





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def  lowerCAmelCase__(		lowercase    :      Tuple				,       lowercase    :      Any=0.9_9_9				,       lowercase    :      List[str]="cosine"				,       )	->      Optional[Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase    :      List[Any]			):
      return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2			) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowercase    :      str			):
      return math.exp(t * -1_2.0			)

  else:
    raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""			)

  __snake_case				:			List[str]            =    []
  for i in range(lowercase			):
    __snake_case				:			str            =    i / num_diffusion_timesteps
    __snake_case				:			List[Any]            =    (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowercase			) / alpha_bar_fn(lowercase			)				,       lowercase			)			)
  return torch.tensor(lowercase				,       dtype=torch.floataa			)




class 			_lowerCamelCase	(      a	,				a      ):



      """simple docstring"""






      UpperCAmelCase_  : Tuple				      =[e.name for e in KarrasDiffusionSchedulers]
      UpperCAmelCase_  : Tuple				      =2







      @register_to_config
      def __init__(	self			,  UpperCAmelCase = 1000			,  UpperCAmelCase = 0.00_085			,  UpperCAmelCase = 0.012			,  UpperCAmelCase = "linear"			,  UpperCAmelCase = None			,  UpperCAmelCase = "epsilon"			,  UpperCAmelCase = "linspace"			,  UpperCAmelCase = 0			,  )    ->				Optional[Any]:






        '''simple docstring'''






        if trained_betas is not None:
          __snake_case				:			Dict            =    torch.tensor(UpperCAmelCase			,  dtype=torch.floataa				)
        elif beta_schedule == "linear":
          __snake_case				:			str            =    torch.linspace(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  dtype=torch.floataa				)
        elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          __snake_case				:			int            =    (
              torch.linspace(beta_start**0.5			,  beta_end**0.5			,  UpperCAmelCase			,  dtype=torch.floataa				) ** 2
          )
        elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          __snake_case				:			str            =    betas_for_alpha_bar(UpperCAmelCase				)
        else:
          raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""				)

        __snake_case				:			str            =    1.0 - self.betas
        __snake_case				:			str            =    torch.cumprod(self.alphas			,  dim=0				)

        #  set all values
        self.set_timesteps(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase=None				)    ->				Union[str, Any]:






        '''simple docstring'''






        if schedule_timesteps is None:
          __snake_case				:			Optional[int]            =    self.timesteps

        __snake_case				:			List[str]            =    (schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter				) == 0:
          __snake_case				:			str            =    1 if len(UpperCAmelCase				) > 1 else 0
        else:
          __snake_case				:			int            =    timestep.cpu().item() if torch.is_tensor(UpperCAmelCase				) else timestep
          __snake_case				:			Dict            =    self._index_counter[timestep_int]

        return indices[pos].item()







      @property
      def   UpperCAmelCase						(	self				)    ->				Optional[int]:






        '''simple docstring'''






        if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  )    ->				torch.FloatTensor:






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    self.index_for_timestep(UpperCAmelCase				)

        if self.state_in_first_order:
          __snake_case				:			Optional[int]            =    self.sigmas[step_index]
        else:
          __snake_case				:			Optional[Any]            =    self.sigmas_interpol[step_index - 1]

        __snake_case				:			str            =    sample / ((sigma**2 + 1) ** 0.5)
        return sample







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  )    ->				Tuple:






        '''simple docstring'''






        __snake_case				:			List[str]            =    num_inference_steps

        __snake_case				:			Union[str, Any]            =    num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
          __snake_case				:			Dict            =    np.linspace(0			,  num_train_timesteps - 1			,  UpperCAmelCase			,  dtype=UpperCAmelCase				)[::-1].copy()
        elif self.config.timestep_spacing == "leading":
          __snake_case				:			str            =    num_train_timesteps // self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          __snake_case				:			int            =    (np.arange(0			,  UpperCAmelCase				) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase				)
          timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
          __snake_case				:			List[str]            =    num_train_timesteps / self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          __snake_case				:			int            =    (np.arange(UpperCAmelCase			,  0			,  -step_ratio				)).round().copy().astype(UpperCAmelCase				)
          timesteps -= 1
        else:
          raise ValueError(
              F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""				)

        __snake_case				:			Optional[Any]            =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
        __snake_case				:			Tuple            =    torch.from_numpy(np.log(UpperCAmelCase				)				).to(UpperCAmelCase				)

        __snake_case				:			Union[str, Any]            =    np.interp(UpperCAmelCase			,  np.arange(0			,  len(UpperCAmelCase				)				)			,  UpperCAmelCase				)
        __snake_case				:			int            =    np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
        __snake_case				:			Optional[int]            =    torch.from_numpy(UpperCAmelCase				).to(device=UpperCAmelCase				)

        # compute up and down sigmas
        __snake_case				:			str            =    sigmas.roll(-1				)
        __snake_case				:			Union[str, Any]            =    0.0
        __snake_case				:			Any            =    (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
        __snake_case				:			Union[str, Any]            =    (sigmas_next**2 - sigmas_up**2) ** 0.5
        __snake_case				:			Tuple            =    0.0

        # compute interpolated sigmas
        __snake_case				:			List[str]            =    sigmas.log().lerp(sigmas_down.log()			,  0.5				).exp()
        __snake_case				:			Union[str, Any]            =    0.0

        # set sigmas
        __snake_case				:			str            =    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
        __snake_case				:			Union[str, Any]            =    torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)
        __snake_case				:			Union[str, Any]            =    torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2				), sigmas_up[-1:]]				)
        __snake_case				:			int            =    torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2				), sigmas_down[-1:]]				)

        if str(UpperCAmelCase				).startswith("mps"				):
          # mps does not support float64
          __snake_case				:			Optional[Any]            =    torch.from_numpy(UpperCAmelCase				).to(UpperCAmelCase			,  dtype=torch.floataa				)
        else:
          __snake_case				:			Union[str, Any]            =    torch.from_numpy(UpperCAmelCase				).to(UpperCAmelCase				)

        __snake_case				:			str            =    self.sigma_to_t(UpperCAmelCase				).to(UpperCAmelCase			,  dtype=timesteps.dtype				)
        __snake_case				:			List[Any]            =    torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])			,  dim=-1				).flatten()

        __snake_case				:			int            =    torch.cat([timesteps[:1], interleaved_timesteps]				)

        __snake_case				:			str            =    None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        __snake_case				:			Any            =    defaultdict(UpperCAmelCase				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				List[str]:






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    sigma.log()

        # get distribution
        __snake_case				:			List[str]            =    log_sigma - self.log_sigmas[:, None]

        # get sigmas range
        __snake_case				:			Tuple            =    dists.ge(0				).cumsum(dim=0				).argmax(dim=0				).clamp(max=self.log_sigmas.shape[0] - 2				)
        __snake_case				:			Tuple            =    low_idx + 1

        __snake_case				:			Optional[Any]            =    self.log_sigmas[low_idx]
        __snake_case				:			int            =    self.log_sigmas[high_idx]

        # interpolate sigmas
        __snake_case				:			Union[str, Any]            =    (low - log_sigma) / (low - high)
        __snake_case				:			Dict            =    w.clamp(0			,  1				)

        # transform interpolation to time range
        __snake_case				:			Dict            =    (1 - w) * low_idx + w * high_idx
        __snake_case				:			Optional[int]            =    t.view(sigma.shape				)
        return t







      @property
      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






        return self.sample is None







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase = None			,  UpperCAmelCase = True			,  )    ->				Union[SchedulerOutput, Tuple]:






        '''simple docstring'''






        __snake_case				:			int            =    self.index_for_timestep(UpperCAmelCase				)

        # advance index counter by 1
        __snake_case				:			List[Any]            =    timestep.cpu().item() if torch.is_tensor(UpperCAmelCase				) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
          __snake_case				:			Optional[Any]            =    self.sigmas[step_index]
          __snake_case				:			int            =    self.sigmas_interpol[step_index]
          __snake_case				:			Dict            =    self.sigmas_up[step_index]
          __snake_case				:			Union[str, Any]            =    self.sigmas_down[step_index - 1]
        else:
          # 2nd order / KPDM2's method
          __snake_case				:			Union[str, Any]            =    self.sigmas[step_index - 1]
          __snake_case				:			Union[str, Any]            =    self.sigmas_interpol[step_index - 1]
          __snake_case				:			Any            =    self.sigmas_up[step_index - 1]
          __snake_case				:			Tuple            =    self.sigmas_down[step_index - 1]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        __snake_case				:			Any            =    0
        __snake_case				:			int            =    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        __snake_case				:			Optional[int]            =    model_output.device
        __snake_case				:			Dict            =    randn_tensor(model_output.shape			,  dtype=model_output.dtype			,  device=UpperCAmelCase			,  generator=UpperCAmelCase				)

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          __snake_case				:			Dict            =    sigma_hat if self.state_in_first_order else sigma_interpol
          __snake_case				:			int            =    sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
          __snake_case				:			Optional[Any]            =    sigma_hat if self.state_in_first_order else sigma_interpol
          __snake_case				:			Optional[int]            =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
              sample / (sigma_input**2 + 1)
          )
        elif self.config.prediction_type == "sample":
          raise NotImplementedError("prediction_type not implemented yet: sample"				)
        else:
          raise ValueError(
              F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

        if self.state_in_first_order:
          # 2. Convert to an ODE derivative for 1st order
          __snake_case				:			Optional[Any]            =    (sample - pred_original_sample) / sigma_hat
          # 3. delta timestep
          __snake_case				:			Tuple            =    sigma_interpol - sigma_hat

          # store for 2nd order step
          __snake_case				:			Tuple            =    sample
          __snake_case				:			Tuple            =    dt
          __snake_case				:			Tuple            =    sample + derivative * dt
        else:
          # DPM-Solver-2
          # 2. Convert to an ODE derivative for 2nd order
          __snake_case				:			Optional[Any]            =    (sample - pred_original_sample) / sigma_interpol
          # 3. delta timestep
          __snake_case				:			Dict            =    sigma_down - sigma_hat

          __snake_case				:			Any            =    self.sample
          __snake_case				:			int            =    None

          __snake_case				:			List[Any]            =    sample + derivative * dt
          __snake_case				:			Optional[int]            =    prev_sample + noise * sigma_up

        if not return_dict:
          return (prev_sample,)

        return SchedulerOutput(prev_sample=UpperCAmelCase				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  )    ->				torch.FloatTensor:






        '''simple docstring'''






        __snake_case				:			Any            =    self.sigmas.to(device=original_samples.device			,  dtype=original_samples.dtype				)
        if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase				):
          # mps does not support float64
          __snake_case				:			Union[str, Any]            =    self.timesteps.to(original_samples.device			,  dtype=torch.floataa				)
          __snake_case				:			Dict            =    timesteps.to(original_samples.device			,  dtype=torch.floataa				)
        else:
          __snake_case				:			Optional[Any]            =    self.timesteps.to(original_samples.device				)
          __snake_case				:			Optional[Any]            =    timesteps.to(original_samples.device				)

        __snake_case				:			List[Any]            =    [self.index_for_timestep(UpperCAmelCase			,  UpperCAmelCase				) for t in timesteps]

        __snake_case				:			Any            =    sigmas[step_indices].flatten()
        while len(sigma.shape				) < len(original_samples.shape				):
          __snake_case				:			str            =    sigma.unsqueeze(-1				)

        __snake_case				:			Any            =    original_samples + noise * sigma
        return noisy_samples







      def __len__(	self				)    ->				str:






        '''simple docstring'''






        return self.config.num_train_timesteps








from __future__ import annotations

import sys




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Union[str, Any]:






        '''simple docstring'''






        __snake_case				:			str            =    letter
        __snake_case				:			int            =    freq
        __snake_case				:			dict[str, str]            =    {}







      def __repr__(	self				)    ->				str:






        '''simple docstring'''






        return F"""{self.letter}:{self.freq}"""




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			int            =    freq
        __snake_case				:			Letter | TreeNode            =    left
        __snake_case				:			Letter | TreeNode            =    right







def  lowerCAmelCase__(		lowercase    :      str			)	->      list[Letter]:
  __snake_case				:			dict[str, int]            =    {}
  with open(lowercase			) as f:
    while True:
      __snake_case				:			List[Any]            =    f.read(1			)
      if not c:
        break
      __snake_case				:			Optional[Any]            =    chars[c] + 1 if c in chars else 1
  return sorted((Letter(lowercase				,       lowercase			) for c, f in chars.items())				,       key=lambda lowercase			: x.freq			)







def  lowerCAmelCase__(		lowercase    :      list[Letter]			)	->      Letter | TreeNode:
  __snake_case				:			list[Letter | TreeNode]            =    letters  # type: ignore
  while len(lowercase			) > 1:
    __snake_case				:			Any            =    response.pop(0			)
    __snake_case				:			Optional[int]            =    response.pop(0			)
    __snake_case				:			str            =    left.freq + right.freq
    __snake_case				:			Dict            =    TreeNode(lowercase				,       lowercase				,       lowercase			)
    response.append(lowercase			)
    response.sort(key=lambda lowercase			: x.freq			)
  return response[0]







def  lowerCAmelCase__(		lowercase    :      Letter | TreeNode				,       lowercase    :      str			)	->      list[Letter]:
  if isinstance(lowercase				,       lowercase			):
    __snake_case				:			List[str]            =    bitstring
    return [root]
  __snake_case				:			TreeNode            =    root  # type: ignore
  __snake_case				:			Union[str, Any]            =    []
  letters += traverse_tree(treenode.left				,       bitstring + "0"			)
  letters += traverse_tree(treenode.right				,       bitstring + "1"			)
  return letters







def  lowerCAmelCase__(		lowercase    :      str			)	->      None:
  __snake_case				:			List[Any]            =    parse_file(lowercase			)
  __snake_case				:			List[str]            =    build_tree(lowercase			)
  __snake_case				:			Optional[int]            =    {
      k: v for letter in traverse_tree(lowercase				,       ""			) for k, v in letter.bitstring.items()
  }
  print(f"""Huffman Coding  of {file_path}: """			)
  with open(lowercase			) as f:
    while True:
      __snake_case				:			int            =    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])

