code: 
  
  import json
  import os
  from functools import lru_cache
  from typing import List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  UpperCAmelCase_       :				List[str]		   =      logging.get_logger(__name__)
  
  UpperCAmelCase_       :				Optional[int]		   =      {
      '''vocab_file''': '''vocab.json''',
      '''merges_file''': '''merges.txt''',
  }
  
  UpperCAmelCase_       :				Optional[Any]		   =      {
      '''vocab_file''': {
          '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
          '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
          '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
          '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
          '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
          '''roberta-large-openai-detector''': (
              '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
          ),
      },
      '''merges_file''': {
          '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
          '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
          '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
          '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
          '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
          '''roberta-large-openai-detector''': (
              '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
          ),
      },
  }
  
  UpperCAmelCase_       :				Tuple		   =      {
      '''roberta-base''': 512,
      '''roberta-large''': 512,
      '''roberta-large-mnli''': 512,
      '''distilroberta-base''': 512,
      '''roberta-base-openai-detector''': 512,
      '''roberta-large-openai-detector''': 512,
  }
  @lru_cache()
  def        __SCREAMING_SNAKE_CASE    (      )				->							Dict:
  	__A   : Optional[Any]								= (
  	    list(range(ord("""!"""					) ,ord("""~"""					) + 1					)					) + list(range(ord("""¡"""					) ,ord("""¬"""					) + 1					)					) + list(range(ord("""®"""					) ,ord("""ÿ"""					) + 1					)					)
  	)
  	__A   : str								= bs[:]
  	__A   : Any								= 0
  	for b in range(2**8					):
  		if b not in bs:
  			bs.append(a__					)
  			cs.append(2**8 + n					)
  			n += 1
  	__A   : Dict								= [chr(a__					) for n in cs]
  	return dict(zip(a__ ,a__					)					)
  def        __SCREAMING_SNAKE_CASE    (      a__						:      int					)				->							int:
  	__A   : List[Any]								= set()
  	__A   : Tuple								= word[0]
  	for char in word[1:]:
  		pairs.add((prev_char, char)					)
  		__A   : int								= char
  	return pairs
  
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      List[str]    				=							VOCAB_FILES_NAMES
  	_lowercase		:      List[str]    				=							PRETRAINED_VOCAB_FILES_MAP
  	_lowercase		:      List[Any]    				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  	_lowercase		:      Union[str, Any]    				=							['''input_ids''', '''attention_mask''']
  
  
  
  
  	def __init__(    self	:    Optional[Any]				,			__A	:    Optional[int]				,			__A	:    Optional[Any]				,			__A	:    List[str]="replace"				,			__A	:    Any="<s>"				,			__A	:    int="</s>"				,			__A	:    Optional[Any]="</s>"				,			__A	:    int="<s>"				,			__A	:    Dict="<unk>"				,			__A	:    Optional[Any]="<pad>"				,			__A	:    Optional[Any]="<mask>"				,			__A	:    int=False				,			**__A	:    Dict				,			):
  		__A   : Dict								= AddedToken(__A				,			lstrip=__A				,			rstrip=__A       ) if isinstance(__A				,			__A       ) else bos_token
  		__A   : Any								= AddedToken(__A				,			lstrip=__A				,			rstrip=__A       ) if isinstance(__A				,			__A       ) else eos_token
  		__A   : List[Any]								= AddedToken(__A				,			lstrip=__A				,			rstrip=__A       ) if isinstance(__A				,			__A       ) else sep_token
  		__A   : Union[str, Any]								= AddedToken(__A				,			lstrip=__A				,			rstrip=__A       ) if isinstance(__A				,			__A       ) else cls_token
  		__A   : Any								= AddedToken(__A				,			lstrip=__A				,			rstrip=__A       ) if isinstance(__A				,			__A       ) else unk_token
  		__A   : List[Any]								= AddedToken(__A				,			lstrip=__A				,			rstrip=__A       ) if isinstance(__A				,			__A       ) else pad_token
  
  		# Mask token behave like a normal word, i.e. include the space before it
  		__A   : int								= AddedToken(__A				,			lstrip=__A				,			rstrip=__A       ) if isinstance(__A				,			__A       ) else mask_token
  
  		super().__init__(
  		    errors=__A				,			bos_token=__A				,			eos_token=__A				,			unk_token=__A				,			sep_token=__A				,			cls_token=__A				,			pad_token=__A				,			mask_token=__A				,			add_prefix_space=__A				,			**__A				,			)
  
  		with open(__A				,			encoding="""utf-8"""       ) as vocab_handle:
  			__A   : Optional[Any]								= json.load(__A       )
  		__A   : List[str]								= {v: k for k, v in self.encoder.items()}
  		__A   : List[str]								= errors  # how to handle errors in decoding
  		__A   : Tuple								= bytes_to_unicode()
  		__A   : List[str]								= {v: k for k, v in self.byte_encoder.items()}
  		with open(__A				,			encoding="""utf-8"""       ) as merges_handle:
  			__A   : List[Any]								= merges_handle.read().split("""\n"""       )[1:-1]
  		__A   : Any								= [tuple(merge.split()       ) for merge in bpe_merges]
  		__A   : List[str]								= dict(zip(__A				,			range(len(__A       )       )       )       )
  		__A   : Tuple								= {}
  		__A   : List[str]								= add_prefix_space
  
  		# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
  		__A   : int								= re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""       )
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Any       ):
  		return len(self.encoder       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    int       ):
  		return dict(self.encoder				,			**self.added_tokens_encoder       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[Any]				,			__A	:    Union[str, Any]       ):
  		if token in self.cache:
  			return self.cache[token]
  		__A   : Tuple								= tuple(__A       )
  		__A   : str								= get_pairs(__A       )
  
  		if not pairs:
  			return token
  
  		while True:
  			__A   : Tuple								= min(__A				,			key=lambda __A       : self.bpe_ranks.get(__A				,			float("""inf"""       )       )       )
  			if bigram not in self.bpe_ranks:
  				break
  			__A						,							__A   : int								= bigram
  			__A   : Any								= []
  			__A   : Optional[Any]								= 0
  			while i < len(__A       ):
  				try:
  					__A   : Optional[int]								= word.index(__A				,			__A       )
  				except ValueError:
  					new_word.extend(word[i:]       )
  					break
  				else:
  					new_word.extend(word[i:j]       )
  					__A   : Tuple								= j
  
  				if word[i] == first and i < len(__A       ) - 1 and word[i + 1] == second:
  					new_word.append(first + second       )
  					i += 2
  				else:
  					new_word.append(word[i]       )
  					i += 1
  			__A   : Tuple								= tuple(__A       )
  			__A   : Union[str, Any]								= new_word
  			if len(__A       ) == 1:
  				break
  			else:
  				__A   : List[Any]								= get_pairs(__A       )
  		__A   : Optional[Any]								= """ """.join(__A       )
  		__A   : Optional[int]								= word
  		return word
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[Any]				,			__A	:    Any       ):
  		__A   : str								= []
  		for token in re.findall(self.pat				,			__A       ):
  			__A   : Union[str, Any]								= """""".join(
  			    self.byte_encoder[b] for b in token.encode("""utf-8"""       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
  			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__A       ).split(""" """       )       )
  		return bpe_tokens
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    int				,			__A	:    List[str]       ):
  		return self.encoder.get(__A				,			self.encoder.get(self.unk_token       )       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[Any]				,			__A	:    int       ):
  		return self.decoder.get(__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    List[str]       ):
  		__A   : Tuple								= """""".join(__A       )
  		__A   : Any								= bytearray([self.byte_decoder[c] for c in text]       ).decode("""utf-8"""				,			errors=self.errors       )
  		return text
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    str				,			__A	:    Optional[str] = None       ):
  		if not os.path.isdir(__A       ):
  			logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
  			return
  		__A   : int								= os.path.join(
  		    __A				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
  		__A   : Optional[int]								= os.path.join(
  		    __A				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )
  
  		with open(__A				,			"""w"""				,			encoding="""utf-8"""       ) as f:
  			f.write(json.dumps(self.encoder				,			indent=2				,			sort_keys=__A				,			ensure_ascii=__A       ) + """\n"""       )
  
  		__A   : List[str]								= 0
  		with open(__A				,			"""w"""				,			encoding="""utf-8"""       ) as writer:
  			writer.write("""#version: 0.2\n"""       )
  			for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,			key=lambda __A       : kv[1]       ):
  				if index != token_index:
  					logger.warning(
  					    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
  					    """ Please check that the tokenizer is not corrupted!"""       )
  					__A   : Tuple								= token_index
  				writer.write(""" """.join(__A       ) + """\n"""       )
  				index += 1
  
  		return vocab_file, merge_file
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    List[int]				,			__A	:    Optional[List[int]] = None       ):
  		if token_ids_a is None:
  			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  		__A   : List[str]								= [self.cls_token_id]
  		__A   : Optional[int]								= [self.sep_token_id]
  		return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    int				,			__A	:    List[int]				,			__A	:    Optional[List[int]] = None				,			__A	:    bool = False       ):
  		if already_has_special_tokens:
  			return super().get_special_tokens_mask(
  			    token_ids_a=__A				,			token_ids_a=__A				,			already_has_special_tokens=__A       )
  
  		if token_ids_a is None:
  			return [1] + ([0] * len(__A       )) + [1]
  		return [1] + ([0] * len(__A       )) + [1, 1] + ([0] * len(__A       )) + [1]
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    List[int]				,			__A	:    Optional[List[int]] = None       ):
  		__A   : Any								= [self.sep_token_id]
  		__A   : Any								= [self.cls_token_id]
  
  		if token_ids_a is None:
  			return len(cls + token_ids_a + sep       ) * [0]
  		return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    str				,			__A	:    List[Any]=False				,			**__A	:    List[Any]       ):
  		__A   : Tuple								= kwargs.pop("""add_prefix_space"""				,			self.add_prefix_space       )
  		if (is_split_into_words or add_prefix_space) and (len(__A       ) > 0 and not text[0].isspace()):
  			__A   : Dict								= """ """ + text
  		return (text, kwargs)
  
  
  
  
  
  
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     lowerCamelCase_			:
  	_lowercase		:      CommonSchedulerState
  	_lowercase		:      jnp.ndarray
  
  	# setable values
  	_lowercase		:      jnp.ndarray
  	_lowercase		:      jnp.ndarray
  	_lowercase		:      Optional[int]    				=							None
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    int				,			__A	:    CommonSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
  		return cls(
  		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)
  
  
  
  
  
  
  @dataclass
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      DDIMSchedulerState
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase	,			_lowercase  ):
  	_lowercase		:      Union[str, Any]    				=							[e.name for e in FlaxKarrasDiffusionSchedulers]
  
  	_lowercase		:      jnp.dtype
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Tuple       ):
  		return True
  
  
  
  
  	@register_to_config
  	def __init__(    self	:    Any				,			__A	:    int = 1000				,			__A	:    float = 0.0_0_0_1				,			__A	:    float = 0.0_2				,			__A	:    str = "linear"				,			__A	:    Optional[jnp.ndarray] = None				,			__A	:    bool = True				,			__A	:    int = 0				,			__A	:    str = "epsilon"				,			__A	:    jnp.dtype = jnp.floataa				,			):
  		__A   : Optional[int]								= dtype
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    Optional[CommonSchedulerState] = None       ):
  		if common is None:
  			__A   : Any								= 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.
  		__A   : Tuple								= (
  		    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
  		__A   : List[str]								= jnp.array(1.0				,			dtype=self.dtype       )
  
  		__A   : Any								= jnp.arange(0				,			self.config.num_train_timesteps       ).round()[::-1]
  
  		return DDIMSchedulerState.create(
  		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    Optional[int] = None       ):
  		return sample
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    DDIMSchedulerState				,			__A	:    int				,			__A	:    Tuple = ()       ):
  		__A   : Union[str, Any]								= 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
  		__A   : Optional[Any]								= (jnp.arange(0				,			__A       ) * step_ratio).round()[::-1] + self.config.steps_offset
  
  		return state.replace(
  		    num_inference_steps=__A				,			timesteps=__A				,			)
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    DDIMSchedulerState				,			__A	:    str				,			__A	:    List[Any]       ):
  		__A   : Tuple								= state.common.alphas_cumprod[timestep]
  		__A   : Tuple								= jnp.where(
  		    prev_timestep >= 0				,			state.common.alphas_cumprod[prev_timestep]				,			state.final_alpha_cumprod       )
  		__A   : List[Any]								= 1 - alpha_prod_t
  		__A   : Tuple								= 1 - alpha_prod_t_prev
  
  		__A   : List[Any]								= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  		return variance
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    int				,			__A	:    jnp.ndarray				,			__A	:    float = 0.0				,			__A	:    bool = 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"""       )
  
  		# 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)
  		__A   : Optional[Any]								= timestep - self.config.num_train_timesteps // state.num_inference_steps
  
  		__A   : Optional[int]								= state.common.alphas_cumprod
  		__A   : Optional[int]								= state.final_alpha_cumprod
  
  		# 2. compute alphas, betas
  		__A   : Optional[int]								= alphas_cumprod[timestep]
  		__A   : str								= jnp.where(prev_timestep >= 0				,			alphas_cumprod[prev_timestep]				,			__A       )
  
  		__A   : Any								= 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":
  			__A   : Any								= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  			__A   : List[Any]								= model_output
  		elif self.config.prediction_type == "sample":
  			__A   : Dict								= model_output
  			__A   : Tuple								= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  		elif self.config.prediction_type == "v_prediction":
  			__A   : int								= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  			__A   : str								= (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)
  		__A   : Union[str, Any]								= self._get_variance(__A				,			__A				,			__A       )
  		__A   : Any								= eta * variance ** 0.5
  
  		# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  		__A   : Any								= (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
  		__A   : Optional[int]								= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
  		if not return_dict:
  			return (prev_sample, state)
  
  		return FlaxDDIMSchedulerOutput(prev_sample=__A				,			state=__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
  		return add_noise_common(state.common				,			__A				,			__A				,			__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
  		return get_velocity_common(state.common				,			__A				,			__A				,			__A       )
  
  
  
  
  	def __len__(    self	:    Union[str, Any]       ):
  		return self.config.num_train_timesteps
  
  
  
  
  
  