

"""simple docstring"""






from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)

class 				__a    (   _SCREAMING_SNAKE_CASE					):



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = 0.93     ,						_lowerCamelCase = True     ,						) ->       int:




										'''simple docstring'''
										__lowercase						      =					mixing_coeff
										__lowercase						      =					leapfrog_steps

										super().__init__()
										self.register_modules(
										    vae=UpperCamelCase__     ,						text_encoder=UpperCamelCase__     ,						tokenizer=UpperCamelCase__     ,						unet=UpperCamelCase__     ,						scheduler=UpperCamelCase__     ,						)

										__lowercase						      =					2 ** (len(self.vae.config.block_out_channels					) - 1)
										__lowercase						      =					VaeImageProcessor(vae_scale_factor=self.vae_scale_factor					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase = None     ,						_lowerCamelCase = False					) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					self.tokenizer(
										    UpperCamelCase__     ,						padding="max_length"     ,						max_length=self.tokenizer.model_max_length     ,						truncation=UpperCamelCase__     ,						return_tensors="pt"     ,						)

										__lowercase						      =					self.text_encoder(text_inputs.input_ids.to(self.device					)					).last_hidden_state

										__lowercase						      =					prompt_embeds.to(dtype=self.text_encoder.dtype     ,						device=self.device					)

										if do_classifier_free_guidance:
													__lowercase						      =					"" if negative_prompt is None else negative_prompt

													__lowercase						      =					self.tokenizer(
													    UpperCamelCase__     ,						padding="max_length"     ,						max_length=self.tokenizer.model_max_length     ,						truncation=UpperCamelCase__     ,						return_tensors="pt"     ,						)

													__lowercase						      =					self.text_encoder(uncond_input.input_ids.to(self.device					)					).last_hidden_state

													__lowercase						      =					torch.cat([negative_prompt_embeds, prompt_embeds]					)

										return prompt_embeds
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Optional[Any]:




										'''simple docstring'''
										__lowercase						      =					self.mixing_coeff * x + (1 - self.mixing_coeff) * y
										__lowercase						      =					self.mixing_coeff * y + (1 - self.mixing_coeff) * x

										return [x, y]
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
										__lowercase						      =					(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

										return [x, y]
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										# as self.alphas_cumprod is always in cpu
										__lowercase						      =					int(UpperCamelCase__					)

										__lowercase						      =					self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

										return alpha_prod, 1 - alpha_prod
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						) ->       List[Any]:




										'''simple docstring'''
										__lowercase						      =					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

										__lowercase      ,				__lowercase						      =					self._get_alpha_and_beta(UpperCamelCase__					)
										__lowercase      ,				__lowercase						      =					self._get_alpha_and_beta(UpperCamelCase__					)

										__lowercase						      =					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
										__lowercase						      =					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

										__lowercase						      =					(base - b_t * model_output) / a_t

										return model_input, next_model_input.to(base.dtype					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						) ->       str:




										'''simple docstring'''
										__lowercase						      =					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

										__lowercase      ,				__lowercase						      =					self._get_alpha_and_beta(UpperCamelCase__					)
										__lowercase      ,				__lowercase						      =					self._get_alpha_and_beta(UpperCamelCase__					)

										__lowercase						      =					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
										__lowercase						      =					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
										__lowercase						      =					a_t * base + b_t * model_output

										return model_input, next_model_input.to(base.dtype					)
							@torch.no_grad()
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Optional[Any]:




										'''simple docstring'''
										__lowercase						      =					1 / self.vae.config.scaling_factor * latents
										__lowercase						      =					self.vae.decode(UpperCamelCase__					).sample
										__lowercase						      =					(image / 2 + 0.5).clamp(0     ,						1					)
										return image
							@torch.no_grad()
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = None     ,						) ->       Dict:




										'''simple docstring'''
										__lowercase						      =					guidance_scale > 1.0

										__lowercase						      =					image.to(device=self.device     ,						dtype=text_embeds.dtype					)
										__lowercase						      =					self.vae.encode(UpperCamelCase__					).latent_dist.sample(UpperCamelCase__					)

										__lowercase						      =					self.vae.config.scaling_factor * latent

										__lowercase						      =					[latent.clone(), latent.clone()]

										for i, t in tqdm(enumerate(UpperCamelCase__					)     ,						total=len(UpperCamelCase__					)					):
													__lowercase						      =					self.noise_mixing_layer(x=coupled_latents[0]     ,						y=coupled_latents[1]					)

													# j - model_input index, k - base index
													for j in range(2					):
																__lowercase						      =					j ^ 1

																if self.leapfrog_steps:
																			if i % 2 == 0:
																						__lowercase      ,				__lowercase						      =					j, k

																__lowercase						      =					coupled_latents[j]
																__lowercase						      =					coupled_latents[k]

																__lowercase						      =					torch.cat([model_input] * 2					) if do_classifier_free_guidance else model_input

																__lowercase						      =					self.unet(UpperCamelCase__     ,						UpperCamelCase__     ,						encoder_hidden_states=UpperCamelCase__					).sample

																if do_classifier_free_guidance:
																			__lowercase      ,				__lowercase						      =					noise_pred.chunk(2					)
																			__lowercase						      =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																__lowercase      ,				__lowercase						      =					self.noise_step(
																    base=UpperCamelCase__     ,						model_input=UpperCamelCase__     ,						model_output=UpperCamelCase__     ,						timestep=UpperCamelCase__     ,						)

																__lowercase						      =					model_input

										return coupled_latents





							@torch.no_grad()
							def __call__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = 3.0     ,						_lowerCamelCase = 50     ,						_lowerCamelCase = 0.8     ,						_lowerCamelCase = None     ,						_lowerCamelCase = None     ,						_lowerCamelCase = "pil"     ,						) ->       Dict:




										'''simple docstring'''
										__lowercase						      =					guidance_scale > 1.0

										__lowercase						      =					self.image_processor.preprocess(UpperCamelCase__					)

										__lowercase						      =					self._encode_prompt(UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__					)
										__lowercase						      =					self._encode_prompt(UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__					)

										self.scheduler.set_timesteps(UpperCamelCase__     ,						self.device					)

										__lowercase						      =					num_inference_steps - int(num_inference_steps * strength					)
										__lowercase						      =					self.scheduler.timesteps[t_limit:]
										__lowercase						      =					fwd_timesteps.flip(0					)

										__lowercase						      =					self.prepare_latents(UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__     ,						UpperCamelCase__					)

										for i, t in tqdm(enumerate(UpperCamelCase__					)     ,						total=len(UpperCamelCase__					)					):
													# j - model_input index, k - base index
													for k in range(2					):
																__lowercase						      =					k ^ 1

																if self.leapfrog_steps:
																			if i % 2 == 1:
																						__lowercase      ,				__lowercase						      =					j, k

																__lowercase						      =					coupled_latents[j]
																__lowercase						      =					coupled_latents[k]

																__lowercase						      =					torch.cat([model_input] * 2					) if do_classifier_free_guidance else model_input

																__lowercase						      =					self.unet(UpperCamelCase__     ,						UpperCamelCase__     ,						encoder_hidden_states=UpperCamelCase__					).sample

																if do_classifier_free_guidance:
																			__lowercase      ,				__lowercase						      =					noise_pred.chunk(2					)
																			__lowercase						      =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																__lowercase      ,				__lowercase						      =					self.denoise_step(
																    base=UpperCamelCase__     ,						model_input=UpperCamelCase__     ,						model_output=UpperCamelCase__     ,						timestep=UpperCamelCase__     ,						)

																__lowercase						      =					model_input

													__lowercase						      =					self.denoise_mixing_layer(x=coupled_latents[0]     ,						y=coupled_latents[1]					)

										# either one is fine
										__lowercase						      =					coupled_latents[0]

										if output_type not in ["latent", "pt", "np", "pil"]:
													__lowercase						      =					(
													    f'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
													    "`pil`, `np`, `pt`, `latent`"
													)
													deprecate("Unsupported output_type"     ,						"1.0.0"     ,						UpperCamelCase__     ,						standard_warn=UpperCamelCase__					)
													__lowercase						      =					"np"

										if output_type == "latent":
													__lowercase						      =					final_latent
										else:
													__lowercase						      =					self.decode_latents(UpperCamelCase__					)
													__lowercase						      =					self.image_processor.postprocess(UpperCamelCase__     ,						output_type=UpperCamelCase__					)

										return image







"""simple docstring"""






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
					from .testing_utils import (
					    floats_tensor,
					    load_hf_numpy,
					    load_image,
					    load_numpy,
					    load_pt,
					    nightly,
					    parse_flag_from_env,
					    print_tensor_test,
					    require_torch_a,
					    require_torch_gpu,
					    skip_mps,
					    slow,
					    torch_all_close,
					    torch_device,
					)
					from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_lowercase							 =							get_logger(__name__)



def     lowerCAmelCase__ (		__magic_name__			)	->Optional[int]:
			if version.parse(__magic_name__			) < version.parse(__magic_name__			):
						if "dev" in min_version:
									__lowercase						      =					(
									    "This example requires a source install from HuggingFace diffusers (see "
									    "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
									)
						else:
									__lowercase						      =					F'''This example requires a minimum version of {min_version},'''
						error_message += F''' but the version found is {__version__}.\n'''
						raise ImportError(__magic_name__			)





