




'''simple docstring'''





# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)




@flax.struct.dataclass
class      UpperCamelCase       :
  a     :CommonSchedulerState
  a     :jnp.ndarray

  # setable values
  a     :jnp.ndarray
  a     :jnp.ndarray
  a     :Optional[int]          =					None



  @classmethod
  def     a_							(			cls		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     ):







      '''simple docstring'''


      return cls(
          common=__lowerCAmelCase		,     final_alpha_cumprod=__lowerCAmelCase		,     init_noise_sigma=__lowerCAmelCase		,     timesteps=__lowerCAmelCase		,     )






@dataclass
class      UpperCamelCase       (	snake_case__       ):
  a     :DDIMSchedulerState






class      UpperCamelCase       (	snake_case__      ,		snake_case__       ):
  a     :List[Any]          =					[e.name for e in FlaxKarrasDiffusionSchedulers]

  a     :jnp.dtype



  @property
  def     a_							(			self ):







      '''simple docstring'''


      return True



  @register_to_config
  def __init__(			self		,     __lowerCAmelCase = 1000		,     __lowerCAmelCase = 0.0001		,     __lowerCAmelCase = 0.02		,     __lowerCAmelCase = "linear"		,     __lowerCAmelCase = None		,     __lowerCAmelCase = True		,     __lowerCAmelCase = 0		,     __lowerCAmelCase = "epsilon"		,     __lowerCAmelCase = jnp.floataa		,     ):







      '''simple docstring'''


      _a				:							Any							       =      dtype



  def     a_							(			self		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      if common is None:
          _a				:							Tuple							       =      CommonSchedulerState.create(self )

      # At every step in ddim, we are looking into the previous alphas_cumprod
      # For the final step, there is no previous alphas_cumprod because we are already at 0
      # `set_alpha_to_one` decides whether we set this parameter simply to one or
      # whether we use the final alpha of the "non-previous" one.
      _a				:							Dict							       =      (
          jnp.array(1.0		,     dtype=self.dtype ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
      )

      # standard deviation of the initial noise distribution
      _a				:							Any							       =      jnp.array(1.0		,     dtype=self.dtype )

      _a				:							Optional[int]							       =      jnp.arange(0		,     self.config.num_train_timesteps ).round()[::-1]

      return DDIMSchedulerState.create(
          common=__lowerCAmelCase		,     final_alpha_cumprod=__lowerCAmelCase		,     init_noise_sigma=__lowerCAmelCase		,     timesteps=__lowerCAmelCase		,     )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      return sample



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase = () ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self.config.num_train_timesteps // num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # rounding to avoid issues when num_inference_step is power of 3
      _a				:							str							       =      (jnp.arange(0		,     __lowerCAmelCase ) * step_ratio).round()[::-1] + self.config.steps_offset

      return state.replace(
          num_inference_steps=__lowerCAmelCase		,     timesteps=__lowerCAmelCase		,     )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      state.common.alphas_cumprod[timestep]
      _a				:							Dict							       =      jnp.where(
          prev_timestep >= 0		,     state.common.alphas_cumprod[prev_timestep]		,     state.final_alpha_cumprod )
      _a				:							int							       =      1 - alpha_prod_t
      _a				:							int							       =      1 - alpha_prod_t_prev

      _a				:							List[Any]							       =      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

      return variance



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase = 0.0		,     __lowerCAmelCase = True		,     ):







      '''simple docstring'''


      if state.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      _a				:							Any							       =      timestep - self.config.num_train_timesteps // state.num_inference_steps

      _a				:							Optional[int]							       =      state.common.alphas_cumprod
      _a				:							Union[str, Any]							       =      state.final_alpha_cumprod

      # 2. compute alphas, betas
      _a				:							int							       =      alphas_cumprod[timestep]
      _a				:							Union[str, Any]							       =      jnp.where(prev_timestep >= 0		,     alphas_cumprod[prev_timestep]		,     __lowerCAmelCase )

      _a				:							Tuple							       =      1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      if self.config.prediction_type == "epsilon":
          _a				:							Optional[int]							       =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          _a				:							str							       =      model_output
      elif self.config.prediction_type == "sample":
          _a				:							Union[str, Any]							       =      model_output
          _a				:							Any							       =      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
          _a				:							List[Any]							       =      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          _a				:							List[Any]							       =      (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
      else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
              """ `v_prediction`""" )

      # 4. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      _a				:							Any							       =      self._get_variance(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
      _a				:							List[Any]							       =      eta * variance ** 0.5

      # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _a				:							Optional[Any]							       =      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

      # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _a				:							List[str]							       =      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if not return_dict:
          return (prev_sample, state)

      return FlaxDDIMSchedulerOutput(prev_sample=__lowerCAmelCase		,     state=__lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     ):







      '''simple docstring'''


      return add_noise_common(state.common		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     ):







      '''simple docstring'''


      return get_velocity_common(state.common		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )



  def __len__(			self ):







      '''simple docstring'''


      return self.config.num_train_timesteps








'''simple docstring'''





from __future__ import annotations

import sys




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


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



  def __repr__(			self ):







      '''simple docstring'''


      return f'''{self.letter}:{self.freq}'''






class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


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






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





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							list[Letter | TreeNode]							       =      letters  # type: ignore
    while len(UpperCAmelCase_						) > 1:
        _a				:							List[Any]							       =      response.pop(0						)
        _a				:							List[str]							       =      response.pop(0						)
        _a				:							Dict							       =      left.freq + right.freq
        _a				:							Any							       =      TreeNode(UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						)
        response.append(UpperCAmelCase_						)
        response.sort(key=lambda UpperCAmelCase_						: x.freq						)
    return response[0]





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    if isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
        _a				:							int							       =      bitstring
        return [root]
    _a				:							TreeNode							       =      root  # type: ignore
    _a				:							Optional[Any]							       =      []
    letters += traverse_tree(treenode.left   ,	bitstring + """0"""						)
    letters += traverse_tree(treenode.right   ,	bitstring + """1"""						)
    return letters





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Optional[Any]							       =      parse_file(UpperCAmelCase_						)
    _a				:							Tuple							       =      build_tree(UpperCAmelCase_						)
    _a				:							Union[str, Any]							       =      {
        k: v for letter in traverse_tree(UpperCAmelCase_   ,	""""""						) for k, v in letter.bitstring.items()
    }
    print(F'''Huffman Coding  of {file_path}: '''						)
    with open(UpperCAmelCase_						) as f:
        while True:
            _a				:							Any							       =      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])


