




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 SwinaSRImageProcessor
		from transformers.image_transforms import get_image_size




class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				def __init__(				self    :					Optional[int] ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					Union[str, Any]=7 ,				__lowerCamelCase    :					int=3 ,				__lowerCamelCase    :					int=18 ,				__lowerCamelCase    :					List[Any]=30 ,				__lowerCamelCase    :					Optional[int]=4_00 ,				__lowerCamelCase    :					Optional[Any]=True ,				__lowerCamelCase    :					int=1 / 2_55 ,				__lowerCamelCase    :					Dict=True ,				__lowerCamelCase    :					str=8 ,				) ->					List[str]:
							a   =      parent
							a   =      batch_size
							a   =      num_channels
							a   =      image_size
							a   =      min_resolution
							a   =      max_resolution
							a   =      do_rescale
							a   =      rescale_factor
							a   =      do_pad
							a   =      pad_size





				def 							__UpperCAmelCase							(				self    :					str							) ->					Any:
							return {
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							    "pad_size": self.pad_size,
							}





				def 							__UpperCAmelCase							(				self    :					Optional[int] ,				__lowerCamelCase    :					List[str]=False ,				__lowerCamelCase    :					int=False ,				__lowerCamelCase    :					Optional[int]=False							) ->					int:
							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(
													        2_55 ,				size=(self.num_channels, self.max_resolution, self.max_resolution) ,				dtype=np.uinta							)							)
							else:
										a   =      []
										for i in range(self.batch_size							):
													a   =      np.random.choice(np.arange(self.min_resolution ,				self.max_resolution							) ,				2							)
													image_inputs.append(np.random.randint(2_55 ,				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    snake_case__							(lowerCAmelCase__   ,	unittest.TestCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Tuple										= SwinaSRImageProcessor if is_vision_available() else None





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Optional[Any]:
							a   =      SwinaSRImageProcessingTester(self							)





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





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Any:
							a   =      self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(UpperCamelCase__ ,				"do_rescale"							)							)
							self.assertTrue(hasattr(UpperCamelCase__ ,				"rescale_factor"							)							)
							self.assertTrue(hasattr(UpperCamelCase__ ,				"do_pad"							)							)
							self.assertTrue(hasattr(UpperCamelCase__ ,				"pad_size"							)							)





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Optional[int]:
							pass





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				__lowerCamelCase    :					List[str]							) ->					int:
							a   =      get_image_size(UpperCamelCase__							)
							a   =      self.image_processor_tester.pad_size

							a   =      (old_height // size + 1) * size - old_height
							a   =      (old_width // size + 1) * size - old_width
							return old_height + pad_height, old_width + pad_width





				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Any:
							# Initialize image_processor
							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_processor(image_inputs[0] ,				return_tensors="pt"							).pixel_values
							a   =      self.calculate_expected_size(np.array(image_inputs[0]							)							)
							self.assertEqual(
							    encoded_images.shape ,				(
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,				)





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Any:
							# Initialize image_processor
							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_processor(image_inputs[0] ,				return_tensors="pt"							).pixel_values
							a   =      self.calculate_expected_size(image_inputs[0]							)
							self.assertEqual(
							    encoded_images.shape ,				(
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,				)





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					List[str]:
							# Initialize image_processor
							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_processor(image_inputs[0] ,				return_tensors="pt"							).pixel_values
							a   =      self.calculate_expected_size(image_inputs[0]							)
							self.assertEqual(
							    encoded_images.shape ,				(
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,				)












import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Dict										= (VQDiffusionScheduler,)





				def 							__UpperCAmelCase							(				self    :					Tuple ,				**__lowerCamelCase    :					Optional[int]							) ->					Optional[Any]:
							a   =      {
							    "num_vec_classes": 40_97,
							    "num_train_timesteps": 1_00,
							}

							config.update(**__lowerCamelCase							)
							return config





				def 							__UpperCAmelCase							(				self    :					List[str] ,				__lowerCamelCase    :					Any							) ->					Union[str, Any]:
							a   =      4
							a   =      8
							a   =      8

							a   =      torch.randint(0 ,				__lowerCamelCase ,				(batch_size, height * width)							)

							return sample





				@property
				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Optional[Any]:
							assert False





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					Optional[int]							) ->					int:
							def model(__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Any ,				*__lowerCamelCase    :					Optional[Any]							):
										a   ,		a   =      sample.shape
										a   =      torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)							)
										a   =      F.log_softmax(logits.double() ,				dim=1							).float()
										return return_value

							return model





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Any:
							for timesteps in [2, 5, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					List[Any]:
							for num_vec_classes in [5, 1_00, 10_00, 40_00]:
										self.check_over_configs(num_vec_classes=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[Any]:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=__lowerCamelCase							)





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






