
"""simple docstring"""


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 				lowerCAmelCase_				(    _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    LEDTokenizer
						_lowerCamelCase:    Optional[int]      =    LEDTokenizerFast
						_lowerCamelCase:    Union[str, Any]      =    True


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[Any]:
							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(A_  ,range(len(A_      )      )      )      )
							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(A_      ) + '\n'      )
							with open(self.merges_file  ,'w'  ,encoding='utf-8'      ) as fp:
								fp.write('\n'.join(A_      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,**A_     :      int      )     ->   str:
							kwargs.update(self.special_tokens_map      )
							return self.tokenizer_class.from_pretrained(self.tmpdirname  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,**A_     :      Any      )     ->   Union[str, Any]:
							kwargs.update(self.special_tokens_map      )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      Optional[Any]      )     ->   Any:
							return "lower newer", "lower newer"


						@cached_property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[Any]:
							return LEDTokenizer.from_pretrained('allenai/led-base-16384'      )


						@cached_property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   str:
							return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'      )


						@require_torch
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Tuple:
							A						   =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							A						   =   [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

								self.assertEqual((2, 9)  ,batch.input_ids.shape      )
								self.assertEqual((2, 9)  ,batch.attention_mask.shape      )
								A						   =   batch.input_ids.tolist()[0]
								self.assertListEqual(A_  ,A_      )


						@require_torch
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[Any]:
							A						   =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A						   =   tokenizer(A_  ,padding=A_  ,return_tensors='pt'      )
								self.assertIn('input_ids'  ,A_      )
								self.assertIn('attention_mask'  ,A_      )
								self.assertNotIn('labels'  ,A_      )
								self.assertNotIn('decoder_attention_mask'  ,A_      )


						@require_torch
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[int]:
							A						   =   [
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A						   =   tokenizer(text_target=A_  ,max_length=32  ,padding='max_length'  ,return_tensors='pt'      )
								self.assertEqual(32  ,targets['input_ids'].shape[1]      )


						@require_torch
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   List[Any]:
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A						   =   tokenizer(
								    ['I am a small frog' * 1024, 'I am a small frog']  ,padding=A_  ,truncation=A_  ,return_tensors='pt'      )
								self.assertIsInstance(A_  ,A_      )
								self.assertEqual(batch.input_ids.shape  ,(2, 5122)      )


						@require_torch
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A						   =   ['A long paragraph for summarization.']
							A						   =   [
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A						   =   tokenizer(A_  ,return_tensors='pt'      )
								A						   =   tokenizer(text_target=A_  ,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 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   int:
							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(A_  ,padding=A_      )
								A						   =   [[0] * len(A_      ) for x in encoded_output['input_ids']]
								A						   =   tokenizer.pad(A_      )
								self.assertSequenceEqual(outputs['global_attention_mask']  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   int:
							pass




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   str:
							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(A_  ,**A_      )
									A						   =   self.tokenizer_class.from_pretrained(A_  ,**A_      )
									A						   =   'A, <mask> AllenNLP sentence.'
									A						   =   tokenizer_r.encode_plus(A_  ,add_special_tokens=A_  ,return_token_type_ids=A_      )
									A						   =   tokenizer_p.encode_plus(A_  ,add_special_tokens=A_  ,return_token_type_ids=A_      )
									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, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )
									self.assertSequenceEqual(tokens_r['input_ids']  ,[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )

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

"""simple docstring"""


def       _snake_case				(		snake_case__   :     list	,      snake_case__   :     list	,      snake_case__   :     int     ):
	A						   =   len(snake_case__     )
	A						   =   [[0] * n for i in range(snake_case__     )]
	for i in range(snake_case__     ):
		A						   =   y_points[i]

	for i in range(2	,      snake_case__     ):
		for j in range(snake_case__	,      snake_case__     ):
			A						   =   (
			    (xa - x_points[j - i + 1]) * q[j][i - 1]
			    - (xa - x_points[j]) * q[j - 1][i - 1]
			) / (x_points[j] - x_points[j - i + 1])

	return [q[n - 1][n - 1], q]


if __name__ == "__main__":
		import doctest

		doctest.testmod()