code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  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 SchedulerMixin
  
  
  a__				: Dict									=    logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  
  @dataclass
  class 							__snake_case    (				snake_case__       ):
  	__lowerCAmelCase						=     42
  
  
  
  
  
  
  class 							__snake_case    (				snake_case__					,					snake_case__       ):
  	__lowerCAmelCase						=     1
  
  
  
  
  
  	@register_to_config
  	def __init__(		self			,					UpperCamelCase_ = 40			,					UpperCamelCase_ = 0.0_0_2			,					UpperCamelCase_ = 8_0.0			,					UpperCamelCase_ = 0.5			,					UpperCamelCase_ = 1.0			,					UpperCamelCase_ = 7.0			,					UpperCamelCase_ = True			,					)			->		str:
  		# standard deviation of the initial noise distribution
  		snake_case__  					=      sigma_max
  
  		snake_case__  					=      np.linspace(0			,					1			,					__snake_case		)
  		snake_case__  					=      self._convert_to_karras(__snake_case		)
  		snake_case__  					=      self.sigma_to_t(__snake_case		)
  
  		# setable values
  		snake_case__  					=      None
  		snake_case__  					=      torch.from_numpy(__snake_case		)
  		snake_case__  					=      torch.from_numpy(__snake_case		)
  		snake_case__  					=      False
  		snake_case__  					=      False
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_=None		)			->		Union[str, Any]:
  		if schedule_timesteps is None:
  			snake_case__  					=      self.timesteps
  
  		snake_case__  					=      (schedule_timesteps == timestep).nonzero()
  		return indices.item()
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_		)			->		Optional[int]:
  		# Get sigma corresponding to timestep
  		if isinstance(__snake_case			,					torch.Tensor		):
  			snake_case__  					=      timestep.to(self.timesteps.device		)
  		snake_case__  					=      self.index_for_timestep(__snake_case		)
  		snake_case__  					=      self.sigmas[step_idx]
  
  		snake_case__  					=      sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
  		snake_case__  					=      True
  		return sample
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_		)			->		Tuple:
  		if not isinstance(__snake_case			,					np.ndarray		):
  			snake_case__  					=      np.array(__snake_case			,					dtype=np.floataa		)
  
  		snake_case__  					=      1000 * 0.2_5 * np.log(sigmas + 1E-4_4		)
  
  		return timesteps
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_ = None			,					UpperCamelCase_ = None			,					UpperCamelCase_ = None			,					)			->		Optional[Any]:
  		if num_inference_steps is None and timesteps is None:
  			raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'		)
  
  		if num_inference_steps is not None and timesteps is not None:
  			raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'		)
  
  		# Follow DDPMScheduler custom timesteps logic
  		if timesteps is not None:
  			for i in range(1			,					len(__snake_case		)		):
  				if timesteps[i] >= timesteps[i - 1]:
  					raise ValueError('`timesteps` must be in descending order.'		)
  
  			if timesteps[0] >= self.config.num_train_timesteps:
  				raise ValueError(
  				    F'''`timesteps` must start before `self.config.train_timesteps`:'''
  				    F''' {self.config.num_train_timesteps}.'''		)
  
  			snake_case__  					=      np.array(__snake_case			,					dtype=np.intaa		)
  			snake_case__  					=      True
  		else:
  			if num_inference_steps > self.config.num_train_timesteps:
  				raise ValueError(
  				    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
  				    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
  				    F''' maximal {self.config.num_train_timesteps} timesteps.'''		)
  
  			snake_case__  					=      num_inference_steps
  
  			snake_case__  					=      self.config.num_train_timesteps // self.num_inference_steps
  			snake_case__  					=      (np.arange(0			,					__snake_case		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
  			snake_case__  					=      False
  
  		# Map timesteps to Karras sigmas directly for multistep sampling
  		# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
  		snake_case__  					=      self.config.num_train_timesteps
  		snake_case__  					=      timesteps[::-1].copy()
  		snake_case__  					=      ramp / (num_train_timesteps - 1)
  		snake_case__  					=      self._convert_to_karras(__snake_case		)
  		snake_case__  					=      self.sigma_to_t(__snake_case		)
  
  		snake_case__  					=      np.concatenate([sigmas, [self.sigma_min]]		).astype(np.floataa		)
  		snake_case__  					=      torch.from_numpy(__snake_case		).to(device=__snake_case		)
  
  		if str(__snake_case		).startswith('mps'		):
  			# mps does not support float64
  			snake_case__  					=      torch.from_numpy(__snake_case		).to(__snake_case			,					dtype=torch.floataa		)
  		else:
  			snake_case__  					=      torch.from_numpy(__snake_case		).to(device=__snake_case		)
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_		)			->		List[str]:
  		snake_case__  					=      self.config.sigma_min
  		snake_case__  					=      self.config.sigma_max
  
  		snake_case__  					=      self.config.rho
  		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      _snake_case      (		self			,					UpperCamelCase_		)			->		List[str]:
  		snake_case__  					=      self.config.sigma_data
  
  		snake_case__  					=      sigma_data**2 / (sigma**2 + sigma_data**2)
  		snake_case__  					=      sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
  		return c_skip, c_out
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_		)			->		Union[str, Any]:
  		snake_case__  					=      self.config.sigma_min
  		snake_case__  					=      self.config.sigma_data
  
  		snake_case__  					=      sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
  		snake_case__  					=      (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
  		return c_skip, c_out
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_ = None			,					UpperCamelCase_ = True			,					)			->		Any:
  
  		if (
  		    isinstance(__snake_case			,					__snake_case		)
  		    or isinstance(__snake_case			,					torch.IntTensor		)
  		    or isinstance(__snake_case			,					torch.LongTensor		)
  		):
  			raise ValueError(
  			    (
  			        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
  			        F''' `{self.__class__}.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(__snake_case			,					torch.Tensor		):
  			snake_case__  					=      timestep.to(self.timesteps.device		)
  
  		snake_case__  					=      self.config.sigma_min
  		snake_case__  					=      self.config.sigma_max
  
  		snake_case__  					=      self.index_for_timestep(__snake_case		)
  
  		# sigma_next corresponds to next_t in original implementation
  		snake_case__  					=      self.sigmas[step_index]
  		if step_index + 1 < self.config.num_train_timesteps:
  			snake_case__  					=      self.sigmas[step_index + 1]
  		else:
  			# Set sigma_next to sigma_min
  			snake_case__  					=      self.sigmas[-1]
  
  		# Get scalings for boundary conditions
  		snake_case__					,     snake_case__  					=      self.get_scalings_for_boundary_condition(__snake_case		)
  
  		# 1. Denoise model output using boundary conditions
  		snake_case__  					=      c_out * model_output + c_skip * sample
  		if self.config.clip_denoised:
  			snake_case__  					=      denoised.clamp(-1			,					1		)
  
  		# 2. Sample z ~ N(0, s_noise^2 * I)
  		# Noise is not used for onestep sampling.
  		if len(self.timesteps		) > 1:
  			snake_case__  					=      randn_tensor(
  			    model_output.shape			,					dtype=model_output.dtype			,					device=model_output.device			,					generator=__snake_case		)
  		else:
  			snake_case__  					=      torch.zeros_like(__snake_case		)
  		snake_case__  					=      noise * self.config.s_noise
  
  		snake_case__  					=      sigma_next.clamp(min=__snake_case			,					max=__snake_case		)
  
  		# 3. Return noisy sample
  		# tau = sigma_hat, eps = sigma_min
  		snake_case__  					=      denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
  		if not return_dict:
  			return (prev_sample,)
  
  		return CMStochasticIterativeSchedulerOutput(prev_sample=__snake_case		)
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					)			->		str:
  		# 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(__snake_case		):
  			# 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[Any]:
  		return self.config.num_train_timesteps
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import sys
  
  from transformers import AutoTokenizer
  
  
  a__				: Optional[int]									=    sys.argv[1]
  a__				: Any									=    sys.argv[2]
  a__				: str									=    int(sys.argv[3])
  
  a__				: str									=    0
  
  a__				: int									=    AutoTokenizer.from_pretrained(model_name_or_path)
  max_len -= tokenizer.num_special_tokens_to_add()
  
  with open(dataset, '''rt''') as f_p:
  			for line in f_p:
  						a__				: Tuple									=    line.rstrip()
  
  						if not line:
  									print(line)
  									a__				: List[str]									=    0
  									continue
  
  						a__				: Optional[int]									=    line.split()[0]
  
  						a__				: Tuple									=    len(tokenizer.tokenize(token))
  
  						# Token contains strange control characters like \x96 or \x95
  						# Just filter out the complete line
  						if current_subwords_len == 0:
  									continue
  
  						if (subword_len_counter + current_subwords_len) > max_len:
  									print('''''')
  									print(line)
  									a__				: List[str]									=    current_subwords_len
  									continue
  
  						subword_len_counter += current_subwords_len
  
  						print(line)
  
  
  
  