






"""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


_lowerCAmelCase     :     List[str]            =	logging.get_logger(__name__)

_lowerCAmelCase     :     int            =	{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

_lowerCAmelCase     :     Dict            =	{
    '''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     :     List[str]            =	{
    '''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__    (   )->						Any:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				(
         list(range(ord("!"							)       , ord("~"							) + 1							)							) + list(range(ord("¡"							)       , ord("¬"							) + 1							)							) + list(range(ord("®"							)       , ord("ÿ"							) + 1							)							)
     )
     UpperCAmelCase__		:  Dict				=				bs[:]
     UpperCAmelCase__		:  int				=				0
     for b in range(2**8							):
          if b not in bs:
               bs.append(UpperCAmelCase__							)
               cs.append(2**8 + n							)
               n += 1
     UpperCAmelCase__		:  Optional[Any]				=				[chr(UpperCAmelCase__							) for n in cs]
     return dict(zip(UpperCAmelCase__       , UpperCAmelCase__							)							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      int							)->						List[str]:






     '''simple docstring'''



     UpperCAmelCase__		:  int				=				set()
     UpperCAmelCase__		:  List[str]				=				word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)							)
          UpperCAmelCase__		:  Tuple				=				char
     return pairs




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      =VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE_					      =PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE_					      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   SCREAMING_SNAKE_CASE_					      =["""input_ids""", """attention_mask"""]
   def __init__(						self	:     Optional[Any]				,       snake_case__	:     str				,       snake_case__	:     Union[str, Any]				,       snake_case__	:     Optional[int]="replace"				,       snake_case__	:     Any="<s>"				,       snake_case__	:     Optional[Any]="</s>"				,       snake_case__	:     Dict="</s>"				,       snake_case__	:     List[Any]="<s>"				,       snake_case__	:     int="<unk>"				,       snake_case__	:     int="<pad>"				,       snake_case__	:     List[Any]="<mask>"				,       snake_case__	:     Union[str, Any]=False				,       **snake_case__	:     int				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Union[str, Any]				=				AddedToken(_UpperCamelCase				,       lstrip=_UpperCamelCase				,       rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase				,       _UpperCamelCase				) else bos_token
        UpperCAmelCase__		:  Optional[int]				=				AddedToken(_UpperCamelCase				,       lstrip=_UpperCamelCase				,       rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase				,       _UpperCamelCase				) else eos_token
        UpperCAmelCase__		:  str				=				AddedToken(_UpperCamelCase				,       lstrip=_UpperCamelCase				,       rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase				,       _UpperCamelCase				) else sep_token
        UpperCAmelCase__		:  Any				=				AddedToken(_UpperCamelCase				,       lstrip=_UpperCamelCase				,       rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase				,       _UpperCamelCase				) else cls_token
        UpperCAmelCase__		:  Union[str, Any]				=				AddedToken(_UpperCamelCase				,       lstrip=_UpperCamelCase				,       rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase				,       _UpperCamelCase				) else unk_token
        UpperCAmelCase__		:  List[str]				=				AddedToken(_UpperCamelCase				,       lstrip=_UpperCamelCase				,       rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase				,       _UpperCamelCase				) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        UpperCAmelCase__		:  List[str]				=				AddedToken(_UpperCamelCase				,       lstrip=_UpperCamelCase				,       rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase				,       _UpperCamelCase				) else mask_token

        super().__init__(
            errors=_UpperCamelCase				,       bos_token=_UpperCamelCase				,       eos_token=_UpperCamelCase				,       unk_token=_UpperCamelCase				,       sep_token=_UpperCamelCase				,       cls_token=_UpperCamelCase				,       pad_token=_UpperCamelCase				,       mask_token=_UpperCamelCase				,       add_prefix_space=_UpperCamelCase				,       **_UpperCamelCase				,       )

        with open(_UpperCamelCase				,       encoding="utf-8"				) as vocab_handle:
             UpperCAmelCase__		:  Any				=				json.load(_UpperCamelCase				)
        UpperCAmelCase__		:  Optional[Any]				=				{v: k for k, v in self.encoder.items()}
        UpperCAmelCase__		:  int				=				errors  # how to handle errors in decoding
        UpperCAmelCase__		:  Optional[int]				=				bytes_to_unicode()
        UpperCAmelCase__		:  Optional[int]				=				{v: k for k, v in self.byte_encoder.items()}
        with open(_UpperCamelCase				,       encoding="utf-8"				) as merges_handle:
             UpperCAmelCase__		:  int				=				merges_handle.read().split("\n"				)[1:-1]
        UpperCAmelCase__		:  Any				=				[tuple(merge.split()				) for merge in bpe_merges]
        UpperCAmelCase__		:  Dict				=				dict(zip(_UpperCamelCase				,       range(len(_UpperCamelCase				)				)				)				)
        UpperCAmelCase__		:  Optional[Any]				=				{}
        UpperCAmelCase__		:  Any				=				add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        UpperCAmelCase__		:  str				=				re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"				)
   @property
   def 	__a				(						self	:     int				):


        '''simple docstring'''
        return len(self.encoder				)
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        return dict(self.encoder				,       **self.added_tokens_encoder				)
   def 	__a				(						self	:     Union[str, Any]				,       snake_case__	:     Tuple				):


        '''simple docstring'''

        if token in self.cache:
             return self.cache[token]
        UpperCAmelCase__		:  Tuple				=				tuple(_UpperCamelCase				)
        UpperCAmelCase__		:  Tuple				=				get_pairs(_UpperCamelCase				)

        if not pairs:
             return token

        while True:
             UpperCAmelCase__		:  Tuple				=				min(_UpperCamelCase				,       key=lambda snake_case__				: self.bpe_ranks.get(_UpperCamelCase				,       float("inf"				)				)				)
             if bigram not in self.bpe_ranks:
                  break
             UpperCAmelCase__				,							UpperCAmelCase__		:  Optional[Any]				=				bigram
             UpperCAmelCase__		:  List[str]				=				[]
             UpperCAmelCase__		:  Union[str, Any]				=				0
             while i < len(_UpperCamelCase				):
                  try:
                       UpperCAmelCase__		:  Dict				=				word.index(_UpperCamelCase				,       _UpperCamelCase				)
                  except ValueError:
                       new_word.extend(word[i:]				)
                       break
                  else:
                       new_word.extend(word[i:j]				)
                       UpperCAmelCase__		:  List[str]				=				j

                  if word[i] == first and i < len(_UpperCamelCase				) - 1 and word[i + 1] == second:
                       new_word.append(first + second				)
                       i += 2
                  else:
                       new_word.append(word[i]				)
                       i += 1
             UpperCAmelCase__		:  Union[str, Any]				=				tuple(_UpperCamelCase				)
             UpperCAmelCase__		:  Union[str, Any]				=				new_word
             if len(_UpperCamelCase				) == 1:
                  break
             else:
                  UpperCAmelCase__		:  Optional[Any]				=				get_pairs(_UpperCamelCase				)
        UpperCAmelCase__		:  Union[str, Any]				=				" ".join(_UpperCamelCase				)
        UpperCAmelCase__		:  Any				=				word
        return word
   def 	__a				(						self	:     Optional[Any]				,       snake_case__	:     Tuple				):


        '''simple docstring'''
        UpperCAmelCase__		:  Any				=				[]
        for token in re.findall(self.pat				,       _UpperCamelCase				):
             UpperCAmelCase__		:  List[str]				=				"".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(_UpperCamelCase				).split(" "				)				)
        return bpe_tokens
   def 	__a				(						self	:     List[str]				,       snake_case__	:     Dict				):


        '''simple docstring'''
        return self.encoder.get(_UpperCamelCase				,       self.encoder.get(self.unk_token				)				)
   def 	__a				(						self	:     str				,       snake_case__	:     str				):


        '''simple docstring'''
        return self.decoder.get(_UpperCamelCase				)
   def 	__a				(						self	:     Dict				,       snake_case__	:     Dict				):


        '''simple docstring'''
        UpperCAmelCase__		:  Tuple				=				"".join(_UpperCamelCase				)
        UpperCAmelCase__		:  Dict				=				bytearray([self.byte_decoder[c] for c in text]				).decode("utf-8"				,       errors=self.errors				)
        return text
   def 	__a				(						self	:     str				,       snake_case__	:     str				,       snake_case__	:     Optional[str] = None				):


        '''simple docstring'''

        if not os.path.isdir(_UpperCamelCase				):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'				)
             return
        UpperCAmelCase__		:  Union[str, Any]				=				os.path.join(
            _UpperCamelCase				,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)
        UpperCAmelCase__		:  int				=				os.path.join(
            _UpperCamelCase				,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]				)

        with open(_UpperCamelCase				,       "w"				,       encoding="utf-8"				) as f:
             f.write(json.dumps(self.encoder				,       indent=2				,       sort_keys=_UpperCamelCase				,       ensure_ascii=_UpperCamelCase				) + "\n"				)

        UpperCAmelCase__		:  Dict				=				0
        with open(_UpperCamelCase				,       "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 snake_case__				: 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!"				)
                       UpperCAmelCase__		:  int				=				token_index
                  writer.write(" ".join(_UpperCamelCase				) + "\n"				)
                  index += 1

        return vocab_file, merge_file
   def 	__a				(						self	:     str				,       snake_case__	:     List[int]				,       snake_case__	:     Optional[List[int]] = None				):


        '''simple docstring'''

        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        UpperCAmelCase__		:  List[Any]				=				[self.cls_token_id]
        UpperCAmelCase__		:  Tuple				=				[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep
   def 	__a				(						self	:     Optional[int]				,       snake_case__	:     List[int]				,       snake_case__	:     Optional[List[int]] = None				,       snake_case__	:     bool = False				):


        '''simple docstring'''

        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCamelCase				,       token_ids_a=_UpperCamelCase				,       already_has_special_tokens=_UpperCamelCase				)

        if token_ids_a is None:
             return [1] + ([0] * len(_UpperCamelCase				)) + [1]
        return [1] + ([0] * len(_UpperCamelCase				)) + [1, 1] + ([0] * len(_UpperCamelCase				)) + [1]
   def 	__a				(						self	:     Any				,       snake_case__	:     List[int]				,       snake_case__	:     Optional[List[int]] = None				):


        '''simple docstring'''
        UpperCAmelCase__		:  Tuple				=				[self.sep_token_id]
        UpperCAmelCase__		:  Optional[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 	__a				(						self	:     Union[str, Any]				,       snake_case__	:     Optional[Any]				,       snake_case__	:     List[Any]=False				,       **snake_case__	:     Any				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				kwargs.pop("add_prefix_space"				,       self.add_prefix_space				)
        if (is_split_into_words or add_prefix_space) and (len(_UpperCamelCase				) > 0 and not text[0].isspace()):
             UpperCAmelCase__		:  Any				=				" " + text
        return (text, kwargs)



import random


class   lowercase			:




							def __init__(  self      :    List[Any]		,							_UpperCamelCase      :    list[list[float]]		,							_UpperCamelCase      :    list[int]		,							_UpperCamelCase      :    float = 0.0_1		,							_UpperCamelCase      :    int = 1_000		,							_UpperCamelCase      :    float = -1		,							)    -> None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				sample
												if len(self.sample					) == 0:
																	raise ValueError("Sample data can not be empty"					)
												SCREAMING_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"					)
												SCREAMING_SNAKE_CASE				=				learning_rate
												SCREAMING_SNAKE_CASE				=				epoch_number
												SCREAMING_SNAKE_CASE				=				bias
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				len(sample[0]					)  # number of columns in dataset
												SCREAMING_SNAKE_CASE				=				[]




							def 		__snake_case(  self      :    Any					)    -> None:



												'''simple docstring'''

												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					)

												SCREAMING_SNAKE_CASE				=				0

												while True:
																	SCREAMING_SNAKE_CASE				=				False
																	for i in range(self.number_sample					):
																						SCREAMING_SNAKE_CASE				=				0
																						for j in range(self.col_sample + 1					):
																											SCREAMING_SNAKE_CASE				=				u + self.weight[j] * self.sample[i][j]
																						SCREAMING_SNAKE_CASE				=				self.sign(_UpperCamelCase					)
																						if y != self.target[i]:
																											for j in range(self.col_sample + 1					):
																																SCREAMING_SNAKE_CASE				=				(
																																    self.weight[j]
																																    + self.learning_rate
																																    * (self.target[i] - y)
																																    * self.sample[i][j]
																																)
																											SCREAMING_SNAKE_CASE				=				True
            # print('Epoch: \n',epoch_count)
																	SCREAMING_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:



												'''simple docstring'''

												if len(self.sample					) == 0:
																	raise ValueError("Sample data can not be empty"					)
												sample.insert(0		,							self.bias					)
												SCREAMING_SNAKE_CASE				=				0
												for i in range(self.col_sample + 1					):
																	SCREAMING_SNAKE_CASE				=				u + self.weight[i] * sample[i]

												SCREAMING_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      :    Dict		,							_UpperCamelCase      :    float					)    -> int:



												'''simple docstring'''

												return 1 if u >= 0 else -1



_lowerCamelCase    :      Union[str, Any]	 =						[
    [-0.6_508, 0.1_097, 4.0_009],
    [-1.4_492, 0.8_896, 4.4_005],
    [2.0_850, 0.6_876, 12.0_710],
    [0.2_626, 1.1_476, 7.7_985],
    [0.6_418, 1.0_234, 7.0_427],
    [0.2_569, 0.6_730, 8.3_265],
    [1.1_155, 0.6_043, 7.4_446],
    [0.0_914, 0.3_399, 7.0_677],
    [0.0_121, 0.5_256, 4.6_316],
    [-0.0_429, 0.4_660, 5.4_323],
    [0.4_340, 0.6_870, 8.2_287],
    [0.2_735, 1.0_287, 7.1_934],
    [0.4_839, 0.4_851, 7.4_850],
    [0.4_089, -0.1_267, 5.5_019],
    [1.4_391, 0.1_614, 8.5_843],
    [-0.9_115, -0.1_973, 2.1_962],
    [0.3_654, 1.0_475, 7.4_858],
    [0.2_144, 0.7_515, 7.1_699],
    [0.2_013, 1.0_014, 6.5_489],
    [0.6_483, 0.2_183, 5.8_991],
    [-0.1_147, 0.2_242, 7.2_435],
    [-0.7_970, 0.8_795, 3.8_762],
    [-1.0_625, 0.6_366, 2.4_707],
    [0.5_307, 0.1_285, 5.6_883],
    [-1.2_200, 0.7_777, 1.7_252],
    [0.3_957, 0.1_076, 5.6_623],
    [-0.1_013, 0.5_989, 7.1_812],
    [2.4_482, 0.9_455, 11.2_095],
    [2.0_149, 0.6_192, 10.9_263],
    [0.2_012, 0.2_611, 5.4_631],
]

_lowerCamelCase    :      Optional[Any]	 =						[
    -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    :      Tuple	 =						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    :      list	 =						[]
														for i in range(len(samples[0])):
																					_lowerCamelCase    :      Dict	 =						input('''value: ''').strip()
																					if user_input == "q":
																												break
																					_lowerCamelCase    :      Any	 =						float(user_input)
																					sample.insert(i, observation)
														network.sort(sample)




