






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

import numpy as np
import torch

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







@dataclass
class 				lowerCamelCase_     (	lowercase_     ):
							SCREAMING_SNAKE_CASE_						     =			42
							SCREAMING_SNAKE_CASE_						     =			42





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_=0.999,  snake_case_="cosine",  )   ->  Dict:


	"""simple docstring"""



	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

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

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

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







class 				lowerCamelCase_     (	lowercase_ ,						lowercase_     ):
							SCREAMING_SNAKE_CASE_						     =			1
							@register_to_config
							def __init__(						self		:					Tuple							,__lowerCamelCase		:					int = 10_00							,__lowerCamelCase		:					float = 0.0_001							,__lowerCamelCase		:					float = 0.02							,__lowerCamelCase		:					str = "linear"							,__lowerCamelCase		:					float = 0.0							,__lowerCamelCase		:					Optional[np.ndarray] = None							,__lowerCamelCase		:					bool = True							,):





								'''simple docstring'''



								if trained_betas is not None:
									a		=			torch.from_numpy(__lowerCamelCase					)
								elif beta_schedule == "linear":
									a		=			torch.linspace(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,dtype=torch.floataa					)
								elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									a		=			(
									    torch.linspace(beta_start**0.5							,beta_end**0.5							,__lowerCamelCase							,dtype=torch.floataa					) ** 2
									)
								elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									a		=			betas_for_alpha_bar(__lowerCamelCase					)
								elif beta_schedule == "sigmoid":
									# GeoDiff sigmoid schedule
									a		=			torch.linspace(-6							,6							,__lowerCamelCase					)
									a		=			torch.sigmoid(__lowerCamelCase					) * (beta_end - beta_start) + beta_start
								else:
									raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""					)

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

								a		=			torch.tensor(1.0					)

								# standard deviation of the initial noise distribution
								a		=			1.0

								# setable values
								a		=			None
								a		=			torch.from_numpy(np.arange(0							,__lowerCamelCase					)[::-1].copy()					)

								a		=			eta
							def  SCREAMING_SNAKE_CASE_ (						self		:					int							,__lowerCamelCase		:					torch.FloatTensor							,__lowerCamelCase		:					Optional[int] = None					):





								'''simple docstring'''



								return sample
							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					int							,__lowerCamelCase		:					int = 10							,__lowerCamelCase		:					int = 10							,__lowerCamelCase		:					Union[str, torch.device] = None							,):





								'''simple docstring'''



								a		=			min(self.config.num_train_timesteps							,__lowerCamelCase					)
								a		=			num_inference_steps

								a		=			[]

								a		=			{}
								for j in range(0							,num_inference_steps - jump_length							,__lowerCamelCase					):
									a		=			jump_n_sample - 1

								a		=			num_inference_steps
								while t >= 1:
									a		=			t - 1
									timesteps.append(__lowerCamelCase					)

									if jumps.get(__lowerCamelCase							,0					) > 0:
										a		=			jumps[t] - 1
										for _ in range(__lowerCamelCase					):
											a		=			t + 1
											timesteps.append(__lowerCamelCase					)

								a		=			np.array(__lowerCamelCase					) * (self.config.num_train_timesteps // self.num_inference_steps)
								a		=			torch.from_numpy(__lowerCamelCase					).to(__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					Optional[int]					):





								'''simple docstring'''



								a		=			t - self.config.num_train_timesteps // self.num_inference_steps

								a		=			self.alphas_cumprod[t]
								a		=			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								a		=			1 - alpha_prod_t
								a		=			1 - alpha_prod_t_prev

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from
								# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
								# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
								# variance to pred_sample
								# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
								# without eta.
								# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
								a		=			(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

								return variance
							def  SCREAMING_SNAKE_CASE_ (						self		:					int							,__lowerCamelCase		:					torch.FloatTensor							,__lowerCamelCase		:					int							,__lowerCamelCase		:					torch.FloatTensor							,__lowerCamelCase		:					torch.FloatTensor							,__lowerCamelCase		:					torch.FloatTensor							,__lowerCamelCase		:					Optional[torch.Generator] = None							,__lowerCamelCase		:					bool = True							,):





								'''simple docstring'''



								a		=			timestep
								a		=			timestep - self.config.num_train_timesteps // self.num_inference_steps

								# 1. compute alphas, betas
								a		=			self.alphas_cumprod[t]
								a		=			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								a		=			1 - alpha_prod_t

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								a		=			(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
									a		=			torch.clamp(__lowerCamelCase							,-1							,1					)

								# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
								# substitute formula (7) in the algorithm coming from DDPM paper
								# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
								# DDIM schedule gives the same results as DDPM with eta = 1.0
								# Noise is being reused in 7. and 8., but no impact on quality has
								# been observed.

								# 5. Add noise
								a		=			model_output.device
								a		=			randn_tensor(model_output.shape							,generator=__lowerCamelCase							,device=__lowerCamelCase							,dtype=model_output.dtype					)
								a		=			self.eta * self._get_variance(__lowerCamelCase					) ** 0.5

								a		=			0
								if t > 0 and self.eta > 0:
									a		=			std_dev_t * noise

								# 6. compute "direction pointing to x_t" of formula (12)
								# from https://arxiv.org/pdf/2010.02502.pdf
								a		=			(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

								# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								a		=			alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

								# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
								a		=			(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

								# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
								a		=			mask * prev_known_part + (1.0 - mask) * prev_unknown_part

								if not return_dict:
									return (
									    pred_prev_sample,
									    pred_original_sample,
									)

								return RePaintSchedulerOutput(prev_sample=__lowerCamelCase							,pred_original_sample=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,__lowerCamelCase		:					int							,__lowerCamelCase		:					Any							,__lowerCamelCase		:					List[str]=None					):





								'''simple docstring'''



								a		=			self.config.num_train_timesteps // self.num_inference_steps

								for i in range(__lowerCamelCase					):
									a		=			self.betas[timestep + i]
									if sample.device.type == "mps":
										# randn does not work reproducibly on mps
										a		=			randn_tensor(sample.shape							,dtype=sample.dtype							,generator=__lowerCamelCase					)
										a		=			noise.to(sample.device					)
									else:
										a		=			randn_tensor(sample.shape							,generator=__lowerCamelCase							,device=sample.device							,dtype=sample.dtype					)

									# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
									a		=			(1 - beta) ** 0.5 * sample + beta**0.5 * noise

								return sample
							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					torch.FloatTensor							,__lowerCamelCase		:					torch.FloatTensor							,__lowerCamelCase		:					torch.IntTensor							,):





								'''simple docstring'''



								raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''					)




							def __len__(						self		:					Dict					):





								'''simple docstring'''



								return self.config.num_train_timesteps













from __future__ import annotations

import sys







class 				lowerCamelCase_     :
							def __init__(						self		:					Optional[int]							,__lowerCamelCase		:					str							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								a		=			letter
								a		=			freq
								a		=			{}




							def __repr__(						self		:					List[str]					):





								'''simple docstring'''



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







class 				lowerCamelCase_     :
							def __init__(						self		:					Dict							,__lowerCamelCase		:					int							,__lowerCamelCase		:					Letter | TreeNode							,__lowerCamelCase		:					Letter | TreeNode					):





								'''simple docstring'''



								a		=			freq
								a		=			left
								a		=			right





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  list[Letter]:


	"""simple docstring"""



	a		=			{}
	with open(snake_case_  ) 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(snake_case_,  snake_case_  ) for c, f in chars.items()),  key=lambda snake_case_  : x.freq  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  Letter | TreeNode:


	"""simple docstring"""



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





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  list[Letter]:


	"""simple docstring"""



	if isinstance(snake_case_,  snake_case_  ):
		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   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  None:


	"""simple docstring"""



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





