



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class    UpperCAmelCase__:

   '''simple docstring'''




   A     :	CommonSchedulerState

   # setable values
   A     :	jnp.ndarray
   A     :	jnp.ndarray
   A     :	Optional[int]												=     None


   @classmethod
   def       UpperCAmelCase		(    cls	:      str      ,					lowerCAmelCase	:      CommonSchedulerState      ,					lowerCAmelCase	:      jnp.ndarray      ,					lowerCAmelCase	:      jnp.ndarray)						->					Tuple:




         """simple docstring"""

         return cls(common=lowerCAmelCase      ,					init_noise_sigma=lowerCAmelCase      ,					timesteps=lowerCAmelCase)



@dataclass
class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	DDPMSchedulerState



class    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase						):

   '''simple docstring'''




   A     :	Optional[Any]												=     [e.name for e in FlaxKarrasDiffusionSchedulers]

   A     :	jnp.dtype


   @property
   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					Tuple:




         """simple docstring"""

         return True


   @register_to_config
   def __init__(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      int = 10_00      ,					lowerCAmelCase	:      float = 0.00_01      ,					lowerCAmelCase	:      float = 0.02      ,					lowerCAmelCase	:      str = "linear"      ,					lowerCAmelCase	:      Optional[jnp.ndarray] = None      ,					lowerCAmelCase	:      str = "fixed_small"      ,					lowerCAmelCase	:      bool = True      ,					lowerCAmelCase	:      str = "epsilon"      ,					lowerCAmelCase	:      jnp.dtype = jnp.floataa      ,					)						->					int:




         """simple docstring"""

         lowercase__  =	dtype


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      Optional[CommonSchedulerState] = None)						->					DDPMSchedulerState:




         """simple docstring"""

         if common is None:
               lowercase__  =	CommonSchedulerState.create(self)

         # standard deviation of the initial noise distribution
         lowercase__  =	jnp.array(1.0      ,					dtype=self.dtype)

         lowercase__  =	jnp.arange(0      ,					self.config.num_train_timesteps).round()[::-1]

         return DDPMSchedulerState.create(
             common=lowerCAmelCase      ,					init_noise_sigma=lowerCAmelCase      ,					timesteps=lowerCAmelCase      ,					)


   def       UpperCAmelCase		(    self	:      Optional[int]      ,					lowerCAmelCase	:      DDPMSchedulerState      ,					lowerCAmelCase	:      jnp.ndarray      ,					lowerCAmelCase	:      Optional[int] = None)						->					jnp.ndarray:




         """simple docstring"""

         return sample


   def       UpperCAmelCase		(    self	:      Optional[int]      ,					lowerCAmelCase	:      DDPMSchedulerState      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      Tuple = ())						->					DDPMSchedulerState:




         """simple docstring"""

         lowercase__  =	self.config.num_train_timesteps // num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # rounding to avoid issues when num_inference_step is power of 3
         lowercase__  =	(jnp.arange(0      ,					lowerCAmelCase) * step_ratio).round()[::-1]

         return state.replace(
             num_inference_steps=lowerCAmelCase      ,					timesteps=lowerCAmelCase      ,					)


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      DDPMSchedulerState      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      Optional[Any]=None      ,					lowerCAmelCase	:      List[Any]=None)						->					Any:




         """simple docstring"""

         lowercase__  =	state.common.alphas_cumprod[t]
         lowercase__  =	jnp.where(t > 0      ,					state.common.alphas_cumprod[t - 1]      ,					jnp.array(1.0      ,					dtype=self.dtype))

         # 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__  =	(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

         if variance_type is None:
               lowercase__  =	self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small":
               lowercase__  =	jnp.clip(lowerCAmelCase      ,					a_min=1E-2_0)
         # for rl-diffuser https://arxiv.org/abs/2205.09991
         elif variance_type == "fixed_small_log":
               lowercase__  =	jnp.log(jnp.clip(lowerCAmelCase      ,					a_min=1E-2_0))
         elif variance_type == "fixed_large":
               lowercase__  =	state.common.betas[t]
         elif variance_type == "fixed_large_log":
               # Glide max_log
               lowercase__  =	jnp.log(state.common.betas[t])
         elif variance_type == "learned":
               return predicted_variance
         elif variance_type == "learned_range":
               lowercase__  =	variance
               lowercase__  =	state.common.betas[t]
               lowercase__  =	(predicted_variance + 1) / 2
               lowercase__  =	frac * max_log + (1 - frac) * min_log

         return variance


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      DDPMSchedulerState      ,					lowerCAmelCase	:      jnp.ndarray      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      jnp.ndarray      ,					lowerCAmelCase	:      Optional[jax.random.KeyArray] = None      ,					lowerCAmelCase	:      bool = True      ,					)						->					Union[FlaxDDPMSchedulerOutput, Tuple]:




         """simple docstring"""

         lowercase__  =	timestep

         if key is None:
               lowercase__  =	jax.random.PRNGKey(0)

         if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
               lowercase__,						lowercase__  =	jnp.split(lowerCAmelCase      ,					sample.shape[1]      ,					axis=1)
         else:
               lowercase__  =	None

         # 1. compute alphas, betas
         lowercase__  =	state.common.alphas_cumprod[t]
         lowercase__  =	jnp.where(t > 0      ,					state.common.alphas_cumprod[t - 1]      ,					jnp.array(1.0      ,					dtype=self.dtype))
         lowercase__  =	1 - alpha_prod_t
         lowercase__  =	1 - alpha_prod_t_prev

         # 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__  =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
               lowercase__  =	model_output
         elif self.config.prediction_type == "v_prediction":
               lowercase__  =	(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         else:
               raise ValueError(
                   f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
                   ' for the FlaxDDPMScheduler.')

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
               lowercase__  =	jnp.clip(lowerCAmelCase      ,					-1      ,					1)

         # 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__  =	(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
         lowercase__  =	state.common.alphas[t] ** 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__  =	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         def random_variance():
               lowercase__  =	jax.random.split(lowerCAmelCase      ,					num=1)
               lowercase__  =	jax.random.normal(lowerCAmelCase      ,					shape=model_output.shape      ,					dtype=self.dtype)
               return (self._get_variance(lowerCAmelCase      ,					lowerCAmelCase      ,					predicted_variance=lowerCAmelCase) ** 0.5) * noise

         lowercase__  =	jnp.where(t > 0      ,					random_variance()      ,					jnp.zeros(model_output.shape      ,					dtype=self.dtype))

         lowercase__  =	pred_prev_sample + variance

         if not return_dict:
               return (pred_prev_sample, state)

         return FlaxDDPMSchedulerOutput(prev_sample=lowerCAmelCase      ,					state=lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      DDPMSchedulerState      ,					lowerCAmelCase	:      jnp.ndarray      ,					lowerCAmelCase	:      jnp.ndarray      ,					lowerCAmelCase	:      jnp.ndarray      ,					)						->					jnp.ndarray:




         """simple docstring"""

         return add_noise_common(state.common      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      DDPMSchedulerState      ,					lowerCAmelCase	:      jnp.ndarray      ,					lowerCAmelCase	:      jnp.ndarray      ,					lowerCAmelCase	:      jnp.ndarray      ,					)						->					jnp.ndarray:




         """simple docstring"""

         return get_velocity_common(state.common      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)



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




         """simple docstring"""

         return self.config.num_train_timesteps





import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	str												=     IFImgaImgSuperResolutionPipeline
   A     :	Dict												=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
   A     :	Any												=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}						)
   A     :	str												=     PipelineTesterMixin.required_optional_params - {"latents"}


   def       UpperCAmelCase		(    self	:      Dict)						->					Dict:




         """simple docstring"""

         return self._get_superresolution_dummy_components()


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Union[str, Any]=0)						->					str:




         """simple docstring"""

         if str(lowerCAmelCase).startswith('mps'):
               lowercase__  =	torch.manual_seed(lowerCAmelCase)
         else:
               lowercase__  =	torch.Generator(device=lowerCAmelCase).manual_seed(lowerCAmelCase)

         lowercase__  =	floats_tensor((1, 3, 32, 32)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)
         lowercase__  =	floats_tensor((1, 3, 16, 16)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)

         lowercase__  =	{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'original_image': original_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       UpperCAmelCase		(    self	:      str)						->					Dict:




         """simple docstring"""

         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)


   def       UpperCAmelCase		(    self	:      List[Any])						->					Dict:




         """simple docstring"""

         self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != 'cuda'      ,					reason='float16 requires CUDA')
   def       UpperCAmelCase		(    self	:      Optional[Any])						->					List[Any]:




         """simple docstring"""

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


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




         """simple docstring"""

         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Tuple:




         """simple docstring"""

         self._test_save_load_local()



   def       UpperCAmelCase		(    self	:      List[str])						->					Tuple:




         """simple docstring"""

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