




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 ChineseCLIPImageProcessor





class     lowercase_	(					unittest.TestCase							):



 '''simple docstring'''

 def __init__(			self	:	Dict   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Optional[int]=7   ,							_UpperCAmelCase	:	int=3   ,							_UpperCAmelCase	:	Optional[Any]=18   ,							_UpperCAmelCase	:	Dict=30   ,							_UpperCAmelCase	:	List[str]=400   ,							_UpperCAmelCase	:	Any=True   ,							_UpperCAmelCase	:	Optional[Any]=None   ,							_UpperCAmelCase	:	Union[str, Any]=True   ,							_UpperCAmelCase	:	Any=None   ,							_UpperCAmelCase	:	List[str]=True   ,							_UpperCAmelCase	:	Any=[0.4814_5466, 0.457_8275, 0.4082_1073]   ,							_UpperCAmelCase	:	int=[0.2686_2954, 0.2613_0258, 0.2757_7711]   ,							_UpperCAmelCase	:	Union[str, Any]=True   ,							):
       _A        =       size if size is not None else {'height': 224, 'width': 224}
       _A        =       crop_size if crop_size is not None else {'height': 18, 'width': 18}
       _A        =       parent
       _A        =       batch_size
       _A        =       num_channels
       _A        =       image_size
       _A        =       min_resolution
       _A        =       max_resolution
       _A        =       do_resize
       _A        =       size
       _A        =       do_center_crop
       _A        =       crop_size
       _A        =       do_normalize
       _A        =       image_mean
       _A        =       image_std
       _A        =       do_convert_rgb

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

       if equal_resolution:
             _A        =       []
             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:
             _A        =       []
             for i in range(self.batch_size		):
                   _A       ,   _A        =       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
             _A        =       [Image.fromarray(np.moveaxis(_UpperCAmelCase   ,							0   ,							-1		)		) for x in image_inputs]

       if torchify:
             _A        =       [torch.from_numpy(_UpperCAmelCase		) for x in image_inputs]

       return image_inputs





@require_torch
@require_vision
class     lowercase_	(					__lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					int				  =     ChineseCLIPImageProcessor if is_vision_available() else None

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       ChineseCLIPImageProcessingTester(self   ,							do_center_crop=_UpperCAmelCase		)

 @property
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       return self.image_processor_tester.prepare_image_processor_dict()

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'do_resize'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'size'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'do_center_crop'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'center_crop'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'do_normalize'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'image_mean'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'image_std'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'do_convert_rgb'		)		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A        =       self.image_processing_class.from_dict(self.image_processor_dict		)
       self.assertEqual(image_processor.size   ,							{'height': 224, 'width': 224}		)
       self.assertEqual(image_processor.crop_size   ,							{'height': 18, 'width': 18}		)

       _A        =       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       lowerCAmelCase_							(			self	:	str		):
       pass

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       # Initialize image_processing
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       _A        =       self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase		)
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase   ,							Image.Image		)

       # Test not batched input
       _A        =       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
       _A        =       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,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )   ,							)

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

       # Test not batched input
       _A        =       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
       _A        =       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,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )   ,							)



 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       # Initialize image_processing
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       _A        =       self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase   ,							torchify=_UpperCAmelCase		)
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase   ,							torch.Tensor		)

       # Test not batched input
       _A        =       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
       _A        =       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,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )   ,							)





@require_torch
@require_vision
class     lowercase_	(					__lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Any				  =     ChineseCLIPImageProcessor if is_vision_available() else None

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       ChineseCLIPImageProcessingTester(self   ,							num_channels=4   ,							do_center_crop=_UpperCAmelCase		)
       _A        =       3

 @property
 def       lowerCAmelCase_							(			self	:	List[str]		):
       return self.image_processor_tester.prepare_image_processor_dict()

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'do_resize'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'size'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'do_center_crop'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'center_crop'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'do_normalize'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'image_mean'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'image_std'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'do_convert_rgb'		)		)

 def       lowerCAmelCase_							(			self	:	str		):
       pass



 def       lowerCAmelCase_							(			self	:	Dict		):
       # Initialize image_processing
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       _A        =       self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase		)
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase   ,							Image.Image		)

       # Test not batched input
       _A        =       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
       _A        =       image_processing(_UpperCAmelCase   ,							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 tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Union[str, Any]				  =     (IPNDMScheduler,)
 UpperCAmelCase   :					Optional[Any]				  =     (('''num_inference_steps''', 50),)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							**_UpperCAmelCase	:	List[Any]		):
       _A        =       {'num_train_timesteps': 1_000}
       config.update(**_UpperCAmelCase		)
       return config

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	Optional[int]=0   ,							**_UpperCAmelCase	:	Union[str, Any]		):
       _A        =       dict(self.forward_default_kwargs		)
       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)
       _A        =       self.dummy_sample
       _A        =       0.1 * sample
       _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config(**_UpperCAmelCase		)
             _A        =       scheduler_class(**_UpperCAmelCase		)
             scheduler.set_timesteps(_UpperCAmelCase		)
             # copy over dummy past residuals
             _A        =       dummy_past_residuals[:]

             if time_step is None:
                   _A        =       scheduler.timesteps[len(scheduler.timesteps		) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase		)
                   _A        =       scheduler_class.from_pretrained(_UpperCAmelCase		)
                   new_scheduler.set_timesteps(_UpperCAmelCase		)
                   # copy over dummy past residuals
                   _A        =       dummy_past_residuals[:]

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

 def       lowerCAmelCase_							(			self	:	str		):
       pass

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Any=0   ,							**_UpperCAmelCase	:	Any		):
       _A        =       dict(self.forward_default_kwargs		)
       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)
       _A        =       self.dummy_sample
       _A        =       0.1 * sample
       _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)
             scheduler.set_timesteps(_UpperCAmelCase		)

             # copy over dummy past residuals (must be after setting timesteps)
             _A        =       dummy_past_residuals[:]

             if time_step is None:
                   _A        =       scheduler.timesteps[len(scheduler.timesteps		) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase		)
                   _A        =       scheduler_class.from_pretrained(_UpperCAmelCase		)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(_UpperCAmelCase		)

                   # copy over dummy past residual (must be after setting timesteps)
                   _A        =       dummy_past_residuals[:]

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	Optional[int]		):
       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config(**_UpperCAmelCase		)
       _A        =       scheduler_class(**_UpperCAmelCase		)

       _A        =       10
       _A        =       self.dummy_model()
       _A        =       self.dummy_sample_deter
       scheduler.set_timesteps(_UpperCAmelCase		)

       for i, t in enumerate(scheduler.timesteps		):
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		).prev_sample

       for i, t in enumerate(scheduler.timesteps		):
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		).prev_sample

       return sample

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       dict(self.forward_default_kwargs		)

       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)

             _A        =       self.dummy_sample
             _A        =       0.1 * sample

             if num_inference_steps is not None and hasattr(_UpperCAmelCase   ,							'set_timesteps'		):
                   scheduler.set_timesteps(_UpperCAmelCase		)
             elif num_inference_steps is not None and not hasattr(_UpperCAmelCase   ,							'set_timesteps'		):
                   _A        =       num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
             _A        =       dummy_past_residuals[:]

             _A        =       scheduler.timesteps[5]
             _A        =       scheduler.timesteps[6]

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             self.assertEqual(output_a.shape   ,							sample.shape		)
             self.assertEqual(output_a.shape   ,							output_a.shape		)

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             self.assertEqual(output_a.shape   ,							sample.shape		)
             self.assertEqual(output_a.shape   ,							output_a.shape		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       for timesteps in [100, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase   ,							time_step=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       for t, num_inference_steps in zip([1, 5, 10]   ,							[10, 50, 100]		):
             self.check_over_forward(num_inference_steps=_UpperCAmelCase   ,							time_step=_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.full_loop()
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_mean.item() - 2_540_529		) < 10

