





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, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import LevitImageProcessor

class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=7	,			__lowerCAmelCase=3	,			__lowerCAmelCase=18	,			__lowerCAmelCase=30	,			__lowerCAmelCase=400	,			__lowerCAmelCase=True	,			__lowerCAmelCase=None	,			__lowerCAmelCase=True	,			__lowerCAmelCase=None	,			__lowerCAmelCase=True	,			__lowerCAmelCase=[0.5, 0.5, 0.5]	,			__lowerCAmelCase=[0.5, 0.5, 0.5]	,			):
														UpperCamelCase__					  =      size if size is not None else {"""shortest_edge""": 18}
														UpperCamelCase__					  =      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
														UpperCamelCase__					  =      parent
														UpperCamelCase__					  =      batch_size
														UpperCamelCase__					  =      num_channels
														UpperCamelCase__					  =      image_size
														UpperCamelCase__					  =      min_resolution
														UpperCamelCase__					  =      max_resolution
														UpperCamelCase__					  =      do_resize
														UpperCamelCase__					  =      size
														UpperCamelCase__					  =      do_center_crop
														UpperCamelCase__					  =      crop_size
														UpperCamelCase__					  =      do_normalize
														UpperCamelCase__					  =      image_mean
														UpperCamelCase__					  =      image_std







							def 					_lowerCamelCase		(    self	):
														return {
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														    "do_normalize": self.do_normalize,
														    "do_resize": self.do_resize,
														    "do_center_crop": self.do_center_crop,
														    "size": self.size,
														    "crop_size": self.crop_size,
														}






@require_torch
@require_vision
class 							__SCREAMING_SNAKE_CASE       (  _a ,		unittest.TestCase							):
							snake_case  :   Optional[Any]      =							LevitImageProcessor if is_vision_available() else None






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      LevitImageProcessingTester(self	)






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






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.image_processing_class(**self.image_processor_dict	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""image_mean"""	)	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""image_std"""	)	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""do_normalize"""	)	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""do_resize"""	)	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""do_center_crop"""	)	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""size"""	)	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.image_processing_class.from_dict(self.image_processor_dict	)
														self.assertEqual(image_processor.size	,			{"""shortest_edge""": 18}	)
														self.assertEqual(image_processor.crop_size	,			{"""height""": 18, """width""": 18}	)

														UpperCamelCase__					  =      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	):
														pass






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

														# Test not batched input
														UpperCamelCase__					  =      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__					  =      image_processing(__lowerCAmelCase	,			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	):
														# Initialize image_processing
														UpperCamelCase__					  =      self.image_processing_class(**self.image_processor_dict	)
														# create random numpy tensors
														UpperCamelCase__					  =      prepare_image_inputs(self.image_processor_tester	,			equal_resolution=__lowerCAmelCase	,			numpify=__lowerCAmelCase	)
														for image in image_inputs:
																					self.assertIsInstance(__lowerCAmelCase	,			np.ndarray	)

														# Test not batched input
														UpperCamelCase__					  =      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__					  =      image_processing(__lowerCAmelCase	,			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	):
														# Initialize image_processing
														UpperCamelCase__					  =      self.image_processing_class(**self.image_processor_dict	)
														# create random PyTorch tensors
														UpperCamelCase__					  =      prepare_image_inputs(self.image_processor_tester	,			equal_resolution=__lowerCAmelCase	,			torchify=__lowerCAmelCase	)
														for image in image_inputs:
																					self.assertIsInstance(__lowerCAmelCase	,			torch.Tensor	)

														# Test not batched input
														UpperCamelCase__					  =      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__					  =      image_processing(__lowerCAmelCase	,			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"""],
														    )	,			)











import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCamelCase__           =			False

class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							pass






@nightly
@require_torch_gpu
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							def 					_lowerCamelCase		(    self	):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""	)
														# remove text_unet
														pipe.remove_unused_weights()
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(__lowerCAmelCase	)
																					UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      generator.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(
														    """shi-labs/versatile-diffusion"""	,			torch_dtype=torch.floataa	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=50	,			output_type="""numpy"""	).images

														UpperCamelCase__					  =      image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														UpperCamelCase__					  =      np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

														assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




