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 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		def __init__(      self     :					Dict	,       __SCREAMING_SNAKE_CASE     :					AutoencoderKL	,       __SCREAMING_SNAKE_CASE     :					CLIPTextModel	,       __SCREAMING_SNAKE_CASE     :					CLIPTokenizer	,       __SCREAMING_SNAKE_CASE     :					UNetaDConditionModel	,       __SCREAMING_SNAKE_CASE     :					DDIMScheduler	,       __SCREAMING_SNAKE_CASE     :					float = 0.9_3	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       ):






					"""simple docstring"""
					_snake_case														=   mixing_coeff
					_snake_case														=   leapfrog_steps

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

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



		def 					_a					(      self     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					str	,       __SCREAMING_SNAKE_CASE     :					Optional[str] = None	,       __SCREAMING_SNAKE_CASE     :					bool = False ):






					"""simple docstring"""
					_snake_case														=   self.tokenizer(
					    __SCREAMING_SNAKE_CASE	,       padding="max_length"	,       max_length=self.tokenizer.model_max_length	,       truncation=__SCREAMING_SNAKE_CASE	,       return_tensors="pt"	,       )

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

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

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

								_snake_case														=   self.tokenizer(
								    __SCREAMING_SNAKE_CASE	,       padding="max_length"	,       max_length=self.tokenizer.model_max_length	,       truncation=__SCREAMING_SNAKE_CASE	,       return_tensors="pt"	,       )

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

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

					return prompt_embeds



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






					"""simple docstring"""
					_snake_case														=   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
					_snake_case														=   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

					return [x, y]



		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					torch.Tensor	,       __SCREAMING_SNAKE_CASE     :					torch.Tensor ):






					"""simple docstring"""
					_snake_case														=   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
					_snake_case														=   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

					return [x, y]



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






					"""simple docstring"""
					# as self.alphas_cumprod is always in cpu
					_snake_case														=   int(__SCREAMING_SNAKE_CASE )

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

					return alpha_prod, 1 - alpha_prod



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






					"""simple docstring"""
					_snake_case														=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					_snake_case							,			_snake_case														=   self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE )
					_snake_case							,			_snake_case														=   self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE )

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

					_snake_case														=   (base - b_t * model_output) / a_t

					return model_input, next_model_input.to(base.dtype )



		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					torch.Tensor	,       __SCREAMING_SNAKE_CASE     :					torch.Tensor	,       __SCREAMING_SNAKE_CASE     :					torch.Tensor	,       __SCREAMING_SNAKE_CASE     :					torch.Tensor	,       ):






					"""simple docstring"""
					_snake_case														=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					_snake_case							,			_snake_case														=   self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE )
					_snake_case							,			_snake_case														=   self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE )

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

					return model_input, next_model_input.to(base.dtype )



		@torch.no_grad()
		def 					_a					(      self     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					torch.Tensor ):






					"""simple docstring"""
					_snake_case														=   1 / self.vae.config.scaling_factor * latents
					_snake_case														=   self.vae.decode(__SCREAMING_SNAKE_CASE ).sample
					_snake_case														=   (image / 2 + 0.5).clamp(0	,       1 )
					return image



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






					"""simple docstring"""
					_snake_case														=   guidance_scale > 1.0

					_snake_case														=   image.to(device=self.device	,       dtype=text_embeds.dtype )
					_snake_case														=   self.vae.encode(__SCREAMING_SNAKE_CASE ).latent_dist.sample(__SCREAMING_SNAKE_CASE )

					_snake_case														=   self.vae.config.scaling_factor * latent

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

					for i, t in tqdm(enumerate(__SCREAMING_SNAKE_CASE )	,       total=len(__SCREAMING_SNAKE_CASE ) ):
								_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 ):
											_snake_case														=   j ^ 1

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

											_snake_case														=   coupled_latents[j]
											_snake_case														=   coupled_latents[k]

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

											_snake_case														=   self.unet(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       encoder_hidden_states=__SCREAMING_SNAKE_CASE ).sample

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

											_snake_case							,			_snake_case														=   self.noise_step(
											    base=__SCREAMING_SNAKE_CASE	,       model_input=__SCREAMING_SNAKE_CASE	,       model_output=__SCREAMING_SNAKE_CASE	,       timestep=__SCREAMING_SNAKE_CASE	,       )

											_snake_case														=   model_input

					return coupled_latents







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






					"""simple docstring"""
					_snake_case														=   guidance_scale > 1.0

					_snake_case														=   self.image_processor.preprocess(__SCREAMING_SNAKE_CASE )

					_snake_case														=   self._encode_prompt(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
					_snake_case														=   self._encode_prompt(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )

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

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

					_snake_case														=   self.prepare_latents(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )

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

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

											_snake_case														=   coupled_latents[j]
											_snake_case														=   coupled_latents[k]

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

											_snake_case														=   self.unet(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       encoder_hidden_states=__SCREAMING_SNAKE_CASE ).sample

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

											_snake_case							,			_snake_case														=   self.denoise_step(
											    base=__SCREAMING_SNAKE_CASE	,       model_input=__SCREAMING_SNAKE_CASE	,       model_output=__SCREAMING_SNAKE_CASE	,       timestep=__SCREAMING_SNAKE_CASE	,       )

											_snake_case														=   model_input

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

					# either one is fine
					_snake_case														=   coupled_latents[0]

					if output_type not in ["latent", "pt", "np", "pil"]:
								_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"	,       __SCREAMING_SNAKE_CASE	,       standard_warn=__SCREAMING_SNAKE_CASE )
								_snake_case														=   "np"

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

					return image


from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							{
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    Dict     						=							[
			    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SamModel''',
			    '''SamPreTrainedModel''',
			]
try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							[
			    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFSamModel''',
			    '''TFSamPreTrainedModel''',
			]
try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    List[Any]     						=							['''SamImageProcessor''']


if TYPE_CHECKING:
			from .configuration_sam import (
			    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SamConfig,
			    SamMaskDecoderConfig,
			    SamPromptEncoderConfig,
			    SamVisionConfig,
			)
			from .processing_sam import SamProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_sam import SamImageProcessor

else:
			import sys

			_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

