




'''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 						a_				(			UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     List[Any]			  =      LEDTokenizer
	lowercase_       :     Tuple			  =      LEDTokenizerFast
	lowercase_       :     str			  =      True




	def   lowercase__   (       self     :  str      ):
							super().setUp()
							__snake_case						 =   [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							__snake_case						 =   dict(zip(__lowerCAmelCase       ,		range(len(__lowerCAmelCase      )      )      )      )
							__snake_case						 =   ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							__snake_case						 =   {'unk_token': '<unk>'}

							__snake_case						 =   os.path.join(self.tmpdirname       ,		VOCAB_FILES_NAMES['vocab_file']      )
							__snake_case						 =   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(__lowerCAmelCase      ) + '\n'      )
							with open(self.merges_file       ,		'w'       ,		encoding='utf-8'      ) as fp:
													fp.write('\n'.join(__lowerCAmelCase      )      )




	def   lowercase__   (       self     :  Union[str, Any]       ,		**__lowerCAmelCase     :  Union[str, Any]      ):
							kwargs.update(self.special_tokens_map      )
							return self.tokenizer_class.from_pretrained(self.tmpdirname       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]       ,		**__lowerCAmelCase     :  str      ):
							kwargs.update(self.special_tokens_map      )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]      ):
							return "lower newer", "lower newer"




	@cached_property
	def   lowercase__   (       self     :  List[str]      ):
							return LEDTokenizer.from_pretrained('allenai/led-base-16384'      )




	@cached_property
	def   lowercase__   (       self     :  List[str]      ):
							return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'      )




	@require_torch
	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							__snake_case						 =   [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

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

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




	@require_torch
	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													__snake_case						 =   tokenizer(__lowerCAmelCase       ,		padding=__lowerCAmelCase       ,		return_tensors='pt'      )
													self.assertIn('input_ids'       ,		__lowerCAmelCase      )
													self.assertIn('attention_mask'       ,		__lowerCAmelCase      )
													self.assertNotIn('labels'       ,		__lowerCAmelCase      )
													self.assertNotIn('decoder_attention_mask'       ,		__lowerCAmelCase      )




	@require_torch
	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   [
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													__snake_case						 =   tokenizer(text_target=__lowerCAmelCase       ,		max_length=3_2       ,		padding='max_length'       ,		return_tensors='pt'      )
													self.assertEqual(3_2       ,		targets['input_ids'].shape[1]      )




	@require_torch
	def   lowercase__   (       self     :  List[Any]      ):
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													__snake_case						 =   tokenizer(
													    ['I am a small frog' * 1_0_2_4, 'I am a small frog']       ,		padding=__lowerCAmelCase       ,		truncation=__lowerCAmelCase       ,		return_tensors='pt'      )
													self.assertIsInstance(__lowerCAmelCase       ,		__lowerCAmelCase      )
													self.assertEqual(batch.input_ids.shape       ,		(2, 5_1_2_2)      )




	@require_torch
	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   ['A long paragraph for summarization.']
							__snake_case						 =   [
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													__snake_case						 =   tokenizer(__lowerCAmelCase       ,		return_tensors='pt'      )
													__snake_case						 =   tokenizer(text_target=__lowerCAmelCase       ,		return_tensors='pt'      )
													__snake_case						 =   inputs['input_ids']
													__snake_case						 =   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   lowercase__   (       self     :  List[str]      ):
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													__snake_case						 =   ['Summary of the text.', 'Another summary.']
													__snake_case						 =   [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

													__snake_case						 =   tokenizer(__lowerCAmelCase       ,		padding=__lowerCAmelCase      )
													__snake_case						 =   [[0] * len(__lowerCAmelCase      ) for x in encoded_output['input_ids']]
													__snake_case						 =   tokenizer.pad(__lowerCAmelCase      )
													self.assertSequenceEqual(outputs['global_attention_mask']       ,		__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[str]      ):
							pass




	def   lowercase__   (       self     :  Tuple      ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																			__snake_case						 =   self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase       ,		**__lowerCAmelCase      )
																			__snake_case						 =   self.tokenizer_class.from_pretrained(__lowerCAmelCase       ,		**__lowerCAmelCase      )
																			__snake_case						 =   'A, <mask> AllenNLP sentence.'
																			__snake_case						 =   tokenizer_r.encode_plus(__lowerCAmelCase       ,		add_special_tokens=__lowerCAmelCase       ,		return_token_type_ids=__lowerCAmelCase      )
																			__snake_case						 =   tokenizer_p.encode_plus(__lowerCAmelCase       ,		add_special_tokens=__lowerCAmelCase       ,		return_token_type_ids=__lowerCAmelCase      )
																			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']      )       ,		)

																			__snake_case						 =   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
																			__snake_case						 =   tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )
																			self.assertSequenceEqual(tokens_p['input_ids']       ,		[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )
																			self.assertSequenceEqual(tokens_r['input_ids']       ,		[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )

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







'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						assert (
						    isinstance(a    ,	a							) and number_of_steps > 0
						), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
						if number_of_steps == 1:
												return 1
						__snake_case ,       __snake_case						 =   1, 1
						for _ in range(number_of_steps - 1							):
												__snake_case ,       __snake_case						 =   current + previous, current
						return current


if __name__ == "__main__":
	import doctest

	doctest.testmod()

