





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   snake_case_							:
  A_     				=       42

  # setable values
  A_     				=       42
  A_     				=       42
  A_     				=       None






  @classmethod
  def  UpperCAmelCase__		(				cls					:	Optional[Any]  ,    _snake_case					:	CommonSchedulerState  ,    _snake_case					:	jnp.ndarray  ,    _snake_case					:	jnp.ndarray     )->str:






   '''simple docstring'''

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






@dataclass
class   snake_case_							(						_UpperCamelCase		):
  A_     				=       42






class   snake_case_							(						_UpperCamelCase	,_UpperCamelCase		):
  A_     				=       [e.name for e in FlaxKarrasDiffusionSchedulers]

  A_     				=       42






  @property
  def  UpperCAmelCase__		(				self					:	Any     )->List[Any]:






   '''simple docstring'''

   return True






  @register_to_config
  def __init__(				self					:	Optional[Any]  ,    _snake_case					:	int = 1000  ,    _snake_case					:	float = 0.0_001  ,    _snake_case					:	float = 0.02  ,    _snake_case					:	str = "linear"  ,    _snake_case					:	Optional[jnp.ndarray] = None  ,    _snake_case					:	str = "fixed_small"  ,    _snake_case					:	bool = True  ,    _snake_case					:	str = "epsilon"  ,    _snake_case					:	jnp.dtype = jnp.floataa  ,    )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase	:			List[Any]         =  dtype






  def  UpperCAmelCase__		(				self					:	int  ,    _snake_case					:	Optional[CommonSchedulerState] = None     )->List[str]:






   '''simple docstring'''

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

   # standard deviation of the initial noise distribution
   __lowerCAmelCase	:			Optional[int]         =  jnp.array(1.0  ,    dtype=self.dtype     )

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

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






  def  UpperCAmelCase__		(				self					:	Optional[int]  ,    _snake_case					:	DDPMSchedulerState  ,    _snake_case					:	jnp.ndarray  ,    _snake_case					:	Optional[int] = None     )->str:






   '''simple docstring'''

   return sample






  def  UpperCAmelCase__		(				self					:	Tuple  ,    _snake_case					:	DDPMSchedulerState  ,    _snake_case					:	int  ,    _snake_case					:	Tuple = ()     )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase	:			int         =  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
   __lowerCAmelCase	:			Optional[int]         =  (jnp.arange(0  ,    _UpperCAmelCase     ) * step_ratio).round()[::-1]

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






  def  UpperCAmelCase__		(				self					:	Union[str, Any]  ,    _snake_case					:	DDPMSchedulerState  ,    _snake_case					:	Dict  ,    _snake_case					:	int=None  ,    _snake_case					:	List[Any]=None     )->int:






   '''simple docstring'''

   __lowerCAmelCase	:			Optional[Any]         =  state.common.alphas_cumprod[t]
   __lowerCAmelCase	:			str         =  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
   __lowerCAmelCase	:			Dict         =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

   if variance_type is None:
    __lowerCAmelCase	:			Tuple         =  self.config.variance_type

   # hacks - were probably added for training stability
   if variance_type == "fixed_small":
    __lowerCAmelCase	:			Dict         =  jnp.clip(_UpperCAmelCase  ,    a_min=1E-20     )
   # for rl-diffuser https://arxiv.org/abs/2205.09991
   elif variance_type == "fixed_small_log":
    __lowerCAmelCase	:			List[Any]         =  jnp.log(jnp.clip(_UpperCAmelCase  ,    a_min=1E-20     )     )
   elif variance_type == "fixed_large":
    __lowerCAmelCase	:			Optional[Any]         =  state.common.betas[t]
   elif variance_type == "fixed_large_log":
    # Glide max_log
    __lowerCAmelCase	:			Optional[Any]         =  jnp.log(state.common.betas[t]     )
   elif variance_type == "learned":
    return predicted_variance
   elif variance_type == "learned_range":
    __lowerCAmelCase	:			Union[str, Any]         =  variance
    __lowerCAmelCase	:			Optional[Any]         =  state.common.betas[t]
    __lowerCAmelCase	:			Dict         =  (predicted_variance + 1) / 2
    __lowerCAmelCase	:			List[Any]         =  frac * max_log + (1 - frac) * min_log

   return variance






  def  UpperCAmelCase__		(				self					:	Any  ,    _snake_case					:	DDPMSchedulerState  ,    _snake_case					:	jnp.ndarray  ,    _snake_case					:	int  ,    _snake_case					:	jnp.ndarray  ,    _snake_case					:	Optional[jax.random.KeyArray] = None  ,    _snake_case					:	bool = True  ,    )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase	:			int         =  timestep

   if key is None:
    __lowerCAmelCase	:			Optional[Any]         =  jax.random.PRNGKey(0     )

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

   # 1. compute alphas, betas
   __lowerCAmelCase	:			str         =  state.common.alphas_cumprod[t]
   __lowerCAmelCase	:			List[str]         =  jnp.where(t > 0  ,    state.common.alphas_cumprod[t - 1]  ,    jnp.array(1.0  ,    dtype=self.dtype     )     )
   __lowerCAmelCase	:			Dict         =  1 - alpha_prod_t
   __lowerCAmelCase	:			List[str]         =  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":
    __lowerCAmelCase	:			Optional[int]         =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif self.config.prediction_type == "sample":
    __lowerCAmelCase	:			Optional[int]         =  model_output
   elif self.config.prediction_type == "v_prediction":
    __lowerCAmelCase	:			List[Any]         =  (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:
    __lowerCAmelCase	:			str         =  jnp.clip(_UpperCAmelCase  ,    -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
   __lowerCAmelCase	:			Any         =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
   __lowerCAmelCase	:			Any         =  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
   __lowerCAmelCase	:			Optional[Any]         =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   def random_variance():
    __lowerCAmelCase	:			List[str]         =  jax.random.split(_UpperCAmelCase  ,    num=1     )
    __lowerCAmelCase	:			Dict         =  jax.random.normal(_UpperCAmelCase  ,    shape=model_output.shape  ,    dtype=self.dtype     )
    return (self._get_variance(_UpperCAmelCase  ,    _UpperCAmelCase  ,    predicted_variance=_UpperCAmelCase     ) ** 0.5) * noise

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

   __lowerCAmelCase	:			Any         =  pred_prev_sample + variance

   if not return_dict:
    return (pred_prev_sample, state)

   return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase  ,    state=_UpperCAmelCase     )






  def  UpperCAmelCase__		(				self					:	List[Any]  ,    _snake_case					:	DDPMSchedulerState  ,    _snake_case					:	jnp.ndarray  ,    _snake_case					:	jnp.ndarray  ,    _snake_case					:	jnp.ndarray  ,    )->Dict:






   '''simple docstring'''

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






  def  UpperCAmelCase__		(				self					:	List[str]  ,    _snake_case					:	DDPMSchedulerState  ,    _snake_case					:	jnp.ndarray  ,    _snake_case					:	jnp.ndarray  ,    _snake_case					:	jnp.ndarray  ,    )->Optional[Any]:






   '''simple docstring'''

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






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






   '''simple docstring'''

   return self.config.num_train_timesteps




'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Any  							=   StableDiffusionInpaintPipeline
							lowerCAmelCase			:       int  							=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							lowerCAmelCase			:       Optional[Any]  							=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowerCAmelCase			:       List[str]  							=   frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCAmelCase			:       Optional[Any]  							=   frozenset([]  )







							def 				__lowercase     (				self   :			Dict						):
									torch.manual_seed(0						)
									_a	: Optional[int]								=				UNetaDConditionModel(
									    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,cross_attention_dim=32      ,attention_head_dim=(2, 4)      ,use_linear_projection=_UpperCAmelCase      ,)
									_a	: Optional[int]								=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase						)
									torch.manual_seed(0						)
									_a	: Dict								=				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      ,sample_size=128      ,)
									torch.manual_seed(0						)
									_a	: Union[str, Any]								=				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      ,hidden_act='gelu'      ,projection_dim=512      ,)
									_a	: str								=				CLIPTextModel(_UpperCAmelCase						)
									_a	: Any								=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									_a	: Optional[Any]								=				{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			List[Any]      ,_UpperCAmelCase   :			int=0						):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									_a	: Any								=				floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
									_a	: Any								=				image.cpu().permute(0      ,2      ,3      ,1						)[0]
									_a	: Optional[int]								=				Image.fromarray(np.uinta(_UpperCAmelCase						)						).convert('RGB'						).resize((64, 64)						)
									_a	: Tuple								=				Image.fromarray(np.uinta(image + 4						)						).convert('RGB'						).resize((64, 64)						)
									if str(_UpperCAmelCase						).startswith('mps'						):
											_a	: str								=				torch.manual_seed(_UpperCAmelCase						)
									else:
											_a	: Dict								=				torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
									_a	: Any								=				{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': init_image,
									    'mask_image': mask_image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Optional[int]								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a	: Dict								=				self.get_dummy_components()
									_a	: int								=				StableDiffusionInpaintPipeline(**_UpperCAmelCase						)
									_a	: Any								=				sd_pipe.to(_UpperCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				self.get_dummy_inputs(_UpperCAmelCase						)
									_a	: Tuple								=				sd_pipe(**_UpperCAmelCase						).images
									_a	: Dict								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: List[Any]								=				np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]						)

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







							def 				__lowercase     (				self   :			List[Any]						):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

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







							def 				__lowercase     (				self   :			List[str]						):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def 				__lowercase     (				self   :			int						):
									_a	: Tuple								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Union[str, Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench.npy'						)

									_a	: List[Any]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Tuple								=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase      ,safety_checker=_UpperCAmelCase						)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: int								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: Any								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: List[str]								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 9E-3







							def 				__lowercase     (				self   :			Tuple						):
									_a	: str								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'						)

									_a	: int								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: List[str]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,torch_dtype=torch.floataa      ,safety_checker=_UpperCAmelCase      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: Dict								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: int								=				torch.manual_seed(0						)
									_a	: Dict								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: str								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 5E-1







							def 				__lowercase     (				self   :			Tuple						):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_a	: Dict								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[int]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)

									_a	: List[str]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Any								=				PNDMScheduler.from_pretrained(_UpperCAmelCase      ,subfolder='scheduler'						)
									_a	: Optional[Any]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,safety_checker=_UpperCAmelCase      ,scheduler=_UpperCAmelCase      ,torch_dtype=torch.floataa      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									_a	: Any								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Optional[Any]								=				torch.manual_seed(0						)
									_a	: int								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: int								=				torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9




