code: 
  
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  from scipy import integrate
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      broadcast_to_shape_from_left,
  )
  
  
  
  
  @flax.struct.dataclass
  class 					_UpperCamelCase :
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  4_2
  
  							# setable values
  							_A  				=  4_2
  							_A  				=  4_2
  							_A  				=  4_2
  							_A  				=  None
  
  							# running values
  							_A  				=  None
  
  
  
  
  
  
  
  							@classmethod
  							def 		_UpperCAmelCase	(		cls  :				int		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				List[str]		):
  													return cls(common=__UpperCamelCase		,				init_noise_sigma=__UpperCamelCase		,				timesteps=__UpperCamelCase		,				sigmas=__UpperCamelCase		)
  
  
  
  
  @dataclass
  class 					_UpperCamelCase (      UpperCAmelCase_	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  4_2
  
  
  
  
  class 					_UpperCamelCase (      UpperCAmelCase_			,							UpperCAmelCase_	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  [e.name for e in FlaxKarrasDiffusionSchedulers]
  
  							_A  				=  4_2
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
  													return True
  
  
  
  
  
  
  
  							@register_to_config
  							def __init__(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				Any = 1_0_0_0		,				SCREAMING_SNAKE_CASE_  :				List[Any] = 0.0001		,				SCREAMING_SNAKE_CASE_  :				List[Any] = 0.02		,				SCREAMING_SNAKE_CASE_  :				List[Any] = "linear"		,				SCREAMING_SNAKE_CASE_  :				Optional[Any] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Any] = "epsilon"		,				SCREAMING_SNAKE_CASE_  :				Dict = jnp.floataa		,				):
  													_a     =			dtype
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any] = None		):
  													if common is None:
  																			_a     =			CommonSchedulerState.create(self		)
  
  													_a     =			jnp.arange(0		,				self.config.num_train_timesteps		).round()[::-1]
  													_a     =			((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5
  
  													# standard deviation of the initial noise distribution
  													_a     =			sigmas.max()
  
  													return LMSDiscreteSchedulerState.create(
  													    common=__UpperCamelCase		,				init_noise_sigma=__UpperCamelCase		,				timesteps=__UpperCamelCase		,				sigmas=__UpperCamelCase		,				)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				Dict		,				SCREAMING_SNAKE_CASE_  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				Dict		):
  													((_a					) ,						)     =			jnp.where(state.timesteps == timestep		,				size=1		)
  													_a     =			step_index[0]
  
  													_a     =			state.sigmas[step_index]
  													_a     =			sample / ((sigma**2 + 1) ** 0.5)
  													return sample
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				int		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				int		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
  
  													def lms_derivative(SCREAMING_SNAKE_CASE_  :				Optional[Any]		):
  																			_a     =			1.0
  																			for k in range(__UpperCamelCase		):
  																									if current_order == k:
  																															continue
  																									prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
  																			return prod
  
  													_a     =			integrate.quad(__UpperCamelCase		,				state.sigmas[t]		,				state.sigmas[t + 1]		,				epsrel=1e-4		)[0]
  
  													return integrated_coeff
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				int		,				SCREAMING_SNAKE_CASE_  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any] = ()		):
  													_a     =			jnp.linspace(self.config.num_train_timesteps - 1		,				0		,				__UpperCamelCase		,				dtype=self.dtype		)
  
  													_a     =			jnp.floor(__UpperCamelCase		).astype(jnp.intaa		)
  													_a     =			jnp.ceil(__UpperCamelCase		).astype(jnp.intaa		)
  
  													_a     =			jnp.mod(__UpperCamelCase		,				1.0		)
  
  													_a     =			((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
  													_a     =			(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
  													_a     =			jnp.concatenate([sigmas, jnp.array([0.0]		,				dtype=self.dtype		)]		)
  
  													_a     =			timesteps.astype(jnp.intaa		)
  
  													# initial running values
  													_a     =			jnp.zeros((0,) + shape		,				dtype=self.dtype		)
  
  													return state.replace(
  													    timesteps=__UpperCamelCase		,				sigmas=__UpperCamelCase		,				num_inference_steps=__UpperCamelCase		,				derivatives=__UpperCamelCase		,				)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				int		,				SCREAMING_SNAKE_CASE_  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any] = 4		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = True		,				):
  													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'		)
  
  													_a     =			state.sigmas[timestep]
  
  													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  													if self.config.prediction_type == "epsilon":
  																			_a     =			sample - sigma * model_output
  													elif self.config.prediction_type == "v_prediction":
  																			# * c_out + input * c_skip
  																			_a     =			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
  													_a     =			(sample - pred_original_sample) / sigma
  													_a     =			state.replace(derivatives=jnp.append(state.derivatives		,				__UpperCamelCase		)		)
  													if len(state.derivatives		) > order:
  																			_a     =			state.replace(derivatives=jnp.delete(state.derivatives		,				0		)		)
  
  													# 3. Compute linear multistep coefficients
  													_a     =			min(timestep + 1		,				__UpperCamelCase		)
  													_a     =			[self.get_lms_coefficient(__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase		) for curr_order in range(__UpperCamelCase		)]
  
  													# 4. Compute previous sample based on the derivatives path
  													_a     =			sample + sum(
  													    coeff * derivative for coeff, derivative in zip(__UpperCamelCase		,				reversed(state.derivatives		)		)		)
  
  													if not return_dict:
  																			return (prev_sample, state)
  
  													return FlaxLMSSchedulerOutput(prev_sample=__UpperCamelCase		,				state=__UpperCamelCase		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				int		,				SCREAMING_SNAKE_CASE_  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				Any		,				):
  													_a     =			state.sigmas[timesteps].flatten()
  													_a     =			broadcast_to_shape_from_left(__UpperCamelCase		,				noise.shape		)
  
  													_a     =			original_samples + noise * sigma
  
  													return noisy_samples
  
  
  
  
  
  
  
  							def __len__(		self  :				List[str]		):
  													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__)
  
  
  