



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       _snake_case				(     snake_case__ ):






   '''simple docstring'''






   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     AutoencoderKL  ,lowerCamelCase_:     CLIPTextModel  ,lowerCamelCase_:     CLIPTokenizer  ,lowerCamelCase_:     UNetaDConditionModel  ,lowerCamelCase_:     DDIMScheduler  ,lowerCamelCase_:     float = 0.9_3  ,lowerCamelCase_:     bool = True  ,)	->      Optional[Any]:
       UpperCAmelCase_     :       Optional[int]  		=					mixing_coeff
       UpperCAmelCase_     :       List[str]  		=					leapfrog_steps

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

       UpperCAmelCase_     :       List[Any]  		=					2 ** (len(self.vae.config.block_out_channels							) - 1)
       UpperCAmelCase_     :       List[str]  		=					VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)


   def       A__    (    self:     str  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Optional[str] = None  ,lowerCamelCase_:     bool = False							)	->      List[str]:
       UpperCAmelCase_     :       Union[str, Any]  		=					self.tokenizer(
           __snake_case  ,padding="""max_length"""  ,max_length=self.tokenizer.model_max_length  ,truncation=__snake_case  ,return_tensors="""pt"""  ,)

       UpperCAmelCase_     :       Union[str, Any]  		=					self.text_encoder(text_inputs.input_ids.to(self.device							)							).last_hidden_state

       UpperCAmelCase_     :       List[str]  		=					prompt_embeds.to(dtype=self.text_encoder.dtype  ,device=self.device							)

       if do_classifier_free_guidance:
           UpperCAmelCase_     :       Tuple  		=					'''''' if negative_prompt is None else negative_prompt

           UpperCAmelCase_     :       List[str]  		=					self.tokenizer(
               __snake_case  ,padding="""max_length"""  ,max_length=self.tokenizer.model_max_length  ,truncation=__snake_case  ,return_tensors="""pt"""  ,)

           UpperCAmelCase_     :       Optional[int]  		=					self.text_encoder(uncond_input.input_ids.to(self.device							)							).last_hidden_state

           UpperCAmelCase_     :       int  		=					torch.cat([negative_prompt_embeds, prompt_embeds]							)

       return prompt_embeds


   def       A__    (    self:     Tuple  ,lowerCamelCase_:     torch.Tensor  ,lowerCamelCase_:     torch.Tensor							)	->      List[str]:
       UpperCAmelCase_     :       Dict  		=					self.mixing_coeff * x + (1 - self.mixing_coeff) * y
       UpperCAmelCase_     :       Union[str, Any]  		=					self.mixing_coeff * y + (1 - self.mixing_coeff) * x

       return [x, y]


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     torch.Tensor  ,lowerCamelCase_:     torch.Tensor							)	->      Tuple:
       UpperCAmelCase_     :       int  		=					(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
       UpperCAmelCase_     :       Optional[int]  		=					(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

       return [x, y]


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     torch.Tensor							)	->      Union[str, Any]:
       UpperCAmelCase_     :       int  		=					int(__snake_case							)

       UpperCAmelCase_     :       List[str]  		=					self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

       return alpha_prod, 1 - alpha_prod


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     torch.Tensor  ,lowerCamelCase_:     torch.Tensor  ,lowerCamelCase_:     torch.Tensor  ,lowerCamelCase_:     torch.Tensor  ,)	->      Tuple:
       UpperCAmelCase_     :       Optional[Any]  		=					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       UpperCAmelCase_     :       int  		=					self._get_alpha_and_beta(__snake_case							)
       UpperCAmelCase_     :       Union[str, Any]  		=					self._get_alpha_and_beta(__snake_case							)

       UpperCAmelCase_     :       int  		=					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
       UpperCAmelCase_     :       Optional[Any]  		=					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

       UpperCAmelCase_     :       Union[str, Any]  		=					(base - b_t * model_output) / a_t

       return model_input, next_model_input.to(base.dtype							)


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     torch.Tensor  ,lowerCamelCase_:     torch.Tensor  ,lowerCamelCase_:     torch.Tensor  ,lowerCamelCase_:     torch.Tensor  ,)	->      Tuple:
       UpperCAmelCase_     :       List[str]  		=					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       UpperCAmelCase_     :       Optional[int]  		=					self._get_alpha_and_beta(__snake_case							)
       UpperCAmelCase_     :       Optional[Any]  		=					self._get_alpha_and_beta(__snake_case							)

       UpperCAmelCase_     :       Optional[int]  		=					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
       UpperCAmelCase_     :       List[str]  		=					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
       UpperCAmelCase_     :       str  		=					a_t * base + b_t * model_output

       return model_input, next_model_input.to(base.dtype							)


   @torch.no_grad()
   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     torch.Tensor							)	->      int:
       UpperCAmelCase_     :       Union[str, Any]  		=					1 / self.vae.config.scaling_factor * latents
       UpperCAmelCase_     :       List[Any]  		=					self.vae.decode(__snake_case							).sample
       UpperCAmelCase_     :       Tuple  		=					(image / 2 + 0.5).clamp(0  ,1							)
       return image


   @torch.no_grad()
   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     Image.Image  ,lowerCamelCase_:     torch.Tensor  ,lowerCamelCase_:     torch.Tensor  ,lowerCamelCase_:     float  ,lowerCamelCase_:     Optional[torch.Generator] = None  ,)	->      Union[str, Any]:
       UpperCAmelCase_     :       Any  		=					guidance_scale > 1.0

       UpperCAmelCase_     :       List[Any]  		=					image.to(device=self.device  ,dtype=text_embeds.dtype							)
       UpperCAmelCase_     :       Any  		=					self.vae.encode(__snake_case							).latent_dist.sample(__snake_case							)

       UpperCAmelCase_     :       int  		=					self.vae.config.scaling_factor * latent

       UpperCAmelCase_     :       Optional[Any]  		=					[latent.clone(), latent.clone()]

       for i, t in tqdm(enumerate(__snake_case							)  ,total=len(__snake_case							)							):
           UpperCAmelCase_     :       List[Any]  		=					self.noise_mixing_layer(x=coupled_latents[0]  ,y=coupled_latents[1]							)

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

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

               UpperCAmelCase_     :       Union[str, Any]  		=					coupled_latents[j]
               UpperCAmelCase_     :       int  		=					coupled_latents[k]

               UpperCAmelCase_     :       List[Any]  		=					torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

               UpperCAmelCase_     :       str  		=					self.unet(__snake_case  ,__snake_case  ,encoder_hidden_states=__snake_case							).sample

               if do_classifier_free_guidance:
                   UpperCAmelCase_     :       Tuple  		=					noise_pred.chunk(2							)
                   UpperCAmelCase_     :       Optional[Any]  		=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

               UpperCAmelCase_     :       Optional[Any]  		=					self.noise_step(
                   base=__snake_case  ,model_input=__snake_case  ,model_output=__snake_case  ,timestep=__snake_case  ,)

               UpperCAmelCase_     :       Tuple  		=					model_input

       return coupled_latents






   @torch.no_grad()
   def __call__(    self:     Union[str, Any]  ,lowerCamelCase_:     str  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Image.Image  ,lowerCamelCase_:     float = 3.0  ,lowerCamelCase_:     int = 50  ,lowerCamelCase_:     float = 0.8  ,lowerCamelCase_:     Optional[str] = None  ,lowerCamelCase_:     Optional[torch.Generator] = None  ,lowerCamelCase_:     Optional[str] = "pil"  ,)	->      List[Any]:
       UpperCAmelCase_     :       str  		=					guidance_scale > 1.0

       UpperCAmelCase_     :       str  		=					self.image_processor.preprocess(__snake_case							)

       UpperCAmelCase_     :       int  		=					self._encode_prompt(__snake_case  ,__snake_case  ,__snake_case							)
       UpperCAmelCase_     :       Union[str, Any]  		=					self._encode_prompt(__snake_case  ,__snake_case  ,__snake_case							)

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

       UpperCAmelCase_     :       List[str]  		=					num_inference_steps - int(num_inference_steps * strength							)
       UpperCAmelCase_     :       int  		=					self.scheduler.timesteps[t_limit:]
       UpperCAmelCase_     :       Tuple  		=					fwd_timesteps.flip(0							)

       UpperCAmelCase_     :       Any  		=					self.prepare_latents(__snake_case  ,__snake_case  ,__snake_case  ,__snake_case  ,__snake_case							)

       for i, t in tqdm(enumerate(__snake_case							)  ,total=len(__snake_case							)							):
           # j - model_input index, k - base index
           for k in range(2							):
               UpperCAmelCase_     :       Optional[Any]  		=					k ^ 1

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

               UpperCAmelCase_     :       Any  		=					coupled_latents[j]
               UpperCAmelCase_     :       str  		=					coupled_latents[k]

               UpperCAmelCase_     :       str  		=					torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

               UpperCAmelCase_     :       Any  		=					self.unet(__snake_case  ,__snake_case  ,encoder_hidden_states=__snake_case							).sample

               if do_classifier_free_guidance:
                   UpperCAmelCase_     :       Optional[Any]  		=					noise_pred.chunk(2							)
                   UpperCAmelCase_     :       Any  		=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

               UpperCAmelCase_     :       Union[str, Any]  		=					self.denoise_step(
                   base=__snake_case  ,model_input=__snake_case  ,model_output=__snake_case  ,timestep=__snake_case  ,)

               UpperCAmelCase_     :       Optional[int]  		=					model_input

           UpperCAmelCase_     :       Dict  		=					self.denoise_mixing_layer(x=coupled_latents[0]  ,y=coupled_latents[1]							)

       # either one is fine
       UpperCAmelCase_     :       List[Any]  		=					coupled_latents[0]

       if output_type not in ["latent", "pt", "np", "pil"]:
           UpperCAmelCase_     :       Any  		=					(
               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"""  ,__snake_case  ,standard_warn=__snake_case							)
           UpperCAmelCase_     :       str  		=					'''np'''

       if output_type == "latent":
           UpperCAmelCase_     :       int  		=					final_latent
       else:
           UpperCAmelCase_     :       Optional[Any]  		=					self.decode_latents(__snake_case							)
           UpperCAmelCase_     :       List[str]  		=					self.image_processor.postprocess(__snake_case  ,output_type=__snake_case							)

       return image




import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
       import tensorflow as tf

       from transformers.activations_tf import get_tf_activation





@require_tf
class 			lowerCAmelCase__(     unittest.TestCase    ):






    '''simple docstring'''





    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       UpperCAmelCase_				:   str							= tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]						)
       UpperCAmelCase_				:   List[Any]							= get_tf_activation('''gelu'''						)
       UpperCAmelCase_				:   Any							= get_tf_activation('''gelu_10'''						)

       UpperCAmelCase_				:   int							= gelu(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= geluaa(__snake_case						)

       UpperCAmelCase_				:   Union[str, Any]							= tf.where(y_gelu_aa < 10.0							, 1.0							, 0.0						)

       self.assertEqual(tf.math.reduce_max(__snake_case						).numpy().item()							, 10.0						)
       self.assertTrue(np.allclose(y_gelu * clipped_mask							, y_gelu_aa * clipped_mask						)						)





    def        _lowerCamelCase			(						self					: Dict						):





       '''simple docstring'''
       get_tf_activation('''gelu'''						)
       get_tf_activation('''gelu_10'''						)
       get_tf_activation('''gelu_fast'''						)
       get_tf_activation('''gelu_new'''						)
       get_tf_activation('''glu'''						)
       get_tf_activation('''mish'''						)
       get_tf_activation('''quick_gelu'''						)
       get_tf_activation('''relu'''						)
       get_tf_activation('''sigmoid'''						)
       get_tf_activation('''silu'''						)
       get_tf_activation('''swish'''						)
       get_tf_activation('''tanh'''						)
       with self.assertRaises(__snake_case						):
          get_tf_activation('''bogus'''						)
       with self.assertRaises(__snake_case						):
          get_tf_activation(__snake_case						)