






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

import flax
import jax.numpy as jnp
from scipy import integrate

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




@flax.struct.dataclass
class 							a_				:


							'''simple docstring'''






							__a:			int									=					4_2

							# setable values
							__a:			Tuple									=					4_2
							__a:			str									=					4_2
							__a:			Dict									=					4_2
							__a:			Optional[Any]									=					None

							# running values
							__a:			Union[str, Any]									=					None

							@classmethod
							def   _lowercase (	cls    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_     )      ->      Optional[int]:
														'''simple docstring'''

														return cls(common=_lowerCAmelCase    ,    init_noise_sigma=_lowerCAmelCase    ,    timesteps=_lowerCAmelCase    ,    sigmas=_lowerCAmelCase     )




@dataclass
class 							a_				(		__lowerCamelCase	):


							'''simple docstring'''






							__a:			Tuple									=					4_2




class 							a_				(		__lowerCamelCase			,			__lowerCamelCase	):


							'''simple docstring'''






							__a:			Any									=					[e.name for e in FlaxKarrasDiffusionSchedulers]

							__a:			Optional[Any]									=					4_2

							@property
							def   _lowercase (	self     )      ->      List[str]:
														'''simple docstring'''

														return True

							@register_to_config
							def __init__(	self    ,    lowercase_ = 1_0_0_0    ,    lowercase_ = 0.00_01    ,    lowercase_ = 0.02    ,    lowercase_ = "linear"    ,    lowercase_ = None    ,    lowercase_ = "epsilon"    ,    lowercase_ = jnp.floataa    ,    )      ->      Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_       		=  dtype

							def   _lowercase (	self    ,    lowercase_ = None     )      ->      List[Any]:
														'''simple docstring'''

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

														lowerCAmelCase_       		=  jnp.arange(0    ,    self.config.num_train_timesteps     ).round()[::-1]
														lowerCAmelCase_       		=  ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

														# standard deviation of the initial noise distribution
														lowerCAmelCase_       		=  sigmas.max()

														return LMSDiscreteSchedulerState.create(
														    common=_lowerCAmelCase    ,    init_noise_sigma=_lowerCAmelCase    ,    timesteps=_lowerCAmelCase    ,    sigmas=_lowerCAmelCase    ,    )

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_     )      ->      int:
														'''simple docstring'''

														(lowerCAmelCase_		)       		=  jnp.where(state.timesteps == timestep    ,    size=1     )
														lowerCAmelCase_       		=  step_index[0]

														lowerCAmelCase_       		=  state.sigmas[step_index]
														lowerCAmelCase_       		=  sample / ((sigma**2 + 1) ** 0.5)
														return sample

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_     )      ->      Tuple:
														'''simple docstring'''

														def lms_derivative(lowercase_     ):
																					lowerCAmelCase_       		=  1.0
																					for k in range(_lowerCAmelCase     ):
																												if current_order == k:
																																			continue
																												prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
																					return prod

														lowerCAmelCase_       		=  integrate.quad(_lowerCAmelCase    ,    state.sigmas[t]    ,    state.sigmas[t + 1]    ,    epsrel=1e-4     )[0]

														return integrated_coeff

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_ = ()     )      ->      Tuple:
														'''simple docstring'''

														lowerCAmelCase_       		=  jnp.linspace(self.config.num_train_timesteps - 1    ,    0    ,    _lowerCAmelCase    ,    dtype=self.dtype     )

														lowerCAmelCase_       		=  jnp.floor(_lowerCAmelCase     ).astype(jnp.intaa     )
														lowerCAmelCase_       		=  jnp.ceil(_lowerCAmelCase     ).astype(jnp.intaa     )

														lowerCAmelCase_       		=  jnp.mod(_lowerCAmelCase    ,    1.0     )

														lowerCAmelCase_       		=  ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
														lowerCAmelCase_       		=  (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
														lowerCAmelCase_       		=  jnp.concatenate([sigmas, jnp.array([0.0]    ,    dtype=self.dtype     )]     )

														lowerCAmelCase_       		=  timesteps.astype(jnp.intaa     )

														# initial running values
														lowerCAmelCase_       		=  jnp.zeros((0,) + shape    ,    dtype=self.dtype     )

														return state.replace(
														    timesteps=_lowerCAmelCase    ,    sigmas=_lowerCAmelCase    ,    num_inference_steps=_lowerCAmelCase    ,    derivatives=_lowerCAmelCase    ,    )

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_ = 4    ,    lowercase_ = True    ,    )      ->      Optional[Any]:
														'''simple docstring'''

														if state.num_inference_steps is None:
																					raise ValueError(
																					    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'     )

														lowerCAmelCase_       		=  state.sigmas[timestep]

														# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
														if self.config.prediction_type == "epsilon":
																					lowerCAmelCase_       		=  sample - sigma * model_output
														elif self.config.prediction_type == "v_prediction":
																					# * c_out + input * c_skip
																					lowerCAmelCase_       		=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
														else:
																					raise ValueError(
																					    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''     )

														# 2. Convert to an ODE derivative
														lowerCAmelCase_       		=  (sample - pred_original_sample) / sigma
														lowerCAmelCase_       		=  state.replace(derivatives=jnp.append(state.derivatives    ,    _lowerCAmelCase     )     )
														if len(state.derivatives     ) > order:
																					lowerCAmelCase_       		=  state.replace(derivatives=jnp.delete(state.derivatives    ,    0     )     )

														# 3. Compute linear multistep coefficients
														lowerCAmelCase_       		=  min(timestep + 1    ,    _lowerCAmelCase     )
														lowerCAmelCase_       		=  [self.get_lms_coefficient(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase     ) for curr_order in range(_lowerCAmelCase     )]

														# 4. Compute previous sample based on the derivatives path
														lowerCAmelCase_       		=  sample + sum(
														    coeff * derivative for coeff, derivative in zip(_lowerCAmelCase    ,    reversed(state.derivatives     )     )     )

														if not return_dict:
																					return (prev_sample, state)

														return FlaxLMSSchedulerOutput(prev_sample=_lowerCAmelCase    ,    state=_lowerCAmelCase     )

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    )      ->      str:
														'''simple docstring'''

														lowerCAmelCase_       		=  state.sigmas[timesteps].flatten()
														lowerCAmelCase_       		=  broadcast_to_shape_from_left(_lowerCAmelCase    ,    noise.shape     )

														lowerCAmelCase_       		=  original_samples + noise * sigma

														return noisy_samples






							def __len__(	self     )      ->      Tuple:
														'''simple docstring'''

														return self.config.num_train_timesteps











from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCamelCase_		  =						logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase_		  =						"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""
def   lowerCamelCase      (		a_				,		a_				,		a_=8			)			->    Tuple:
							lowerCAmelCase_       		=  height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							lowerCAmelCase_       		=  width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor
def   lowerCamelCase      (		a_				,		a_=512				,		a_=512			)			->    Any:
							lowerCAmelCase_       		=  pil_image.resize((w, h)				,		resample=Image.BICUBIC				,		reducing_gap=1			)
							lowerCAmelCase_       		=  np.array(pil_image.convert('RGB'			)			)
							lowerCAmelCase_       		=  arr.astype(np.floataa			) / 127.5 - 1
							lowerCAmelCase_       		=  np.transpose(a_				,		[2, 0, 1]			)
							lowerCAmelCase_       		=  torch.from_numpy(a_			).unsqueeze(0			)
							return image




class 							a_				(		a_	):


							'''simple docstring'''






							def __init__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    )      ->      List[Any]:
														'''simple docstring'''

														super().__init__()

														self.register_modules(
														    unet=lowercase_    ,    scheduler=lowercase_    ,    movq=lowercase_    ,    )
														lowerCAmelCase_       		=  2 ** (len(self.movq.config.block_out_channels     ) - 1)

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_     )      ->      Tuple:
														'''simple docstring'''

														lowerCAmelCase_       		=  min(int(num_inference_steps * strength     )    ,    lowercase_     )

														lowerCAmelCase_       		=  max(num_inference_steps - init_timestep    ,    0     )
														lowerCAmelCase_       		=  self.scheduler.timesteps[t_start:]

														return timesteps, num_inference_steps - t_start

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_=None     )      ->      str:
														'''simple docstring'''

														if not isinstance(lowercase_    ,    (torch.Tensor, PIL.Image.Image, list)     ):
																					raise ValueError(
																					    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase_     )}'''     )

														lowerCAmelCase_       		=  image.to(device=lowercase_    ,    dtype=lowercase_     )

														lowerCAmelCase_       		=  batch_size * num_images_per_prompt

														if image.shape[1] == 4:
																					lowerCAmelCase_       		=  image

														else:
																					if isinstance(lowercase_    ,    lowercase_     ) and len(lowercase_     ) != batch_size:
																												raise ValueError(
																												    f'''You have passed a list of generators of length {len(lowercase_     )}, but requested an effective batch'''
																												    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

																					elif isinstance(lowercase_    ,    lowercase_     ):
																												lowerCAmelCase_       		=  [
																												    self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(lowercase_     )
																												]
																												lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )
																					else:
																												lowerCAmelCase_       		=  self.movq.encode(lowercase_     ).latent_dist.sample(lowercase_     )

																					lowerCAmelCase_       		=  self.movq.config.scaling_factor * init_latents

														lowerCAmelCase_       		=  torch.cat([init_latents]    ,    dim=0     )

														lowerCAmelCase_       		=  init_latents.shape
														lowerCAmelCase_       		=  randn_tensor(lowercase_    ,    generator=lowercase_    ,    device=lowercase_    ,    dtype=lowercase_     )

														# get latents
														lowerCAmelCase_       		=  self.scheduler.add_noise(lowercase_    ,    lowercase_    ,    lowercase_     )

														lowerCAmelCase_       		=  init_latents

														return latents

							def   _lowercase (	self    ,    lowercase_=0     )      ->      int:
														'''simple docstring'''

														if is_accelerate_available():
																					from accelerate import cpu_offload
														else:
																					raise ImportError('Please install accelerate via `pip install accelerate`'     )

														lowerCAmelCase_       		=  torch.device(f'''cuda:{gpu_id}'''     )

														lowerCAmelCase_       		=  [
														    self.unet,
														    self.movq,
														]
														for cpu_offloaded_model in models:
																					if cpu_offloaded_model is not None:
																												cpu_offload(lowercase_    ,    lowercase_     )

							def   _lowercase (	self    ,    lowercase_=0     )      ->      Union[str, Any]:
														'''simple docstring'''

														if is_accelerate_available() and is_accelerate_version('>='    ,    '0.17.0.dev0'     ):
																					from accelerate import cpu_offload_with_hook
														else:
																					raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'     )

														lowerCAmelCase_       		=  torch.device(f'''cuda:{gpu_id}'''     )

														if self.device.type != "cpu":
																					self.to('cpu'    ,    silence_dtype_warnings=lowercase_     )
																					torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

														lowerCAmelCase_       		=  None
														for cpu_offloaded_model in [self.unet, self.movq]:
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  cpu_offload_with_hook(lowercase_    ,    lowercase_    ,    prev_module_hook=lowercase_     )

														# We'll offload the last model manually.
														lowerCAmelCase_       		=  hook

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def   _lowercase (	self     )      ->      Dict:
														'''simple docstring'''

														if not hasattr(self.unet    ,    '_hf_hook'     ):
																					return self.device
														for module in self.unet.modules():
																					if (
																					    hasattr(lowercase_    ,    '_hf_hook'     )
																					    and hasattr(module._hf_hook    ,    'execution_device'     )
																					    and module._hf_hook.execution_device is not None
																					):
																												return torch.device(module._hf_hook.execution_device     )
														return self.device






							@torch.no_grad()
							@replace_example_docstring(lowercase_     )
							def __call__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_ = 5_1_2    ,    lowercase_ = 5_1_2    ,    lowercase_ = 1_0_0    ,    lowercase_ = 4.0    ,    lowercase_ = 0.3    ,    lowercase_ = 1    ,    lowercase_ = None    ,    lowercase_ = "pil"    ,    lowercase_ = True    ,    )      ->      int:
														'''simple docstring'''

														lowerCAmelCase_       		=  self._execution_device

														lowerCAmelCase_       		=  guidance_scale > 1.0

														if isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )
														if isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )
														if isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )

														lowerCAmelCase_       		=  image_embeds.shape[0]

														if do_classifier_free_guidance:
																					lowerCAmelCase_       		=  image_embeds.repeat_interleave(lowercase_    ,    dim=0     )
																					lowerCAmelCase_       		=  negative_image_embeds.repeat_interleave(lowercase_    ,    dim=0     )
																					lowerCAmelCase_       		=  hint.repeat_interleave(lowercase_    ,    dim=0     )

														lowerCAmelCase_       		=  torch.cat([negative_image_embeds, image_embeds]    ,    dim=0     ).to(dtype=self.unet.dtype    ,    device=lowercase_     )
														lowerCAmelCase_       		=  torch.cat([hint, hint]    ,    dim=0     ).to(dtype=self.unet.dtype    ,    device=lowercase_     )

														if not isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  [image]
														if not all(isinstance(lowercase_    ,    (PIL.Image.Image, torch.Tensor)     ) for i in image     ):
																					raise ValueError(
																					    f'''Input is in incorrect format: {[type(lowercase_     ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''     )

														lowerCAmelCase_       		=  torch.cat([prepare_image(lowercase_    ,    lowercase_    ,    lowercase_     ) for i in image]    ,    dim=0     )
														lowerCAmelCase_       		=  image.to(dtype=image_embeds.dtype    ,    device=lowercase_     )

														lowerCAmelCase_       		=  self.movq.encode(lowercase_     )['latents']
														lowerCAmelCase_       		=  latents.repeat_interleave(lowercase_    ,    dim=0     )
														self.scheduler.set_timesteps(lowercase_    ,    device=lowercase_     )
														lowerCAmelCase_		,     lowerCAmelCase_       		=  self.get_timesteps(lowercase_    ,    lowercase_    ,    lowercase_     )
														lowerCAmelCase_       		=  timesteps[:1].repeat(batch_size * num_images_per_prompt     )
														lowerCAmelCase_		,     lowerCAmelCase_       		=  downscale_height_and_width(lowercase_    ,    lowercase_    ,    self.movq_scale_factor     )
														lowerCAmelCase_       		=  self.prepare_latents(
														    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    image_embeds.dtype    ,    lowercase_    ,    lowercase_     )
														for i, t in enumerate(self.progress_bar(lowercase_     )     ):
																					# expand the latents if we are doing classifier free guidance
																					lowerCAmelCase_       		=  torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

																					lowerCAmelCase_       		=  {'image_embeds': image_embeds, 'hint': hint}
																					lowerCAmelCase_       		=  self.unet(
																					    sample=lowercase_    ,    timestep=lowercase_    ,    encoder_hidden_states=lowercase_    ,    added_cond_kwargs=lowercase_    ,    return_dict=lowercase_    ,    )[0]

																					if do_classifier_free_guidance:
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  noise_pred.split(latents.shape[1]    ,    dim=1     )
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  noise_pred.chunk(2     )
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  variance_pred.chunk(2     )
																												lowerCAmelCase_       		=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																												lowerCAmelCase_       		=  torch.cat([noise_pred, variance_pred_text]    ,    dim=1     )

																					if not (
																					    hasattr(self.scheduler.config    ,    'variance_type'     )
																					    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																					):
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  noise_pred.split(latents.shape[1]    ,    dim=1     )

																					# compute the previous noisy sample x_t -> x_t-1

																					lowerCAmelCase_       		=  self.scheduler.step(
																					    lowercase_    ,    lowercase_    ,    lowercase_    ,    generator=lowercase_    ,    )[0]

														# post-processing
														lowerCAmelCase_       		=  self.movq.decode(lowercase_    ,    force_not_quantize=lowercase_     )['sample']

														if output_type not in ["pt", "np", "pil"]:
																					raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''     )

														if output_type in ["np", "pil"]:
																					lowerCAmelCase_       		=  image * 0.5 + 0.5
																					lowerCAmelCase_       		=  image.clamp(0    ,    1     )
																					lowerCAmelCase_       		=  image.cpu().permute(0    ,    2    ,    3    ,    1     ).float().numpy()

														if output_type == "pil":
																					lowerCAmelCase_       		=  self.numpy_to_pil(lowercase_     )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowercase_     )



