code: 
  
  
  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
  
  
  lowercase_								=    logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  @dataclass
  class 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  42
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__			,							lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  1
  
  
  
  
  
  
  
  							@register_to_config
  							def __init__(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				int = 4_0		,				SCREAMING_SNAKE_CASE_  :				float = 0.002		,				SCREAMING_SNAKE_CASE_  :				float = 80.0		,				SCREAMING_SNAKE_CASE_  :				float = 0.5		,				SCREAMING_SNAKE_CASE_  :				float = 1.0		,				SCREAMING_SNAKE_CASE_  :				float = 7.0		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				):
  													# standard deviation of the initial noise distribution
  													_a     =			sigma_max
  
  													_a     =			np.linspace(0		,				1		,				SCREAMING_SNAKE_CASE_		)
  													_a     =			self._convert_to_karras(SCREAMING_SNAKE_CASE_		)
  													_a     =			self.sigma_to_t(SCREAMING_SNAKE_CASE_		)
  
  													# setable values
  													_a     =			None
  													_a     =			torch.from_numpy(SCREAMING_SNAKE_CASE_		)
  													_a     =			torch.from_numpy(SCREAMING_SNAKE_CASE_		)
  													_a     =			False
  													_a     =			False
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				List[str]=None		):
  													if schedule_timesteps is None:
  																			_a     =			self.timesteps
  
  													_a     =			(schedule_timesteps == timestep).nonzero()
  													return indices.item()
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				torch.FloatTensor		,				SCREAMING_SNAKE_CASE_  :				Union[float, torch.FloatTensor]		):
  													# Get sigma corresponding to timestep
  													if isinstance(SCREAMING_SNAKE_CASE_		,				torch.Tensor		):
  																			_a     =			timestep.to(self.timesteps.device		)
  													_a     =			self.index_for_timestep(SCREAMING_SNAKE_CASE_		)
  													_a     =			self.sigmas[step_idx]
  
  													_a     =			sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
  													_a     =			True
  													return sample
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Union[float, np.ndarray]		):
  													if not isinstance(SCREAMING_SNAKE_CASE_		,				np.ndarray		):
  																			_a     =			np.array(SCREAMING_SNAKE_CASE_		,				dtype=np.floataa		)
  
  													_a     =			1_0_0_0 * 0.25 * np.log(sigmas + 1e-4_4		)
  
  													return timesteps
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = None		,				SCREAMING_SNAKE_CASE_  :				Union[str, torch.device] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[List[int]] = None		,				):
  													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(SCREAMING_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}."""		)
  
  																			_a     =			np.array(SCREAMING_SNAKE_CASE_		,				dtype=np.intaa		)
  																			_a     =			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."""		)
  
  																			_a     =			num_inference_steps
  
  																			_a     =			self.config.num_train_timesteps // self.num_inference_steps
  																			_a     =			(np.arange(0		,				SCREAMING_SNAKE_CASE_		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
  																			_a     =			False
  
  													# Map timesteps to Karras sigmas directly for multistep sampling
  													# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
  													_a     =			self.config.num_train_timesteps
  													_a     =			timesteps[::-1].copy()
  													_a     =			ramp / (num_train_timesteps - 1)
  													_a     =			self._convert_to_karras(SCREAMING_SNAKE_CASE_		)
  													_a     =			self.sigma_to_t(SCREAMING_SNAKE_CASE_		)
  
  													_a     =			np.concatenate([sigmas, [self.sigma_min]]		).astype(np.floataa		)
  													_a     =			torch.from_numpy(SCREAMING_SNAKE_CASE_		).to(device=SCREAMING_SNAKE_CASE_		)
  
  													if str(SCREAMING_SNAKE_CASE_		).startswith('mps'		):
  																			# mps does not support float64
  																			_a     =			torch.from_numpy(SCREAMING_SNAKE_CASE_		).to(SCREAMING_SNAKE_CASE_		,				dtype=torch.floataa		)
  													else:
  																			_a     =			torch.from_numpy(SCREAMING_SNAKE_CASE_		).to(device=SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				int		):
  													_a     =			self.config.sigma_min
  													_a     =			self.config.sigma_max
  
  													_a     =			self.config.rho
  													_a     =			sigma_min ** (1 / rho)
  													_a     =			sigma_max ** (1 / rho)
  													_a     =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
  													return sigmas
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				Any		):
  													_a     =			self.config.sigma_data
  
  													_a     =			sigma_data**2 / (sigma**2 + sigma_data**2)
  													_a     =			sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
  													return c_skip, c_out
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				int		,				SCREAMING_SNAKE_CASE_  :				int		):
  													_a     =			self.config.sigma_min
  													_a     =			self.config.sigma_data
  
  													_a     =			sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
  													_a     =			(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
  													return c_skip, c_out
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				torch.FloatTensor		,				SCREAMING_SNAKE_CASE_  :				Union[float, torch.FloatTensor]		,				SCREAMING_SNAKE_CASE_  :				torch.FloatTensor		,				SCREAMING_SNAKE_CASE_  :				Optional[torch.Generator] = None		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				):
  
  													if (
  													    isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  													    or isinstance(SCREAMING_SNAKE_CASE_		,				torch.IntTensor		)
  													    or isinstance(SCREAMING_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(SCREAMING_SNAKE_CASE_		,				torch.Tensor		):
  																			_a     =			timestep.to(self.timesteps.device		)
  
  													_a     =			self.config.sigma_min
  													_a     =			self.config.sigma_max
  
  													_a     =			self.index_for_timestep(SCREAMING_SNAKE_CASE_		)
  
  													# sigma_next corresponds to next_t in original implementation
  													_a     =			self.sigmas[step_index]
  													if step_index + 1 < self.config.num_train_timesteps:
  																			_a     =			self.sigmas[step_index + 1]
  													else:
  																			# Set sigma_next to sigma_min
  																			_a     =			self.sigmas[-1]
  
  													# Get scalings for boundary conditions
  													_a ,	_a     =			self.get_scalings_for_boundary_condition(SCREAMING_SNAKE_CASE_		)
  
  													# 1. Denoise model output using boundary conditions
  													_a     =			c_out * model_output + c_skip * sample
  													if self.config.clip_denoised:
  																			_a     =			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:
  																			_a     =			randn_tensor(
  																			    model_output.shape		,				dtype=model_output.dtype		,				device=model_output.device		,				generator=SCREAMING_SNAKE_CASE_		)
  													else:
  																			_a     =			torch.zeros_like(SCREAMING_SNAKE_CASE_		)
  													_a     =			noise * self.config.s_noise
  
  													_a     =			sigma_next.clamp(min=SCREAMING_SNAKE_CASE_		,				max=SCREAMING_SNAKE_CASE_		)
  
  													# 3. Return noisy sample
  													# tau = sigma_hat, eps = sigma_min
  													_a     =			denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
  													if not return_dict:
  																			return (prev_sample,)
  
  													return CMStochasticIterativeSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				torch.FloatTensor		,				SCREAMING_SNAKE_CASE_  :				torch.FloatTensor		,				SCREAMING_SNAKE_CASE_  :				torch.FloatTensor		,				):
  													# Make sure sigmas and timesteps have the same device and dtype as original_samples
  													_a     =			self.sigmas.to(device=original_samples.device		,				dtype=original_samples.dtype		)
  													if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_		):
  																			# mps does not support float64
  																			_a     =			self.timesteps.to(original_samples.device		,				dtype=torch.floataa		)
  																			_a     =			timesteps.to(original_samples.device		,				dtype=torch.floataa		)
  													else:
  																			_a     =			self.timesteps.to(original_samples.device		)
  																			_a     =			timesteps.to(original_samples.device		)
  
  													_a     =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
  													_a     =			sigmas[step_indices].flatten()
  													while len(sigma.shape		) < len(original_samples.shape		):
  																			_a     =			sigma.unsqueeze(-1		)
  
  													_a     =			original_samples + noise * sigma
  													return noisy_samples
  
  
  
  
  
  
  
  							def __len__(		self  :				List[Any]		):
  													return self.config.num_train_timesteps
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  lowercase_								=    {
      'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
      'tokenization_codegen': ['CodeGenTokenizer'],
  }
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    ['CodeGenTokenizerFast']
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    [
  		    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
  		    'CodeGenForCausalLM',
  		    'CodeGenModel',
  		    'CodeGenPreTrainedModel',
  		]
  
  if TYPE_CHECKING:
  		from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
  		from .tokenization_codegen import CodeGenTokenizer
  
  		try:
  				if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_codegen_fast import CodeGenTokenizerFast
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_codegen import (
  				    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    CodeGenForCausalLM,
  				    CodeGenModel,
  				    CodeGenPreTrainedModel,
  				)
  
  else:
  		import sys
  
  		lowercase_								=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  