

from typing import List, Optional, Union

import torch

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
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=8      ):

					"""simple docstring"""






					__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





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A					,						_A					,						):
												super().__init__()

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




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A							):
												if latents is None:
																	__lowerCAmelCase					=							randn_tensor(_A					,						generator=_A					,						device=_A					,						dtype=_A							)
												else:
																	if latents.shape != shape:
																						raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""							)
																	__lowerCAmelCase					=							latents.to(_A							)

												__lowerCAmelCase					=							latents * scheduler.init_noise_sigma
												return latents




							def        A__  ( self					,						_A=0							):
												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(_A					,						_A							)




							def        A__  ( self					,						_A=0							):
												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=_A							)
																	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(_A					,						_A					,						prev_module_hook=_A							)

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




							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        A__  ( self							):
												if not hasattr(self.unet					,						'_hf_hook'							):
																	return self.device
												for module in self.unet.modules():
																	if (
																	    hasattr(_A					,						'_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(_A							)
							def __call__( self					,						_A					,						_A					,						_A = 5_1_2					,						_A = 5_1_2					,						_A = 1_0_0					,						_A = 4.0					,						_A = 1					,						_A = None					,						_A = None					,						_A = "pil"					,						_A = True					,						):
												__lowerCAmelCase					=							self._execution_device

												__lowerCAmelCase					=							guidance_scale > 1.0

												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)
												__lowerCAmelCase					=							image_embeds.shape[0] * num_images_per_prompt
												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)

												if do_classifier_free_guidance:
																	__lowerCAmelCase					=							image_embeds.repeat_interleave(_A					,						dim=0							)
																	__lowerCAmelCase					=							negative_image_embeds.repeat_interleave(_A					,						dim=0							)

												__lowerCAmelCase					=							torch.cat([negative_image_embeds, image_embeds]					,						dim=0							).to(dtype=self.unet.dtype					,						device=_A							)

												self.scheduler.set_timesteps(_A					,						device=_A							)
												__lowerCAmelCase					=							self.scheduler.timesteps

												__lowerCAmelCase					=							self.unet.config.in_channels

												__lowerCAmelCase,	__lowerCAmelCase					=							downscale_height_and_width(_A					,						_A					,						self.movq_scale_factor							)

												# create initial latent
												__lowerCAmelCase					=							self.prepare_latents(
												    (batch_size, num_channels_latents, height, width)					,						image_embeds.dtype					,						_A					,						_A					,						_A					,						self.scheduler					,						)

												for i, t in enumerate(self.progress_bar(_A							)							):
																	# 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}
																	__lowerCAmelCase					=							self.unet(
																	    sample=_A					,						timestep=_A					,						encoder_hidden_states=_A					,						added_cond_kwargs=_A					,						return_dict=_A					,						)[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(
																	    _A					,						_A					,						_A					,						generator=_A					,						)[0]
												# post-processing
												__lowerCAmelCase					=							self.movq.decode(_A					,						force_not_quantize=_A							)['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(_A							)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_A							)









from __future__ import annotations

from collections.abc import MutableSequence





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A							):
												if len(_A							) != degree + 1:
																	raise ValueError(
																	    'The number of coefficients should be equal to the degree + 1.'							)

												__lowerCAmelCase					=							list(_A							)
												__lowerCAmelCase					=							degree




							def __add__( self					,						_A							):

												if self.degree > polynomial_a.degree:
																	__lowerCAmelCase					=							self.coefficients[:]
																	for i in range(polynomial_a.degree + 1							):
																						coefficients[i] += polynomial_a.coefficients[i]
																	return Polynomial(self.degree					,						_A							)
												else:
																	__lowerCAmelCase					=							polynomial_a.coefficients[:]
																	for i in range(self.degree + 1							):
																						coefficients[i] += self.coefficients[i]
																	return Polynomial(polynomial_a.degree					,						_A							)




							def __sub__( self					,						_A							):
												return self + polynomial_a * Polynomial(0					,						[-1]							)




							def __neg__( self							):
												return Polynomial(self.degree					,						[-c for c in self.coefficients]							)




							def __mul__( self					,						_A							):
												__lowerCAmelCase					=							[0] * (self.degree + polynomial_a.degree + 1)
												for i in range(self.degree + 1							):
																	for j in range(polynomial_a.degree + 1							):
																						coefficients[i + j] += (
																						    self.coefficients[i] * polynomial_a.coefficients[j]
																						)

												return Polynomial(self.degree + polynomial_a.degree					,						_A							)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							0
												for i in range(self.degree + 1							):
																	result += self.coefficients[i] * (substitution**i)
												return result




							def __str__( self							):
												__lowerCAmelCase					=							''
												for i in range(self.degree					,						-1					,						-1							):
																	if self.coefficients[i] == 0:
																						continue
																	elif self.coefficients[i] > 0:
																						if polynomial:
																											polynomial += " + "
																	else:
																						polynomial += " - "

																	if i == 0:
																						polynomial += str(abs(self.coefficients[i]							)							)
																	elif i == 1:
																						polynomial += str(abs(self.coefficients[i]							)							) + "x"
																	else:
																						polynomial += str(abs(self.coefficients[i]							)							) + "x^" + str(_A							)

												return polynomial




							def __repr__( self							):
												return self.__str__()




							def        A__  ( self							):
												__lowerCAmelCase					=							[0] * self.degree
												for i in range(self.degree							):
																	__lowerCAmelCase					=							self.coefficients[i + 1] * (i + 1)
												return Polynomial(self.degree - 1					,						_A							)




							def        A__  ( self					,						_A = 0							):
												__lowerCAmelCase					=							[0] * (self.degree + 2)
												__lowerCAmelCase					=							constant
												for i in range(self.degree + 1							):
																	__lowerCAmelCase					=							self.coefficients[i] / (i + 1)
												return Polynomial(self.degree + 1					,						_A							)




							def __eq__( self					,						_A							):
												if not isinstance(_A					,						_A							):
																	return False

												if self.degree != polynomial_a.degree:
																	return False

												for i in range(self.degree + 1							):
																	if self.coefficients[i] != polynomial_a.coefficients[i]:
																						return False

												return True




							def __ne__( self					,						_A							):
												return not self.__eq__(_A							)






