






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_big_bird import BigBirdTokenizer
else:
	__lowerCamelCase      :    Dict   		=   None

__lowerCamelCase      :    Tuple   		=   logging.get_logger(__name__)
__lowerCamelCase      :    Dict   		=   {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase      :    Optional[Any]   		=   {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

__lowerCamelCase      :    int   		=   {
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}


__lowerCamelCase      :    List[str]   		=   """▁"""



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	VOCAB_FILES_NAMES
					a_			=	PRETRAINED_VOCAB_FILES_MAP
					a_			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_			=	BigBirdTokenizer
					a_			=	["input_ids", "attention_mask"]
					a_			=	[]



					def __init__(      self  :						int							,	__A  :						Dict=None							,	__A  :						int=None							,	__A  :						Dict="<unk>"							,	__A  :						Union[str, Any]="<s>"							,	__A  :						Tuple="</s>"							,	__A  :						List[str]="<pad>"							,	__A  :						List[Any]="[SEP]"							,	__A  :						List[Any]="[MASK]"							,	__A  :						List[str]="[CLS]"							,	**__A  :						Union[str, Any]							,	):
						snake_case__				:   Tuple     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else bos_token
						snake_case__				:   Union[str, Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else eos_token
						snake_case__				:   Union[str, Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else unk_token
						snake_case__				:   Optional[int]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else pad_token
						snake_case__				:   int     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else cls_token
						snake_case__				:   str     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else sep_token

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

						super().__init__(
						    __A							,	tokenizer_file=__A							,	bos_token=__A							,	eos_token=__A							,	unk_token=__A							,	sep_token=__A							,	pad_token=__A							,	cls_token=__A							,	mask_token=__A							,	**__A							,	)

						snake_case__				:   List[Any]     =						vocab_file
						snake_case__				:   Tuple     =						False if not self.vocab_file else True



					def  _lowercase  (      self  :						Any							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None ):
						snake_case__				:   str     =						[self.sep_token_id]
						snake_case__				:   List[str]     =						[self.cls_token_id]
						if token_ids_a is None:
							return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep



					def  _lowercase  (      self  :						int							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None							,	__A  :						bool = False ):

						if already_has_special_tokens:
							if token_ids_a is not None:
								raise ValueError(
								    "You should not supply a second sequence if the provided sequence of "
								    "ids is already formatted with special tokens for the model." )
							return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is None:
							return [1] + ([0] * len(__A )) + [1]
						return [1] + ([0] * len(__A )) + [1] + ([0] * len(__A )) + [1]



					def  _lowercase  (      self  :						Any							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None ):
						snake_case__				:   Union[str, Any]     =						[self.sep_token_id]
						snake_case__				:   List[str]     =						[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 ) * [0] + len(token_ids_a + sep ) * [1]






					def  _lowercase  (      self  :						List[Any]							,	__A  :						str							,	__A  :						Optional[str] = None ):
						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer." )

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

						if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ):
							copyfile(self.vocab_file							,	__A )

						return (out_vocab_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   SCREAMING_SNAKE_CASE       (				snake_case_      :							dict     ):
	return (data["data"], data["target"])







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							np.ndarray  ,						snake_case_      :							np.ndarray     ):
	snake_case__				:   Optional[int]     =						XGBClassifier()
	classifier.fit(snake_case_  ,						snake_case_     )
	return classifier







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Any     =						load_iris()
	snake_case__,		snake_case__				:   str     =						data_handling(snake_case_     )
	snake_case__,		snake_case__,		snake_case__,		snake_case__				:   int     =						train_test_split(
	    snake_case_  ,						snake_case_  ,						test_size=0.25     )

	snake_case__				:   Dict     =						iris["target_names"]

	# Create an XGBoost Classifier from the training data
	snake_case__				:   Dict     =						xgboost(snake_case_  ,						snake_case_     )

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


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()



