
'''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							(       __UpperCAmelCase				):



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = 0.93       ,     lowerCamelCase__ = True       ,     )			->		Union[str, Any]:




									'''simple docstring'''



									lowercase__							 =       mixing_coeff
									lowercase__							 =       leapfrog_steps

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

									lowercase__							 =       2 ** (len(self.vae.config.block_out_channels       ) - 1)
									lowercase__							 =       VaeImageProcessor(vae_scale_factor=self.vae_scale_factor       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = False       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       self.tokenizer(
									    lowerCamelCase__       ,     padding="""max_length"""       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowerCamelCase__       ,     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(
															    lowerCamelCase__       ,     padding="""max_length"""       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowerCamelCase__       ,     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 A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Dict:




									'''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 A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[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 A__ (					self       ,     lowerCamelCase__       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       int(lowerCamelCase__       )

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

									return alpha_prod, 1 - alpha_prod



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     )			->		Optional[int]:




									'''simple docstring'''



									lowercase__							 =       timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

									lowercase__ ,						lowercase__							 =       self._get_alpha_and_beta(lowerCamelCase__       )
									lowercase__ ,						lowercase__							 =       self._get_alpha_and_beta(lowerCamelCase__       )

									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 A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     )			->		Tuple:




									'''simple docstring'''



									lowercase__							 =       timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

									lowercase__ ,						lowercase__							 =       self._get_alpha_and_beta(lowerCamelCase__       )
									lowercase__ ,						lowercase__							 =       self._get_alpha_and_beta(lowerCamelCase__       )

									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 A__ (					self       ,     lowerCamelCase__       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       1 / self.vae.config.scaling_factor * latents
									lowercase__							 =       self.vae.decode(lowerCamelCase__       ).sample
									lowercase__							 =       (image / 2 + 0.5).clamp(0       ,     1       )
									return image



			@torch.no_grad()
			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     )			->		Union[str, Any]:




									'''simple docstring'''



									lowercase__							 =       guidance_scale > 1.0

									lowercase__							 =       image.to(device=self.device       ,     dtype=text_embeds.dtype       )
									lowercase__							 =       self.vae.encode(lowerCamelCase__       ).latent_dist.sample(lowerCamelCase__       )

									lowercase__							 =       self.vae.config.scaling_factor * latent

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

									for i, t in tqdm(enumerate(lowerCamelCase__       )       ,     total=len(lowerCamelCase__       )       ):
															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(lowerCamelCase__       ,     lowerCamelCase__       ,     encoder_hidden_states=lowerCamelCase__       ).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=lowerCamelCase__       ,     model_input=lowerCamelCase__       ,     model_output=lowerCamelCase__       ,     timestep=lowerCamelCase__       ,     )

																					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"       ,     )			->		str:




									'''simple docstring'''



									lowercase__							 =       guidance_scale > 1.0

									lowercase__							 =       self.image_processor.preprocess(lowerCamelCase__       )

									lowercase__							 =       self._encode_prompt(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									lowercase__							 =       self._encode_prompt(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )

									self.scheduler.set_timesteps(lowerCamelCase__       ,     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(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )

									for i, t in tqdm(enumerate(lowerCamelCase__       )       ,     total=len(lowerCamelCase__       )       ):
															# 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(lowerCamelCase__       ,     lowerCamelCase__       ,     encoder_hidden_states=lowerCamelCase__       ).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=lowerCamelCase__       ,     model_input=lowerCamelCase__       ,     model_output=lowerCamelCase__       ,     timestep=lowerCamelCase__       ,     )

																					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"""       ,     lowerCamelCase__       ,     standard_warn=lowerCamelCase__       )
															lowercase__							 =       """np"""

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

									return image








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__A          =    {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A          =    ["DetrFeatureExtractor"]
				__A          =    ["DetrImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A          =    [
				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "DetrForObjectDetection",
				    "DetrForSegmentation",
				    "DetrModel",
				    "DetrPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_detr import DetrFeatureExtractor
								from .image_processing_detr import DetrImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_detr import (
								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetrForObjectDetection,
								    DetrForSegmentation,
								    DetrModel,
								    DetrPreTrainedModel,
								)

else:
				import sys

				__A          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






