






import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


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


__lowerCamelCase      :    Optional[Any]   		=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
__lowerCamelCase      :    Tuple   		=   {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase      :    Dict   		=   {
    """allenai/led-base-16384""": 1_6384,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[int]     =						(
	    list(range(ord("!"     )  ,						ord("~"     ) + 1     )     ) + list(range(ord("¡"     )  ,						ord("¬"     ) + 1     )     ) + list(range(ord("®"     )  ,						ord("ÿ"     ) + 1     )     )
	)
	snake_case__				:   Optional[int]     =						bs[:]
	snake_case__				:   Any     =						0
	for b in range(2**8     ):
		if b not in bs:
			bs.append(snake_case_     )
			cs.append(2**8 + n     )
			n += 1
	snake_case__				:   Dict     =						[chr(snake_case_     ) for n in cs]
	return dict(zip(snake_case_  ,						snake_case_     )     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]     ):
	snake_case__				:   Dict     =						set()
	snake_case__				:   Tuple     =						word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)     )
		snake_case__				:   List[Any]     =						char
	return pairs





class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






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



					def __init__(      self  :						List[str]							,	__A  :						Any							,	__A  :						List[str]							,	__A  :						Optional[Any]="replace"							,	__A  :						Optional[int]="<s>"							,	__A  :						Union[str, Any]="</s>"							,	__A  :						Tuple="</s>"							,	__A  :						List[Any]="<s>"							,	__A  :						Dict="<unk>"							,	__A  :						Any="<pad>"							,	__A  :						Optional[int]="<mask>"							,	__A  :						List[str]=False							,	**__A  :						Union[str, Any]							,	):
						snake_case__				:   List[Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else bos_token
						snake_case__				:   List[str]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else eos_token
						snake_case__				:   Any     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else sep_token
						snake_case__				:   List[Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else cls_token
						snake_case__				:   Tuple     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else unk_token
						snake_case__				:   List[Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else pad_token

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

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

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

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



					@property
					# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
					def  _lowercase  (      self  :						List[Any] ):
						return len(self.encoder )



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



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Optional[int] ):
						if token in self.cache:
							return self.cache[token]
						snake_case__				:   Union[str, Any]     =						tuple(__A )
						snake_case__				:   List[Any]     =						get_pairs(__A )

						if not pairs:
							return token

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

								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
							snake_case__				:   str     =						tuple(__A )
							snake_case__				:   int     =						new_word
							if len(__A ) == 1:
								break
							else:
								snake_case__				:   List[str]     =						get_pairs(__A )
						snake_case__				:   List[Any]     =						" ".join(__A )
						snake_case__				:   Optional[int]     =						word
						return word



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Optional[Any] ):
						snake_case__				:   List[str]     =						[]
						for token in re.findall(self.pat							,	__A ):
							snake_case__				:   Dict     =						"".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(__A ).split(" " ) )
						return bpe_tokens



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Optional[int] ):
						return self.encoder.get(__A							,	self.encoder.get(self.unk_token ) )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						Optional[Any] ):
						return self.decoder.get(__A )



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Dict ):
						snake_case__				:   Optional[Any]     =						"".join(__A )
						snake_case__				:   int     =						bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8"							,	errors=self.errors )
						return text



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						str							,	__A  :						Optional[str] = None ):
						if not os.path.isdir(__A ):
							logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
							return
						snake_case__				:   List[Any]     =						os.path.join(
						    __A							,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
						snake_case__				:   str     =						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" )

						snake_case__				:   str     =						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!" )
									snake_case__				:   int     =						token_index
								writer.write(" ".join(__A ) + "\n" )
								index += 1

						return vocab_file, merge_file



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



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None							,	__A  :						bool = False ):
						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__A							,	token_ids_a=__A							,	already_has_special_tokens=__A )

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



					def  _lowercase  (      self  :						List[Any]							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None ):
						snake_case__				:   Any     =						[self.sep_token_id]
						snake_case__				:   List[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  _lowercase  (      self  :						Optional[Any]							,	__A  :						int							,	__A  :						int=False							,	**__A  :						Dict ):
						snake_case__				:   Optional[int]     =						kwargs.pop("add_prefix_space"							,	self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(__A ) > 0 and not text[0].isspace()):
							snake_case__				:   Optional[int]     =						" " + text
						return (text, kwargs)






					def  _lowercase  (      self  :						Any							,	__A  :						Union[Dict[str, EncodedInput], BatchEncoding]							,	__A  :						Optional[int] = None							,	__A  :						PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,	__A  :						Optional[int] = None							,	__A  :						Optional[bool] = None							,	):
						snake_case__				:   Optional[Any]     =						super()._pad(
						    encoded_inputs=__A							,	max_length=__A							,	padding_strategy=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	)

						# Load from model defaults
						if return_attention_mask is None:
							snake_case__				:   Union[str, Any]     =						"attention_mask" in self.model_input_names

						if return_attention_mask and "global_attention_mask" in encoded_inputs:
							snake_case__				:   Union[str, Any]     =						encoded_inputs[self.model_input_names[0]]
							# `global_attention_mask` need to have the same length as other (sequential) inputs.
							snake_case__				:   Tuple     =						len(encoded_inputs["global_attention_mask"] ) != len(__A )

							if needs_to_be_padded:
								snake_case__				:   int     =						len(__A ) - len(encoded_inputs["global_attention_mask"] )

								if self.padding_side == "right":
									# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
									snake_case__				:   int     =						(
									    encoded_inputs["global_attention_mask"] + [-1] * difference
									)
								elif self.padding_side == "left":
									snake_case__				:   Tuple     =						[-1] * difference + encoded_inputs[
									    "global_attention_mask"
									]
								else:
									raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

						return encoded_inputs











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()



