'''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 LevitImageProcessor



class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=7     ,       __lowerCAmelCase=3     ,       __lowerCAmelCase=18     ,       __lowerCAmelCase=30     ,       __lowerCAmelCase=400     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=[0.5, 0.5, 0.5]     ,       __lowerCAmelCase=[0.5, 0.5, 0.5]     ,       )  ->  Optional[int]:
        lowercase__    :   List[str]						= size if size is not None else {'''shortest_edge''': 18}
        lowercase__    :   Tuple						= crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        lowercase__    :   List[str]						= parent
        lowercase__    :   Union[str, Any]						= batch_size
        lowercase__    :   Tuple						= num_channels
        lowercase__    :   Optional[Any]						= image_size
        lowercase__    :   int						= min_resolution
        lowercase__    :   int						= max_resolution
        lowercase__    :   int						= do_resize
        lowercase__    :   Optional[Any]						= size
        lowercase__    :   Union[str, Any]						= do_center_crop
        lowercase__    :   str						= crop_size
        lowercase__    :   Union[str, Any]						= do_normalize
        lowercase__    :   Union[str, Any]						= image_mean
        lowercase__    :   Any						= image_std


       def _lowerCAmelCase(						self			)  ->  int:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "do_center_crop": self.do_center_crop,
            "size": self.size,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   LevitImageProcessor if is_vision_available() else None

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   int						= LevitImageProcessingTester(self			)

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

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   Dict						= self.image_processing_class(**self.image_processor_dict			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''image_mean'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''image_std'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''do_normalize'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''do_resize'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''do_center_crop'''			)			)
        self.assertTrue(hasattr(__lowerCAmelCase     ,       '''size'''			)			)

       def _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   List[Any]						= self.image_processing_class.from_dict(self.image_processor_dict			)
        self.assertEqual(image_processor.size     ,       {'''shortest_edge''': 18}			)
        self.assertEqual(image_processor.crop_size     ,       {'''height''': 18, '''width''': 18}			)

        lowercase__    :   Any						= 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 _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        pass

       def _lowerCAmelCase(						self			)  ->  List[str]:
        # Initialize image_processing
        lowercase__    :   Any						= self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        lowercase__    :   Tuple						= prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=__lowerCAmelCase			)
        for image in image_inputs:
         self.assertIsInstance(__lowerCAmelCase     ,       Image.Image			)

        # Test not batched input
        lowercase__    :   str						= 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
        lowercase__    :   str						= image_processing(__lowerCAmelCase     ,       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 _lowerCAmelCase(						self			)  ->  Any:
        # Initialize image_processing
        lowercase__    :   int						= self.image_processing_class(**self.image_processor_dict			)
        # create random numpy tensors
        lowercase__    :   List[str]						= prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=__lowerCAmelCase     ,       numpify=__lowerCAmelCase			)
        for image in image_inputs:
         self.assertIsInstance(__lowerCAmelCase     ,       np.ndarray			)

        # Test not batched input
        lowercase__    :   Union[str, Any]						= 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
        lowercase__    :   str						= image_processing(__lowerCAmelCase     ,       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 _lowerCAmelCase(						self			)  ->  int:
        # Initialize image_processing
        lowercase__    :   int						= self.image_processing_class(**self.image_processor_dict			)
        # create random PyTorch tensors
        lowercase__    :   Any						= prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=__lowerCAmelCase     ,       torchify=__lowerCAmelCase			)
        for image in image_inputs:
         self.assertIsInstance(__lowerCAmelCase     ,       torch.Tensor			)

        # Test not batched input
        lowercase__    :   List[Any]						= 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
        lowercase__    :   str						= image_processing(__lowerCAmelCase     ,       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 math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   42
       SCREAMING_SNAKE_CASE 		=   None


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=0.9_9_9		,					UpperCAmelCase="cosine"		,					):
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.exp(t * -1_2.0					)

 else:
  raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

 lowercase__    :   str						= []
 for i in range(UpperCAmelCase					):
  lowercase__    :   int						= i / num_diffusion_timesteps
  lowercase__    :   Tuple						= (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(UpperCAmelCase					) / alpha_bar_fn(UpperCAmelCase					)		,					UpperCAmelCase					)					)
 return torch.tensor(UpperCAmelCase		,					dtype=torch.floataa					)





class       UpperCAmelCase  (						a__   ,						a__ ):





       '''simple docstring'''



       @register_to_config
       def __init__(						self     ,       __lowerCAmelCase = 1000     ,       __lowerCAmelCase = "fixed_small_log"     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = 1.0     ,       __lowerCAmelCase = "epsilon"     ,       __lowerCAmelCase = "squaredcos_cap_v2"     ,       )  ->  Optional[int]:
        if beta_schedule != "squaredcos_cap_v2":
         raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''			)

        lowercase__    :   Union[str, Any]						= betas_for_alpha_bar(__lowerCAmelCase			)

        lowercase__    :   List[Any]						= 1.0 - self.betas
        lowercase__    :   int						= torch.cumprod(self.alphas     ,       dim=0			)
        lowercase__    :   str						= torch.tensor(1.0			)

        # standard deviation of the initial noise distribution
        lowercase__    :   Optional[Any]						= 1.0

        # setable values
        lowercase__    :   Optional[Any]						= None
        lowercase__    :   List[Any]						= torch.from_numpy(np.arange(0     ,       __lowerCAmelCase			)[::-1].copy()			)

        lowercase__    :   Tuple						= variance_type

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  torch.FloatTensor:
        return sample

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Optional[int]:
        lowercase__    :   List[str]						= num_inference_steps
        lowercase__    :   List[str]						= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
        lowercase__    :   List[str]						= (np.arange(0     ,       __lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
        lowercase__    :   str						= torch.from_numpy(__lowerCAmelCase			).to(__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None			)  ->  Tuple:
        if prev_timestep is None:
         lowercase__    :   Any						= t - 1

        lowercase__    :   Any						= self.alphas_cumprod[t]
        lowercase__    :   List[Any]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   str						= 1 - alpha_prod_t
        lowercase__    :   int						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Tuple						= self.betas[t]
        else:
         lowercase__    :   Dict						= 1 - alpha_prod_t / alpha_prod_t_prev

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
        # and sample from it to get previous sample
        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
        lowercase__    :   Any						= beta_prod_t_prev / beta_prod_t * beta

        if variance_type is None:
         lowercase__    :   Union[str, Any]						= self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small_log":
         lowercase__    :   int						= torch.log(torch.clamp(__lowerCAmelCase     ,       min=1E-20			)			)
         lowercase__    :   Dict						= torch.exp(0.5 * variance			)
        elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         lowercase__    :   Union[str, Any]						= variance.log()
         lowercase__    :   Optional[int]						= beta.log()

         lowercase__    :   Tuple						= (predicted_variance + 1) / 2
         lowercase__    :   Dict						= frac * max_log + (1 - frac) * min_log

        return variance

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase = True     ,       )  ->  Union[UnCLIPSchedulerOutput, Tuple]:
        lowercase__    :   Tuple						= timestep

        if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         lowercase__			,							lowercase__    :   str						= torch.split(__lowerCAmelCase     ,       sample.shape[1]     ,       dim=1			)
        else:
         lowercase__    :   Dict						= None

        # 1. compute alphas, betas
        if prev_timestep is None:
         lowercase__    :   int						= t - 1

        lowercase__    :   Optional[int]						= self.alphas_cumprod[t]
        lowercase__    :   Optional[int]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   Optional[int]						= 1 - alpha_prod_t
        lowercase__    :   List[Any]						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Optional[int]						= self.betas[t]
         lowercase__    :   Optional[Any]						= self.alphas[t]
        else:
         lowercase__    :   Any						= 1 - alpha_prod_t / alpha_prod_t_prev
         lowercase__    :   Optional[int]						= 1 - beta

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        if self.config.prediction_type == "epsilon":
         lowercase__    :   int						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
         lowercase__    :   Dict						= model_output
        else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
             ''' for the UnCLIPScheduler.'''			)

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
         lowercase__    :   List[Any]						= torch.clamp(
             __lowerCAmelCase     ,       -self.config.clip_sample_range     ,       self.config.clip_sample_range			)

        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        lowercase__    :   Optional[Any]						= (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
        lowercase__    :   Tuple						= alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

        # 5. Compute predicted previous sample µ_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        lowercase__    :   List[Any]						= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        lowercase__    :   List[Any]						= 0
        if t > 0:
         lowercase__    :   Dict						= randn_tensor(
             model_output.shape     ,       dtype=model_output.dtype     ,       generator=__lowerCAmelCase     ,       device=model_output.device			)

         lowercase__    :   Union[str, Any]						= self._get_variance(
             __lowerCAmelCase     ,       predicted_variance=__lowerCAmelCase     ,       prev_timestep=__lowerCAmelCase     ,       )

         if self.variance_type == "fixed_small_log":
          lowercase__    :   List[Any]						= variance
         elif self.variance_type == "learned_range":
          lowercase__    :   int						= (0.5 * variance).exp()
         else:
          raise ValueError(
              F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
              ''' for the UnCLIPScheduler.'''			)

         lowercase__    :   List[str]						= variance * variance_noise

        lowercase__    :   Union[str, Any]						= pred_prev_sample + variance

        if not return_dict:
         return (pred_prev_sample,)

        return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase     ,       pred_original_sample=__lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  torch.FloatTensor:
        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
        lowercase__    :   Tuple						= self.alphas_cumprod.to(device=original_samples.device     ,       dtype=original_samples.dtype			)
        lowercase__    :   str						= timesteps.to(original_samples.device			)

        lowercase__    :   Union[str, Any]						= alphas_cumprod[timesteps] ** 0.5
        lowercase__    :   List[str]						= sqrt_alpha_prod.flatten()
        while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
         lowercase__    :   List[str]						= sqrt_alpha_prod.unsqueeze(-1			)

        lowercase__    :   int						= (1 - alphas_cumprod[timesteps]) ** 0.5
        lowercase__    :   List[Any]						= sqrt_one_minus_alpha_prod.flatten()
        while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
         lowercase__    :   int						= sqrt_one_minus_alpha_prod.unsqueeze(-1			)

        lowercase__    :   Optional[int]						= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
        return noisy_samples

