




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 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Any		,     A__     :      AutoencoderKL		,     A__     :      CLIPTextModel		,     A__     :      CLIPTokenizer		,     A__     :      UNetaDConditionModel		,     A__     :      DDIMScheduler		,     A__     :      float = 0.93		,     A__     :      bool = True		,     ) ->				int:
           lowerCamelCase__					     = mixing_coeff
           lowerCamelCase__					     = leapfrog_steps

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

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




     def        __snake_case      (			self     :      int		,     A__     :      str		,     A__     :      Optional[str] = None		,     A__     :      bool = False	) ->				Dict:
           lowerCamelCase__					     = self.tokenizer(
               A__		,     padding='max_length'		,     max_length=self.tokenizer.model_max_length		,     truncation=A__		,     return_tensors='pt'		,     )

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

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

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

                 lowerCamelCase__					     = self.tokenizer(
                     A__		,     padding='max_length'		,     max_length=self.tokenizer.model_max_length		,     truncation=A__		,     return_tensors='pt'		,     )

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

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

           return prompt_embeds




     def        __snake_case      (			self     :      Optional[int]		,     A__     :      torch.Tensor		,     A__     :      torch.Tensor	) ->				Tuple:
           lowerCamelCase__					     = self.mixing_coeff * x + (1 - self.mixing_coeff) * y
           lowerCamelCase__					     = self.mixing_coeff * y + (1 - self.mixing_coeff) * x

           return [x, y]




     def        __snake_case      (			self     :      str		,     A__     :      torch.Tensor		,     A__     :      torch.Tensor	) ->				str:
           lowerCamelCase__					     = (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
           lowerCamelCase__					     = (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

           return [x, y]




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      torch.Tensor	) ->				Optional[Any]:
           # as self.alphas_cumprod is always in cpu
           lowerCamelCase__					     = int(A__	)

           lowerCamelCase__					     = 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		,     ) ->				List[str]:
           lowerCamelCase__					     = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

           lowerCamelCase__     ,						lowerCamelCase__					     = self._get_alpha_and_beta(A__	)
           lowerCamelCase__     ,						lowerCamelCase__					     = self._get_alpha_and_beta(A__	)

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

           lowerCamelCase__					     = (base - b_t * model_output) / a_t

           return model_input, next_model_input.to(base.dtype	)




     def        __snake_case      (			self     :      Any		,     A__     :      torch.Tensor		,     A__     :      torch.Tensor		,     A__     :      torch.Tensor		,     A__     :      torch.Tensor		,     ) ->				List[str]:
           lowerCamelCase__					     = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

           lowerCamelCase__     ,						lowerCamelCase__					     = self._get_alpha_and_beta(A__	)
           lowerCamelCase__     ,						lowerCamelCase__					     = self._get_alpha_and_beta(A__	)

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

           return model_input, next_model_input.to(base.dtype	)




     @torch.no_grad()
     def        __snake_case      (			self     :      Any		,     A__     :      torch.Tensor	) ->				Optional[int]:
           lowerCamelCase__					     = 1 / self.vae.config.scaling_factor * latents
           lowerCamelCase__					     = self.vae.decode(A__	).sample
           lowerCamelCase__					     = (image / 2 + 0.5).clamp(0		,     1	)
           return image




     @torch.no_grad()
     def        __snake_case      (			self     :      Tuple		,     A__     :      Image.Image		,     A__     :      torch.Tensor		,     A__     :      torch.Tensor		,     A__     :      float		,     A__     :      Optional[torch.Generator] = None		,     ) ->				Optional[int]:
           lowerCamelCase__					     = guidance_scale > 1.0

           lowerCamelCase__					     = image.to(device=self.device		,     dtype=text_embeds.dtype	)
           lowerCamelCase__					     = self.vae.encode(A__	).latent_dist.sample(A__	)

           lowerCamelCase__					     = self.vae.config.scaling_factor * latent

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

           for i, t in tqdm(enumerate(A__	)		,     total=len(A__	)	):
                 lowerCamelCase__					     = self.noise_mixing_layer(x=coupled_latents[0]		,     y=coupled_latents[1]	)

                 # j - model_input index, k - base index
                 for j in range(2	):
                       lowerCamelCase__					     = j ^ 1

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

                       lowerCamelCase__					     = coupled_latents[j]
                       lowerCamelCase__					     = coupled_latents[k]

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

                       lowerCamelCase__					     = self.unet(A__		,     A__		,     encoder_hidden_states=A__	).sample

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

                       lowerCamelCase__     ,						lowerCamelCase__					     = self.noise_step(
                           base=A__		,     model_input=A__		,     model_output=A__		,     timestep=A__		,     )

                       lowerCamelCase__					     = model_input

           return coupled_latents




     @torch.no_grad()
     def __call__(			self     :      int		,     A__     :      str		,     A__     :      str		,     A__     :      Image.Image		,     A__     :      float = 3.0		,     A__     :      int = 5_0		,     A__     :      float = 0.8		,     A__     :      Optional[str] = None		,     A__     :      Optional[torch.Generator] = None		,     A__     :      Optional[str] = "pil"		,     ) ->				List[str]:
           lowerCamelCase__					     = guidance_scale > 1.0

           lowerCamelCase__					     = self.image_processor.preprocess(A__	)

           lowerCamelCase__					     = self._encode_prompt(A__		,     A__		,     A__	)
           lowerCamelCase__					     = self._encode_prompt(A__		,     A__		,     A__	)

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

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

           lowerCamelCase__					     = 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	):
                       lowerCamelCase__					     = k ^ 1

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

                       lowerCamelCase__					     = coupled_latents[j]
                       lowerCamelCase__					     = coupled_latents[k]

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

                       lowerCamelCase__					     = self.unet(A__		,     A__		,     encoder_hidden_states=A__	).sample

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

                       lowerCamelCase__     ,						lowerCamelCase__					     = self.denoise_step(
                           base=A__		,     model_input=A__		,     model_output=A__		,     timestep=A__		,     )

                       lowerCamelCase__					     = model_input

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

           # either one is fine
           lowerCamelCase__					     = coupled_latents[0]

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

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

           return image





from __future__ import annotations

import sys






class 	A	:
     """simple docstring"""

     def __init__(			self     :      str		,     A__     :      str		,     A__     :      int	) ->				str:
           lowerCamelCase__					     = letter
           lowerCamelCase__					     = freq
           lowerCamelCase__					     = {}




     def __repr__(			self     :      Any	) ->				str:
           return F"""{self.letter}:{self.freq}"""






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Optional[Any]		,     A__     :      int		,     A__     :      Letter | TreeNode		,     A__     :      Letter | TreeNode	) ->				Any:
           lowerCamelCase__					     = freq
           lowerCamelCase__					     = left
           lowerCamelCase__					     = right

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	list[Letter]:


      '''simple docstring'''




      lowerCamelCase__					     = {}
      with open(lowerCamelCase  ) as f:
            while True:
                  lowerCamelCase__					     = f.read(1  )
                  if not c:
                        break
                  lowerCamelCase__					     = chars[c] + 1 if c in chars else 1
      return sorted((Letter(lowerCamelCase   , lowerCamelCase  ) for c, f in chars.items())   , key=lambda lowerCamelCase  : x.freq  )

def 	lowerCAmelCase_	(     lowerCamelCase:				list[Letter]  )    ->	Letter | TreeNode:


      '''simple docstring'''




      lowerCamelCase__					     = letters  # type: ignore
      while len(lowerCamelCase  ) > 1:
            lowerCamelCase__					     = response.pop(0  )
            lowerCamelCase__					     = response.pop(0  )
            lowerCamelCase__					     = left.freq + right.freq
            lowerCamelCase__					     = TreeNode(lowerCamelCase   , lowerCamelCase   , lowerCamelCase  )
            response.append(lowerCamelCase  )
            response.sort(key=lambda lowerCamelCase  : x.freq  )
      return response[0]

def 	lowerCAmelCase_	(     lowerCamelCase:				Letter | TreeNode   , lowerCamelCase:				str  )    ->	list[Letter]:


      '''simple docstring'''




      if isinstance(lowerCamelCase   , lowerCamelCase  ):
            lowerCamelCase__					     = bitstring
            return [root]
      lowerCamelCase__					     = root  # type: ignore
      lowerCamelCase__					     = []
      letters += traverse_tree(treenode.left   , bitstring + '0'  )
      letters += traverse_tree(treenode.right   , bitstring + '1'  )
      return letters

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	None:


      '''simple docstring'''




      lowerCamelCase__					     = parse_file(lowerCamelCase  )
      lowerCamelCase__					     = build_tree(lowerCamelCase  )
      lowerCamelCase__					     = {
          k: v for letter in traverse_tree(lowerCamelCase   , ''  ) for k, v in letter.bitstring.items()
      }
      print(f"""Huffman Coding  of {file_path}: """  )
      with open(lowerCamelCase  ) as f:
            while True:
                  lowerCamelCase__					     = f.read(1  )
                  if not c:
                        break
                  print(letters[c]   , end=' '  )
      print()


if __name__ == "__main__":
      # pass the file path to the huffman function
      huffman(sys.argv[1])