


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class        snake_case_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Optional[int]          =			LEDTokenizer
					UpperCAmelCase__					:    int          =			LEDTokenizerFast
					UpperCAmelCase__					:    Dict          =			True
					def  lowerCamelCase__(      self   :Dict      )					->    Tuple:
												super().setUp()
												a__             =     [
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '<unk>',
												]
												a__             =     dict(zip(__snake_case			,range(len(__snake_case      )      )      )      )
												a__             =     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												a__             =     {'unk_token': '<unk>'}

												a__             =     os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['vocab_file']      )
												a__             =     os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['merges_file']      )
												with open(self.vocab_file			,'w'			,encoding='utf-8'      ) as fp:
																			fp.write(json.dumps(__snake_case      ) + '\n'      )
												with open(self.merges_file			,'w'			,encoding='utf-8'      ) as fp:
																			fp.write('\n'.join(__snake_case      )      )
					def  lowerCamelCase__(      self   :int			,**__snake_case   :Optional[int]      )					->    Tuple:
												kwargs.update(self.special_tokens_map      )
												return self.tokenizer_class.from_pretrained(self.tmpdirname			,**__snake_case      )
					def  lowerCamelCase__(      self   :List[str]			,**__snake_case   :List[Any]      )					->    List[Any]:
												kwargs.update(self.special_tokens_map      )
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,**__snake_case      )
					def  lowerCamelCase__(      self   :str			,__snake_case   :Union[str, Any]      )					->    Union[str, Any]:
												return "lower newer", "lower newer"
					@cached_property
					def  lowerCamelCase__(      self   :str      )					->    Any:
												return LEDTokenizer.from_pretrained('allenai/led-base-16384'      )
					@cached_property
					def  lowerCamelCase__(      self   :int      )					->    Any:
												return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'      )
					@require_torch
					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[str]:
												a__             =     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
												a__             =     [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__             =     tokenizer(__snake_case			,max_length=len(__snake_case      )			,padding=__snake_case			,return_tensors='pt'      )
																			self.assertIsInstance(__snake_case			,__snake_case      )

																			self.assertEqual((2, 9)			,batch.input_ids.shape      )
																			self.assertEqual((2, 9)			,batch.attention_mask.shape      )
																			a__             =     batch.input_ids.tolist()[0]
																			self.assertListEqual(__snake_case			,__snake_case      )
					@require_torch
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Dict:
												a__             =     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__             =     tokenizer(__snake_case			,padding=__snake_case			,return_tensors='pt'      )
																			self.assertIn('input_ids'			,__snake_case      )
																			self.assertIn('attention_mask'			,__snake_case      )
																			self.assertNotIn('labels'			,__snake_case      )
																			self.assertNotIn('decoder_attention_mask'			,__snake_case      )
					@require_torch
					def  lowerCamelCase__(      self   :List[str]      )					->    Tuple:
												a__             =     [
												    'Summary of the text.',
												    'Another summary.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__             =     tokenizer(text_target=__snake_case			,max_length=32			,padding='max_length'			,return_tensors='pt'      )
																			self.assertEqual(32			,targets['input_ids'].shape[1]      )
					@require_torch
					def  lowerCamelCase__(      self   :Dict      )					->    List[Any]:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__             =     tokenizer(
																			    ['I am a small frog' * 10_24, 'I am a small frog']			,padding=__snake_case			,truncation=__snake_case			,return_tensors='pt'      )
																			self.assertIsInstance(__snake_case			,__snake_case      )
																			self.assertEqual(batch.input_ids.shape			,(2, 51_22)      )
					@require_torch
					def  lowerCamelCase__(      self   :str      )					->    Optional[Any]:
												a__             =     ['A long paragraph for summarization.']
												a__             =     [
												    'Summary of the text.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__             =     tokenizer(__snake_case			,return_tensors='pt'      )
																			a__             =     tokenizer(text_target=__snake_case			,return_tensors='pt'      )
																			a__             =     inputs['input_ids']
																			a__             =     targets['input_ids']
																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )
					@require_torch
					def  lowerCamelCase__(      self   :List[Any]      )					->    List[str]:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__             =     ['Summary of the text.', 'Another summary.']
																			a__             =     [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																			a__             =     tokenizer(__snake_case			,padding=__snake_case      )
																			a__             =     [[0] * len(__snake_case      ) for x in encoded_output['input_ids']]
																			a__             =     tokenizer.pad(__snake_case      )
																			self.assertSequenceEqual(outputs['global_attention_mask']			,__snake_case      )
					def  lowerCamelCase__(      self   :Tuple      )					->    Any:
												pass



					def  lowerCamelCase__(      self   :int      )					->    Dict:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																										a__             =     self.rust_tokenizer_class.from_pretrained(__snake_case			,**__snake_case      )
																										a__             =     self.tokenizer_class.from_pretrained(__snake_case			,**__snake_case      )
																										a__             =     'A, <mask> AllenNLP sentence.'
																										a__             =     tokenizer_r.encode_plus(__snake_case			,add_special_tokens=__snake_case			,return_token_type_ids=__snake_case      )
																										a__             =     tokenizer_p.encode_plus(__snake_case			,add_special_tokens=__snake_case			,return_token_type_ids=__snake_case      )
																										self.assertEqual(sum(tokens_r['token_type_ids']      )			,sum(tokens_p['token_type_ids']      )      )
																										self.assertEqual(
																										    sum(tokens_r['attention_mask']      ) / len(tokens_r['attention_mask']      )			,sum(tokens_p['attention_mask']      ) / len(tokens_p['attention_mask']      )			,)

																										a__             =     tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
																										a__             =     tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )
																										self.assertSequenceEqual(tokens_p['input_ids']			,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )
																										self.assertSequenceEqual(tokens_r['input_ids']			,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )

																										self.assertSequenceEqual(
																										    __snake_case			,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
																										self.assertSequenceEqual(
																										    __snake_case			,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )








def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ):

							def get_matched_characters(__lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ) -> str:
														a__             =     []
														a__             =     min(len(_stra   )					,    len(_stra   )   ) // 2
														for i, l in enumerate(_stra   ):
																					a__             =     int(max(0					,    i - limit   )   )
																					a__             =     int(min(i + limit + 1					,    len(_stra   )   )   )
																					if l in _stra[left:right]:
																												matched.append(__lowerCAmelCase   )
																												a__             =     F'{_stra[0:_stra.index(__lowerCAmelCase   )]} {_stra[_stra.index(__lowerCAmelCase   ) + 1:]}'

														return "".join(__lowerCAmelCase   )

							# matching characters
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     len(__lowerCAmelCase   )

							# transposition
							a__             =     (
							    len([(ca, ca) for ca, ca in zip(__lowerCAmelCase					,    __lowerCAmelCase   ) if ca != ca]   ) // 2
							)

							if not match_count:
														a__             =     0.0
							else:
														a__             =     (
														    1
														    / 3
														    * (
														        match_count / len(__lowerCAmelCase   )
														        + match_count / len(__lowerCAmelCase   )
														        + (match_count - transpositions) / match_count
														    )
														)

							# common prefix up to 4 characters
							a__             =     0
							for ca, ca in zip(stra[:4]					,    stra[:4]   ):
														if ca == ca:
																					prefix_len += 1
														else:
																					break

							return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(jaro_winkler('''hello''', '''world'''))




