code: 
  
  
  '''simple docstring'''
  
  
  
  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
  
  
  __lowercase			:		Optional[int] =			logging.get_logger(__name__)
  
  __lowercase			:		List[str] =			{
      """vocab_file""": """vocab.json""",
      """merges_file""": """merges.txt""",
  }
  
  __lowercase			:		str =			{
      """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"""
          ),
      },
  }
  
  __lowercase			:		Dict =			{
      """roberta-base""": 5_12,
      """roberta-large""": 5_12,
      """roberta-large-mnli""": 5_12,
      """distilroberta-base""": 5_12,
      """roberta-base-openai-detector""": 5_12,
      """roberta-large-openai-detector""": 5_12,
  }
  
  
  
  
  
  
  @lru_cache()
  def 						lowerCamelCase							():
  					__a    : str      					=						(
  					    list(range(ord('!'     )		,   ord('~'     ) + 1     )     ) + list(range(ord('¡'     )		,   ord('¬'     ) + 1     )     ) + list(range(ord('®'     )		,   ord('ÿ'     ) + 1     )     )
  					)
  					__a    : int      					=						bs[:]
  					__a    : Optional[int]      					=						0
  					for b in range(2**8     ):
  										if b not in bs:
  															bs.append(_SCREAMING_SNAKE_CASE     )
  															cs.append(2**8 + n     )
  															n += 1
  					__a    : Any      					=						[chr(_SCREAMING_SNAKE_CASE     ) for n in cs]
  					return dict(zip(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict     ):
  					__a    : str      					=						set()
  					__a    : Any      					=						word[0]
  					for char in word[1:]:
  										pairs.add((prev_char, char)     )
  										__a    : Any      					=						char
  					return pairs
  
  
  
  
  
  class 							__UpperCamelCase       (	a__      ):
  	A_					    =					VOCAB_FILES_NAMES
  	A_					    =					PRETRAINED_VOCAB_FILES_MAP
  	A_					    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  	A_					    =					["input_ids", "attention_mask"]
  
  
  
  
  	def __init__(     self		,       __a		,       __a		,       __a="replace"		,       __a="<s>"		,       __a="</s>"		,       __a="</s>"		,       __a="<s>"		,       __a="<unk>"		,       __a="<pad>"		,       __a="<mask>"		,       __a=False		,       **__a		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Tuple      					=						AddedToken(__A		,       lstrip=__A		,       rstrip=__A							) if isinstance(__A		,       __A							) else bos_token
  						__a    : Optional[Any]      					=						AddedToken(__A		,       lstrip=__A		,       rstrip=__A							) if isinstance(__A		,       __A							) else eos_token
  						__a    : int      					=						AddedToken(__A		,       lstrip=__A		,       rstrip=__A							) if isinstance(__A		,       __A							) else sep_token
  						__a    : str      					=						AddedToken(__A		,       lstrip=__A		,       rstrip=__A							) if isinstance(__A		,       __A							) else cls_token
  						__a    : List[Any]      					=						AddedToken(__A		,       lstrip=__A		,       rstrip=__A							) if isinstance(__A		,       __A							) else unk_token
  						__a    : Optional[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    : Union[str, Any]      					=						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[Any]      					=						{v: k for k, v in self.encoder.items()}
  						__a    : Any      					=						errors  # how to handle errors in decoding
  						__a    : Optional[Any]      					=						bytes_to_unicode()
  						__a    : Optional[int]      					=						{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    : Union[str, Any]      					=						[tuple(merge.split()							) for merge in bpe_merges]
  						__a    : Dict      					=						dict(zip(__A		,       range(len(__A							)							)							)							)
  						__a    : Any      					=						{}
  						__a    : List[Any]      					=						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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return len(self.encoder							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return dict(self.encoder		,       **self.added_tokens_encoder							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if token in self.cache:
  											return self.cache[token]
  						__a    : Union[str, Any]      					=						tuple(__A							)
  						__a    : List[str]      					=						get_pairs(__A							)
  
  						if not pairs:
  											return token
  
  						while True:
  											__a    : Dict      					=						min(__A		,       key=lambda __a							: self.bpe_ranks.get(__A		,       float('inf'							)							)							)
  											if bigram not in self.bpe_ranks:
  																break
  											__a    : Tuple      					=						bigram
  											__a    : List[Any]      					=						[]
  											__a    : Dict      					=						0
  											while i < len(__A							):
  																try:
  																					__a    : List[str]      					=						word.index(__A		,       __A							)
  																except ValueError:
  																					new_word.extend(word[i:]							)
  																					break
  																else:
  																					new_word.extend(word[i:j]							)
  																					__a    : Optional[int]      					=						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    : Union[str, Any]      					=						tuple(__A							)
  											__a    : Optional[Any]      					=						new_word
  											if len(__A							) == 1:
  																break
  											else:
  																__a    : Optional[Any]      					=						get_pairs(__A							)
  						__a    : Optional[int]      					=						" ".join(__A							)
  						__a    : Optional[Any]      					=						word
  						return word
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						[]
  						for token in re.findall(self.pat		,       __A							):
  											__a    : Optional[int]      					=						"".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    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return self.encoder.get(__A		,       self.encoder.get(self.unk_token							)							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return self.decoder.get(__A							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[str]      					=						"".join(__A							)
  						__a    : List[Any]      					=						bytearray([self.byte_decoder[c] for c in text]							).decode('utf-8'		,       errors=self.errors							)
  						return text
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a = None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if not os.path.isdir(__A							):
  											logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
  											return
  						__a    : Optional[Any]      					=						os.path.join(
  						    __A		,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)
  						__a    : Any      					=						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    : Tuple      					=						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    : Optional[Any]      					=						token_index
  																writer.write(' '.join(__A							) + '\n'							)
  																index += 1
  
  						return vocab_file, merge_file
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a = None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						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    : Any      					=						[self.sep_token_id]
  						return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a = None		,       __a = False							):
  
  						'''simple docstring'''
  
  
  
  
  
  						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    __UpperCAmelCase							(     self		,       __a		,       __a = None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Tuple      					=						[self.sep_token_id]
  						__a    : Union[str, 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    __UpperCAmelCase							(     self		,       __a		,       __a=False		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[Any]      					=						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    : Optional[Any]      					=						" " + text
  						return (text, kwargs)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	A_			    =					(KDPMaAncestralDiscreteScheduler,)
  	A_			    =					10
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						{
  						    'num_train_timesteps': 1100,
  						    'beta_start': 0.0001,
  						    'beta_end': 0.02,
  						    'beta_schedule': 'linear',
  						}
  
  						config.update(**__a							)
  						return config
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for timesteps in [10, 50, 100, 1000]:
  											self.check_over_configs(num_train_timesteps=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,       [0.0002, 0.002, 0.02]							):
  											self.check_over_configs(beta_start=__a		,       beta_end=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for schedule in ["linear", "scaled_linear"]:
  											self.check_over_configs(beta_schedule=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : Dict      					=						self.scheduler_classes[0]
  						__a    : Tuple      					=						self.get_scheduler_config()
  						__a    : Optional[int]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps							)
  
  						__a    : int      					=						torch.manual_seed(0							)
  
  						__a    : Dict      					=						self.dummy_model()
  						__a    : List[Any]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
  						__a    : int      					=						sample.to(__a							)
  
  						for i, t in enumerate(scheduler.timesteps							):
  											__a    : Any      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : int      					=						model(__a		,       __a							)
  
  											__a    : Dict      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : int      					=						output.prev_sample
  
  						__a    : Tuple      					=						torch.sum(torch.abs(__a							)							)
  						__a    : List[str]      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 13849.3877							) < 1E-2
  						assert abs(result_mean.item() - 18.0331							) < 5E-3
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for prediction_type in ["epsilon", "v_prediction"]:
  											self.check_over_configs(prediction_type=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : Dict      					=						self.scheduler_classes[0]
  						__a    : Dict      					=						self.get_scheduler_config(prediction_type='v_prediction'							)
  						__a    : List[str]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps							)
  
  						__a    : Dict      					=						self.dummy_model()
  						__a    : List[str]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
  						__a    : List[str]      					=						sample.to(__a							)
  
  						__a    : Tuple      					=						torch.manual_seed(0							)
  
  						for i, t in enumerate(scheduler.timesteps							):
  											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : Optional[int]      					=						model(__a		,       __a							)
  
  											__a    : Union[str, Any]      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : Union[str, Any]      					=						output.prev_sample
  
  						__a    : Any      					=						torch.sum(torch.abs(__a							)							)
  						__a    : Optional[int]      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 328.9970							) < 1E-2
  						assert abs(result_mean.item() - 0.4284							) < 1E-3
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : List[str]      					=						self.scheduler_classes[0]
  						__a    : Union[str, Any]      					=						self.get_scheduler_config()
  						__a    : List[Any]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps		,       device=__a							)
  						__a    : Union[str, Any]      					=						torch.manual_seed(0							)
  
  						__a    : List[str]      					=						self.dummy_model()
  						__a    : Any      					=						self.dummy_sample_deter.to(__a							) * scheduler.init_noise_sigma
  
  						for t in scheduler.timesteps:
  											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : str      					=						model(__a		,       __a							)
  
  											__a    : str      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : Any      					=						output.prev_sample
  
  						__a    : Union[str, Any]      					=						torch.sum(torch.abs(__a							)							)
  						__a    : str      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 13849.3818							) < 1E-1
  						assert abs(result_mean.item() - 18.0331							) < 1E-3
  
  
  
  