





import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
	lowerCAmelCase_    =    '''pt'''
elif is_tf_available():
	lowerCAmelCase_    =    '''tf'''
else:
	lowerCAmelCase_    =    '''jax'''



class  __lowerCAmelCase	(      _a,					unittest.TestCase					):
			lowerCamelCase_					:					Dict	      =				ByTaTokenizer
			lowerCamelCase_					:					Union[str, Any]	      =				False






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							super().setUp()
							snake_case_						:   List[Any]           =  ByTaTokenizer()
							tokenizer.save_pretrained(self.tmpdirname   )






			@cached_property
			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							return ByTaTokenizer.from_pretrained('''google/byt5-small'''   )






			def       lowerCamelCase  (self ,							**__magic_name__   )					->       ByTaTokenizer:


							'''simple docstring'''



							return self.tokenizer_class.from_pretrained(self.tmpdirname ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=False ,							__magic_name__=20 ,							__magic_name__=5   )					->       Tuple[str, list]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  []
							for i in range(len(__magic_name__   )   ):
											try:
															snake_case_						:   str           =  tokenizer.decode([i] ,							clean_up_tokenization_spaces=__magic_name__   )
											except UnicodeDecodeError:
															pass
											toks.append((i, tok)   )

							snake_case_						:   Dict           =  list(filter(lambda __magic_name__   : re.match(R'''^[ a-zA-Z]+$''' ,							t[1]   ) ,							__magic_name__   )   )
							snake_case_						:   List[str]           =  list(filter(lambda __magic_name__   : [t[0]] == tokenizer.encode(t[1] ,							add_special_tokens=__magic_name__   ) ,							__magic_name__   )   )
							if max_length is not None and len(__magic_name__   ) > max_length:
											snake_case_						:   Tuple           =  toks[:max_length]
							if min_length is not None and len(__magic_name__   ) < min_length and len(__magic_name__   ) > 0:
											while len(__magic_name__   ) < min_length:
															snake_case_						:   Any           =  toks + toks
        # toks_str = [t[1] for t in toks]
							snake_case_						:   Union[str, Any]           =  [t[0] for t in toks]

							# Ensure consistency
							snake_case_						:   Union[str, Any]           =  tokenizer.decode(__magic_name__ ,							clean_up_tokenization_spaces=__magic_name__   )
							if " " not in output_txt and len(__magic_name__   ) > 1:
											snake_case_						:   int           =  (
											    tokenizer.decode([toks_ids[0]] ,							clean_up_tokenization_spaces=__magic_name__   )
											    + ''' '''
											    + tokenizer.decode(toks_ids[1:] ,							clean_up_tokenization_spaces=__magic_name__   )
											)
							if with_prefix_space:
											snake_case_						:   str           =  ''' ''' + output_txt
							snake_case_						:   Tuple           =  tokenizer.encode(__magic_name__ ,							add_special_tokens=__magic_name__   )
							return output_txt, output_ids






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self.ta_base_tokenizer
							snake_case_						:   List[str]           =  tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>''']   )
							snake_case_						:   List[Any]           =  tokenizer(['''hi''', '''I went to the gym''', '''''']   )
							self.assertListEqual(batch_with_eos_added['''input_ids'''] ,							batch_without_eos_added['''input_ids''']   )






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   int           =  self.ta_base_tokenizer
							snake_case_						:   Any           =  '''Unicode €.'''
							snake_case_						:   Tuple           =  tokenizer(__magic_name__   )
							snake_case_						:   str           =  [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
							self.assertEqual(encoded['''input_ids'''] ,							__magic_name__   )

							# decoding
							snake_case_						:   Union[str, Any]           =  tokenizer.decode(__magic_name__   )
							self.assertEqual(__magic_name__ ,							'''Unicode €.</s>'''   )

							snake_case_						:   Tuple           =  tokenizer('''e è é ê ë'''   )
							snake_case_						:   str           =  [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
							self.assertEqual(encoded['''input_ids'''] ,							__magic_name__   )
							# decoding
							snake_case_						:   List[str]           =  tokenizer.decode(__magic_name__   )
							self.assertEqual(__magic_name__ ,							'''e è é ê ë</s>'''   )

							# encode/decode, but with `encode` instead of `__call__`
							self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''   )   ) ,							'''e è é ê ë</s>'''   )






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  self.ta_base_tokenizer
							snake_case_						:   List[str]           =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							# fmt: off
							snake_case_						:   List[Any]           =  [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
							# fmt: on
							snake_case_						:   str           =  tokenizer(__magic_name__ ,							padding=__magic_name__ ,							return_tensors=__magic_name__   )
							self.assertIsInstance(__magic_name__ ,							__magic_name__   )

							if FRAMEWORK != "jax":
											snake_case_						:   int           =  list(batch.input_ids.numpy()[0]   )
							else:
											snake_case_						:   List[Any]           =  list(batch.input_ids.tolist()[0]   )

							self.assertListEqual(__magic_name__ ,							__magic_name__   )

							self.assertEqual((2, 37) ,							batch.input_ids.shape   )
							self.assertEqual((2, 37) ,							batch.attention_mask.shape   )






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   str           =  self.ta_base_tokenizer
							snake_case_						:   List[Any]           =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							snake_case_						:   Optional[Any]           =  tokenizer(__magic_name__ ,							padding=__magic_name__ ,							return_tensors=__magic_name__   )
							# check if input_ids are returned and no decoder_input_ids
							self.assertIn('''input_ids''' ,							__magic_name__   )
							self.assertIn('''attention_mask''' ,							__magic_name__   )
							self.assertNotIn('''decoder_input_ids''' ,							__magic_name__   )
							self.assertNotIn('''decoder_attention_mask''' ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self.ta_base_tokenizer
							snake_case_						:   Optional[Any]           =  [
							    '''Summary of the text.''',
							    '''Another summary.''',
							]
							snake_case_						:   Dict           =  tokenizer(
							    text_target=__magic_name__ ,							max_length=32 ,							padding='''max_length''' ,							truncation=__magic_name__ ,							return_tensors=__magic_name__   )
							self.assertEqual(32 ,							targets['''input_ids'''].shape[1]   )






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self.ta_base_tokenizer
							snake_case_						:   Optional[int]           =  ['''A long paragraph for summarization. </s>''']
							snake_case_						:   Optional[Any]           =  ['''Summary of the text. </s>''']
							# fmt: off
							snake_case_						:   int           =  [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
							snake_case_						:   str           =  [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
							# fmt: on

							snake_case_						:   Tuple           =  tokenizer(__magic_name__ ,							text_target=__magic_name__   )

							self.assertEqual(__magic_name__ ,							batch['''input_ids'''][0]   )
							self.assertEqual(__magic_name__ ,							batch['''labels'''][0]   )






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self.get_tokenizers()
							for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
															self.assertNotEqual(tokenizer.model_max_length ,							42   )

        # Now let's start the test
							snake_case_						:   str           =  self.get_tokenizers()
							for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
															# Isolate this from the other tests because we save additional tokens/etc
															snake_case_						:   Optional[int]           =  tempfile.mkdtemp()

															snake_case_						:   Tuple           =  ''' He is very happy, UNwant\u00E9d,running'''
															snake_case_						:   Union[str, Any]           =  tokenizer.encode(__magic_name__ ,							add_special_tokens=__magic_name__   )
															tokenizer.save_pretrained(__magic_name__   )

															snake_case_						:   List[str]           =  tokenizer.__class__.from_pretrained(__magic_name__   )
															snake_case_						:   int           =  after_tokenizer.encode(__magic_name__ ,							add_special_tokens=__magic_name__   )
															self.assertListEqual(__magic_name__ ,							__magic_name__   )

															shutil.rmtree(__magic_name__   )

							snake_case_						:   int           =  self.get_tokenizers(model_max_length=42   )
							for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
															# Isolate this from the other tests because we save additional tokens/etc
															snake_case_						:   Optional[Any]           =  tempfile.mkdtemp()

															snake_case_						:   Union[str, Any]           =  ''' He is very happy, UNwant\u00E9d,running'''
															tokenizer.add_tokens(['''bim''', '''bambam''']   )
															snake_case_						:   Union[str, Any]           =  tokenizer.additional_special_tokens
															additional_special_tokens.append('''new_additional_special_token'''   )
															tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}   )
															snake_case_						:   int           =  tokenizer.encode(__magic_name__ ,							add_special_tokens=__magic_name__   )
															tokenizer.save_pretrained(__magic_name__   )

															snake_case_						:   int           =  tokenizer.__class__.from_pretrained(__magic_name__   )
															snake_case_						:   Tuple           =  after_tokenizer.encode(__magic_name__ ,							add_special_tokens=__magic_name__   )
															self.assertListEqual(__magic_name__ ,							__magic_name__   )
															self.assertIn('''new_additional_special_token''' ,							after_tokenizer.additional_special_tokens   )
															self.assertEqual(after_tokenizer.model_max_length ,							42   )

															snake_case_						:   Tuple           =  tokenizer.__class__.from_pretrained(__magic_name__ ,							model_max_length=43   )
															self.assertEqual(tokenizer.model_max_length ,							43   )

															shutil.rmtree(__magic_name__   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   int           =  []
							if self.test_slow_tokenizer:
											tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())   )

							if self.test_rust_tokenizer:
											tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())   )

							for tokenizer_class, tokenizer_utils in tokenizer_list:
											with tempfile.TemporaryDirectory() as tmp_dir:
															tokenizer_utils.save_pretrained(__magic_name__   )

															with open(os.path.join(__magic_name__ ,							'''special_tokens_map.json'''   ) ,							encoding='''utf-8'''   ) as json_file:
																			snake_case_						:   Tuple           =  json.load(__magic_name__   )

															with open(os.path.join(__magic_name__ ,							'''tokenizer_config.json'''   ) ,							encoding='''utf-8'''   ) as json_file:
																			snake_case_						:   Optional[Any]           =  json.load(__magic_name__   )

															snake_case_						:   Optional[int]           =  [F'''<extra_id_{i}>''' for i in range(125   )]

															snake_case_						:   Optional[int]           =  added_tokens_extra_ids + [
															    '''an_additional_special_token'''
															]
															snake_case_						:   str           =  added_tokens_extra_ids + [
															    '''an_additional_special_token'''
															]

															with open(os.path.join(__magic_name__ ,							'''special_tokens_map.json'''   ) ,							'''w''' ,							encoding='''utf-8'''   ) as outfile:
																			json.dump(__magic_name__ ,							__magic_name__   )
															with open(os.path.join(__magic_name__ ,							'''tokenizer_config.json'''   ) ,							'''w''' ,							encoding='''utf-8'''   ) as outfile:
																			json.dump(__magic_name__ ,							__magic_name__   )

															# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
															# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
															# "special_tokens_map.json" files
															snake_case_						:   Optional[int]           =  tokenizer_class.from_pretrained(
															    __magic_name__ ,							)
															self.assertIn(
															    '''an_additional_special_token''' ,							tokenizer_without_change_in_init.additional_special_tokens   )
															# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
															self.assertEqual(
															    ['''an_additional_special_token'''] ,							tokenizer_without_change_in_init.convert_ids_to_tokens(
															        tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']   )   ) ,							)

															# Now we test that we can change the value of additional_special_tokens in the from_pretrained
															snake_case_						:   Optional[Any]           =  added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' ,							lstrip=__magic_name__   )]
															snake_case_						:   int           =  tokenizer_class.from_pretrained(
															    __magic_name__ ,							additional_special_tokens=__magic_name__ ,							)

															self.assertIn('''a_new_additional_special_token''' ,							tokenizer.additional_special_tokens   )
															self.assertEqual(
															    ['''a_new_additional_special_token'''] ,							tokenizer.convert_ids_to_tokens(
															        tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']   )   ) ,							)






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  []
							if self.test_slow_tokenizer:
											tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())   )

							if self.test_rust_tokenizer:
											tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())   )

							for tokenizer_class, tokenizer_utils in tokenizer_list:
											with tempfile.TemporaryDirectory() as tmp_dir:
															tokenizer_utils.save_pretrained(__magic_name__   )

															snake_case_						:   Any           =  tokenizer_class.from_pretrained(__magic_name__   )

															self.assertTrue(tokenizer.decode([255]   ) == ''''''   )






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							pass






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							pass






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							pass






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							pass






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.get_tokenizers(fast=__magic_name__ ,							do_lower_case=__magic_name__   )
							for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
															snake_case_						:   Any           =  ['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
															snake_case_						:   Any           =  tokenizer.convert_tokens_to_string(__magic_name__   )

															self.assertIsInstance(__magic_name__ ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.get_tokenizers()
							for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
															snake_case_						:   str           =  [
															    '''bos_token''',
															    '''eos_token''',
															    '''unk_token''',
															    '''sep_token''',
															    '''pad_token''',
															    '''cls_token''',
															    '''mask_token''',
															]

															snake_case_						:   List[Any]           =  0
															snake_case_						:   int           =  tokenizer.convert_ids_to_tokens(
															    __magic_name__ ,							skip_special_tokens=__magic_name__   )

															for attr in attributes_list:
																			setattr(__magic_name__ ,							attr + '''_id''' ,							__magic_name__   )
																			self.assertEqual(getattr(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
																			self.assertEqual(getattr(__magic_name__ ,							attr + '''_id'''   ) ,							__magic_name__   )

																			setattr(__magic_name__ ,							attr + '''_id''' ,							__magic_name__   )
																			self.assertEqual(getattr(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
																			self.assertEqual(getattr(__magic_name__ ,							attr + '''_id'''   ) ,							__magic_name__   )

															setattr(__magic_name__ ,							'''additional_special_tokens_ids''' ,							[]   )
															self.assertListEqual(getattr(__magic_name__ ,							'''additional_special_tokens'''   ) ,							[]   )
															self.assertListEqual(getattr(__magic_name__ ,							'''additional_special_tokens_ids'''   ) ,							[]   )

															setattr(__magic_name__ ,							'''additional_special_tokens_ids''' ,							[token_id_to_test_setters]   )
															self.assertListEqual(getattr(__magic_name__ ,							'''additional_special_tokens'''   ) ,							[token_to_test_setters]   )
															self.assertListEqual(getattr(__magic_name__ ,							'''additional_special_tokens_ids'''   ) ,							[token_id_to_test_setters]   )







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			)		->						tuple:






				"""simple docstring"""


				return (data["data"], data["target"])






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						XGBClassifier:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  XGBClassifier()
				classifier.fit(_UpperCamelCase			,    _UpperCamelCase			)
				return classifier






def 					lowerCamelCase_						(    )		->						None:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  load_iris()
				snake_case_      ,							snake_case_						:   str           =  data_handling(_UpperCamelCase			)
				snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Dict           =  train_test_split(
				    _UpperCamelCase			,    _UpperCamelCase			,    test_size=0.25			)

				snake_case_						:   List[str]           =  iris['''target_names''']

				# Create an XGBoost Classifier from the training data
				snake_case_						:   int           =  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()
