


'''simple docstring'''
import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


lowerCAmelCase  =				logging.get_logger(__name__)

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

lowerCAmelCase  =				{
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
}

lowerCAmelCase  =				{
    """openai-gpt""": 512,
}

def 				lowerCamelCase_   (   __UpperCamelCase				:					List[str]		)				->  Optional[int]:







     """simple docstring"""
     _A				=       text.strip()
     if not text:
          return []
     _A				=       text.split()
     return tokens






class 					lowerCAmelCase_     (    UpperCAmelCase  ):






       def __init__(   self ,    _UpperCamelCase=True ,    _UpperCamelCase=None ,    _UpperCamelCase=True ,    _UpperCamelCase=None ,    _UpperCamelCase=True ,    )->			int:
            if never_split is None:
                 _A				=       []
            _A				=       do_lower_case
            _A				=       set(__lowerCamelCase      )
            _A				=       tokenize_chinese_chars
            _A				=       strip_accents
            _A				=       do_split_on_punc






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase=None      )->			Dict:
            _A				=       self.never_split.union(set(__lowerCamelCase      )      ) if never_split else self.never_split
            _A				=       self._clean_text(__lowerCamelCase      )

            # This was added on November 1st, 2018 for the multilingual and Chinese
            # models. This is also applied to the English models now, but it doesn't
            # matter since the English models were not trained on any Chinese data
            # and generally don't have any Chinese data in them (there are Chinese
            # characters in the vocabulary because Wikipedia does have some Chinese
            # words in the English Wikipedia.).
            if self.tokenize_chinese_chars:
                 _A				=       self._tokenize_chinese_chars(__lowerCamelCase      )
            # prevents treating the same character with different unicode codepoints as different characters
            _A				=       unicodedata.normalize('NFC' ,    __lowerCamelCase      )
            _A				=       whitespace_tokenize(__lowerCamelCase      )
            _A				=       []
            for token in orig_tokens:
                 if token not in never_split:
                      if self.do_lower_case:
                           _A				=       token.lower()
                           if self.strip_accents is not False:
                                _A				=       self._run_strip_accents(__lowerCamelCase      )
                      elif self.strip_accents:
                           _A				=       self._run_strip_accents(__lowerCamelCase      )
                 split_tokens.extend(self._run_split_on_punc(__lowerCamelCase ,    __lowerCamelCase      )      )

            _A				=       whitespace_tokenize(' '.join(__lowerCamelCase      )      )
            return output_tokens






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			List[Any]:
            _A				=       unicodedata.normalize('NFD' ,    __lowerCamelCase      )
            _A				=       []
            for char in text:
                 _A				=       unicodedata.category(__lowerCamelCase      )
                 if cat == "Mn":
                      continue
                 output.append(__lowerCamelCase      )
            return "".join(__lowerCamelCase      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase=None      )->			List[str]:
            if not self.do_split_on_punc or (never_split is not None and text in never_split):
                 return [text]
            _A				=       list(__lowerCamelCase      )
            _A				=       0
            _A				=       True
            _A				=       []
            while i < len(__lowerCamelCase      ):
                 _A				=       chars[i]
                 if _is_punctuation(__lowerCamelCase      ):
                      output.append([char]      )
                      _A				=       True
                 else:
                      if start_new_word:
                           output.append([]      )
                      _A				=       False
                      output[-1].append(__lowerCamelCase      )
                 i += 1

            return ["".join(__lowerCamelCase      ) for x in output]






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			List[Any]:
            _A				=       []
            for char in text:
                 _A				=       ord(__lowerCamelCase      )
                 if self._is_chinese_char(__lowerCamelCase      ):
                      output.append(' '      )
                      output.append(__lowerCamelCase      )
                      output.append(' '      )
                 else:
                      output.append(__lowerCamelCase      )
            return "".join(__lowerCamelCase      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			List[str]:
            # This defines a "chinese character" as anything in the CJK Unicode block:
            #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
            #
            # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
            # despite its name. The modern Korean Hangul alphabet is a different block,
            # as is Japanese Hiragana and Katakana. Those alphabets are used to write
            # space-separated words, so they are not treated specially and handled
            # like the all of the other languages.
            if (
                (cp >= 0X4_e00 and cp <= 0X9_fff)
                or (cp >= 0X3_400 and cp <= 0X4_dbf)  #
                or (cp >= 0X20_000 and cp <= 0X2a_6df)  #
                or (cp >= 0X2a_700 and cp <= 0X2b_73f)  #
                or (cp >= 0X2b_740 and cp <= 0X2b_81f)  #
                or (cp >= 0X2b_820 and cp <= 0X2c_eaf)  #
                or (cp >= 0Xf_900 and cp <= 0Xf_aff)
                or (cp >= 0X2f_800 and cp <= 0X2f_a1f)  #
            ):  #
                 return True

            return False






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			int:
            _A				=       []
            for char in text:
                 _A				=       ord(__lowerCamelCase      )
                 if cp == 0 or cp == 0Xf_ffd or _is_control(__lowerCamelCase      ):
                      continue
                 if _is_whitespace(__lowerCamelCase      ):
                      output.append(' '      )
                 else:
                      output.append(__lowerCamelCase      )
            return "".join(__lowerCamelCase      )






def 				lowerCamelCase_   (   __UpperCamelCase				:					Any		)				->  Union[str, Any]:







     """simple docstring"""
     _A				=       set()
     _A				=       word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)		)
          _A				=       char
     return pairs

def 				lowerCamelCase_   (   __UpperCamelCase				:					int		)				->  Dict:







     """simple docstring"""
     _A				=       text.replace('—'       ,						'-'		)
     _A				=       text.replace('–'       ,						'-'		)
     _A				=       text.replace('―'       ,						'-'		)
     _A				=       text.replace('…'       ,						'...'		)
     _A				=       text.replace('´'       ,						'\''		)
     _A				=       re.sub(r'(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'       ,						r' \1 '       ,						UpperCamelCase__		)
     _A				=       re.sub(r'\s*\n\s*'       ,						' \n '       ,						UpperCamelCase__		)
     _A				=       re.sub(r'[^\S\n]+'       ,						' '       ,						UpperCamelCase__		)
     return text.strip()






class 					lowerCAmelCase_     (    UpperCAmelCase  ):
       __UpperCAmelCase				=VOCAB_FILES_NAMES
       __UpperCAmelCase				=PRETRAINED_VOCAB_FILES_MAP
       __UpperCAmelCase				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCAmelCase				=['input_ids', 'attention_mask']






       def __init__(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase="<unk>" ,    **_UpperCamelCase      )->			Dict:
            super().__init__(unk_token=__lowerCamelCase ,    **__lowerCamelCase      )

            try:
                 import ftfy
                 from spacy.lang.en import English

                 _A				=       English()
                 _A				=       _nlp.tokenizer
                 _A				=       ftfy.fix_text
            except ImportError:
                 logger.warning('ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'      )
                 _A				=       BasicTokenizer(do_lower_case=__lowerCamelCase      )
                 _A				=       None

            with open(__lowerCamelCase ,    encoding='utf-8'      ) as vocab_handle:
                 _A				=       json.load(__lowerCamelCase      )
            _A				=       {v: k for k, v in self.encoder.items()}
            with open(__lowerCamelCase ,    encoding='utf-8'      ) as merges_handle:
                 _A				=       merges_handle.read().split('\n'      )[1:-1]
            _A				=       [tuple(merge.split()      ) for merge in merges]
            _A				=       dict(zip(__lowerCamelCase ,    range(len(__lowerCamelCase      )      )      )      )
            _A				=       {}






       @property
       def      UpperCamelCase	(   self      )->			str:
            return True






       @property
       def      UpperCamelCase	(   self      )->			str:
            return len(self.encoder      )






       def      UpperCamelCase	(   self      )->			Union[str, Any]:
            return dict(self.encoder ,    **self.added_tokens_encoder      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			Tuple:
            _A				=       tuple(token[:-1]      ) + (token[-1] + "</w>",)
            if token in self.cache:
                 return self.cache[token]
            _A				=       get_pairs(__lowerCamelCase      )

            if not pairs:
                 return token + "</w>"

            while True:
                 _A				=       min(__lowerCamelCase ,    key=lambda _UpperCamelCase      : self.bpe_ranks.get(__lowerCamelCase ,    float('inf'      )      )      )
                 if bigram not in self.bpe_ranks:
                      break
                 _A				=       bigram
                 _A				=       []
                 _A				=       0
                 while i < len(__lowerCamelCase      ):
                      try:
                           _A				=       word.index(__lowerCamelCase ,    __lowerCamelCase      )
                      except ValueError:
                           new_word.extend(word[i:]      )
                           break
                      else:
                           new_word.extend(word[i:j]      )
                           _A				=       j

                      if word[i] == first and i < len(__lowerCamelCase      ) - 1 and word[i + 1] == second:
                           new_word.append(first + second      )
                           i += 2
                      else:
                           new_word.append(word[i]      )
                           i += 1
                 _A				=       tuple(__lowerCamelCase      )
                 _A				=       new_word
                 if len(__lowerCamelCase      ) == 1:
                      break
                 else:
                      _A				=       get_pairs(__lowerCamelCase      )
            _A				=       " ".join(__lowerCamelCase      )
            if word == "\n  </w>":
                 _A				=       "\n</w>"
            _A				=       word
            return word






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			Any:
            _A				=       []
            if self.fix_text is None:
                 # Using BERT's BasicTokenizer
                 _A				=       self.nlp.tokenize(__lowerCamelCase      )
                 for token in text:
                      split_tokens.extend(list(self.bpe(__lowerCamelCase      ).split(' '      )      )      )
            else:
                 # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
                 _A				=       self.nlp(text_standardize(self.fix_text(__lowerCamelCase      )      )      )
                 for token in text:
                      split_tokens.extend(list(self.bpe(token.text.lower()      ).split(' '      )      )      )
            return split_tokens






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			str:
            return self.encoder.get(__lowerCamelCase ,    self.encoder.get(self.unk_token      )      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			Tuple:
            return self.decoder.get(__lowerCamelCase ,    self.unk_token      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			Union[str, Any]:
            _A				=       "".join(__lowerCamelCase      ).replace('</w>' ,    ' '      ).strip()
            return out_string






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase = None      )->			Tuple[str]:
            if not os.path.isdir(__lowerCamelCase      ):
                 logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
                 return
            _A				=       os.path.join(
                __lowerCamelCase ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
            _A				=       os.path.join(
                __lowerCamelCase ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

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

            _A				=       0
            with open(__lowerCamelCase ,    '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!'      )
                           _A				=       token_index
                      writer.write(' '.join(__lowerCamelCase      ) + '\n'      )
                      index += 1

            return vocab_file, merge_file









'''simple docstring'''
import numpy as np
import requests

def 				lowerCamelCase_   (   )				->  int:







     """simple docstring"""
     _A				=       requests.get(
         'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
         'master/Week1/ADRvsRating.csv'		)
     _A				=       response.text.splitlines()
     _A				=       []
     for item in lines:
          _A				=       item.split(','		)
          data.append(__UpperCamelCase		)
     data.pop(0		)  # This is for removing the labels from the list
     _A				=       np.matrix(__UpperCamelCase		)
     return dataset

def 				lowerCamelCase_   (   __UpperCamelCase				:					Union[str, Any]       ,						__UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					int       ,						__UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Dict		)				->  int:







     """simple docstring"""
     _A				=       len_data

     _A				=       np.dot(__UpperCamelCase       ,						data_x.transpose()		)
     prod -= data_y.transpose()
     _A				=       np.dot(__UpperCamelCase       ,						__UpperCamelCase		)
     _A				=       theta - (alpha / n) * sum_grad
     return theta

def 				lowerCamelCase_   (   __UpperCamelCase				:					Optional[int]       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					Optional[int]		)				->  Dict:







     """simple docstring"""
     _A				=       np.dot(__UpperCamelCase       ,						data_x.transpose()		)
     prod -= data_y.transpose()
     _A				=       np.sum(np.square(__UpperCamelCase		)		)
     _A				=       sum_elem / (2 * len_data)
     return error

def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]		)				->  int:







     """simple docstring"""
     _A				=       1_0_0_0_0_0
     _A				=       0.0_00_15_50

     _A				=       data_x.shape[1]
     _A				=       data_x.shape[0] - 1

     _A				=       np.zeros((1, no_features)		)

     for i in range(0       ,						__UpperCamelCase		):
          _A				=       run_steep_gradient_descent(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
          _A				=       sum_of_square_error(__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase		)
          print(F'At Iteration {i + 1} - Error is {error:.5f}'		)

     return theta

def 				lowerCamelCase_   (   __UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					Tuple		)				->  Union[str, Any]:







     """simple docstring"""
     _A				=       sum(abs(y - predicted_y[i]		) for i, y in enumerate(__UpperCamelCase		)		)
     return total / len(__UpperCamelCase		)

def 				lowerCamelCase_   (   )				->  Optional[Any]:







     """simple docstring"""
     _A				=       collect_dataset()

     _A				=       data.shape[0]
     _A				=       np.c_[np.ones(__UpperCamelCase		), data[:, :-1]].astype(__UpperCamelCase		)
     _A				=       data[:, -1].astype(__UpperCamelCase		)

     _A				=       run_linear_regression(__UpperCamelCase       ,						__UpperCamelCase		)
     _A				=       theta.shape[1]
     print('Resultant Feature vector : '		)
     for i in range(0       ,						__UpperCamelCase		):
          print(F'{theta[0, i]:.5f}'		)


if __name__ == "__main__":
    main()





