


"""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 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	42
 SCREAMING_SNAKE_CASE__				=	None







def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[Any]			,  UpperCAmelCase_						:		List[str]=0.999			,  UpperCAmelCase_						:		str="cosine"			,  ):




      """simple docstring"""





      if alpha_transform_type == "cosine":

            def alpha_bar_fn(UpperCAmelCase_						:		List[str]						):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(UpperCAmelCase_						:		List[Any]						):
                  return math.exp(t * -12.0						)

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

      a						:Union[str, Any]										=	[]
      for i in range(UpperCAmelCase_						):
            a						:Optional[Any]										=	i / num_diffusion_timesteps
            a						:int										=	(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 		_snake_case  (				_snake_case						,   _snake_case							):




 @register_to_config
 def __init__(		self ,  _lowerCamelCase = 1000 ,  _lowerCamelCase = "fixed_small_log" ,  _lowerCamelCase = True ,  _lowerCamelCase = 1.0 ,  _lowerCamelCase = "epsilon" ,  _lowerCamelCase = "squaredcos_cap_v2" ,  ):
       if beta_schedule != "squaredcos_cap_v2":
             raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''   )

       a						:List[Any]										=	betas_for_alpha_bar(_lowerCamelCase   )

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

       # standard deviation of the initial noise distribution
       a						:List[Any]										=	1.0

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

       a						:List[str]										=	variance_type




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       return sample




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       a						:Optional[int]										=	num_inference_steps
       a						:Union[str, Any]										=	(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       a						:int										=	(np.arange(0 ,  _lowerCamelCase   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
       a						:List[str]										=	torch.from_numpy(_lowerCamelCase   ).to(_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=None   ):
       if prev_timestep is None:
             a						:Union[str, Any]										=	t - 1

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

       if prev_timestep == t - 1:
             a						:int										=	self.betas[t]
       else:
             a						:Tuple										=	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
       a						:str										=	beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
             a						:Optional[int]										=	self.config.variance_type

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

             a						:List[Any]										=	(predicted_variance + 1) / 2
             a						:Dict										=	frac * max_log + (1 - frac) * min_log

       return variance




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase=None ,  _lowerCamelCase = True ,  ):
       a						:Optional[int]										=	timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
             a						:Optional[int]										=	torch.split(_lowerCamelCase ,  sample.shape[1] ,  dim=1   )
       else:
             a						:int										=	None

       # 1. compute alphas, betas
       if prev_timestep is None:
             a						:Any										=	t - 1

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

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

       # 6. Add noise
       a						:Dict										=	0
       if t > 0:
             a						:Optional[int]										=	randn_tensor(
                 model_output.shape ,  dtype=model_output.dtype ,  generator=_lowerCamelCase ,  device=model_output.device   )

             a						:Dict										=	self._get_variance(
                 _lowerCamelCase ,  predicted_variance=_lowerCamelCase ,  prev_timestep=_lowerCamelCase ,  )

             if self.variance_type == "fixed_small_log":
                   a						:Optional[int]										=	variance
             elif self.variance_type == "learned_range":
                   a						:Union[str, 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.'''   )

             a						:Optional[int]										=	variance * variance_noise

       a						:List[str]										=	pred_prev_sample + variance

       if not return_dict:
             return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=_lowerCamelCase ,  pred_original_sample=_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  ):
       # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
       a						:List[Any]										=	self.alphas_cumprod.to(device=original_samples.device ,  dtype=original_samples.dtype   )
       a						:Optional[Any]										=	timesteps.to(original_samples.device   )

       a						:Tuple										=	alphas_cumprod[timesteps] ** 0.5
       a						:List[Any]										=	sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape   ) < len(original_samples.shape   ):
             a						:Any										=	sqrt_alpha_prod.unsqueeze(-1   )

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

       a						:str										=	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples









import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 		_snake_case  (				_snake_case						,   _snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	CycleDiffusionPipeline
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     'negative_prompt',
     'height',
     'width',
     'negative_prompt_embeds',
 }
 SCREAMING_SNAKE_CASE__				=	PipelineTesterMixin.required_optional_params - {'latents'}
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}							)
 SCREAMING_SNAKE_CASE__				=	IMAGE_TO_IMAGE_IMAGE_PARAMS
 SCREAMING_SNAKE_CASE__				=	IMAGE_TO_IMAGE_IMAGE_PARAMS




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:Optional[Any]										=	UNetaDConditionModel(
           block_out_channels=(32, 64) ,  layers_per_block=2 ,  sample_size=32 ,  in_channels=4 ,  out_channels=4 ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,  cross_attention_dim=32 ,  )
       a						:List[str]										=	DDIMScheduler(
           beta_start=0.0_0085 ,  beta_end=0.012 ,  beta_schedule='''scaled_linear''' ,  num_train_timesteps=1000 ,  clip_sample=_lowerCamelCase ,  set_alpha_to_one=_lowerCamelCase ,  )
       torch.manual_seed(0   )
       a						:List[str]										=	AutoencoderKL(
           block_out_channels=[32, 64] ,  in_channels=3 ,  out_channels=3 ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,  latent_channels=4 ,  )
       torch.manual_seed(0   )
       a						:Dict										=	CLIPTextConfig(
           bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=32 ,  intermediate_size=37 ,  layer_norm_eps=1e-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=1000 ,  )
       a						:str										=	CLIPTextModel(_lowerCamelCase   )
       a						:List[str]										=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       a						:Union[str, Any]										=	{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=0   ):
       a						:Tuple										=	floats_tensor((1, 3, 32, 32) ,  rng=random.Random(_lowerCamelCase   )   ).to(_lowerCamelCase   )
       a						:Tuple										=	image / 2 + 0.5
       if str(_lowerCamelCase   ).startswith('''mps'''   ):
             a						:List[str]										=	torch.manual_seed(_lowerCamelCase   )
       else:
             a						:Any										=	torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
       a						:int										=	{
           '''prompt''': '''An astronaut riding an elephant''',
           '''source_prompt''': '''An astronaut riding a horse''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''eta''': 0.1,
           '''strength''': 0.8,
           '''guidance_scale''': 3,
           '''source_guidance_scale''': 1,
           '''output_type''': '''numpy''',
       }
       return inputs




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       a						:Optional[Any]										=	self.get_dummy_components()
       a						:Dict										=	CycleDiffusionPipeline(**_lowerCamelCase   )
       a						:Optional[Any]										=	pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:List[str]										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Any										=	pipe(**_lowerCamelCase   )
       a						:List[Any]										=	output.images

       a						:str										=	images[0, -3:, -3:, -1]

       assert images.shape == (1, 32, 32, 3)
       a						:List[Any]										=	np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2




 @unittest.skipIf(torch_device != '''cuda''' ,  '''This test requires a GPU'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	self.get_dummy_components()
       for name, module in components.items():
             if hasattr(_lowerCamelCase ,  '''half'''   ):
                   a						:Union[str, Any]										=	module.half()
       a						:List[Any]										=	CycleDiffusionPipeline(**_lowerCamelCase   )
       a						:Dict										=	pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Tuple										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Optional[int]										=	pipe(**_lowerCamelCase   )
       a						:Optional[Any]										=	output.images

       a						:List[Any]										=	images[0, -3:, -3:, -1]

       assert images.shape == (1, 32, 32, 3)
       a						:str										=	np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_save_load_local()




 @unittest.skip('''non-deterministic pipeline'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_inference_batch_single_identical()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_dict_tuple_outputs_equivalent()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_save_load_optional_components()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_attention_slicing_forward_pass()







@slow
@require_torch_gpu
class 		_snake_case  (				unittest.TestCase							):




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/cycle-diffusion/black_colored_car.png'''   )
       a						:Optional[int]										=	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''   )
       a						:Optional[Any]										=	init_image.resize((512, 512)   )

       a						:List[str]										=	'''CompVis/stable-diffusion-v1-4'''
       a						:List[str]										=	DDIMScheduler.from_pretrained(_lowerCamelCase ,  subfolder='''scheduler'''   )
       a						:Tuple										=	CycleDiffusionPipeline.from_pretrained(
           _lowerCamelCase ,  scheduler=_lowerCamelCase ,  safety_checker=_lowerCamelCase ,  torch_dtype=torch.floataa ,  revision='''fp16'''   )

       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )
       pipe.enable_attention_slicing()

       a						:Optional[Any]										=	'''A black colored car'''
       a						:Any										=	'''A blue colored car'''

       a						:str										=	torch.manual_seed(0   )
       a						:List[Any]										=	pipe(
           prompt=_lowerCamelCase ,  source_prompt=_lowerCamelCase ,  image=_lowerCamelCase ,  num_inference_steps=100 ,  eta=0.1 ,  strength=0.85 ,  guidance_scale=3 ,  source_guidance_scale=1 ,  generator=_lowerCamelCase ,  output_type='''np''' ,  )
       a						:int										=	output.images

       # the values aren't exactly equal, but the images look the same visually
       assert np.abs(image - expected_image   ).max() < 5e-1




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/cycle-diffusion/black_colored_car.png'''   )
       a						:Optional[int]										=	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''   )
       a						:List[str]										=	init_image.resize((512, 512)   )

       a						:List[str]										=	'''CompVis/stable-diffusion-v1-4'''
       a						:Any										=	DDIMScheduler.from_pretrained(_lowerCamelCase ,  subfolder='''scheduler'''   )
       a						:int										=	CycleDiffusionPipeline.from_pretrained(_lowerCamelCase ,  scheduler=_lowerCamelCase ,  safety_checker=_lowerCamelCase   )

       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )
       pipe.enable_attention_slicing()

       a						:Optional[int]										=	'''A black colored car'''
       a						:Any										=	'''A blue colored car'''

       a						:Optional[int]										=	torch.manual_seed(0   )
       a						:Union[str, Any]										=	pipe(
           prompt=_lowerCamelCase ,  source_prompt=_lowerCamelCase ,  image=_lowerCamelCase ,  num_inference_steps=100 ,  eta=0.1 ,  strength=0.85 ,  guidance_scale=3 ,  source_guidance_scale=1 ,  generator=_lowerCamelCase ,  output_type='''np''' ,  )
       a						:Optional[int]										=	output.images

       assert np.abs(image - expected_image   ).max() < 2e-2





