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				(  _SCREAMING_SNAKE_CASE				):






		"""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     :					int	,       __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     :					List[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     :					Dict	,       __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     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					torch.Tensor ):






					"""simple docstring"""
					_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     :					Union[str, 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														=   self._get_alpha_and_beta(__SCREAMING_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     :					str	,       __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														=   self._get_alpha_and_beta(__SCREAMING_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     :					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     :					Optional[int]	,       __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														=   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														=   noise_pred.chunk(2 )
														_snake_case														=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											_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     :					Union[str, Any]	,       __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														=   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														=   noise_pred.chunk(2 )
														_snake_case														=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											_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



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__ :		str					={'configuration_t5': ['T5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'T5Config', 'T5OnnxConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Optional[int]					=['T5Tokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=['T5TokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         'T5_PRETRAINED_MODEL_ARCHIVE_LIST',
         'T5EncoderModel',
         'T5ForConditionalGeneration',
         'T5Model',
         'T5PreTrainedModel',
         'load_tf_weights_in_t5',
         'T5ForQuestionAnswering',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		List[str]					=[
         'TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFT5EncoderModel',
         'TFT5ForConditionalGeneration',
         'TFT5Model',
         'TFT5PreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         'FlaxT5EncoderModel',
         'FlaxT5ForConditionalGeneration',
         'FlaxT5Model',
         'FlaxT5PreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_ta import TaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_ta_fast import TaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ta import (
              T5_PRETRAINED_MODEL_ARCHIVE_LIST,
              TaEncoderModel,
              TaForConditionalGeneration,
              TaForQuestionAnswering,
              TaModel,
              TaPreTrainedModel,
              load_tf_weights_in_ta,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_ta import (
              TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTaEncoderModel,
              TFTaForConditionalGeneration,
              TFTaModel,
              TFTaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_ta import (
              FlaxTaEncoderModel,
              FlaxTaForConditionalGeneration,
              FlaxTaModel,
              FlaxTaPreTrainedModel,
          )


else:
     import sys

     A__ :		Dict					=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



