


'''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)







'''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



