

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


lowerCAmelCase_ 			=			logging.get_logger(__name__)

lowerCAmelCase_ 			=			{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

lowerCAmelCase_ 			=			{
    """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"""
        ),
    },
}

lowerCAmelCase_ 			=			{
    """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 						__SCREAMING_SNAKE_CASE      ():
						snake_case_     =			(
						    list(range(ord('''!''' )						,	ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' )						,	ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' )						,	ord('''ÿ''' ) + 1 ) )
						)
						snake_case_     =			bs[:]
						snake_case_     =			0
						for b in range(2**8 ):
												if b not in bs:
																		bs.append(SCREAMING_SNAKE_CASE__ )
																		cs.append(2**8 + n )
																		n += 1
						snake_case_     =			[chr(SCREAMING_SNAKE_CASE__ ) for n in cs]
						return dict(zip(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) )


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			set()
						snake_case_     =			word[0]
						for char in word[1:]:
												pairs.add((prev_char, char) )
												snake_case_     =			char
						return pairs





class 			snake_case_     (	a__      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     List[Any]		    =       VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE  :     List[Any]		    =       PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE  :     Tuple		    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       ["input_ids", "attention_mask"]







			def __init__(  self				:       Any							,     _UpperCamelCase				:       Tuple							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       int="replace"							,     _UpperCamelCase				:       List[Any]="<s>"							,     _UpperCamelCase				:       List[str]="</s>"							,     _UpperCamelCase				:       Optional[int]="</s>"							,     _UpperCamelCase				:       Tuple="<s>"							,     _UpperCamelCase				:       Optional[Any]="<unk>"							,     _UpperCamelCase				:       List[Any]="<pad>"							,     _UpperCamelCase				:       Optional[int]="<mask>"							,     _UpperCamelCase				:       Any=False							,     **_UpperCamelCase				:       Optional[int]							,     )   ->List[Any]:
									snake_case_     =			AddedToken(__A							,     lstrip=__A							,     rstrip=__A							) if isinstance(__A							,     __A							) else bos_token
									snake_case_     =			AddedToken(__A							,     lstrip=__A							,     rstrip=__A							) if isinstance(__A							,     __A							) else eos_token
									snake_case_     =			AddedToken(__A							,     lstrip=__A							,     rstrip=__A							) if isinstance(__A							,     __A							) else sep_token
									snake_case_     =			AddedToken(__A							,     lstrip=__A							,     rstrip=__A							) if isinstance(__A							,     __A							) else cls_token
									snake_case_     =			AddedToken(__A							,     lstrip=__A							,     rstrip=__A							) if isinstance(__A							,     __A							) else unk_token
									snake_case_     =			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
									snake_case_     =			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:
															snake_case_     =			json.load(__A							)
									snake_case_     =			{v: k for k, v in self.encoder.items()}
									snake_case_     =			errors  # how to handle errors in decoding
									snake_case_     =			bytes_to_unicode()
									snake_case_     =			{v: k for k, v in self.byte_encoder.items()}
									with open(__A							,     encoding='''utf-8'''							) as merges_handle:
															snake_case_     =			merges_handle.read().split('''\n'''							)[1:-1]
									snake_case_     =			[tuple(merge.split()							) for merge in bpe_merges]
									snake_case_     =			dict(zip(__A							,     range(len(__A							)							)							)							)
									snake_case_     =			{}
									snake_case_     =			add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									snake_case_     =			re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''							)







			@property
			def        snake_case__(  self				:       Tuple							)   ->Optional[Any]:
									return len(self.encoder							)







			def        snake_case__(  self				:       int							)   ->Tuple:
									return dict(self.encoder							,     **self.added_tokens_encoder							)







			def        snake_case__(  self				:       Dict							,     _UpperCamelCase				:       List[Any]							)   ->List[Any]:







									if token in self.cache:
															return self.cache[token]
									snake_case_     =			tuple(__A							)
									snake_case_     =			get_pairs(__A							)

									if not pairs:
															return token

									while True:
															snake_case_     =			min(__A							,     key=lambda _UpperCamelCase							: self.bpe_ranks.get(__A							,     float('''inf'''							)							)							)
															if bigram not in self.bpe_ranks:
																					break
															snake_case_     =			bigram
															snake_case_     =			[]
															snake_case_     =			0
															while i < len(__A							):
																					try:
																											snake_case_     =			word.index(__A							,     __A							)
																					except ValueError:
																											new_word.extend(word[i:]							)
																											break
																					else:
																											new_word.extend(word[i:j]							)
																											snake_case_     =			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
															snake_case_     =			tuple(__A							)
															snake_case_     =			new_word
															if len(__A							) == 1:
																					break
															else:
																					snake_case_     =			get_pairs(__A							)
									snake_case_     =			" ".join(__A							)
									snake_case_     =			word
									return word







			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       Tuple							)   ->List[str]:
									snake_case_     =			[]
									for token in re.findall(self.pat							,     __A							):
															snake_case_     =			"".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        snake_case__(  self				:       List[Any]							,     _UpperCamelCase				:       List[str]							)   ->List[str]:
									return self.encoder.get(__A							,     self.encoder.get(self.unk_token							)							)







			def        snake_case__(  self				:       Dict							,     _UpperCamelCase				:       List[str]							)   ->Any:
									return self.decoder.get(__A							)







			def        snake_case__(  self				:       Optional[int]							,     _UpperCamelCase				:       Tuple							)   ->Union[str, Any]:
									snake_case_     =			"".join(__A							)
									snake_case_     =			bytearray([self.byte_decoder[c] for c in text]							).decode('''utf-8'''							,     errors=self.errors							)
									return text







			def        snake_case__(  self				:       Dict							,     _UpperCamelCase				:       int							,     _UpperCamelCase				:       Any = None							)   ->Tuple[str]:







									if not os.path.isdir(__A							):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
															return
									snake_case_     =			os.path.join(
									    __A							,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
									snake_case_     =			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'''							)

									snake_case_     =			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 _UpperCamelCase							: 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!'''							)
																											snake_case_     =			token_index
																					writer.write(''' '''.join(__A							) + '''\n'''							)
																					index += 1

									return vocab_file, merge_file







			def        snake_case__(  self				:       int							,     _UpperCamelCase				:       str							,     _UpperCamelCase				:       Dict = None							)   ->List[int]:







									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									snake_case_     =			[self.cls_token_id]
									snake_case_     =			[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep







			def        snake_case__(  self				:       Optional[int]							,     _UpperCamelCase				:       str							,     _UpperCamelCase				:       str = None							,     _UpperCamelCase				:       int = False							)   ->List[int]:







									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        snake_case__(  self				:       Any							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       int = None							)   ->List[int]:
									snake_case_     =			[self.sep_token_id]
									snake_case_     =			[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        snake_case__(  self				:       Tuple							,     _UpperCamelCase				:       Tuple							,     _UpperCamelCase				:       List[Any]=False							,     **_UpperCamelCase				:       int							)   ->Optional[Any]:
									snake_case_     =			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()):
															snake_case_     =			" " + text
									return (text, kwargs)


import random





class 			snake_case_     :







			'''simple docstring'''

			def __init__(  self				:       List[str]							,     _UpperCamelCase				:       list[list[float]]							,     _UpperCamelCase				:       list[int]							,     _UpperCamelCase				:       float = 0.01							,     _UpperCamelCase				:       int = 1_0_0_0							,     _UpperCamelCase				:       float = -1							,     )   ->None:
									snake_case_     =			sample
									if len(self.sample							) == 0:
															raise ValueError('''Sample data can not be empty'''							)
									snake_case_     =			target
									if len(self.target							) == 0:
															raise ValueError('''Target data can not be empty'''							)
									if len(self.sample							) != len(self.target							):
															raise ValueError('''Sample data and Target data do not have matching lengths'''							)
									snake_case_     =			learning_rate
									snake_case_     =			epoch_number
									snake_case_     =			bias
									snake_case_     =			len(_UpperCamelCase							)
									snake_case_     =			len(sample[0]							)  # number of columns in dataset
									snake_case_     =			[]







			def        snake_case__(  self				:       List[str]							)   ->None:
									for sample in self.sample:
															sample.insert(0							,     self.bias							)

									for _ in range(self.col_sample							):
															self.weight.append(random.random()							)

									self.weight.insert(0							,     self.bias							)

									snake_case_     =			0

									while True:
															snake_case_     =			False
															for i in range(self.number_sample							):
																					snake_case_     =			0
																					for j in range(self.col_sample + 1							):
																											snake_case_     =			u + self.weight[j] * self.sample[i][j]
																					snake_case_     =			self.sign(_UpperCamelCase							)
																					if y != self.target[i]:
																											for j in range(self.col_sample + 1							):
																																	snake_case_     =			(
																																	    self.weight[j]
																																	    + self.learning_rate
																																	    * (self.target[i] - y)
																																	    * self.sample[i][j]
																																	)
																											snake_case_     =			True
            # print('Epoch: \n',epoch_count)
															snake_case_     =			epoch_count + 1
															# if you want control the epoch or just by error
															if not has_misclassified:
																					print(('''\nEpoch:\n''', epoch_count)							)
																					print('''------------------------\n'''							)
																					# if epoch_count > self.epoch_number or not error:
																					break







			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       list[float]							)   ->None:
									if len(self.sample							) == 0:
															raise ValueError('''Sample data can not be empty'''							)
									sample.insert(0							,     self.bias							)
									snake_case_     =			0
									for i in range(self.col_sample + 1							):
															snake_case_     =			u + self.weight[i] * sample[i]

									snake_case_     =			self.sign(_UpperCamelCase							)

									if y == -1:
															print(('''Sample: ''', sample)							)
															print('''classification: P1'''							)
									else:
															print(('''Sample: ''', sample)							)
															print('''classification: P2'''							)







			def        snake_case__(  self				:       str							,     _UpperCamelCase				:       float							)   ->int:
									return 1 if u >= 0 else -1






lowerCAmelCase_ 			=			[
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

lowerCAmelCase_ 			=			[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowerCAmelCase_ 			=			Perceptron(
					    sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
					)
					network.training()
					print('''Finished training perceptron''')
					print('''Enter values to predict or q to exit''')
					while True:
										lowerCAmelCase_ 			=			[]
										for i in range(len(samples[0])):
															lowerCAmelCase_ 			=			input('''value: ''').strip()
															if user_input == "q":
																				break
															lowerCAmelCase_ 			=			float(user_input)
															sample.insert(i, observation)
										network.sort(sample)