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 ViTImageProcessor
class 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 def __init__(     self   :    Dict     ,       lowercase__   :    Tuple     ,       lowercase__   :    Optional[Any]=7     ,       lowercase__   :    Optional[int]=3     ,       lowercase__   :    str=18     ,       lowercase__   :    str=30     ,       lowercase__   :    List[str]=400     ,       lowercase__   :    Optional[int]=True     ,       lowercase__   :    Dict=None     ,       lowercase__   :    Union[str, Any]=True     ,       lowercase__   :    Union[str, Any]=[0.5, 0.5, 0.5]     ,       lowercase__   :    Optional[Any]=[0.5, 0.5, 0.5]     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   size if size is not None else {'height': 18, 'width': 18}
        lowerCAmelCase__	      =   parent
        lowerCAmelCase__	      =   batch_size
        lowerCAmelCase__	      =   num_channels
        lowerCAmelCase__	      =   image_size
        lowerCAmelCase__	      =   min_resolution
        lowerCAmelCase__	      =   max_resolution
        lowerCAmelCase__	      =   do_resize
        lowerCAmelCase__	      =   size
        lowerCAmelCase__	      =   do_normalize
        lowerCAmelCase__	      =   image_mean
        lowerCAmelCase__	      =   image_std




 def 						__snake_case		(     self   :    Union[str, Any]):







        '''simple docstring'''



        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }






@require_torch
@require_vision
class 							a_					(		SCREAMING_SNAKE_CASE			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   ViTImageProcessor if is_vision_available() else None




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        lowerCAmelCase__	      =   ViTImageProcessingTester(self)




 @property
 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase__     ,       'image_mean'))
        self.assertTrue(hasattr(lowercase__     ,       'image_std'))
        self.assertTrue(hasattr(lowercase__     ,       'do_normalize'))
        self.assertTrue(hasattr(lowercase__     ,       'do_resize'))
        self.assertTrue(hasattr(lowercase__     ,       'size'))




 def 						__snake_case		(     self   :    Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size     ,       {'height': 18, 'width': 18})

        lowerCAmelCase__	      =   self.image_processing_class.from_dict(self.image_processor_dict     ,       size=42)
        self.assertEqual(image_processor.size     ,       {'height': 42, 'width': 42})




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        pass




 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        lowerCAmelCase__	      =   prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=lowercase__)
        for image in image_inputs:
               self.assertIsInstance(lowercase__     ,       Image.Image)

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

        # Test batched
        lowerCAmelCase__	      =   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.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        lowerCAmelCase__	      =   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
        lowerCAmelCase__	      =   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.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )

        # Test batched
        lowerCAmelCase__	      =   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.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        lowerCAmelCase__	      =   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
        lowerCAmelCase__	      =   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.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )

        # Test batched
        lowerCAmelCase__	      =   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.size['height'],
                self.image_processor_tester.size['width'],
            )     ,       )

from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 def __init__(     self   :    List[Any]     ,       lowercase__   :    AutoencoderKL     ,       lowercase__   :    CLIPTextModel     ,       lowercase__   :    CLIPTokenizer     ,       lowercase__   :    UNetaDConditionModel     ,       lowercase__   :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]     ,       ):







        '''simple docstring'''



        super().__init__()

        self.register_modules(vae=lowercase__     ,       text_encoder=lowercase__     ,       tokenizer=lowercase__     ,       unet=lowercase__     ,       scheduler=lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    str):







        '''simple docstring'''



        with torch.no_grad():
               lowerCAmelCase__	      =   self.vae.encode(tfms.ToTensor()(lowercase__).unsqueeze(0).to(self.device) * 2 - 1)
               lowerCAmelCase__	      =   0.18_215 * latent.latent_dist.sample()
        return latent




 def 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   (1 / 0.18_215) * latent
        with torch.no_grad():
               lowerCAmelCase__	      =   self.vae.decode(lowercase__).sample
        lowerCAmelCase__	      =   (img / 2 + 0.5).clamp(0     ,       1)
        lowerCAmelCase__	      =   img.detach().cpu().permute(0     ,       2     ,       3     ,       1).numpy()
        lowerCAmelCase__	      =   (img * 255).round().astype('uint8')
        return Image.fromarray(img[0])




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.tokenizer(
            lowercase__     ,       padding='max_length'     ,       max_length=self.tokenizer.model_max_length     ,       truncation=lowercase__     ,       return_tensors='pt'     ,       )

        lowerCAmelCase__	      =   self.text_encoder(text_input.input_ids.to(self.device))[0]

        lowerCAmelCase__	      =   self.tokenizer(
            ''     ,       padding='max_length'     ,       max_length=self.tokenizer.model_max_length     ,       truncation=lowercase__     ,       return_tensors='pt'     ,       )

        lowerCAmelCase__	      =   self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        return torch.cat([uncond_embedding, text_embedding])




 def __call__(     self   :    List[Any]     ,       lowercase__   :    Image.Image     ,       lowercase__   :    str     ,       lowercase__   :    float = 0.3     ,       lowercase__   :    float = 0.6     ,       lowercase__   :    float = 0.5     ,       lowercase__   :    int = 42     ,       lowercase__   :    int = 50     ,       lowercase__   :    float = 7.5     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   steps - int(kmin * steps)
        lowerCAmelCase__	      =   steps - int(kmax * steps)

        lowerCAmelCase__	      =   self.prep_text(lowercase__)

        self.scheduler.set_timesteps(lowercase__)

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   img.size
        lowerCAmelCase__	      =   self.encode(lowercase__)

        torch.manual_seed(lowercase__)
        lowerCAmelCase__	      =   torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)     ,       ).to(self.device)

        lowerCAmelCase__	      =   self.scheduler.add_noise(
            lowercase__     ,       lowercase__     ,       timesteps=self.scheduler.timesteps[tmax]     ,       )

        lowerCAmelCase__	      =   torch.cat([latents] * 2)

        lowerCAmelCase__	      =   self.scheduler.scale_model_input(lowercase__     ,       self.scheduler.timesteps[tmax])

        with torch.no_grad():
               lowerCAmelCase__	      =   self.unet(
                   lowercase__     ,       self.scheduler.timesteps[tmax]     ,       encoder_hidden_states=lowercase__     ,       ).sample

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   pred.chunk(2)
        lowerCAmelCase__	      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        lowerCAmelCase__	      =   self.scheduler.step(lowercase__     ,       self.scheduler.timesteps[tmax]     ,       lowercase__).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
               if i > tmax:
                      if i < tmin:  # layout generation phase
                             lowerCAmelCase__	      =   self.scheduler.add_noise(
                                 lowercase__     ,       lowercase__     ,       timesteps=lowercase__     ,       )

                             lowerCAmelCase__	      =   (mix_factor * latents) + (
                                 1 - mix_factor
                             ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                             lowerCAmelCase__	      =   torch.cat([input] * 2)

                      else:  # content generation phase
                             lowerCAmelCase__	      =   torch.cat([latents] * 2)

                      lowerCAmelCase__	      =   self.scheduler.scale_model_input(lowercase__     ,       lowercase__)

                      with torch.no_grad():
                             lowerCAmelCase__	      =   self.unet(
                                 lowercase__     ,       lowercase__     ,       encoder_hidden_states=lowercase__     ,       ).sample

                      lowerCAmelCase__      ,   lowerCAmelCase__	      =   pred.chunk(2)
                      lowerCAmelCase__	      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

                      lowerCAmelCase__	      =   self.scheduler.step(lowercase__     ,       lowercase__     ,       lowercase__).prev_sample

        return self.decode(lowercase__)
