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__       (				_UpperCamelCase							):



   def __init__(				self   :   Any			,	A__   :   AutoencoderKL			,	A__   :   CLIPTextModel			,	A__   :   CLIPTokenizer			,	A__   :   UNetaDConditionModel			,	A__   :   DDIMScheduler			,	A__   :   float = 0.93			,	A__   :   bool = True			,	)							->     List[str]:




         '''simple docstring'''




         snake_case_     :			Dict       =       mixing_coeff
         snake_case_     :			Tuple       =       leapfrog_steps

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

         snake_case_     :			Tuple       =       2 ** (len(self.vae.config.block_out_channels   ) - 1)
         snake_case_     :			Tuple       =       VaeImageProcessor(vae_scale_factor=self.vae_scale_factor   )



   def 					UpperCAmelCase__       (				self   :   List[Any]			,	A__   :   str			,	A__   :   Optional[str] = None			,	A__   :   bool = False   )							->     List[Any]:




         '''simple docstring'''




         snake_case_     :			Optional[Any]       =       self.tokenizer(
             A__			,	padding="max_length"			,	max_length=self.tokenizer.model_max_length			,	truncation=A__			,	return_tensors="pt"			,	)

         snake_case_     :			List[str]       =       self.text_encoder(text_inputs.input_ids.to(self.device   )   ).last_hidden_state

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

         if do_classifier_free_guidance:
               snake_case_     :			List[Any]       =       "" if negative_prompt is None else negative_prompt

               snake_case_     :			str       =       self.tokenizer(
                   A__			,	padding="max_length"			,	max_length=self.tokenizer.model_max_length			,	truncation=A__			,	return_tensors="pt"			,	)

               snake_case_     :			List[Any]       =       self.text_encoder(uncond_input.input_ids.to(self.device   )   ).last_hidden_state

               snake_case_     :			Dict       =       torch.cat([negative_prompt_embeds, prompt_embeds]   )

         return prompt_embeds



   def 					UpperCAmelCase__       (				self   :   str			,	A__   :   torch.Tensor			,	A__   :   torch.Tensor   )							->     str:




         '''simple docstring'''




         snake_case_     :			Tuple       =       self.mixing_coeff * x + (1 - self.mixing_coeff) * y
         snake_case_     :			List[Any]       =       self.mixing_coeff * y + (1 - self.mixing_coeff) * x

         return [x, y]



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   torch.Tensor			,	A__   :   torch.Tensor   )							->     Union[str, Any]:




         '''simple docstring'''




         snake_case_     :			Dict       =       (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
         snake_case_     :			Any       =       (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

         return [x, y]



   def 					UpperCAmelCase__       (				self   :   Tuple			,	A__   :   torch.Tensor   )							->     Optional[int]:




         '''simple docstring'''




         snake_case_     :			int       =       int(A__   )

         snake_case_     :			Optional[Any]       =       self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

         return alpha_prod, 1 - alpha_prod



   def 					UpperCAmelCase__       (				self   :   List[str]			,	A__   :   torch.Tensor			,	A__   :   torch.Tensor			,	A__   :   torch.Tensor			,	A__   :   torch.Tensor			,	)							->     List[Any]:




         '''simple docstring'''




         snake_case_     :			str       =       timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         snake_case_  ,snake_case_     :			List[str]       =       self._get_alpha_and_beta(A__   )
         snake_case_  ,snake_case_     :			Any       =       self._get_alpha_and_beta(A__   )

         snake_case_     :			Tuple       =       (alpha_prod_t_prev / alpha_prod_t) ** 0.5
         snake_case_     :			Dict       =       -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

         snake_case_     :			Any       =       (base - b_t * model_output) / a_t

         return model_input, next_model_input.to(base.dtype   )



   def 					UpperCAmelCase__       (				self   :   Dict			,	A__   :   torch.Tensor			,	A__   :   torch.Tensor			,	A__   :   torch.Tensor			,	A__   :   torch.Tensor			,	)							->     int:




         '''simple docstring'''




         snake_case_     :			str       =       timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         snake_case_  ,snake_case_     :			List[str]       =       self._get_alpha_and_beta(A__   )
         snake_case_  ,snake_case_     :			int       =       self._get_alpha_and_beta(A__   )

         snake_case_     :			Tuple       =       (alpha_prod_t_prev / alpha_prod_t) ** 0.5
         snake_case_     :			Union[str, Any]       =       -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
         snake_case_     :			Dict       =       a_t * base + b_t * model_output

         return model_input, next_model_input.to(base.dtype   )



   @torch.no_grad()
   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   torch.Tensor   )							->     Union[str, Any]:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       1 / self.vae.config.scaling_factor * latents
         snake_case_     :			Union[str, Any]       =       self.vae.decode(A__   ).sample
         snake_case_     :			int       =       (image / 2 + 0.5).clamp(0			,	1   )
         return image



   @torch.no_grad()
   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   Image.Image			,	A__   :   torch.Tensor			,	A__   :   torch.Tensor			,	A__   :   float			,	A__   :   Optional[torch.Generator] = None			,	)							->     Tuple:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       guidance_scale > 1.0

         snake_case_     :			int       =       image.to(device=self.device			,	dtype=text_embeds.dtype   )
         snake_case_     :			Any       =       self.vae.encode(A__   ).latent_dist.sample(A__   )

         snake_case_     :			List[str]       =       self.vae.config.scaling_factor * latent

         snake_case_     :			List[str]       =       [latent.clone(), latent.clone()]

         for i, t in tqdm(enumerate(A__   )			,	total=len(A__   )   ):
               snake_case_     :			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   ):
                     snake_case_     :			Optional[Any]       =       j ^ 1

                     if self.leapfrog_steps:
                           if i % 2 == 0:
                                 snake_case_  ,snake_case_     :			Union[str, Any]       =       j, k

                     snake_case_     :			Tuple       =       coupled_latents[j]
                     snake_case_     :			Optional[Any]       =       coupled_latents[k]

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

                     snake_case_     :			Optional[Any]       =       self.unet(A__			,	A__			,	encoder_hidden_states=A__   ).sample

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

                     snake_case_  ,snake_case_     :			Union[str, Any]       =       self.noise_step(
                         base=A__			,	model_input=A__			,	model_output=A__			,	timestep=A__			,	)

                     snake_case_     :			str       =       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[Any]:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       guidance_scale > 1.0

         snake_case_     :			Optional[Any]       =       self.image_processor.preprocess(A__   )

         snake_case_     :			Optional[Any]       =       self._encode_prompt(A__			,	A__			,	A__   )
         snake_case_     :			Optional[int]       =       self._encode_prompt(A__			,	A__			,	A__   )

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

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

         snake_case_     :			Any       =       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   ):
                     snake_case_     :			Dict       =       k ^ 1

                     if self.leapfrog_steps:
                           if i % 2 == 1:
                                 snake_case_  ,snake_case_     :			Union[str, Any]       =       j, k

                     snake_case_     :			Any       =       coupled_latents[j]
                     snake_case_     :			Any       =       coupled_latents[k]

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

                     snake_case_     :			str       =       self.unet(A__			,	A__			,	encoder_hidden_states=A__   ).sample

                     if do_classifier_free_guidance:
                           snake_case_  ,snake_case_     :			List[str]       =       noise_pred.chunk(2   )
                           snake_case_     :			int       =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                     snake_case_  ,snake_case_     :			Optional[int]       =       self.denoise_step(
                         base=A__			,	model_input=A__			,	model_output=A__			,	timestep=A__			,	)

                     snake_case_     :			Dict       =       model_input

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

         # either one is fine
         snake_case_     :			Tuple       =       coupled_latents[0]

         if output_type not in ["latent", "pt", "np", "pil"]:
               snake_case_     :			Union[str, 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"			,	A__			,	standard_warn=A__   )
               snake_case_     :			Dict       =       "np"

         if output_type == "latent":
               snake_case_     :			Union[str, Any]       =       final_latent
         else:
               snake_case_     :			str       =       self.decode_latents(A__   )
               snake_case_     :			int       =       self.image_processor.postprocess(A__			,	output_type=A__   )

         return image




from typing import TYPE_CHECKING

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


UpperCAmelCase							=       {
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       [
         "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "NystromformerForMaskedLM",
         "NystromformerForMultipleChoice",
         "NystromformerForQuestionAnswering",
         "NystromformerForSequenceClassification",
         "NystromformerForTokenClassification",
         "NystromformerLayer",
         "NystromformerModel",
         "NystromformerPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nystromformer import (
              NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              NystromformerForMaskedLM,
              NystromformerForMultipleChoice,
              NystromformerForQuestionAnswering,
              NystromformerForSequenceClassification,
              NystromformerForTokenClassification,
              NystromformerLayer,
              NystromformerModel,
              NystromformerPreTrainedModel,
          )


else:
     import sys

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



