



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 							lowerCAmelCase_		(		lowerCamelCase__     ):







		def __init__(    self  :   List[str]       ,		UpperCAmelCase_  :   Tuple       ,		UpperCAmelCase_  :   Dict       ,		UpperCAmelCase_  :   Dict       ,		UpperCAmelCase_  :   List[str]       ,		UpperCAmelCase_  :   Any       ,		UpperCAmelCase_  :   str = 0.93       ,		UpperCAmelCase_  :   str = True       ,		)       ->		List[Any]:


						'''simple docstring'''







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







		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   List[str]       ,		UpperCAmelCase_  :   Optional[int] = None       ,		UpperCAmelCase_  :   List[str] = False					)       ->		Optional[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[Any]     					=	self.tokenizer(
						    snake_case__       ,		padding='''max_length'''       ,		max_length=self.tokenizer.model_max_length       ,		truncation=snake_case__       ,		return_tensors='''pt'''       ,		)

						_UpperCAmelCase						:  Tuple     					=	self.text_encoder(text_inputs.input_ids.to(self.device					)					).last_hidden_state

						_UpperCAmelCase						:  List[Any]     					=	prompt_embeds.to(dtype=self.text_encoder.dtype       ,		device=self.device					)

						if do_classifier_free_guidance:
										_UpperCAmelCase						:  List[str]     					=	'''''' if negative_prompt is None else negative_prompt

										_UpperCAmelCase						:  Dict     					=	self.tokenizer(
										    snake_case__       ,		padding='''max_length'''       ,		max_length=self.tokenizer.model_max_length       ,		truncation=snake_case__       ,		return_tensors='''pt'''       ,		)

										_UpperCAmelCase						:  List[str]     					=	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  :   Optional[int]       ,		UpperCAmelCase_  :   Optional[int]       ,		UpperCAmelCase_  :   Tuple					)       ->		Union[str, Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[int]     					=	self.mixing_coeff * x + (1 - self.mixing_coeff) * y
						_UpperCAmelCase						:  int     					=	self.mixing_coeff * y + (1 - self.mixing_coeff) * x

						return [x, y]







		def 					a_  (    self  :   str       ,		UpperCAmelCase_  :   Optional[int]       ,		UpperCAmelCase_  :   List[Any]					)       ->		Any:


						'''simple docstring'''







						_UpperCAmelCase						:  str     					=	(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
						_UpperCAmelCase						:  Union[str, Any]     					=	(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

						return [x, y]







		def 					a_  (    self  :   int       ,		UpperCAmelCase_  :   Tuple					)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  int     					=	int(snake_case__					)

						_UpperCAmelCase						:  Dict     					=	self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

						return alpha_prod, 1 - alpha_prod







		def 					a_  (    self  :   List[str]       ,		UpperCAmelCase_  :   List[Any]       ,		UpperCAmelCase_  :   Tuple       ,		UpperCAmelCase_  :   Optional[int]       ,		UpperCAmelCase_  :   Any       ,		)       ->		Dict:


						'''simple docstring'''







						_UpperCAmelCase						:  List[Any]     					=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

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

						return model_input, next_model_input.to(base.dtype					)







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   List[str]       ,		UpperCAmelCase_  :   str       ,		)       ->		int:


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

						_UpperCAmelCase						:  Union[str, Any]     					=	self._get_alpha_and_beta(snake_case__					)
						_UpperCAmelCase						:  Optional[int]     					=	self._get_alpha_and_beta(snake_case__					)

						_UpperCAmelCase						:  str     					=	(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						:  List[str]     					=	a_t * base + b_t * model_output

						return model_input, next_model_input.to(base.dtype					)







		@torch.no_grad()
		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   int					)       ->		Dict:


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	1 / self.vae.config.scaling_factor * latents
						_UpperCAmelCase						:  List[Any]     					=	self.vae.decode(snake_case__					).sample
						_UpperCAmelCase						:  str     					=	(image / 2 + 0.5).clamp(0       ,		1					)
						return image







		@torch.no_grad()
		def 					a_  (    self  :   str       ,		UpperCAmelCase_  :   List[Any]       ,		UpperCAmelCase_  :   List[str]       ,		UpperCAmelCase_  :   Union[str, Any]       ,		UpperCAmelCase_  :   Dict       ,		UpperCAmelCase_  :   Any = None       ,		)       ->		Optional[int]:


						'''simple docstring'''







						_UpperCAmelCase						:  Dict     					=	guidance_scale > 1.0

						_UpperCAmelCase						:  str     					=	image.to(device=self.device       ,		dtype=text_embeds.dtype					)
						_UpperCAmelCase						:  Union[str, Any]     					=	self.vae.encode(snake_case__					).latent_dist.sample(snake_case__					)

						_UpperCAmelCase						:  Optional[Any]     					=	self.vae.config.scaling_factor * latent

						_UpperCAmelCase						:  Optional[int]     					=	[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						:  Optional[Any]     					=	j ^ 1

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

														_UpperCAmelCase						:  Any     					=	coupled_latents[j]
														_UpperCAmelCase						:  Tuple     					=	coupled_latents[k]

														_UpperCAmelCase						:  List[Any]     					=	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						:  Dict     					=	noise_pred.chunk(2					)
																		_UpperCAmelCase						:  str     					=	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						:  str     					=	model_input

						return coupled_latents







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


						'''simple docstring'''







						_UpperCAmelCase						:  str     					=	guidance_scale > 1.0

						_UpperCAmelCase						:  List[str]     					=	self.image_processor.preprocess(snake_case__					)

						_UpperCAmelCase						:  str     					=	self._encode_prompt(snake_case__       ,		snake_case__       ,		snake_case__					)
						_UpperCAmelCase						:  List[Any]     					=	self._encode_prompt(snake_case__       ,		snake_case__       ,		snake_case__					)

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

						_UpperCAmelCase						:  int     					=	num_inference_steps - int(num_inference_steps * strength					)
						_UpperCAmelCase						:  Union[str, Any]     					=	self.scheduler.timesteps[t_limit:]
						_UpperCAmelCase						:  Any     					=	fwd_timesteps.flip(0					)

						_UpperCAmelCase						:  List[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[int]     					=	k ^ 1

														if self.leapfrog_steps:
																		if i % 2 == 1:
																						_UpperCAmelCase						:  Optional[Any]     					=	j, k

														_UpperCAmelCase						:  Optional[Any]     					=	coupled_latents[j]
														_UpperCAmelCase						:  str     					=	coupled_latents[k]

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

														_UpperCAmelCase						:  List[str]     					=	self.unet(snake_case__       ,		snake_case__       ,		encoder_hidden_states=snake_case__					).sample

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

														_UpperCAmelCase						:  int     					=	self.denoise_step(
														    base=snake_case__       ,		model_input=snake_case__       ,		model_output=snake_case__       ,		timestep=snake_case__       ,		)

														_UpperCAmelCase						:  int     					=	model_input

										_UpperCAmelCase						:  Union[str, Any]     					=	self.denoise_mixing_layer(x=coupled_latents[0]       ,		y=coupled_latents[1]					)

						# either one is fine
						_UpperCAmelCase						:  List[str]     					=	coupled_latents[0]

						if output_type not in ["latent", "pt", "np", "pil"]:
										_UpperCAmelCase						:  Dict     					=	(
										    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						:  Optional[int]     					=	'''np'''

						if output_type == "latent":
										_UpperCAmelCase						:  Dict     					=	final_latent
						else:
										_UpperCAmelCase						:  Optional[int]     					=	self.decode_latents(snake_case__					)
										_UpperCAmelCase						:  Tuple     					=	self.image_processor.postprocess(snake_case__       ,		output_type=snake_case__					)

						return image

from typing import TYPE_CHECKING

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


__snake_case         =     {
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MobileViTV2ForImageClassification''',
         '''MobileViTV2ForSemanticSegmentation''',
         '''MobileViTV2Model''',
         '''MobileViTV2PreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_mobilevitva import (
         MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileViTVaConfig,
         MobileViTVaOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilevitva import (
              MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileViTVaForImageClassification,
              MobileViTVaForSemanticSegmentation,
              MobileViTVaModel,
              MobileViTVaPreTrainedModel,
          )

else:
     import sys

     __snake_case         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
