

'''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, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor






class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase=7,     _lowercase=3,     _lowercase=18,     _lowercase=30,     _lowercase=400,     _lowercase=True,     _lowercase=None,     _lowercase=True,     _lowercase=None,     )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				size if size is not None else {'shortest_edge': 20}
											SCREAMING_SNAKE_CASE_				=				crop_size if crop_size is not None else {'height': 18, 'width': 18}
											SCREAMING_SNAKE_CASE_				=				parent
											SCREAMING_SNAKE_CASE_				=				batch_size
											SCREAMING_SNAKE_CASE_				=				num_channels
											SCREAMING_SNAKE_CASE_				=				image_size
											SCREAMING_SNAKE_CASE_				=				min_resolution
											SCREAMING_SNAKE_CASE_				=				max_resolution
											SCREAMING_SNAKE_CASE_				=				do_resize
											SCREAMING_SNAKE_CASE_				=				size
											SCREAMING_SNAKE_CASE_				=				do_center_crop
											SCREAMING_SNAKE_CASE_				=				crop_size





				def   a__					(    self       )     ->							Union[str, Any]:
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}






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






				"""simple docstring"""
				_a    	=     MobileNetVaImageProcessor if is_vision_available() else None



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				MobileNetVaImageProcessingTester(self       )



				@property
				def   a__					(    self       )     ->							Dict:
											return self.image_processor_tester.prepare_image_processor_dict()



				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				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,     'crop_size'       )       )



				def   a__					(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				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}       )

											SCREAMING_SNAKE_CASE_				=				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   a__					(    self       )     ->							Tuple:
											pass



				def   a__					(    self       )     ->							List[Any]:
											# Initialize image_processing
											SCREAMING_SNAKE_CASE_				=				self.image_processing_class(**self.image_processor_dict       )
											# create random PIL images
											SCREAMING_SNAKE_CASE_				=				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_				=				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
											SCREAMING_SNAKE_CASE_				=				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   a__					(    self       )     ->							Optional[Any]:
											# Initialize image_processing
											SCREAMING_SNAKE_CASE_				=				self.image_processing_class(**self.image_processor_dict       )
											# create random numpy tensors
											SCREAMING_SNAKE_CASE_				=				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_				=				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
											SCREAMING_SNAKE_CASE_				=				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   a__					(    self       )     ->							Dict:
											# Initialize image_processing
											SCREAMING_SNAKE_CASE_				=				self.image_processing_class(**self.image_processor_dict       )
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE_				=				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_				=				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
											SCREAMING_SNAKE_CASE_				=				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'],
											    ),     )








'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE_				=				latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(_lowercase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





