'''simple docstring'''




import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

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

_a   :	Optional[Any] 							=   {
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

_a   :	Tuple 							=   {
    "ctrl": 256,
}

_a   :	Dict 							=   {
    "Pregnancy": 168629,
    "Christianity": 7675,
    "Explain": 106423,
    "Fitness": 63440,
    "Saving": 63163,
    "Ask": 27171,
    "Ass": 95985,
    "Joke": 163509,
    "Questions": 45622,
    "Thoughts": 49605,
    "Retail": 52342,
    "Feminism": 164338,
    "Writing": 11992,
    "Atheism": 192263,
    "Netflix": 48616,
    "Computing": 39639,
    "Opinion": 43213,
    "Alone": 44967,
    "Funny": 58917,
    "Gaming": 40358,
    "Human": 4088,
    "India": 1331,
    "Joker": 77138,
    "Diet": 36206,
    "Legal": 11859,
    "Norman": 4939,
    "Tip": 72689,
    "Weight": 52343,
    "Movies": 46273,
    "Running": 23425,
    "Science": 2090,
    "Horror": 37793,
    "Confession": 60572,
    "Finance": 12250,
    "Politics": 16360,
    "Scary": 191985,
    "Support": 12654,
    "Technologies": 32516,
    "Teenage": 66160,
    "Event": 32769,
    "Learned": 67460,
    "Notion": 182770,
    "Wikipedia": 37583,
    "Books": 6665,
    "Extract": 76050,
    "Confessions": 102701,
    "Conspiracy": 75932,
    "Links": 63674,
    "Narcissus": 150425,
    "Relationship": 54766,
    "Relationships": 134796,
    "Reviews": 41671,
    "News": 4256,
    "Translation": 26820,
    "multilingual": 128406,
}

def _lowercase	(      lowerCamelCase__						)					->       Any:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		set()
       __UpperCAmelCase  :				int       				=		word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)						)
              __UpperCAmelCase  :				Optional[int]       				=		char

       __UpperCAmelCase  :				List[Any]       				=		set(lowerCamelCase__						)
       return pairs






class 		__A       (__magic_name__   ):
       snake_case       :List[Any]				    =					VOCAB_FILES_NAMES
       snake_case       :Optional[int]				    =					PRETRAINED_VOCAB_FILES_MAP
       snake_case       :Optional[Any]				    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case       :Tuple				    =					CONTROL_CODES






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_="<unk>"				,     **UpperCamelCase_      ):
              super().__init__(unk_token=UpperCamelCase_				,     **UpperCamelCase_      )

              with open(UpperCamelCase_				,     encoding="utf-8"      ) as vocab_handle:
                     __UpperCAmelCase  :				Union[str, Any]       				=		json.load(UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		{v: k for k, v in self.encoder.items()}
              with open(UpperCamelCase_				,     encoding="utf-8"      ) as merges_handle:
                     __UpperCAmelCase  :				List[Any]       				=		merges_handle.read().split("\n"      )[1:-1]
              __UpperCAmelCase  :				Optional[int]       				=		[tuple(merge.split()      ) for merge in merges]
              __UpperCAmelCase  :				str       				=		dict(zip(UpperCamelCase_				,     range(len(UpperCamelCase_      )      )      )      )
              __UpperCAmelCase  :				List[Any]       				=		{}






       @property
       def 			_snake_case						(      self      ):
              return len(self.encoder      )






       def 			_snake_case						(      self      ):
              return dict(self.encoder				,     **self.added_tokens_encoder      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if token in self.cache:
                     return self.cache[token]
              __UpperCAmelCase  :				List[Any]       				=		tuple(UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		tuple(list(word[:-1]      ) + [word[-1] + "</w>"]      )
              __UpperCAmelCase  :				Tuple       				=		get_pairs(UpperCamelCase_      )

              if not pairs:
                     return token

              while True:
                     __UpperCAmelCase  :				int       				=		min(UpperCamelCase_				,     key=lambda UpperCamelCase_      : self.bpe_ranks.get(UpperCamelCase_				,     float("inf"      )      )      )
                     if bigram not in self.bpe_ranks:
                            break
                     __UpperCAmelCase  :				Optional[int]       				=		bigram
                     __UpperCAmelCase  :				int       				=		[]
                     __UpperCAmelCase  :				Optional[Any]       				=		0
                     while i < len(UpperCamelCase_      ):
                            try:
                                   __UpperCAmelCase  :				Any       				=		word.index(UpperCamelCase_				,     UpperCamelCase_      )
                            except ValueError:
                                   new_word.extend(word[i:]      )
                                   break
                            else:
                                   new_word.extend(word[i:j]      )
                                   __UpperCAmelCase  :				Optional[Any]       				=		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  :				Dict       				=		tuple(UpperCamelCase_      )
                     __UpperCAmelCase  :				int       				=		new_word
                     if len(UpperCamelCase_      ) == 1:
                            break
                     else:
                            __UpperCAmelCase  :				str       				=		get_pairs(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		"@@ ".join(UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		word[:-4]
              __UpperCAmelCase  :				Optional[Any]       				=		word
              return word






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		[]

              __UpperCAmelCase  :				str       				=		re.findall(r"\S+\n?"				,     UpperCamelCase_      )

              for token in words:
                     split_tokens.extend(list(self.bpe(UpperCamelCase_      ).split(" "      )      )      )
              return split_tokens






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return self.encoder.get(UpperCamelCase_				,     self.encoder.get(self.unk_token      )      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return self.decoder.get(UpperCamelCase_				,     self.unk_token      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[Any]       				=		" ".join(UpperCamelCase_      ).replace("@@ "				,     ""      ).strip()
              return out_string







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              if not os.path.isdir(UpperCamelCase_      ):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
                     return
              __UpperCAmelCase  :				int       				=		os.path.join(
                  UpperCamelCase_				,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
              __UpperCAmelCase  :				List[Any]       				=		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  :				List[str]       				=		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 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!"      )
                                   __UpperCAmelCase  :				List[str]       				=		token_index
                            writer.write(" ".join(UpperCamelCase_      ) + "\n"      )
                            index += 1

              return vocab_file, merge_file

       # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
       #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
       #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
       #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
       #     return ''.join(tokens_generated_so_far)







'''simple docstring'''




from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_a   :	str 							=   datasets.load_iris()

_a   :	List[Any] 							=   np.array(data["data"])
_a   :	Optional[Any] 							=   np.array(data["target"])
_a   :	Dict 							=   data["target_names"]

_a							,					_a							,					_a							,					_a   :	Any 							=   train_test_split(X, y)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


       return np.linalg.norm(np.array(lowerCamelCase__						) - np.array(lowerCamelCase__						)						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=5						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		zip(lowerCamelCase__							,    lowerCamelCase__						)
       # List of distances of all points from the point to be classified
       __UpperCAmelCase  :				int       				=		[]
       for data_point in data:
              __UpperCAmelCase  :				Optional[Any]       				=		euclidean_distance(data_point[0]							,    lowerCamelCase__						)
              distances.append((distance, data_point[1])						)
       # Choosing 'k' points with the least distances.
       __UpperCAmelCase  :				Union[str, Any]       				=		[i[1] for i in sorted(lowerCamelCase__						)[:k]]
       # Most commonly occurring class among them
       # is the class into which the point is classified
       __UpperCAmelCase  :				Dict       				=		Counter(lowerCamelCase__						).most_common(1						)[0][0]
       return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))






