

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 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      torch.FloatTensor
  __lowerCamelCase			:      Optional[torch.FloatTensor]					=   None







def       lowercase					(	__A :					Optional[Any] ,    __A :					Optional[Any]=0.999 ,    __A :					List[str]="cosine" ,    )				->			List[Any]:





    '''simple docstring'''




    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__A :					Union[str, Any]		):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__A :					int		):
            return math.exp(t * -12.0		)

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

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



class 						_A     (				snake_case						,    snake_case				):





  '''simple docstring'''



  @register_to_config
  def __init__(  self   ,SCREAMING_SNAKE_CASE_ = 1000   ,SCREAMING_SNAKE_CASE_ = "fixed_small_log"   ,SCREAMING_SNAKE_CASE_ = True   ,SCREAMING_SNAKE_CASE_ = 1.0   ,SCREAMING_SNAKE_CASE_ = "epsilon"   ,SCREAMING_SNAKE_CASE_ = "squaredcos_cap_v2"   ,):

      '''simple docstring'''



      if beta_schedule != "squaredcos_cap_v2":
          raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'"""    )

      snake_case							:		Optional[int]        =		betas_for_alpha_bar(SCREAMING_SNAKE_CASE_    )

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

      # standard deviation of the initial noise distribution
      snake_case							:		Any        =		1.0

      # setable values
      snake_case							:		Dict        =		None
      snake_case							:		int        =		torch.from_numpy(np.arange(0   ,SCREAMING_SNAKE_CASE_    )[::-1].copy()    )

      snake_case							:		Any        =		variance_type





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None    ):

      '''simple docstring'''
      return sample





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None    ):

      '''simple docstring'''
      snake_case							:		str        =		num_inference_steps
      snake_case							:		Dict        =		(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
      snake_case							:		Any        =		(np.arange(0   ,SCREAMING_SNAKE_CASE_    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
      snake_case							:		str        =		torch.from_numpy(SCREAMING_SNAKE_CASE_    ).to(SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=None   ,SCREAMING_SNAKE_CASE_=None   ,SCREAMING_SNAKE_CASE_=None    ):

      '''simple docstring'''



      if prev_timestep is None:
          snake_case							:		Optional[int]        =		t - 1

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

      if prev_timestep == t - 1:
          snake_case							:		Dict        =		self.betas[t]
      else:
          snake_case							:		Any        =		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
      snake_case							:		Union[str, Any]        =		beta_prod_t_prev / beta_prod_t * beta

      if variance_type is None:
          snake_case							:		List[str]        =		self.config.variance_type

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

          snake_case							:		Tuple        =		(predicted_variance + 1) / 2
          snake_case							:		int        =		frac * max_log + (1 - frac) * min_log

      return variance





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_=None   ,SCREAMING_SNAKE_CASE_ = True   ,):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
          snake_case      ,		snake_case							:		Union[str, Any]        =		torch.split(SCREAMING_SNAKE_CASE_   ,sample.shape[1]   ,dim=1    )
      else:
          snake_case							:		Optional[Any]        =		None

      # 1. compute alphas, betas
      if prev_timestep is None:
          snake_case							:		List[str]        =		t - 1

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

      if prev_timestep == t - 1:
          snake_case							:		str        =		self.betas[t]
          snake_case							:		int        =		self.alphas[t]
      else:
          snake_case							:		int        =		1 - alpha_prod_t / alpha_prod_t_prev
          snake_case							:		str        =		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":
          snake_case							:		Dict        =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
          snake_case							:		int        =		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:
          snake_case							:		Dict        =		torch.clamp(
              SCREAMING_SNAKE_CASE_   ,-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
      snake_case							:		Optional[Any]        =		(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
      snake_case							:		List[str]        =		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
      snake_case							:		str        =		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

          snake_case							:		Union[str, Any]        =		self._get_variance(
              SCREAMING_SNAKE_CASE_   ,predicted_variance=SCREAMING_SNAKE_CASE_   ,prev_timestep=SCREAMING_SNAKE_CASE_   ,)

          if self.variance_type == "fixed_small_log":
              snake_case							:		int        =		variance
          elif self.variance_type == "learned_range":
              snake_case							:		Optional[Any]        =		(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."""    )

          snake_case							:		int        =		variance * variance_noise

      snake_case							:		int        =		pred_prev_sample + variance

      if not return_dict:
          return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_   ,pred_original_sample=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      snake_case							:		str        =		self.alphas_cumprod.to(device=original_samples.device   ,dtype=original_samples.dtype    )
      snake_case							:		Optional[int]        =		timesteps.to(original_samples.device    )

      snake_case							:		Any        =		alphas_cumprod[timesteps] ** 0.5
      snake_case							:		int        =		sqrt_alpha_prod.flatten()
      while len(sqrt_alpha_prod.shape    ) < len(original_samples.shape    ):
          snake_case							:		Optional[int]        =		sqrt_alpha_prod.unsqueeze(-1    )

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

      snake_case							:		Dict        =		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples








'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class __A     (    A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			List[Any]		=			IFInpaintingSuperResolutionPipeline
 __lowerCamelCase       :			Tuple		=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
 __lowerCamelCase       :			Optional[Any]		=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}		)
 __lowerCamelCase       :			str		=			PipelineTesterMixin.required_optional_params - {'latents'}



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      return self._get_superresolution_dummy_components()



 def    a__    (self					,  A					,  A=0			)       ->	List[Any]:


      """simple docstring"""



      if str(A			).startswith('''mps'''			):
           _a           =    torch.manual_seed(A			)
      else:
           _a           =    torch.Generator(device=A			).manual_seed(A			)

      _a           =    floats_tensor((1, 3, 16, 16)					,  rng=random.Random(A			)			).to(A			)
      _a           =    floats_tensor((1, 3, 32, 32)					,  rng=random.Random(A			)			).to(A			)
      _a           =    floats_tensor((1, 3, 32, 32)					,  rng=random.Random(A			)			).to(A			)

      _a           =    {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': image,
          '''original_image''': original_image,
          '''mask_image''': mask_image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }

      return inputs



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()					,  reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,  )
 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != '''cuda'''					,  reason='''float16 requires CUDA'''			)
 def    a__    (self			)       ->	str:


      """simple docstring"""



      super().test_save_load_floataa(expected_max_diff=1E-1			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      self._test_save_load_local()



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2					,  )



