


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	(_a		):






       """simple docstring"""




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






            '''simple docstring'''




            __UpperCamelCase				=	mixing_coeff
            __UpperCamelCase				=	leapfrog_steps

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

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







       def 						snake_case_					(	self:				str,A_:				str,A_:				Optional[str] = None,A_:				bool = False			):






            '''simple docstring'''




            __UpperCamelCase				=	self.tokenizer(
                A_,padding='max_length',max_length=self.tokenizer.model_max_length,truncation=A_,return_tensors='pt',)

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

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

            if do_classifier_free_guidance:
                 __UpperCamelCase				=	'' if negative_prompt is None else negative_prompt

                 __UpperCamelCase				=	self.tokenizer(
                     A_,padding='max_length',max_length=self.tokenizer.model_max_length,truncation=A_,return_tensors='pt',)

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

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

            return prompt_embeds







       def 						snake_case_					(	self:				Tuple,A_:				torch.Tensor,A_:				torch.Tensor			):






            '''simple docstring'''




            __UpperCamelCase				=	self.mixing_coeff * x + (1 - self.mixing_coeff) * y
            __UpperCamelCase				=	self.mixing_coeff * y + (1 - self.mixing_coeff) * x

            return [x, y]







       def 						snake_case_					(	self:				List[Any],A_:				torch.Tensor,A_:				torch.Tensor			):






            '''simple docstring'''




            __UpperCamelCase				=	(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
            __UpperCamelCase				=	(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

            return [x, y]







       def 						snake_case_					(	self:				str,A_:				torch.Tensor			):






            '''simple docstring'''




            __UpperCamelCase				=	int(A_			)

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

            return alpha_prod, 1 - alpha_prod







       def 						snake_case_					(	self:				int,A_:				torch.Tensor,A_:				torch.Tensor,A_:				torch.Tensor,A_:				torch.Tensor,):






            '''simple docstring'''




            __UpperCamelCase				=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

            __UpperCamelCase,  __UpperCamelCase				=	self._get_alpha_and_beta(A_			)
            __UpperCamelCase,  __UpperCamelCase				=	self._get_alpha_and_beta(A_			)

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

            __UpperCamelCase				=	(base - b_t * model_output) / a_t

            return model_input, next_model_input.to(base.dtype			)







       def 						snake_case_					(	self:				Union[str, Any],A_:				torch.Tensor,A_:				torch.Tensor,A_:				torch.Tensor,A_:				torch.Tensor,):






            '''simple docstring'''




            __UpperCamelCase				=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

            __UpperCamelCase,  __UpperCamelCase				=	self._get_alpha_and_beta(A_			)
            __UpperCamelCase,  __UpperCamelCase				=	self._get_alpha_and_beta(A_			)

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

            return model_input, next_model_input.to(base.dtype			)







       @torch.no_grad()
       def 						snake_case_					(	self:				Optional[int],A_:				torch.Tensor			):






            '''simple docstring'''




            __UpperCamelCase				=	1 / self.vae.config.scaling_factor * latents
            __UpperCamelCase				=	self.vae.decode(A_			).sample
            __UpperCamelCase				=	(image / 2 + 0.5).clamp(0,1			)
            return image







       @torch.no_grad()
       def 						snake_case_					(	self:				str,A_:				Image.Image,A_:				torch.Tensor,A_:				torch.Tensor,A_:				float,A_:				Optional[torch.Generator] = None,):






            '''simple docstring'''




            __UpperCamelCase				=	guidance_scale > 1.0

            __UpperCamelCase				=	image.to(device=self.device,dtype=text_embeds.dtype			)
            __UpperCamelCase				=	self.vae.encode(A_			).latent_dist.sample(A_			)

            __UpperCamelCase				=	self.vae.config.scaling_factor * latent

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

            for i, t in tqdm(enumerate(A_			),total=len(A_			)			):
                 __UpperCamelCase				=	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				=	j ^ 1

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

                      __UpperCamelCase				=	coupled_latents[j]
                      __UpperCamelCase				=	coupled_latents[k]

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

                      __UpperCamelCase				=	self.unet(A_,A_,encoder_hidden_states=A_			).sample

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

                      __UpperCamelCase,  __UpperCamelCase				=	self.noise_step(
                          base=A_,model_input=A_,model_output=A_,timestep=A_,)

                      __UpperCamelCase				=	model_input

            return coupled_latents







       @torch.no_grad()
       def __call__(	self:				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",):






            '''simple docstring'''




            __UpperCamelCase				=	guidance_scale > 1.0

            __UpperCamelCase				=	self.image_processor.preprocess(A_			)

            __UpperCamelCase				=	self._encode_prompt(A_,A_,A_			)
            __UpperCamelCase				=	self._encode_prompt(A_,A_,A_			)

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

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

            __UpperCamelCase				=	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			):
                      __UpperCamelCase				=	k ^ 1

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

                      __UpperCamelCase				=	coupled_latents[j]
                      __UpperCamelCase				=	coupled_latents[k]

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

                      __UpperCamelCase				=	self.unet(A_,A_,encoder_hidden_states=A_			).sample

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

                      __UpperCamelCase,  __UpperCamelCase				=	self.denoise_step(
                          base=A_,model_input=A_,model_output=A_,timestep=A_,)

                      __UpperCamelCase				=	model_input

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

            # either one is fine
            __UpperCamelCase				=	coupled_latents[0]

            if output_type not in ["latent", "pt", "np", "pil"]:
                 __UpperCamelCase				=	(
                     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_			)
                 __UpperCamelCase				=	'np'

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

            return image






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__snake_case     =		{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case     =		[
        '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ConvNextV2ForImageClassification''',
        '''ConvNextV2Model''',
        '''ConvNextV2PreTrainedModel''',
        '''ConvNextV2Backbone''',
    ]


if TYPE_CHECKING:
    from .configuration_convnextva import (
        CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ConvNextVaConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnextva import (
            CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextVaBackbone,
            ConvNextVaForImageClassification,
            ConvNextVaModel,
            ConvNextVaPreTrainedModel,
        )

else:
    import sys

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


