


"""simple docstring"""




from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)







class 					lowerCAmelCase__	(    _lowerCAmelCase):




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase = 0.9_3			,						__lowerCamelCase = True			,						)      ->    Optional[int]:
            _A				:	List[str]       =			mixing_coeff
            _A				:	List[str]       =			leapfrog_steps

            super().__init__()
            self.register_modules(
                vae=_lowerCamelCase			,						text_encoder=_lowerCamelCase			,						tokenizer=_lowerCamelCase			,						unet=_lowerCamelCase			,						scheduler=_lowerCamelCase			,						)

            _A				:	int       =			2 ** (len(self.vae.config.block_out_channels) - 1)
            _A				:	Union[str, Any]       =			VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase = None			,						__lowerCamelCase = False)      ->    int:
            _A				:	Union[str, Any]       =			self.tokenizer(
                _lowerCamelCase			,						padding="max_length"			,						max_length=self.tokenizer.model_max_length			,						truncation=_lowerCamelCase			,						return_tensors="pt"			,						)

            _A				:	List[str]       =			self.text_encoder(text_inputs.input_ids.to(self.device)).last_hidden_state

            _A				:	Any       =			prompt_embeds.to(dtype=self.text_encoder.dtype			,						device=self.device)

            if do_classifier_free_guidance:
                   _A				:	Tuple       =			"" if negative_prompt is None else negative_prompt

                   _A				:	Tuple       =			self.tokenizer(
                       _lowerCamelCase			,						padding="max_length"			,						max_length=self.tokenizer.model_max_length			,						truncation=_lowerCamelCase			,						return_tensors="pt"			,						)

                   _A				:	str       =			self.text_encoder(uncond_input.input_ids.to(self.device)).last_hidden_state

                   _A				:	Union[str, Any]       =			torch.cat([negative_prompt_embeds, prompt_embeds])

            return prompt_embeds


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Any:
            _A				:	Tuple       =			self.mixing_coeff * x + (1 - self.mixing_coeff) * y
            _A				:	str       =			self.mixing_coeff * y + (1 - self.mixing_coeff) * x

            return [x, y]


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Optional[Any]:
            _A				:	Tuple       =			(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
            _A				:	Tuple       =			(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

            return [x, y]


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Optional[int]:
            _A				:	int       =			int(_lowerCamelCase)

            _A				:	Optional[Any]       =			self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

            return alpha_prod, 1 - alpha_prod


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						)      ->    Union[str, Any]:
            _A				:	Optional[Any]       =			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

            _A   ,      _A				:	Tuple       =			self._get_alpha_and_beta(_lowerCamelCase)
            _A   ,      _A				:	List[Any]       =			self._get_alpha_and_beta(_lowerCamelCase)

            _A				:	str       =			(alpha_prod_t_prev / alpha_prod_t) ** 0.5
            _A				:	Any       =			-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

            _A				:	Optional[Any]       =			(base - b_t * model_output) / a_t

            return model_input, next_model_input.to(base.dtype)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						)      ->    Tuple:
            _A				:	Optional[int]       =			timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

            _A   ,      _A				:	Tuple       =			self._get_alpha_and_beta(_lowerCamelCase)
            _A   ,      _A				:	List[str]       =			self._get_alpha_and_beta(_lowerCamelCase)

            _A				:	Optional[int]       =			(alpha_prod_t_prev / alpha_prod_t) ** 0.5
            _A				:	Dict       =			-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
            _A				:	Optional[Any]       =			a_t * base + b_t * model_output

            return model_input, next_model_input.to(base.dtype)


     @torch.no_grad()
     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    int:
            _A				:	Any       =			1 / self.vae.config.scaling_factor * latents
            _A				:	List[str]       =			self.vae.decode(_lowerCamelCase).sample
            _A				:	Union[str, Any]       =			(image / 2 + 0.5).clamp(0			,						1)
            return image


     @torch.no_grad()
     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase = None			,						)      ->    Dict:
            _A				:	Any       =			guidance_scale > 1.0

            _A				:	List[str]       =			image.to(device=self.device			,						dtype=text_embeds.dtype)
            _A				:	Any       =			self.vae.encode(_lowerCamelCase).latent_dist.sample(_lowerCamelCase)

            _A				:	List[str]       =			self.vae.config.scaling_factor * latent

            _A				:	Dict       =			[latent.clone(), latent.clone()]

            for i, t in tqdm(enumerate(_lowerCamelCase)			,						total=len(_lowerCamelCase)):
                   _A				:	int       =			self.noise_mixing_layer(x=coupled_latents[0]			,						y=coupled_latents[1])

                   # j - model_input index, k - base index
                   for j in range(2):
                          _A				:	Union[str, Any]       =			j ^ 1

                          if self.leapfrog_steps:
                                 if i % 2 == 0:
                                        _A   ,      _A				:	List[str]       =			j, k

                          _A				:	Tuple       =			coupled_latents[j]
                          _A				:	int       =			coupled_latents[k]

                          _A				:	str       =			torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input

                          _A				:	List[Any]       =			self.unet(_lowerCamelCase			,						_lowerCamelCase			,						encoder_hidden_states=_lowerCamelCase).sample

                          if do_classifier_free_guidance:
                                 _A   ,      _A				:	Union[str, Any]       =			noise_pred.chunk(2)
                                 _A				:	str       =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                          _A   ,      _A				:	List[Any]       =			self.noise_step(
                              base=_lowerCamelCase			,						model_input=_lowerCamelCase			,						model_output=_lowerCamelCase			,						timestep=_lowerCamelCase			,						)

                          _A				:	Dict       =			model_input

            return coupled_latents


     @torch.no_grad()
     def __call__(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase = 3.0			,						__lowerCamelCase = 5_0			,						__lowerCamelCase = 0.8			,						__lowerCamelCase = None			,						__lowerCamelCase = None			,						__lowerCamelCase = "pil"			,						)      ->    str:
            _A				:	Optional[Any]       =			guidance_scale > 1.0

            _A				:	Union[str, Any]       =			self.image_processor.preprocess(_lowerCamelCase)

            _A				:	List[str]       =			self._encode_prompt(_lowerCamelCase			,						_lowerCamelCase			,						_lowerCamelCase)
            _A				:	Union[str, Any]       =			self._encode_prompt(_lowerCamelCase			,						_lowerCamelCase			,						_lowerCamelCase)

            self.scheduler.set_timesteps(_lowerCamelCase			,						self.device)

            _A				:	List[Any]       =			num_inference_steps - int(num_inference_steps * strength)
            _A				:	List[Any]       =			self.scheduler.timesteps[t_limit:]
            _A				:	Union[str, Any]       =			fwd_timesteps.flip(0)

            _A				:	int       =			self.prepare_latents(_lowerCamelCase			,						_lowerCamelCase			,						_lowerCamelCase			,						_lowerCamelCase			,						_lowerCamelCase)

            for i, t in tqdm(enumerate(_lowerCamelCase)			,						total=len(_lowerCamelCase)):
                   # j - model_input index, k - base index
                   for k in range(2):
                          _A				:	int       =			k ^ 1

                          if self.leapfrog_steps:
                                 if i % 2 == 1:
                                        _A   ,      _A				:	str       =			j, k

                          _A				:	Union[str, Any]       =			coupled_latents[j]
                          _A				:	str       =			coupled_latents[k]

                          _A				:	Optional[Any]       =			torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input

                          _A				:	List[Any]       =			self.unet(_lowerCamelCase			,						_lowerCamelCase			,						encoder_hidden_states=_lowerCamelCase).sample

                          if do_classifier_free_guidance:
                                 _A   ,      _A				:	Any       =			noise_pred.chunk(2)
                                 _A				:	Optional[Any]       =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                          _A   ,      _A				:	Optional[Any]       =			self.denoise_step(
                              base=_lowerCamelCase			,						model_input=_lowerCamelCase			,						model_output=_lowerCamelCase			,						timestep=_lowerCamelCase			,						)

                          _A				:	int       =			model_input

                   _A				:	Optional[Any]       =			self.denoise_mixing_layer(x=coupled_latents[0]			,						y=coupled_latents[1])

            # either one is fine
            _A				:	Union[str, Any]       =			coupled_latents[0]

            if output_type not in ["latent", "pt", "np", "pil"]:
                   _A				:	List[Any]       =			(
                       F"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: "
                       "`pil`, `np`, `pt`, `latent`"
                   )
                   deprecate("Unsupported output_type"			,						"1.0.0"			,						_lowerCamelCase			,						standard_warn=_lowerCamelCase)
                   _A				:	Union[str, Any]       =			"np"

            if output_type == "latent":
                   _A				:	int       =			final_latent
            else:
                   _A				:	Any       =			self.decode_latents(_lowerCamelCase)
                   _A				:	Optional[Any]       =			self.image_processor.postprocess(_lowerCamelCase			,						output_type=_lowerCamelCase)

            return image




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


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


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase=False			,						__lowerCamelCase=False			,						__lowerCamelCase=False)      ->    str:
            assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

            if equal_resolution:
                   _A				:	List[str]       =			[]
                   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:
                   _A				:	List[str]       =			[]
                   for i in range(self.batch_size):
                          _A   ,      _A				:	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
                   _A				:	Optional[Any]       =			[Image.fromarray(np.moveaxis(__lowerCamelCase			,						0			,						-1)) for x in image_inputs]

            if torchify:
                   _A				:	Union[str, Any]       =			[torch.from_numpy(__lowerCamelCase) for x in image_inputs]

            return image_inputs







@require_torch
@require_vision
class 					lowerCAmelCase__	(    a					,   unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			SwinaSRImageProcessor if is_vision_available() else None


     def 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	int       =			SwinaSRImageProcessingTester(self)


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


     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            _A				:	Tuple       =			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 			_lowerCamelCase			(						self)      ->    Dict:
            pass


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Tuple:
            _A   ,      _A				:	Dict       =			get_image_size(__lowerCamelCase)
            _A				:	Tuple       =			self.image_processor_tester.pad_size

            _A				:	Dict       =			(old_height // size + 1) * size - old_height
            _A				:	Any       =			(old_width // size + 1) * size - old_width
            return old_height + pad_height, old_width + pad_width


     def 			_lowerCamelCase			(						self)      ->    Dict:
            # Initialize image_processor
            _A				:	Optional[int]       =			self.image_processing_class(**self.image_processor_dict)
            # create random PIL images
            _A				:	Union[str, Any]       =			self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase)
            for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase			,						Image.Image)

            # Test not batched input
            _A				:	str       =			image_processor(image_inputs[0]			,						return_tensors="pt").pixel_values
            _A   ,      _A				:	int       =			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 			_lowerCamelCase			(						self)      ->    int:
            # Initialize image_processor
            _A				:	Any       =			self.image_processing_class(**self.image_processor_dict)
            # create random numpy tensors
            _A				:	int       =			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
            _A				:	Union[str, Any]       =			image_processor(image_inputs[0]			,						return_tensors="pt").pixel_values
            _A   ,      _A				:	Union[str, Any]       =			self.calculate_expected_size(image_inputs[0])
            self.assertEqual(
                encoded_images.shape			,						(
                    1,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )			,						)


     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            # Initialize image_processor
            _A				:	str       =			self.image_processing_class(**self.image_processor_dict)
            # create random PyTorch tensors
            _A				:	Tuple       =			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
            _A				:	List[str]       =			image_processor(image_inputs[0]			,						return_tensors="pt").pixel_values
            _A   ,      _A				:	List[str]       =			self.calculate_expected_size(image_inputs[0])
            self.assertEqual(
                encoded_images.shape			,						(
                    1,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )			,						)
