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  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def __init__(      self							:		Optional[int]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int=7							,      lowerCamelCase							:		Any=3							,      lowerCamelCase							:		List[Any]=1_8							,      lowerCamelCase							:		List[Any]=3_0							,      lowerCamelCase							:		Any=4_0_0							,      lowerCamelCase							:		Optional[Any]=True							,      lowerCamelCase							:		int=1 / 2_5_5							,      lowerCamelCase							:		List[str]=True							,      lowerCamelCase							:		Optional[int]=8							,      )			->				Dict:
        _UpperCamelCase  :							List[Any]	    =	parent
        _UpperCamelCase  :							Dict	    =	batch_size
        _UpperCamelCase  :							Optional[Any]	    =	num_channels
        _UpperCamelCase  :							Union[str, Any]	    =	image_size
        _UpperCamelCase  :							int	    =	min_resolution
        _UpperCamelCase  :							List[str]	    =	max_resolution
        _UpperCamelCase  :							str	    =	do_rescale
        _UpperCamelCase  :							Tuple	    =	rescale_factor
        _UpperCamelCase  :							Optional[int]	    =	do_pad
        _UpperCamelCase  :							int	    =	pad_size

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Any:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		str=False							,      lowerCamelCase							:		Union[str, Any]=False							,      lowerCamelCase							:		List[str]=False	)			->				Dict:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
               _UpperCamelCase  :							Optional[Any]	    =	[]
               for i in range(self.batch_size	):
                      image_inputs.append(
                          np.random.randint(
                              2_5_5							,      size=(self.num_channels, self.max_resolution, self.max_resolution)							,      dtype=np.uinta	)	)
        else:
               _UpperCamelCase  :							Any	    =	[]
               for i in range(self.batch_size	):
                      _UpperCamelCase    ,   _UpperCamelCase  :							int	    =	np.random.choice(np.arange(self.min_resolution							,      self.max_resolution	)							,      2	)
                      image_inputs.append(np.random.randint(2_5_5							,      size=(self.num_channels, width, height)							,      dtype=np.uinta	)	)

        if not numpify and not torchify:
               # PIL expects the channel dimension as last dimension
               _UpperCamelCase  :							Tuple	    =	[Image.fromarray(np.moveaxis(lowerCamelCase							,      0							,      -1	)	) for x in image_inputs]

        if torchify:
               _UpperCamelCase  :							int	    =	[torch.from_numpy(lowerCamelCase	) for x in image_inputs]

        return image_inputs







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



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			SwinaSRImageProcessor if is_vision_available() else None

 def        __snake_case       (      self							:		Union[str, Any]	)			->				List[Any]:
        _UpperCamelCase  :							int	    =	SwinaSRImageProcessingTester(self	)

 @property
 def        __snake_case       (      self							:		Union[str, Any]	)			->				int:
        return self.image_processor_tester.prepare_image_processor_dict()

 def        __snake_case       (      self							:		int	)			->				int:
        _UpperCamelCase  :							str	    =	self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(lowerCamelCase							,      'do_rescale'	)	)
        self.assertTrue(hasattr(lowerCamelCase							,      'rescale_factor'	)	)
        self.assertTrue(hasattr(lowerCamelCase							,      'do_pad'	)	)
        self.assertTrue(hasattr(lowerCamelCase							,      'pad_size'	)	)

 def        __snake_case       (      self							:		Any	)			->				Dict:
        pass

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Tuple	)			->				int:
        _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	get_image_size(lowerCamelCase	)
        _UpperCamelCase  :							int	    =	self.image_processor_tester.pad_size

        _UpperCamelCase  :							List[Any]	    =	(old_height // size + 1) * size - old_height
        _UpperCamelCase  :							Union[str, Any]	    =	(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width

 def        __snake_case       (      self							:		Any	)			->				Optional[Any]:
        # Initialize image_processor
        _UpperCamelCase  :							List[str]	    =	self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        _UpperCamelCase  :							Any	    =	self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase	)
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase							,      Image.Image	)

        # Test not batched input
        _UpperCamelCase  :							Dict	    =	image_processor(image_inputs[0]							,      return_tensors='pt'	).pixel_values
        _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	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        __snake_case       (      self							:		Any	)			->				Tuple:
        # Initialize image_processor
        _UpperCamelCase  :							Optional[int]	    =	self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        _UpperCamelCase  :							Tuple	    =	self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase							,      numpify=lowerCamelCase	)
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase							,      np.ndarray	)

        # Test not batched input
        _UpperCamelCase  :							Optional[Any]	    =	image_processor(image_inputs[0]							,      return_tensors='pt'	).pixel_values
        _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	self.calculate_expected_size(image_inputs[0]	)
        self.assertEqual(
            encoded_images.shape							,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							,      )

 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        # Initialize image_processor
        _UpperCamelCase  :							Optional[Any]	    =	self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        _UpperCamelCase  :							Any	    =	self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase							,      torchify=lowerCamelCase	)
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase							,      torch.Tensor	)

        # Test not batched input
        _UpperCamelCase  :							List[str]	    =	image_processor(image_inputs[0]							,      return_tensors='pt'	).pixel_values
        _UpperCamelCase    ,   _UpperCamelCase  :							Optional[Any]	    =	self.calculate_expected_size(image_inputs[0]	)
        self.assertEqual(
            encoded_images.shape							,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_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__)
