


'''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				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Optional[Any]			      = LEDTokenizer
	_lowerCamelCase   :					Tuple			      = LEDTokenizerFast
	_lowerCamelCase   :					List[Any]			      = True



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

		lowerCAmelCase					:    Union[str, Any]      							= os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['vocab_file']    )
		lowerCAmelCase					:    Tuple      							= 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 				lowercase     (					self ,					**snake_case__    ):
		kwargs.update(self.special_tokens_map    )
		return self.tokenizer_class.from_pretrained(self.tmpdirname ,					**snake_case__    )



	def 				lowercase     (					self ,					**snake_case__    ):
		kwargs.update(self.special_tokens_map    )
		return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,					**snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		return "lower newer", "lower newer"



	@cached_property
	def 				lowercase     (					self    ):
		return LEDTokenizer.from_pretrained('allenai/led-base-16384'    )



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



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

		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			lowerCAmelCase					:    Union[str, Any]      							= 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    )
			lowerCAmelCase					:    Union[str, Any]      							= batch.input_ids.tolist()[0]
			self.assertListEqual(snake_case__ ,					snake_case__    )



	@require_torch
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			lowerCAmelCase					:    Any      							= 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 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= [
		    'Summary of the text.',
		    'Another summary.',
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			lowerCAmelCase					:    Union[str, Any]      							= 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 				lowercase     (					self    ):
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			lowerCAmelCase					:    Optional[int]      							= tokenizer(
			    ['I am a small frog' * 1024, '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, 5122)    )



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

			lowerCAmelCase					:    Tuple      							= tokenizer(snake_case__ ,					padding=snake_case__    )
			lowerCAmelCase					:    Optional[int]      							= [[0] * len(snake_case__    ) for x in encoded_output['input_ids']]
			lowerCAmelCase					:    List[str]      							= tokenizer.pad(snake_case__    )
			self.assertSequenceEqual(outputs['global_attention_mask'] ,					snake_case__    )



	def 				lowercase     (					self    ):
		pass



	def 				lowercase     (					self    ):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"    ):
				lowerCAmelCase					:    Optional[Any]      							= self.rust_tokenizer_class.from_pretrained(snake_case__ ,					**snake_case__    )
				lowerCAmelCase					:    Optional[int]      							= self.tokenizer_class.from_pretrained(snake_case__ ,					**snake_case__    )
				lowerCAmelCase					:    Union[str, Any]      							= 'A, <mask> AllenNLP sentence.'
				lowerCAmelCase					:    Optional[int]      							= tokenizer_r.encode_plus(snake_case__ ,					add_special_tokens=snake_case__ ,					return_token_type_ids=snake_case__    )
				lowerCAmelCase					:    List[str]      							= 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']    ) ,					)

				lowerCAmelCase					:    Any      							= tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']    )
				lowerCAmelCase					:    Union[str, Any]      							= 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(
				    snake_case__ ,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']    )
				self.assertSequenceEqual(
				    snake_case__ ,					['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']    )




'''simple docstring'''




def 			__UpperCamelCase    (    _A    :				int				)    ->      bool:




	"""simple docstring"""





	return number & 1 == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()
