


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__(     snake_case__    ):






    '''simple docstring'''





    def __init__(						self					: Optional[Any]							, __snake_case					: AutoencoderKL							, __snake_case					: CLIPTextModel							, __snake_case					: CLIPTokenizer							, __snake_case					: UNetaDConditionModel							, __snake_case					: DDIMScheduler							, __snake_case					: float = 0.93							, __snake_case					: bool = True							, ):





       '''simple docstring'''
       UpperCAmelCase_				:   Optional[int]							= mixing_coeff
       UpperCAmelCase_				:   List[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_				:   List[Any]							= 2 ** (len(self.vae.config.block_out_channels						) - 1)
       UpperCAmelCase_				:   List[str]							= VaeImageProcessor(vae_scale_factor=self.vae_scale_factor						)





    def        _lowerCamelCase			(						self					: str							, __snake_case					: str							, __snake_case					: Optional[str] = None							, __snake_case					: bool = False						):





       '''simple docstring'''
       UpperCAmelCase_				:   Union[str, Any]							= self.tokenizer(
           __snake_case							, padding='''max_length'''							, max_length=self.tokenizer.model_max_length							, truncation=__snake_case							, return_tensors='''pt'''							, )

       UpperCAmelCase_				:   Union[str, Any]							= self.text_encoder(text_inputs.input_ids.to(self.device						)						).last_hidden_state

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

       if do_classifier_free_guidance:
          UpperCAmelCase_				:   Tuple							= '''''' if negative_prompt is None else negative_prompt

          UpperCAmelCase_				:   List[str]							= self.tokenizer(
              __snake_case							, padding='''max_length'''							, max_length=self.tokenizer.model_max_length							, truncation=__snake_case							, return_tensors='''pt'''							, )

          UpperCAmelCase_				:   Optional[int]							= 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        _lowerCamelCase			(						self					: Tuple							, __snake_case					: torch.Tensor							, __snake_case					: torch.Tensor						):





       '''simple docstring'''
       UpperCAmelCase_				:   Dict							= self.mixing_coeff * x + (1 - self.mixing_coeff) * y
       UpperCAmelCase_				:   Union[str, Any]							= self.mixing_coeff * y + (1 - self.mixing_coeff) * x

       return [x, y]





    def        _lowerCamelCase			(						self					: Optional[int]							, __snake_case					: torch.Tensor							, __snake_case					: torch.Tensor						):





       '''simple docstring'''
       UpperCAmelCase_				:   int							= (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
       UpperCAmelCase_				:   Optional[int]							= (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

       return [x, y]





    def        _lowerCamelCase			(						self					: List[str]							, __snake_case					: torch.Tensor						):





       '''simple docstring'''
       # as self.alphas_cumprod is always in cpu
       UpperCAmelCase_				:   int							= int(__snake_case						)

       UpperCAmelCase_				:   List[str]							= self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

       return alpha_prod, 1 - alpha_prod





    def        _lowerCamelCase			(						self					: Optional[int]							, __snake_case					: torch.Tensor							, __snake_case					: torch.Tensor							, __snake_case					: torch.Tensor							, __snake_case					: torch.Tensor							, ):





       '''simple docstring'''
       UpperCAmelCase_				:   Optional[Any]							= timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       UpperCAmelCase_		,   UpperCAmelCase_				:   int							= self._get_alpha_and_beta(__snake_case						)
       UpperCAmelCase_		,   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_				:   Union[str, Any]							= (base - b_t * model_output) / a_t

       return model_input, next_model_input.to(base.dtype						)





    def        _lowerCamelCase			(						self					: List[str]							, __snake_case					: torch.Tensor							, __snake_case					: torch.Tensor							, __snake_case					: torch.Tensor							, __snake_case					: torch.Tensor							, ):





       '''simple docstring'''
       UpperCAmelCase_				:   List[str]							= timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

       UpperCAmelCase_		,   UpperCAmelCase_				:   Optional[int]							= self._get_alpha_and_beta(__snake_case						)
       UpperCAmelCase_		,   UpperCAmelCase_				:   Optional[Any]							= self._get_alpha_and_beta(__snake_case						)

       UpperCAmelCase_				:   Optional[int]							= (alpha_prod_t_prev / alpha_prod_t) ** 0.5
       UpperCAmelCase_				:   List[str]							= -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
       UpperCAmelCase_				:   str							= a_t * base + b_t * model_output

       return model_input, next_model_input.to(base.dtype						)





    @torch.no_grad()
    def        _lowerCamelCase			(						self					: Optional[Any]							, __snake_case					: torch.Tensor						):





       '''simple docstring'''
       UpperCAmelCase_				:   Union[str, Any]							= 1 / self.vae.config.scaling_factor * latents
       UpperCAmelCase_				:   List[Any]							= self.vae.decode(__snake_case						).sample
       UpperCAmelCase_				:   Tuple							= (image / 2 + 0.5).clamp(0							, 1						)
       return image





    @torch.no_grad()
    def        _lowerCamelCase			(						self					: Optional[int]							, __snake_case					: Image.Image							, __snake_case					: torch.Tensor							, __snake_case					: torch.Tensor							, __snake_case					: float							, __snake_case					: Optional[torch.Generator] = None							, ):





       '''simple docstring'''
       UpperCAmelCase_				:   Any							= guidance_scale > 1.0

       UpperCAmelCase_				:   List[Any]							= image.to(device=self.device							, dtype=text_embeds.dtype						)
       UpperCAmelCase_				:   Any							= self.vae.encode(__snake_case						).latent_dist.sample(__snake_case						)

       UpperCAmelCase_				:   int							= self.vae.config.scaling_factor * latent

       UpperCAmelCase_				:   Optional[Any]							= [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_				:   int							= j ^ 1

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

             UpperCAmelCase_				:   Union[str, Any]							= coupled_latents[j]
             UpperCAmelCase_				:   int							= coupled_latents[k]

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

             UpperCAmelCase_				:   str							= self.unet(__snake_case							, __snake_case							, encoder_hidden_states=__snake_case						).sample

             if do_classifier_free_guidance:
                UpperCAmelCase_		,   UpperCAmelCase_				:   Tuple							= noise_pred.chunk(2						)
                UpperCAmelCase_				:   Optional[Any]							= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             UpperCAmelCase_		,   UpperCAmelCase_				:   Optional[Any]							= self.noise_step(
                 base=__snake_case							, model_input=__snake_case							, model_output=__snake_case							, timestep=__snake_case							, )

             UpperCAmelCase_				:   Tuple							= model_input

       return coupled_latents





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





       '''simple docstring'''
       UpperCAmelCase_				:   str							= guidance_scale > 1.0

       UpperCAmelCase_				:   str							= self.image_processor.preprocess(__snake_case						)

       UpperCAmelCase_				:   int							= self._encode_prompt(__snake_case							, __snake_case							, __snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= self._encode_prompt(__snake_case							, __snake_case							, __snake_case						)

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

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

       UpperCAmelCase_				:   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[Any]							= k ^ 1

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

             UpperCAmelCase_				:   Any							= coupled_latents[j]
             UpperCAmelCase_				:   str							= coupled_latents[k]

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

             UpperCAmelCase_		,   UpperCAmelCase_				:   Union[str, Any]							= self.denoise_step(
                 base=__snake_case							, model_input=__snake_case							, model_output=__snake_case							, timestep=__snake_case							, )

             UpperCAmelCase_				:   Optional[int]							= model_input

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

       # either one is fine
       UpperCAmelCase_				:   List[Any]							= coupled_latents[0]

       if output_type not in ["latent", "pt", "np", "pil"]:
          UpperCAmelCase_				:   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'''							, __snake_case							, standard_warn=__snake_case						)
          UpperCAmelCase_				:   str							= '''np'''

       if output_type == "latent":
          UpperCAmelCase_				:   int							= final_latent
       else:
          UpperCAmelCase_				:   Optional[Any]							= self.decode_latents(__snake_case						)
          UpperCAmelCase_				:   List[str]							= self.image_processor.postprocess(__snake_case							, output_type=__snake_case						)

       return image



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()
def   snake_case_    (			__lowercase      ,							__lowercase							):
   # Initialize PyTorch model
   UpperCAmelCase_				:   Optional[Any]							= CanineConfig()
   UpperCAmelCase_				:   Any							= CanineModel(__lowercase							)
   model.eval()

   print(F'''Building PyTorch model from configuration: {config}'''							)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(__lowercase      ,							__lowercase      ,							__lowercase							)

   # Save pytorch-model (weights and configuration)
   print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
   model.save_pretrained(__lowercase							)

   # Save tokenizer files
   UpperCAmelCase_				:   List[Any]							= CanineTokenizer()
   print(F'''Save tokenizer files to {pytorch_dump_path}'''							)
   tokenizer.save_pretrained(__lowercase							)


if __name__ == "__main__":
       __UpperCamelCase					:	int          =				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path',
           default=None,
           type=str,
           required=True,
           help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
       )
       parser.add_argument(
           '--pytorch_dump_path',
           default=None,
           type=str,
           required=True,
           help='Path to a folder where the PyTorch model will be placed.',
       )
       __UpperCamelCase					:	str          =				parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)