


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   : int			,      _a   : AutoencoderKL			,      _a   : CLIPTextModel			,      _a   : CLIPTokenizer			,      _a   : UNetaDConditionModel			,      _a   : DDIMScheduler			,      _a   : float = 0.93			,      _a   : bool = True			,      )							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=mixing_coeff
										_SCREAMING_SNAKE_CASE     					=leapfrog_steps

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

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




						def 							__UpperCamelCase			(    self   : Tuple			,      _a   : str			,      _a   : Optional[str] = None			,      _a   : bool = False					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.tokenizer(
										    _a			,      padding='''max_length'''			,      max_length=self.tokenizer.model_max_length			,      truncation=_a			,      return_tensors='''pt'''			,      )

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

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

										if do_classifier_free_guidance:
														_SCREAMING_SNAKE_CASE     					='''''' if negative_prompt is None else negative_prompt

														_SCREAMING_SNAKE_CASE     					=self.tokenizer(
														    _a			,      padding='''max_length'''			,      max_length=self.tokenizer.model_max_length			,      truncation=_a			,      return_tensors='''pt'''			,      )

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

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

										return prompt_embeds




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : torch.Tensor			,      _a   : torch.Tensor					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.mixing_coeff * x + (1 - self.mixing_coeff) * y
										_SCREAMING_SNAKE_CASE     					=self.mixing_coeff * y + (1 - self.mixing_coeff) * x

										return [x, y]




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : torch.Tensor			,      _a   : torch.Tensor					)							->       List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
										_SCREAMING_SNAKE_CASE     					=(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

										return [x, y]




						def 							__UpperCamelCase			(    self   : Tuple			,      _a   : torch.Tensor					)							->       Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=int(_a					)

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

										return alpha_prod, 1 - alpha_prod




						def 							__UpperCamelCase			(    self   : Union[str, Any]			,      _a   : torch.Tensor			,      _a   : torch.Tensor			,      _a   : torch.Tensor			,      _a   : torch.Tensor			,      )							->       Dict:


										"""simple docstring"""





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

										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self._get_alpha_and_beta(_a					)
										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self._get_alpha_and_beta(_a					)

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

										_SCREAMING_SNAKE_CASE     					=(base - b_t * model_output) / a_t

										return model_input, next_model_input.to(base.dtype					)




						def 							__UpperCamelCase			(    self   : List[str]			,      _a   : torch.Tensor			,      _a   : torch.Tensor			,      _a   : torch.Tensor			,      _a   : torch.Tensor			,      )							->       Optional[int]:


										"""simple docstring"""





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

										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self._get_alpha_and_beta(_a					)
										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self._get_alpha_and_beta(_a					)

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

										return model_input, next_model_input.to(base.dtype					)




						@torch.no_grad()
						def 							__UpperCamelCase			(    self   : List[Any]			,      _a   : torch.Tensor					)							->       Dict:


										"""simple docstring"""





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




						@torch.no_grad()
						def 							__UpperCamelCase			(    self   : Optional[int]			,      _a   : Image.Image			,      _a   : torch.Tensor			,      _a   : torch.Tensor			,      _a   : float			,      _a   : Optional[torch.Generator] = None			,      )							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=guidance_scale > 1.0

										_SCREAMING_SNAKE_CASE     					=image.to(device=self.device			,      dtype=text_embeds.dtype					)
										_SCREAMING_SNAKE_CASE     					=self.vae.encode(_a					).latent_dist.sample(_a					)

										_SCREAMING_SNAKE_CASE     					=self.vae.config.scaling_factor * latent

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

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

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

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

																		_SCREAMING_SNAKE_CASE     					=coupled_latents[j]
																		_SCREAMING_SNAKE_CASE     					=coupled_latents[k]

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

																		_SCREAMING_SNAKE_CASE     					=self.unet(_a			,      _a			,      encoder_hidden_states=_a					).sample

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

																		_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.noise_step(
																		    base=_a			,      model_input=_a			,      model_output=_a			,      timestep=_a			,      )

																		_SCREAMING_SNAKE_CASE     					=model_input

										return coupled_latents




						@torch.no_grad()
						def __call__(    self   : List[str]			,      _a   : str			,      _a   : str			,      _a   : Image.Image			,      _a   : float = 3.0			,      _a   : int = 50			,      _a   : float = 0.8			,      _a   : Optional[str] = None			,      _a   : Optional[torch.Generator] = None			,      _a   : Optional[str] = "pil"			,      )							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=guidance_scale > 1.0

										_SCREAMING_SNAKE_CASE     					=self.image_processor.preprocess(_a					)

										_SCREAMING_SNAKE_CASE     					=self._encode_prompt(_a			,      _a			,      _a					)
										_SCREAMING_SNAKE_CASE     					=self._encode_prompt(_a			,      _a			,      _a					)

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

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

										_SCREAMING_SNAKE_CASE     					=self.prepare_latents(_a			,      _a			,      _a			,      _a			,      _a					)

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

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

																		_SCREAMING_SNAKE_CASE     					=coupled_latents[j]
																		_SCREAMING_SNAKE_CASE     					=coupled_latents[k]

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

																		_SCREAMING_SNAKE_CASE     					=self.unet(_a			,      _a			,      encoder_hidden_states=_a					).sample

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

																		_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.denoise_step(
																		    base=_a			,      model_input=_a			,      model_output=_a			,      timestep=_a			,      )

																		_SCREAMING_SNAKE_CASE     					=model_input

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

										# either one is fine
										_SCREAMING_SNAKE_CASE     					=coupled_latents[0]

										if output_type not in ["latent", "pt", "np", "pil"]:
														_SCREAMING_SNAKE_CASE     					=(
														    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'''			,      _a			,      standard_warn=_a					)
														_SCREAMING_SNAKE_CASE     					='''np'''

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

										return image



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


snake_case_  :     Tuple							  =							logging.get_logger(__name__)

snake_case_  :     int							  =							{
    '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class   A__							(						UpperCamelCase__					):
						UpperCAmelCase     =  "plbart"
						UpperCAmelCase     =  ["past_key_values"]
						UpperCAmelCase     =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




						def __init__(    self   : Union[str, Any]			,      _a   : List[str]=5_0005			,      _a   : List[str]=1024			,      _a   : int=6			,      _a   : Union[str, Any]=3072			,      _a   : List[str]=12			,      _a   : int=6			,      _a   : Optional[int]=3072			,      _a   : Any=12			,      _a   : Any=0.0			,      _a   : Optional[Any]=0.0			,      _a   : List[Any]=True			,      _a   : Any=True			,      _a   : Any="gelu"			,      _a   : int=768			,      _a   : int=0.1			,      _a   : int=0.1			,      _a   : List[str]=0.0			,      _a   : Optional[int]=0.02			,      _a   : Dict=0.0			,      _a   : List[str]=True			,      _a   : List[Any]=1			,      _a   : Tuple=0			,      _a   : Tuple=2			,      _a   : Optional[int]=2			,      **_a   : Optional[int]			,      )							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=vocab_size
										_SCREAMING_SNAKE_CASE     					=max_position_embeddings
										_SCREAMING_SNAKE_CASE     					=d_model
										_SCREAMING_SNAKE_CASE     					=encoder_ffn_dim
										_SCREAMING_SNAKE_CASE     					=encoder_layers
										_SCREAMING_SNAKE_CASE     					=encoder_attention_heads
										_SCREAMING_SNAKE_CASE     					=decoder_ffn_dim
										_SCREAMING_SNAKE_CASE     					=decoder_layers
										_SCREAMING_SNAKE_CASE     					=decoder_attention_heads
										_SCREAMING_SNAKE_CASE     					=dropout
										_SCREAMING_SNAKE_CASE     					=attention_dropout
										_SCREAMING_SNAKE_CASE     					=activation_dropout
										_SCREAMING_SNAKE_CASE     					=activation_function
										_SCREAMING_SNAKE_CASE     					=init_std
										_SCREAMING_SNAKE_CASE     					=encoder_layerdrop
										_SCREAMING_SNAKE_CASE     					=decoder_layerdrop
										_SCREAMING_SNAKE_CASE     					=classifier_dropout
										_SCREAMING_SNAKE_CASE     					=use_cache
										_SCREAMING_SNAKE_CASE     					=encoder_layers
										_SCREAMING_SNAKE_CASE     					=scale_embedding  # scale factor will be sqrt(d_model) if True
										super().__init__(
										    pad_token_id=_a			,      bos_token_id=_a			,      eos_token_id=_a			,      is_encoder_decoder=_a			,      forced_eos_token_id=_a			,      **_a			,      )

class   A__							(						UpperCamelCase__					):




						@property
						def 							__UpperCamelCase			(    self   : int					)							->       Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										return OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
										    ]					)




						@property
						def 							__UpperCamelCase			(    self   : List[str]					)							->       Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.use_past:
														return OrderedDict(
														    [
														        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
														        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														    ]					)
										else:
														return OrderedDict(
														    [
														        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														    ]					)