




import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


UpperCamelCase__       				=	logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase__       				=	'''
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 15.0
        >>> prompt = "a shark"

        >>> images = pipe(
        ...     prompt,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")
        ```
'''
@dataclass
class 							__lowercase   (			a__    ):
     _lowerCAmelCase		       =						42



class 							__lowercase   (			a__    ):



     def __init__(  self   :							List[Any]      ,		lowercase__   :							PriorTransformer      ,		lowercase__   :							CLIPTextModelWithProjection      ,		lowercase__   :							CLIPTokenizer      ,		lowercase__   :							HeunDiscreteScheduler      ,		lowercase__   :							ShapERenderer      ,		):
       super().__init__()

       self.register_modules(
           prior=lowercase__      ,		text_encoder=lowercase__      ,		tokenizer=lowercase__      ,		scheduler=lowercase__      ,		renderer=lowercase__      ,		)



     def 				__magic_name__		(  self   :							int      ,		lowercase__   :							str      ,		lowercase__   :							str      ,		lowercase__   :							Optional[int]      ,		lowercase__   :							int      ,		lowercase__   :							List[Any]      ,		lowercase__   :							int       ):
       if latents is None:
         a_		    =  randn_tensor(lowercase__      ,		generator=lowercase__      ,		device=lowercase__      ,		dtype=lowercase__       )
       else:
         if latents.shape != shape:
           raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"       )
         a_		    =  latents.to(lowercase__       )

       a_		    =  latents * scheduler.init_noise_sigma
       return latents



     def 				__magic_name__		(  self   :							Any      ,		lowercase__   :							List[Any]=0       ):
       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError('''Please install accelerate via `pip install accelerate`'''       )

       a_		    =  torch.device(f"cuda:{gpu_id}"       )

       a_		    =  [self.text_encoder, self.prior]
       for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
           cpu_offload(lowercase__      ,		lowercase__       )



     def 				__magic_name__		(  self   :							Any      ,		lowercase__   :							int=0       ):
       if is_accelerate_available() and is_accelerate_version('''>='''      ,		'''0.17.0.dev0'''       ):
         from accelerate import cpu_offload_with_hook
       else:
         raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''       )

       a_		    =  torch.device(f"cuda:{gpu_id}"       )

       if self.device.type != "cpu":
         self.to('''cpu'''      ,		silence_dtype_warnings=lowercase__       )
         torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       a_		    =  None
       for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
         a_    , a_		    =  cpu_offload_with_hook(lowercase__      ,		lowercase__      ,		prev_module_hook=lowercase__       )

       if self.safety_checker is not None:
         a_    , a_		    =  cpu_offload_with_hook(self.safety_checker      ,		lowercase__      ,		prev_module_hook=lowercase__       )

       # We'll offload the last model manually.
       a_		    =  hook



     @property
     def 				__magic_name__		(  self   :							Any       ):
       if self.device != torch.device('''meta'''       ) or not hasattr(self.text_encoder      ,		'''_hf_hook'''       ):
         return self.device
       for module in self.text_encoder.modules():
         if (
             hasattr(lowercase__      ,		'''_hf_hook'''       )
             and hasattr(module._hf_hook      ,		'''execution_device'''       )
             and module._hf_hook.execution_device is not None
         ):
           return torch.device(module._hf_hook.execution_device       )
       return self.device



     def 				__magic_name__		(  self   :							List[Any]      ,		lowercase__   :							Union[str, Any]      ,		lowercase__   :							int      ,		lowercase__   :							Optional[int]      ,		lowercase__   :							List[str]      ,		):
       len(lowercase__       ) if isinstance(lowercase__      ,		lowercase__       ) else 1

       # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
       a_		    =  0
       # get prompt text embeddings
       a_		    =  self.tokenizer(
           lowercase__      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=lowercase__      ,		return_tensors='''pt'''      ,		)
       a_		    =  text_inputs.input_ids
       a_		    =  self.tokenizer(lowercase__      ,		padding='''longest'''      ,		return_tensors='''pt'''       ).input_ids

       if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowercase__      ,		lowercase__       ):
         a_		    =  self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]       )
         logger.warning(
             '''The following part of your input was truncated because CLIP can only handle sequences up to'''
             f" {self.tokenizer.model_max_length} tokens: {removed_text}"       )

       a_		    =  self.text_encoder(text_input_ids.to(lowercase__       )       )
       a_		    =  text_encoder_output.text_embeds

       a_		    =  prompt_embeds.repeat_interleave(lowercase__      ,		dim=0       )
       # in Shap-E it normalize the prompt_embeds and then later rescale it
       a_		    =  prompt_embeds / torch.linalg.norm(lowercase__      ,		dim=-1      ,		keepdim=lowercase__       )

       if do_classifier_free_guidance:
         a_		    =  torch.zeros_like(lowercase__       )

         # For classifier free guidance, we need to do two forward passes.
         # Here we concatenate the unconditional and text embeddings into a single batch
         # to avoid doing two forward passes
         a_		    =  torch.cat([negative_prompt_embeds, prompt_embeds]       )

       # Rescale the features to have unit variance
       a_		    =  math.sqrt(prompt_embeds.shape[1]       ) * prompt_embeds

       return prompt_embeds



     @torch.no_grad()
     @replace_example_docstring(lowercase__       )
     def __call__(  self   :							Union[str, Any]      ,		lowercase__   :							str      ,		lowercase__   :							int = 1      ,		lowercase__   :							int = 2_5      ,		lowercase__   :							Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,		lowercase__   :							Optional[torch.FloatTensor] = None      ,		lowercase__   :							float = 4.0      ,		lowercase__   :							int = 6_4      ,		lowercase__   :							Optional[str] = "pil"      ,		lowercase__   :							bool = True      ,		):

       if isinstance(lowercase__      ,		lowercase__       ):
         a_		    =  1
       elif isinstance(lowercase__      ,		lowercase__       ):
         a_		    =  len(lowercase__       )
       else:
         raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(lowercase__       )}"       )

       a_		    =  self._execution_device

       a_		    =  batch_size * num_images_per_prompt

       a_		    =  guidance_scale > 1.0
       a_		    =  self._encode_prompt(lowercase__      ,		lowercase__      ,		lowercase__      ,		lowercase__       )

       # prior

       self.scheduler.set_timesteps(lowercase__      ,		device=lowercase__       )
       a_		    =  self.scheduler.timesteps

       a_		    =  self.prior.config.num_embeddings
       a_		    =  self.prior.config.embedding_dim

       a_		    =  self.prepare_latents(
           (batch_size, num_embeddings * embedding_dim)      ,		prompt_embeds.dtype      ,		lowercase__      ,		lowercase__      ,		lowercase__      ,		self.scheduler      ,		)

       # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
       a_		    =  latents.reshape(latents.shape[0]      ,		lowercase__      ,		lowercase__       )

       for i, t in enumerate(self.progress_bar(lowercase__       )       ):
         # expand the latents if we are doing classifier free guidance
         a_		    =  torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
         a_		    =  self.scheduler.scale_model_input(lowercase__      ,		lowercase__       )

         a_		    =  self.prior(
             lowercase__      ,		timestep=lowercase__      ,		proj_embedding=lowercase__      ,		).predicted_image_embedding

         # remove the variance
         a_    , a_		    =  noise_pred.split(
             scaled_model_input.shape[2]      ,		dim=2       )  # batch_size, num_embeddings, embedding_dim

         if do_classifier_free_guidance is not None:
           a_    , a_		    =  noise_pred.chunk(2       )
           a_		    =  noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

         a_		    =  self.scheduler.step(
             lowercase__      ,		timestep=lowercase__      ,		sample=lowercase__      ,		).prev_sample

       if output_type == "latent":
         return ShapEPipelineOutput(images=lowercase__       )

       a_		    =  []
       for i, latent in enumerate(lowercase__       ):
         a_		    =  self.renderer.decode(
             latent[None, :]      ,		lowercase__      ,		size=lowercase__      ,		ray_batch_size=4_0_9_6      ,		n_coarse_samples=6_4      ,		n_fine_samples=1_2_8      ,		)
         images.append(lowercase__       )

       a_		    =  torch.stack(lowercase__       )

       if output_type not in ["np", "pil"]:
         raise ValueError(f"Only the output types `pil` and `np` are supported not output_type={output_type}"       )

       a_		    =  images.cpu().numpy()

       if output_type == "pil":
         a_		    =  [self.numpy_to_pil(lowercase__       ) for image in images]

       # Offload last model to CPU
       if hasattr(self      ,		'''final_offload_hook'''       ) and self.final_offload_hook is not None:
         self.final_offload_hook.offload()

       if not return_dict:
         return (images,)

       return ShapEPipelineOutput(images=lowercase__       )






from __future__ import annotations

import sys
class 							__lowercase   :



     def __init__(  self   :							Any      ,		lowercase__   :							str      ,		lowercase__   :							int       ):
       a_		    =  letter
       a_		    =  freq
       a_		    =  {}



     def __repr__(  self   :							List[Any]       ):
       return f"{self.letter}:{self.freq}"



class 							__lowercase   :



     def __init__(  self   :							Optional[int]      ,		lowercase__   :							int      ,		lowercase__   :							Letter | TreeNode      ,		lowercase__   :							Letter | TreeNode       ):
       a_		    =  freq
       a_		    =  left
       a_		    =  right






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  parse_file(_A			)
  a_		    =  build_tree(_A			)
  a_		    =  {
      k: v for letter in traverse_tree(_A      ,      ''''''			) for k, v in letter.bitstring.items()
  }
  print(f"Huffman Coding  of {file_path}: "			)
  with open(_A			) as f:
    while True:
      a_		    =  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])
