



import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

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


_lowercase						  =		logging.get_logger(__name__)


_lowercase						  =		{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_lowercase						  =		{
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

_lowercase						  =		{"facebook/blenderbot_small-90M": 512}



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Any	)->  Optional[Any]:
    A__		 =					set()
    A__		 =					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)	)
        A__		 =					char

    A__		 =					set(UpperCamelCase_	)
    return pairs



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				VOCAB_FILES_NAMES
     UpperCamelCase__		    =				PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase__		    =				['''input_ids''', '''attention_mask''']




     def __init__(				self ,						a__ ,						a__ ,						a__="__start__" ,						a__="__end__" ,						a__="__unk__" ,						a__="__null__" ,						**a__ ,						):
         super().__init__(unk_token=a__ ,						bos_token=a__ ,						eos_token=a__ ,						pad_token=a__ ,						**a__)

         with open(a__ ,						encoding='''utf-8''') as vocab_handle:
             A__		 =					json.load(a__)
         A__		 =					{v: k for k, v in self.encoder.items()}
         with open(a__ ,						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(a__ ,						range(len(a__))))
         A__		 =					{}




     @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 ,						a__):
         if token in self.cache:
             return self.cache[token]
         A__		 =					re.sub('''([.,!?()])''' ,						R''' \1''' ,						a__)
         A__		 =					re.sub('''(\')''' ,						R''' \1 ''' ,						a__)
         A__		 =					re.sub(R'''\s{2,}''' ,						''' ''' ,						a__)
         if "\n" in token:
             A__		 =					token.replace('''\n''' ,						''' __newln__''')

         A__		 =					token.split(''' ''')
         A__		 =					[]
         for token in tokens:
             if not len(a__):
                 continue

             A__		 =					token.lower()
             A__		 =					tuple(a__)
             A__		 =					tuple(list(word[:-1]) + [word[-1] + '''</w>'''])
             A__		 =					get_pairs(a__)

             if not pairs:
                 words.append(a__)
                 continue

             while True:
                 A__		 =					min(a__ ,						key=lambda a__: self.bpe_ranks.get(a__ ,						float('''inf''')))
                 if bigram not in self.bpe_ranks:
                     break
                 A__ ,	A__		 =					bigram
                 A__		 =					[]
                 A__		 =					0

                 while i < len(a__):
                     try:
                         A__		 =					word.index(a__ ,						a__)
                         new_word.extend(word[i:j])
                         A__		 =					j
                     except ValueError:
                         new_word.extend(word[i:])
                         break

                     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__		 =					tuple(a__)
                 A__		 =					new_word
                 if len(a__) == 1:
                     break
                 else:
                     A__		 =					get_pairs(a__)
             A__		 =					'''@@ '''.join(a__)
             A__		 =					word[:-4]

             A__		 =					word
             words.append(a__)
         return " ".join(a__)




     def 		snake_case_		(				self ,						a__):
         A__		 =					[]

         A__		 =					re.findall(R'''\S+\n?''' ,						a__)

         for token in words:
             split_tokens.extend(list(self.bpe(a__).split(''' ''')))
         return split_tokens




     def 		snake_case_		(				self ,						a__):
         A__		 =					token.lower()
         return self.encoder.get(a__ ,						self.encoder.get(self.unk_token))




     def 		snake_case_		(				self ,						a__):
         return self.decoder.get(a__ ,						self.unk_token)




     def 		snake_case_		(				self ,						a__):
         A__		 =					''' '''.join(a__).replace('''@@ ''' ,						'''''').strip()
         return out_string




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         if not os.path.isdir(a__):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory")
             return
         A__		 =					os.path.join(
             a__ ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
         A__		 =					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__		 =					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__		 =					token_index
                 writer.write(''' '''.join(a__) + '''\n''')
                 index += 1

         return vocab_file, merge_file





import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier



def 		lowerCAmelCase__    (					UpperCamelCase_				:       dict	)->  tuple:
    return (data["data"], data["target"])



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.ndarray ,       UpperCamelCase_				:       np.ndarray	)->  XGBClassifier:
    A__		 =					XGBClassifier()
    classifier.fit(UpperCamelCase_ ,       UpperCamelCase_	)
    return classifier



def 		lowerCAmelCase__    (					)->  None:
    A__		 =					load_iris()
    A__ ,	A__		 =					data_handling(UpperCamelCase_	)
    A__ ,	A__ ,	A__ ,	A__		 =					train_test_split(
        UpperCamelCase_ ,       UpperCamelCase_ ,       test_size=0.25	)

    A__		 =					iris['''target_names''']

    # Create an XGBoost Classifier from the training data
    A__		 =					xgboost(UpperCamelCase_ ,       UpperCamelCase_	)

    # Display the confusion matrix of the classifier with both training and test sets
    ConfusionMatrixDisplay.from_estimator(
        UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       display_labels=UpperCamelCase_ ,       cmap='''Blues''' ,       normalize='''true''' ,       )
    plt.title('''Normalized Confusion Matrix - IRIS Dataset'''	)
    plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()
