code: 
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __A       					=		{
      '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
  }
  
  try:
  	if not is_sentencepiece_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A       					=		['''CamembertTokenizer''']
  
  try:
  	if not is_tokenizers_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A       					=		['''CamembertTokenizerFast''']
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A       					=		[
  	    '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
  	    '''CamembertForCausalLM''',
  	    '''CamembertForMaskedLM''',
  	    '''CamembertForMultipleChoice''',
  	    '''CamembertForQuestionAnswering''',
  	    '''CamembertForSequenceClassification''',
  	    '''CamembertForTokenClassification''',
  	    '''CamembertModel''',
  	    '''CamembertPreTrainedModel''',
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A       					=		[
  	    '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
  	    '''TFCamembertForCausalLM''',
  	    '''TFCamembertForMaskedLM''',
  	    '''TFCamembertForMultipleChoice''',
  	    '''TFCamembertForQuestionAnswering''',
  	    '''TFCamembertForSequenceClassification''',
  	    '''TFCamembertForTokenClassification''',
  	    '''TFCamembertModel''',
  	    '''TFCamembertPreTrainedModel''',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
  	try:
  		if not is_sentencepiece_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert import CamembertTokenizer
  
  	try:
  		if not is_tokenizers_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert_fast import CamembertTokenizerFast
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_camembert import (
  		    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    CamembertForCausalLM,
  		    CamembertForMaskedLM,
  		    CamembertForMultipleChoice,
  		    CamembertForQuestionAnswering,
  		    CamembertForSequenceClassification,
  		    CamembertForTokenClassification,
  		    CamembertModel,
  		    CamembertPreTrainedModel,
  		)
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_camembert import (
  		    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    TFCamembertForCausalLM,
  		    TFCamembertForMaskedLM,
  		    TFCamembertForMultipleChoice,
  		    TFCamembertForQuestionAnswering,
  		    TFCamembertForSequenceClassification,
  		    TFCamembertForTokenClassification,
  		    TFCamembertModel,
  		    TFCamembertPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	__A       					=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
style_context: 
  
  
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  
  
  
  
  @flax.struct.dataclass
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      CommonSchedulerState
  	a__   :      jnp.ndarray
  
  	# setable values
  	a__   :      jnp.ndarray
  	a__   :      jnp.ndarray
  	a__   :      Optional[int]	     =							None
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		Tuple		,						__UpperCAmelCase			:		CommonSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				int:
  							return cls(
  							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  
  
  @dataclass
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      DDIMSchedulerState
  
  
  
  
  
  
  class 	lowercase		(				snake_case__				,					snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Dict	     =							[e.name for e in FlaxKarrasDiffusionSchedulers]
  
  	a__   :      jnp.dtype
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
  							return True
  
  
  
  
  	@register_to_config
  	def __init__(			self			:		List[Any]		,						__UpperCAmelCase			:		int = 1_000		,						__UpperCAmelCase			:		float = 0.0_001		,						__UpperCAmelCase			:		float = 0.02		,						__UpperCAmelCase			:		str = "linear"		,						__UpperCAmelCase			:		Optional[jnp.ndarray] = None		,						__UpperCAmelCase			:		bool = True		,						__UpperCAmelCase			:		int = 0		,						__UpperCAmelCase			:		str = "epsilon"		,						__UpperCAmelCase			:		jnp.dtype = jnp.floataa		,						)    ->				Union[str, Any]:
  							UpperCAmelCase_=     dtype
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		Optional[CommonSchedulerState] = None  )    ->				DDIMSchedulerState:
  							if common is None:
  													UpperCAmelCase_=     CommonSchedulerState.create(self  )
  
  							# At every step in ddim, we are looking into the previous alphas_cumprod
  							# For the final step, there is no previous alphas_cumprod because we are already at 0
  							# `set_alpha_to_one` decides whether we set this parameter simply to one or
  							# whether we use the final alpha of the "non-previous" one.
  							UpperCAmelCase_=     (
  							    jnp.array(1.0		,						dtype=self.dtype  ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
  							)
  
  							# standard deviation of the initial noise distribution
  							UpperCAmelCase_=     jnp.array(1.0		,						dtype=self.dtype  )
  
  							UpperCAmelCase_=     jnp.arange(0		,						self.config.num_train_timesteps  ).round()[::-1]
  
  							return DDIMSchedulerState.create(
  							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		Optional[int] = None  )    ->				jnp.ndarray:
  							return sample
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Tuple = ()  )    ->				DDIMSchedulerState:
  							UpperCAmelCase_=     self.config.num_train_timesteps // num_inference_steps
  							# creates integer timesteps by multiplying by ratio
  							# rounding to avoid issues when num_inference_step is power of 3
  							UpperCAmelCase_=     (jnp.arange(0		,						__UpperCAmelCase  ) * step_ratio).round()[::-1] + self.config.steps_offset
  
  							return state.replace(
  							    num_inference_steps=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Tuple  )    ->				int:
  							UpperCAmelCase_=     state.common.alphas_cumprod[timestep]
  							UpperCAmelCase_=     jnp.where(
  							    prev_timestep >= 0		,						state.common.alphas_cumprod[prev_timestep]		,						state.final_alpha_cumprod  )
  							UpperCAmelCase_=     1 - alpha_prod_t
  							UpperCAmelCase_=     1 - alpha_prod_t_prev
  
  							UpperCAmelCase_=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  							return variance
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		float = 0.0		,						__UpperCAmelCase			:		bool = True		,						)    ->				Union[FlaxDDIMSchedulerOutput, Tuple]:
  							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"""  )
  
  							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  							# Ideally, read DDIM paper in-detail understanding
  
  							# Notation (<variable name> -> <name in paper>
  							# - pred_noise_t -> e_theta(x_t, t)
  							# - pred_original_sample -> f_theta(x_t, t) or x_0
  							# - std_dev_t -> sigma_t
  							# - eta -> η
  							# - pred_sample_direction -> "direction pointing to x_t"
  							# - pred_prev_sample -> "x_t-1"
  
  							# 1. get previous step value (=t-1)
  							UpperCAmelCase_=     timestep - self.config.num_train_timesteps // state.num_inference_steps
  
  							UpperCAmelCase_=     state.common.alphas_cumprod
  							UpperCAmelCase_=     state.final_alpha_cumprod
  
  							# 2. compute alphas, betas
  							UpperCAmelCase_=     alphas_cumprod[timestep]
  							UpperCAmelCase_=     jnp.where(prev_timestep >= 0		,						alphas_cumprod[prev_timestep]		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     1 - alpha_prod_t
  
  							# 3. compute predicted original sample from predicted noise also called
  							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							if self.config.prediction_type == "epsilon":
  													UpperCAmelCase_=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  													UpperCAmelCase_=     model_output
  							elif self.config.prediction_type == "sample":
  													UpperCAmelCase_=     model_output
  													UpperCAmelCase_=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  							elif self.config.prediction_type == "v_prediction":
  													UpperCAmelCase_=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  													UpperCAmelCase_=     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
  							else:
  													raise ValueError(
  													    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
  													    """ `v_prediction`"""  )
  
  							# 4. compute variance: "sigma_t(η)" -> see formula (16)
  							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  							UpperCAmelCase_=     self._get_variance(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     eta * variance ** 0.5
  
  							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							UpperCAmelCase_=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
  							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							UpperCAmelCase_=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
  							if not return_dict:
  													return (prev_sample, state)
  
  							return FlaxDDIMSchedulerOutput(prev_sample=__UpperCAmelCase		,						state=__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
  							return add_noise_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
  							return get_velocity_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def __len__(			self			:		Union[str, Any]  )    ->				Optional[int]:
  							return self.config.num_train_timesteps
  
  
  
  
  