
'''simple docstring'''
from __future__ import annotations

import sys


class     _lowerCAmelCase					:







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     Optional[int]:
														_snake_case     =						letter
														_snake_case     =						freq
														_snake_case     =						{}





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


class     _lowerCAmelCase					:







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     Optional[Any]:
														_snake_case     =						freq
														_snake_case     =						left
														_snake_case     =						right






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






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






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							if isinstance(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
														_snake_case     =						bitstring
														return [root]
							_snake_case     =						root  # type: ignore
							_snake_case     =						[]
							letters += traverse_tree(treenode.left							,      bitstring + """0"""   )
							letters += traverse_tree(treenode.right							,      bitstring + """1"""   )
							return letters






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

'''simple docstring'''
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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


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


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		42
							lowerCAmelCase_      		=		None






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=0.999							,      _SCREAMING_SNAKE_CASE="cosine"							,      ):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_SCREAMING_SNAKE_CASE   ):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(_SCREAMING_SNAKE_CASE   ):
																					return math.exp(t * -12.0   )

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

							_snake_case     =						[]
							for i in range(_SCREAMING_SNAKE_CASE   ):
														_snake_case     =						i / num_diffusion_timesteps
														_snake_case     =						(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE   ) / alpha_bar_fn(_SCREAMING_SNAKE_CASE   )							,      _SCREAMING_SNAKE_CASE   )   )
							return torch.tensor(_SCREAMING_SNAKE_CASE							,      dtype=torch.floataa   )


class     _lowerCAmelCase					(					__snake_case  ,							__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		[e.name for e in KarrasDiffusionSchedulers]
							lowerCAmelCase_      		=		1

							@register_to_config
							def __init__(self						,						UpperCAmelCase = 1000						,						UpperCAmelCase = 0.0001						,						UpperCAmelCase = 0.02						,						UpperCAmelCase = "linear"						,						UpperCAmelCase = None						,						UpperCAmelCase = "epsilon"						,						UpperCAmelCase = "linear"						,						UpperCAmelCase = False						,						UpperCAmelCase = "linspace"						,						UpperCAmelCase = 0						,						)  ->     Tuple:
														if trained_betas is not None:
																					_snake_case     =						torch.tensor(UpperCAmelCase						,						dtype=torch.floataa						)
														elif beta_schedule == "linear":
																					_snake_case     =						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     =						(
																					    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     =						betas_for_alpha_bar(UpperCAmelCase						)
														else:
																					raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""						)

														_snake_case     =						1.0 - self.betas
														_snake_case     =						torch.cumprod(self.alphas						,						dim=0						)

														_snake_case     =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
														_snake_case     =						np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
														_snake_case     =						torch.from_numpy(UpperCAmelCase						)

														# setable values
														_snake_case     =						None
														_snake_case     =						np.linspace(0						,						num_train_timesteps - 1						,						UpperCAmelCase						,						dtype=UpperCAmelCase						)[::-1].copy()
														_snake_case     =						torch.from_numpy(UpperCAmelCase						)
														_snake_case     =						False
														_snake_case     =						use_karras_sigmas

							@property
							def  lowercase							(self						)  ->     str:
														# standard deviation of the initial noise distribution
														if self.config.timestep_spacing in ["linspace", "trailing"]:
																					return self.sigmas.max()

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

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     torch.FloatTensor:
														if isinstance(UpperCAmelCase						,						torch.Tensor						):
																					_snake_case     =						timestep.to(self.timesteps.device						)
														_snake_case     =						(self.timesteps == timestep).nonzero().item()
														_snake_case     =						self.sigmas[step_index]

														_snake_case     =						sample / ((sigma**2 + 1) ** 0.5)

														_snake_case     =						True
														return sample

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     Any:
														_snake_case     =						num_inference_steps

														# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
														if self.config.timestep_spacing == "linspace":
																					_snake_case     =						np.linspace(0						,						self.config.num_train_timesteps - 1						,						UpperCAmelCase						,						dtype=UpperCAmelCase						)[
																					    ::-1
																					].copy()
														elif self.config.timestep_spacing == "leading":
																					_snake_case     =						self.config.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     =						(np.arange(0						,						UpperCAmelCase						) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase						)
																					timesteps += self.config.steps_offset
														elif self.config.timestep_spacing == "trailing":
																					_snake_case     =						self.config.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     =						(np.arange(self.config.num_train_timesteps						,						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     =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
														_snake_case     =						np.log(UpperCAmelCase						)

														if self.config.interpolation_type == "linear":
																					_snake_case     =						np.interp(UpperCAmelCase						,						np.arange(0						,						len(UpperCAmelCase						)						)						,						UpperCAmelCase						)
														elif self.config.interpolation_type == "log_linear":
																					_snake_case     =						torch.linspace(np.log(sigmas[-1]						)						,						np.log(sigmas[0]						)						,						num_inference_steps + 1						).exp()
														else:
																					raise ValueError(
																					    f"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
																					    """ 'linear' or 'log_linear'"""						)

														if self.use_karras_sigmas:
																					_snake_case     =						self._convert_to_karras(in_sigmas=UpperCAmelCase						,						num_inference_steps=self.num_inference_steps						)
																					_snake_case     =						np.array([self._sigma_to_t(UpperCAmelCase						,						UpperCAmelCase						) for sigma in sigmas]						)

														_snake_case     =						np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
														_snake_case     =						torch.from_numpy(UpperCAmelCase						).to(device=UpperCAmelCase						)
														if str(UpperCAmelCase						).startswith("""mps"""						):
																					# mps does not support float64
																					_snake_case     =						torch.from_numpy(UpperCAmelCase						).to(UpperCAmelCase						,						dtype=torch.floataa						)
														else:
																					_snake_case     =						torch.from_numpy(UpperCAmelCase						).to(device=UpperCAmelCase						)

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     str:
														# get log sigma
														_snake_case     =						np.log(UpperCAmelCase						)

														# get distribution
														_snake_case     =						log_sigma - log_sigmas[:, np.newaxis]

														# get sigmas range
														_snake_case     =						np.cumsum((dists >= 0)						,						axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
														_snake_case     =						low_idx + 1

														_snake_case     =						log_sigmas[low_idx]
														_snake_case     =						log_sigmas[high_idx]

														# interpolate sigmas
														_snake_case     =						(low - log_sigma) / (low - high)
														_snake_case     =						np.clip(UpperCAmelCase						,						0						,						1						)

														# transform interpolation to time range
														_snake_case     =						(1 - w) * low_idx + w * high_idx
														_snake_case     =						t.reshape(sigma.shape						)
														return t

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     torch.FloatTensor:
														_snake_case     =						in_sigmas[-1].item()
														_snake_case     =						in_sigmas[0].item()

														_snake_case     =						7.0  # 7.0 is the value used in the paper
														_snake_case     =						np.linspace(0						,						1						,						UpperCAmelCase						)
														_snake_case     =						sigma_min ** (1 / rho)
														_snake_case     =						sigma_max ** (1 / rho)
														_snake_case     =						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
														return sigmas

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase = 0.0						,						UpperCAmelCase = 0.0						,						UpperCAmelCase = float("""inf"""						)						,						UpperCAmelCase = 1.0						,						UpperCAmelCase = None						,						UpperCAmelCase = True						,						)  ->     Union[EulerDiscreteSchedulerOutput, Tuple]:

														if (
														    isinstance(UpperCAmelCase						,						UpperCAmelCase						)
														    or isinstance(UpperCAmelCase						,						torch.IntTensor						)
														    or isinstance(UpperCAmelCase						,						torch.LongTensor						)
														):
																					raise ValueError(
																					    (
																					        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
																					        """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
																					        """ one of the `scheduler.timesteps` as a timestep."""
																					    )						,						)

														if not self.is_scale_input_called:
																					logger.warning(
																					    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
																					    """See `StableDiffusionPipeline` for a usage example."""						)

														if isinstance(UpperCAmelCase						,						torch.Tensor						):
																					_snake_case     =						timestep.to(self.timesteps.device						)

														_snake_case     =						(self.timesteps == timestep).nonzero().item()
														_snake_case     =						self.sigmas[step_index]

														_snake_case     =						min(s_churn / (len(self.sigmas						) - 1)						,						2**0.5 - 1						) if s_tmin <= sigma <= s_tmax else 0.0

														_snake_case     =						randn_tensor(
														    model_output.shape						,						dtype=model_output.dtype						,						device=model_output.device						,						generator=UpperCAmelCase						)

														_snake_case     =						noise * s_noise
														_snake_case     =						sigma * (gamma + 1)

														if gamma > 0:
																					_snake_case     =						sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

														# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
														# NOTE: "original_sample" should not be an expected prediction_type but is left in for
														# backwards compatibility
														if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																					_snake_case     =						model_output
														elif self.config.prediction_type == "epsilon":
																					_snake_case     =						sample - sigma_hat * model_output
														elif self.config.prediction_type == "v_prediction":
																					# * c_out + input * c_skip
																					_snake_case     =						model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
														else:
																					raise ValueError(
																					    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""						)

														# 2. Convert to an ODE derivative
														_snake_case     =						(sample - pred_original_sample) / sigma_hat

														_snake_case     =						self.sigmas[step_index + 1] - sigma_hat

														_snake_case     =						sample + derivative * dt

														if not return_dict:
																					return (prev_sample,)

														return EulerDiscreteSchedulerOutput(prev_sample=UpperCAmelCase						,						pred_original_sample=UpperCAmelCase						)

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						,						)  ->     torch.FloatTensor:
														# Make sure sigmas and timesteps have the same device and dtype as original_samples
														_snake_case     =						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     =						self.timesteps.to(original_samples.device						,						dtype=torch.floataa						)
																					_snake_case     =						timesteps.to(original_samples.device						,						dtype=torch.floataa						)
														else:
																					_snake_case     =						self.timesteps.to(original_samples.device						)
																					_snake_case     =						timesteps.to(original_samples.device						)

														_snake_case     =						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

														_snake_case     =						original_samples + noise * sigma
														return noisy_samples





							def __len__(self						)  ->     Optional[int]:
														return self.config.num_train_timesteps