
'''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__      ):





	def __init__(   self     :    int ,							__magic_name__     :    AutoencoderKL ,							__magic_name__     :    CLIPTextModel ,							__magic_name__     :    CLIPTokenizer ,							__magic_name__     :    UNetaDConditionModel ,							__magic_name__     :    DDIMScheduler ,							__magic_name__     :    float = 0.93 ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							mixing_coeff
				lowerCAmelCase__									=							leapfrog_steps

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

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any] ,							__magic_name__     :    str ,							__magic_name__     :    Optional[str] = None ,							__magic_name__     :    bool = False							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.tokenizer(
				    __magic_name__ ,							padding="max_length" ,							max_length=self.tokenizer.model_max_length ,							truncation=__magic_name__ ,							return_tensors="pt" ,							)

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

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

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

							lowerCAmelCase__									=							self.tokenizer(
							    __magic_name__ ,							padding="max_length" ,							max_length=self.tokenizer.model_max_length ,							truncation=__magic_name__ ,							return_tensors="pt" ,							)

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

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

				return prompt_embeds





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any] ,							__magic_name__     :    torch.Tensor ,							__magic_name__     :    torch.Tensor							):


				"""simple docstring"""

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

				return [x, y]





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    torch.Tensor ,							__magic_name__     :    torch.Tensor							):


				"""simple docstring"""

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

				return [x, y]





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    torch.Tensor							):


				"""simple docstring"""

				lowerCAmelCase__									=							int(__magic_name__							)

				lowerCAmelCase__									=							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     :    List[Any] ,							__magic_name__     :    torch.Tensor ,							__magic_name__     :    torch.Tensor ,							__magic_name__     :    torch.Tensor ,							__magic_name__     :    torch.Tensor ,							):


				"""simple docstring"""

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

				lowerCAmelCase__		,lowerCAmelCase__									=							self._get_alpha_and_beta(__magic_name__							)
				lowerCAmelCase__		,lowerCAmelCase__									=							self._get_alpha_and_beta(__magic_name__							)

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

				lowerCAmelCase__									=							(base - b_t * model_output) / a_t

				return model_input, next_model_input.to(base.dtype							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    torch.Tensor ,							__magic_name__     :    torch.Tensor ,							__magic_name__     :    torch.Tensor ,							__magic_name__     :    torch.Tensor ,							):


				"""simple docstring"""

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

				lowerCAmelCase__		,lowerCAmelCase__									=							self._get_alpha_and_beta(__magic_name__							)
				lowerCAmelCase__		,lowerCAmelCase__									=							self._get_alpha_and_beta(__magic_name__							)

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

				return model_input, next_model_input.to(base.dtype							)





	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							__magic_name__     :    torch.Tensor							):


				"""simple docstring"""

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





	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							__magic_name__     :    Image.Image ,							__magic_name__     :    torch.Tensor ,							__magic_name__     :    torch.Tensor ,							__magic_name__     :    float ,							__magic_name__     :    Optional[torch.Generator] = None ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							guidance_scale > 1.0

				lowerCAmelCase__									=							image.to(device=self.device ,							dtype=text_embeds.dtype							)
				lowerCAmelCase__									=							self.vae.encode(__magic_name__							).latent_dist.sample(__magic_name__							)

				lowerCAmelCase__									=							self.vae.config.scaling_factor * latent

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

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

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

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

										lowerCAmelCase__									=							coupled_latents[j]
										lowerCAmelCase__									=							coupled_latents[k]

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

										lowerCAmelCase__									=							self.unet(__magic_name__ ,							__magic_name__ ,							encoder_hidden_states=__magic_name__							).sample

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

										lowerCAmelCase__		,lowerCAmelCase__									=							self.noise_step(
										    base=__magic_name__ ,							model_input=__magic_name__ ,							model_output=__magic_name__ ,							timestep=__magic_name__ ,							)

										lowerCAmelCase__									=							model_input

				return coupled_latents







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


				"""simple docstring"""

				lowerCAmelCase__									=							guidance_scale > 1.0

				lowerCAmelCase__									=							self.image_processor.preprocess(__magic_name__							)

				lowerCAmelCase__									=							self._encode_prompt(__magic_name__ ,							__magic_name__ ,							__magic_name__							)
				lowerCAmelCase__									=							self._encode_prompt(__magic_name__ ,							__magic_name__ ,							__magic_name__							)

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

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

				lowerCAmelCase__									=							self.prepare_latents(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__							)

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

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

										lowerCAmelCase__									=							coupled_latents[j]
										lowerCAmelCase__									=							coupled_latents[k]

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

										lowerCAmelCase__									=							self.unet(__magic_name__ ,							__magic_name__ ,							encoder_hidden_states=__magic_name__							).sample

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

										lowerCAmelCase__		,lowerCAmelCase__									=							self.denoise_step(
										    base=__magic_name__ ,							model_input=__magic_name__ ,							model_output=__magic_name__ ,							timestep=__magic_name__ ,							)

										lowerCAmelCase__									=							model_input

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

				# either one is fine
				lowerCAmelCase__									=							coupled_latents[0]

				if output_type not in ["latent", "pt", "np", "pil"]:
							lowerCAmelCase__									=							(
							    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" ,							__magic_name__ ,							standard_warn=__magic_name__							)
							lowerCAmelCase__									=							"np"

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

				return image







'''simple docstring'''

# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/





