

'''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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import TvltImageProcessor

def 			__UpperCamelCase							(		lowercase__  :					Optional[int],   lowercase__  :					int=10,   lowercase__  :					List[Any]=10,   lowercase__  :					Union[str, Any]=False,   lowercase__  :					int=False	):






     '''simple docstring'''





     __lowercase     						=[]
     for i in range(image_processor_tester.num_frames	):
          video.append(np.random.randint(2_55,   size=(image_processor_tester.num_channels, width, height),   dtype=np.uinta	)	)

     if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          __lowercase     						=[Image.fromarray(np.moveaxis(lowercase__,   0,   -1	)	) for frame in video]

     if torchify:
          __lowercase     						=[torch.from_numpy(lowercase__	) for frame in video]

     return video

def 			__UpperCamelCase							(		lowercase__  :					List[Any],   lowercase__  :					int=False,   lowercase__  :					Union[str, Any]=False,   lowercase__  :					str=False	):






     '''simple docstring'''





     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     __lowercase     						=[]
     for i in range(image_processor_tester.batch_size	):
          if equal_resolution:
               __lowercase     						=image_processor_tester.max_resolution
          else:
               __lowercase     						=np.random.choice(
                   np.arange(image_processor_tester.min_resolution,   image_processor_tester.max_resolution	),   2	)
               __lowercase     						=prepare_video(
                   image_processor_tester=lowercase__,   width=lowercase__,   height=lowercase__,   numpify=lowercase__,   torchify=lowercase__,   )
          video_inputs.append(lowercase__	)

     return video_inputs


class 					lowerCAmelCase       ( unittest.TestCase				):







       def __init__(							self     :	Tuple  , __lowercase     :	str  , __lowercase     :	Optional[int]=7  , __lowercase     :	Tuple=3  , __lowercase     :	List[str]=4  , __lowercase     :	Union[str, Any]=18  , __lowercase     :	Any=30  , __lowercase     :	Dict=400  , __lowercase     :	Dict=True  , __lowercase     :	List[str]=None  , __lowercase     :	Optional[Any]=True  , __lowercase     :	List[Any]=[0.5, 0.5, 0.5]  , __lowercase     :	List[str]=[0.5, 0.5, 0.5]  , __lowercase     :	Optional[Any]=True  , __lowercase     :	int=None  , ):







            """simple docstring"""







            __lowercase     						=size if size is not None else {'''shortest_edge''': 18}
            __lowercase     						=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

            __lowercase     						=parent
            __lowercase     						=batch_size
            __lowercase     						=num_channels
            __lowercase     						=num_frames
            __lowercase     						=image_size
            __lowercase     						=min_resolution
            __lowercase     						=max_resolution
            __lowercase     						=do_resize
            __lowercase     						=size
            __lowercase     						=do_normalize
            __lowercase     						=image_mean
            __lowercase     						=image_std
            __lowercase     						=do_center_crop
            __lowercase     						=crop_size







       def      snake_case				(							self     :	List[str] ):







            """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,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
            }






@require_torch
@require_vision
class 					lowerCAmelCase       ( lowerCAmelCase__				,     unittest.TestCase				):
       lowerCAmelCase_				=	TvltImageProcessor if is_vision_available() else None







       def      snake_case				(							self     :	Any ):







            """simple docstring"""







            __lowercase     						=TvltImageProcessorTester(self )







       @property
       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.image_processing_class(**self.image_processor_dict )
            self.assertTrue(hasattr(_A  , 'image_mean' ) )
            self.assertTrue(hasattr(_A  , 'image_std' ) )
            self.assertTrue(hasattr(_A  , 'do_normalize' ) )
            self.assertTrue(hasattr(_A  , 'do_resize' ) )
            self.assertTrue(hasattr(_A  , 'do_center_crop' ) )
            self.assertTrue(hasattr(_A  , 'size' ) )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.image_processing_class(**self.image_processor_dict )
            # create random PIL videos
            __lowercase     						=prepare_video_inputs(self.image_processor_tester  , equal_resolution=_A )
            for video in video_inputs:
                 self.assertIsInstance(_A  , _A )
                 self.assertIsInstance(video[0]  , Image.Image )

            # Test not batched input
            __lowercase     						=image_processor(video_inputs[0]  , return_tensors='pt' ).pixel_values
            self.assertEqual(
                encoded_videos.shape  , (
                    1,
                    self.image_processor_tester.num_frames,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )  , )

            # Test batched
            __lowercase     						=image_processor(_A  , return_tensors='pt' ).pixel_values
            self.assertEqual(
                encoded_videos.shape  , (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_frames,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )  , )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.image_processing_class(**self.image_processor_dict )
            # create random numpy tensors
            __lowercase     						=prepare_video_inputs(self.image_processor_tester  , equal_resolution=_A  , numpify=_A )
            for video in video_inputs:
                 self.assertIsInstance(_A  , _A )
                 self.assertIsInstance(video[0]  , np.ndarray )

            # Test not batched input
            __lowercase     						=image_processor(video_inputs[0]  , return_tensors='pt' ).pixel_values
            self.assertEqual(
                encoded_videos.shape  , (
                    1,
                    self.image_processor_tester.num_frames,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )  , )

            # Test batched
            __lowercase     						=image_processor(_A  , return_tensors='pt' ).pixel_values
            self.assertEqual(
                encoded_videos.shape  , (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_frames,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )  , )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.image_processing_class(**self.image_processor_dict )
            # create random PyTorch tensors
            __lowercase     						=prepare_video_inputs(self.image_processor_tester  , equal_resolution=_A  , torchify=_A )
            for video in video_inputs:
                 self.assertIsInstance(_A  , _A )
                 self.assertIsInstance(video[0]  , torch.Tensor )

            # Test not batched input
            __lowercase     						=image_processor(video_inputs[0]  , return_tensors='pt' ).pixel_values
            self.assertEqual(
                encoded_videos.shape  , (
                    1,
                    self.image_processor_tester.num_frames,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )  , )

            # Test batched
            __lowercase     						=image_processor(_A  , return_tensors='pt' ).pixel_values
            self.assertEqual(
                encoded_videos.shape  , (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_frames,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )  , )





'''simple docstring'''

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 					lowerCAmelCase       ( A				):







       def __init__(							self     :	Union[str, 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     :	str  , __lowercase     :	Optional[int] ):







            """simple docstring"""







            with torch.no_grad():
                 __lowercase     						=self.vae.encode(tfms.ToTensor()(__lowercase ).unsqueeze(0 ).to(self.device ) * 2 - 1 )
                 __lowercase     						=0.1_8_2_1_5 * latent.latent_dist.sample()
            return latent







       def      snake_case				(							self     :	List[str]  , __lowercase     :	Tuple ):







            """simple docstring"""







            __lowercase     						=(1 / 0.1_8_2_1_5) * latent
            with torch.no_grad():
                 __lowercase     						=self.vae.decode(__lowercase ).sample
            __lowercase     						=(img / 2 + 0.5).clamp(0  , 1 )
            __lowercase     						=img.detach().cpu().permute(0  , 2  , 3  , 1 ).numpy()
            __lowercase     						=(img * 255).round().astype('uint8' )
            return Image.fromarray(img[0] )







       def      snake_case				(							self     :	int  , __lowercase     :	Optional[int] ):







            """simple docstring"""







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

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

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

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

            return torch.cat([uncond_embedding, text_embedding] )







       def __call__(							self     :	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"""







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

            __lowercase     						=self.prep_text(__lowercase )

            self.scheduler.set_timesteps(__lowercase )

            __lowercase						,       __lowercase     						=img.size
            __lowercase     						=self.encode(__lowercase )

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

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

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

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

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

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

            __lowercase     						=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
                           __lowercase     						=self.scheduler.add_noise(
                               __lowercase  , __lowercase  , timesteps=__lowercase  , )

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

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

                      __lowercase     						=self.scheduler.scale_model_input(__lowercase  , __lowercase )

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

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

                      __lowercase     						=self.scheduler.step(__lowercase  , __lowercase  , __lowercase ).prev_sample

            return self.decode(__lowercase )


