	int=True	,     lowercase_					:	Union[str, Any]=None	,     lowercase_					:	Optional[Any]=True	,     lowercase_					:	Tuple=[0.4814_5466, 0.457_8275, 0.4082_1073]	,     lowercase_					:	Optional[int]=[0.2686_2954, 0.2613_0258, 0.2757_7711]	,     lowercase_					:	str=True	,     )		->      List[Any]:
      UpperCAmelCase			:      List[Any]		   =					size if size is not None else {'shortest_edge': 20}
      UpperCAmelCase			:      List[str]		   =					crop_size if crop_size is not None else {'height': 18, 'width': 18}
      UpperCAmelCase			:      str		   =					parent
      UpperCAmelCase			:      int		   =					batch_size
      UpperCAmelCase			:      Optional[int]		   =					num_channels
      UpperCAmelCase			:      List[str]		   =					image_size
      UpperCAmelCase			:      Optional[Any]		   =					min_resolution
      UpperCAmelCase			:      int		   =					max_resolution
      UpperCAmelCase			:      Tuple		   =					do_resize
      UpperCAmelCase			:      Tuple		   =					size
      UpperCAmelCase			:      List[str]		   =					do_center_crop
      UpperCAmelCase			:      Tuple		   =					crop_size
      UpperCAmelCase			:      Optional[int]		   =					do_normalize
      UpperCAmelCase			:      List[str]		   =					image_mean
      UpperCAmelCase			:      Any		   =					image_std
      UpperCAmelCase			:      str		   =					do_convert_rgb


     def   UpperCAmelCase_			(						self					:	str			)		->      Optional[int]:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
      }




     def   UpperCAmelCase_			(						self					:	Union[str, Any]	,     lowercase_					:	int=False	,     lowercase_					:	List[str]=False	,     lowercase_					:	List[str]=False			)		->      Optional[int]:
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
       UpperCAmelCase			:      List[str]		   =					[]
       for i in range(self.batch_size			):
        image_inputs.append(
            np.random.randint(
                255	,     size=(self.num_channels, self.max_resolution, self.max_resolution)	,     dtype=np.uinta			)			)
      else:
       UpperCAmelCase			:      List[Any]		   =					[]
       for i in range(self.batch_size			):
        UpperCAmelCase   ,					UpperCAmelCase			:      List[str]		   =					np.random.choice(np.arange(self.min_resolution	,     self.max_resolution			)	,     2			)
        image_inputs.append(np.random.randint(255	,     size=(self.num_channels, width, height)	,     dtype=np.uinta			)			)

      if not numpify and not torchify:
       # PIL expects the channel dimension as last dimension
       UpperCAmelCase			:      List[Any]		   =					[Image.fromarray(np.moveaxis(lowercase_	,     0	,     -1			)			) for x in image_inputs]

      if torchify:
       UpperCAmelCase			:      Optional[Any]		   =					[torch.from_numpy(lowercase_			) for x in image_inputs]

      return image_inputs




@require_torch
@require_vision
class 			A_					(							_snake_case			,  unittest.TestCase				):






     '''simple docstring'''



     UpperCAmelCase_		:      List[str]    						=    CLIPImageProcessor if is_vision_available() else None


     def   UpperCAmelCase_			(						self					:	Optional[Any]			)		->      Union[str, Any]:
      UpperCAmelCase			:      List[Any]		   =					CLIPImageProcessingTester(self			)


     @property
     def   UpperCAmelCase_			(						self					:	Union[str, Any]			)		->      List[str]:
      return self.image_processor_tester.prepare_image_processor_dict()


     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      List[Any]:
      UpperCAmelCase			:      List[str]		   =					self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(lowercase_	,     'do_resize'			)			)
      self.assertTrue(hasattr(lowercase_	,     'size'			)			)
      self.assertTrue(hasattr(lowercase_	,     'do_center_crop'			)			)
      self.assertTrue(hasattr(lowercase_	,     'center_crop'			)			)
      self.assertTrue(hasattr(lowercase_	,     'do_normalize'			)			)
      self.assertTrue(hasattr(lowercase_	,     'image_mean'			)			)
      self.assertTrue(hasattr(lowercase_	,     'image_std'			)			)
      self.assertTrue(hasattr(lowercase_	,     'do_convert_rgb'			)			)


     def   UpperCAmelCase_			(						self					:	str			)		->      Union[str, Any]:
      UpperCAmelCase			:      Optional[Any]		   =					self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size	,     {'shortest_edge': 20}			)
      self.assertEqual(image_processor.crop_size	,     {'height': 18, 'width': 18}			)

      UpperCAmelCase			:      Union[str, Any]		   =					self.image_processing_class.from_dict(self.image_processor_dict	,     size=42	,     crop_size=84			)
      self.assertEqual(image_processor.size	,     {'shortest_edge': 42}			)
      self.assertEqual(image_processor.crop_size	,     {'height': 84, 'width': 84}			)


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


     def   UpperCAmelCase_			(						self					:	Optional[Any]			)		->      str:
      # Initialize image_processing
      UpperCAmelCase			:      List[str]		   =					self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      UpperCAmelCase			:      Dict		   =					self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_			)
      for image in image_inputs:
       self.assertIsInstance(lowercase_	,     Image.Image			)

      # Test not batched input
      UpperCAmelCase			:      str		   =					image_processing(image_inputs[0]	,     return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape	,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )	,     )

      # Test batched
      UpperCAmelCase			:      Dict		   =					image_processing(lowercase_	,     return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape	,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )	,     )


     def   UpperCAmelCase_			(						self					:	Union[str, Any]			)		->      Union[str, Any]:
      # Initialize image_processing
      UpperCAmelCase			:      List[str]		   =					self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      UpperCAmelCase			:      List[str]		   =					self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_	,     numpify=lowercase_			)
      for image in image_inputs:
       self.assertIsInstance(lowercase_	,     np.ndarray			)

      # Test not batched input
      UpperCAmelCase			:      Optional[Any]		   =					image_processing(image_inputs[0]	,     return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape	,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )	,     )

      # Test batched
      UpperCAmelCase			:      List[Any]		   =					image_processing(lowercase_	,     return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape	,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )	,     )




     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      Optional[int]:
      # Initialize image_processing
      UpperCAmelCase			:      List[str]		   =					self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      UpperCAmelCase			:      Optional[Any]		   =					self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_	,     torchify=lowercase_			)
      for image in image_inputs:
       self.assertIsInstance(lowercase_	,     torch.Tensor			)

      # Test not batched input
      UpperCAmelCase			:      Optional[Any]		   =					image_processing(image_inputs[0]	,     return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape	,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )	,     )

      # Test batched
      UpperCAmelCase			:      Union[str, Any]		   =					image_processing(lowercase_	,     return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape	,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )	,     )




@require_torch
@require_vision
class 			A_					(							_snake_case			,  unittest.TestCase				):






     '''simple docstring'''



     UpperCAmelCase_		:      Tuple    						=    CLIPImageProcessor if is_vision_available() else None


     def   UpperCAmelCase_			(						self					:	Optional[Any]			)		->      Optional[Any]:
      UpperCAmelCase			:      Tuple		   =					CLIPImageProcessingTester(self	,     num_channels=4			)
      UpperCAmelCase			:      List[str]		   =					3


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


     def   UpperCAmelCase_			(						self					:	Any			)		->      Dict:
      UpperCAmelCase			:      Optional[int]		   =					self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(lowercase_	,     'do_resize'			)			)
      self.assertTrue(hasattr(lowercase_	,     'size'			)			)
      self.assertTrue(hasattr(lowercase_	,     'do_center_crop'			)			)
      self.assertTrue(hasattr(lowercase_	,     'center_crop'			)			)
      self.assertTrue(hasattr(lowercase_	,     'do_normalize'			)			)
      self.assertTrue(hasattr(lowercase_	,     'image_mean'			)			)
      self.assertTrue(hasattr(lowercase_	,     'image_std'			)			)
      self.assertTrue(hasattr(lowercase_	,     'do_convert_rgb'			)			)


     def   UpperCAmelCase_			(						self					:	int			)		->      Any:
      pass




     def   UpperCAmelCase_			(						self					:	str			)		->      List[str]:
      # Initialize image_processing
      UpperCAmelCase			:      Tuple		   =					self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      UpperCAmelCase			:      List[str]		   =					self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_			)
      for image in image_inputs:
       self.assertIsInstance(lowercase_	,     Image.Image			)

      # Test not batched input
      UpperCAmelCase			:      List[Any]		   =					image_processing(image_inputs[0]	,     return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape	,     (
              1,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )	,     )

      # Test batched
      UpperCAmelCase			:      Optional[Any]		   =					image_processing(lowercase_	,     return_tensors='pt'			).pixel_values
      self.assertEqual(
          encoded_images.shape	,     (
              self.image_processor_tester.batch_size,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )	,     )







'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      Dict    						=    (LMSDiscreteScheduler,)
     UpperCAmelCase_		:      Any    						=    10


     def   UpperCAmelCase_			(						self					:	str	,     **lowercase_					:	List[str]			)		->      str:
      UpperCAmelCase			:      Optional[Any]		   =					{
          'num_train_timesteps': 1_100,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**lowercase_			)
      return config


     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      Tuple:
      for timesteps in [10, 50, 100, 1_000]:
       self.check_over_configs(num_train_timesteps=lowercase_			)


     def   UpperCAmelCase_			(						self					:	int			)		->      Tuple:
      for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]			):
       self.check_over_configs(beta_start=lowercase_	,     beta_end=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Dict			)		->      Union[str, Any]:
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Tuple			)		->      List[Any]:
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=lowercase_			)


     def   UpperCAmelCase_			(						self					:	str			)		->      Union[str, Any]:
      for t in [0, 500, 800]:
       self.check_over_forward(time_step=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Union[str, Any]			)		->      List[str]:
      UpperCAmelCase			:      Any		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Tuple		   =					self.get_scheduler_config()
      UpperCAmelCase			:      Any		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCAmelCase			:      str		   =					self.dummy_model()
      UpperCAmelCase			:      str		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      Optional[Any]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Union[str, Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Dict		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[str]		   =					output.prev_sample

      UpperCAmelCase			:      Optional[Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Optional[Any]		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 1006.388			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3


     def   UpperCAmelCase_			(						self					:	str			)		->      Dict:
      UpperCAmelCase			:      Any		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Dict		   =					self.get_scheduler_config(prediction_type='v_prediction'			)
      UpperCAmelCase			:      Optional[int]		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCAmelCase			:      Optional[Any]		   =					self.dummy_model()
      UpperCAmelCase			:      List[str]		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      List[Any]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Optional[Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Dict		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[str]		   =					output.prev_sample

      UpperCAmelCase			:      Union[str, Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Any		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 0.0017			) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06			) < 1E-3


     def   UpperCAmelCase_			(						self					:	Optional[Any]			)		->      Dict:
      UpperCAmelCase			:      Dict		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Optional[int]		   =					self.get_scheduler_config()
      UpperCAmelCase			:      List[str]		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps	,     device=lowercase_			)

      UpperCAmelCase			:      Tuple		   =					self.dummy_model()
      UpperCAmelCase			:      Tuple		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      UpperCAmelCase			:      Any		   =					sample.to(lowercase_			)

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      Dict		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Optional[Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Tuple		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[Any]		   =					output.prev_sample

      UpperCAmelCase			:      Union[str, Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Union[str, Any]		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 1006.388			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3




     def   UpperCAmelCase_			(						self					:	Dict			)		->      Optional[Any]:
      UpperCAmelCase			:      int		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Union[str, Any]		   =					self.get_scheduler_config()
      UpperCAmelCase			:      Optional[Any]		   =					scheduler_class(**lowercase_	,     use_karras_sigmas=lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps	,     device=lowercase_			)

      UpperCAmelCase			:      List[Any]		   =					self.dummy_model()
      UpperCAmelCase			:      Dict		   =					self.dummy_sample_deter.to(lowercase_			) * scheduler.init_noise_sigma
      UpperCAmelCase			:      Union[str, Any]		   =					sample.to(lowercase_			)

      for t in scheduler.timesteps:
       UpperCAmelCase			:      Optional[int]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      List[str]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      List[Any]		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      Any		   =					output.prev_sample

      UpperCAmelCase			:      List[Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Any		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 3812.9927			) < 2E-2
      assert abs(result_mean.item() - 4.9648			) < 1E-3




