
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        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Union[str, Any]          = LEDTokenizer
	_UpperCAmelCase	:		str          = LEDTokenizerFast
	_UpperCAmelCase	:		Tuple          = True



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				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(lowercase      ,  range(len(lowercase     )     )     )     )
				_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(lowercase     ) + '\n'     )
				with open(self.merges_file      ,  'w'      ,  encoding='utf-8'     ) as fp:
							fp.write('\n'.join(lowercase     )     )



	def 			A							(  self     :   List[str]      ,  **lowercase     :   Any     ):


				'''simple docstring'''


				kwargs.update(self.special_tokens_map     )
				return self.tokenizer_class.from_pretrained(self.tmpdirname      ,  **lowercase     )



	def 			A							(  self     :   List[Any]      ,  **lowercase     :   int     ):


				'''simple docstring'''


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



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   Any     ):


				'''simple docstring'''


				return "lower newer", "lower newer"



	@cached_property
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				return LEDTokenizer.from_pretrained('allenai/led-base-16384'     )



	@cached_property
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'     )



	@require_torch
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
				_snake_case          =      [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

							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(lowercase      ,  lowercase     )



	@require_torch
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							_snake_case          =      tokenizer(lowercase      ,  padding=lowercase      ,  return_tensors='pt'     )
							self.assertIn('input_ids'      ,  lowercase     )
							self.assertIn('attention_mask'      ,  lowercase     )
							self.assertNotIn('labels'      ,  lowercase     )
							self.assertNotIn('decoder_attention_mask'      ,  lowercase     )



	@require_torch
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      [
				    'Summary of the text.',
				    'Another summary.',
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							_snake_case          =      tokenizer(text_target=lowercase      ,  max_length=32      ,  padding='max_length'      ,  return_tensors='pt'     )
							self.assertEqual(32      ,  targets['input_ids'].shape[1]     )



	@require_torch
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							_snake_case          =      tokenizer(
							    ['I am a small frog' * 1_024, 'I am a small frog']      ,  padding=lowercase      ,  truncation=lowercase      ,  return_tensors='pt'     )
							self.assertIsInstance(lowercase      ,  lowercase     )
							self.assertEqual(batch.input_ids.shape      ,  (2, 5_122)     )



	@require_torch
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_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(lowercase      ,  return_tensors='pt'     )
							_snake_case          =      tokenizer(text_target=lowercase      ,  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 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


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



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				pass





	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				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(lowercase      ,  **lowercase     )
										_snake_case          =      self.tokenizer_class.from_pretrained(lowercase      ,  **lowercase     )
										_snake_case          =      'A, <mask> AllenNLP sentence.'
										_snake_case          =      tokenizer_r.encode_plus(lowercase      ,  add_special_tokens=lowercase      ,  return_token_type_ids=lowercase     )
										_snake_case          =      tokenizer_p.encode_plus(lowercase      ,  add_special_tokens=lowercase      ,  return_token_type_ids=lowercase     )
										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, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]     )
										self.assertSequenceEqual(tokens_r['input_ids']      ,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]     )

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

from __future__ import annotations

_lowerCamelCase	:    Optional[Any]          =      [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCamelCase	:    Optional[int]          =      [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			_snake_case          =      len(__lowercase			)

			for i in range(__lowercase			):
						_snake_case          =      -1
						for j in range(i + 1	, __lowercase			):
									if arr[i] < arr[j]:
												_snake_case          =      arr[j]
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			for i, outer in enumerate(__lowercase			):
						_snake_case          =      -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												_snake_case          =      inner
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      len(__lowercase			)
			_snake_case          =      []
			_snake_case          =      [-1] * arr_size

			for index in reversed(range(__lowercase			)			):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									_snake_case          =      stack[-1]
						stack.append(arr[index]			)
			return result


if __name__ == "__main__":
			from doctest import testmod
			from timeit import timeit

			testmod()
			print(next_greatest_element_slow(arr))
			print(next_greatest_element_fast(arr))
			print(next_greatest_element(arr))

			_lowerCamelCase	:    Union[str, Any]          =      (
			    '''from __main__ import arr, next_greatest_element_slow, '''
			    '''next_greatest_element_fast, next_greatest_element'''
			)
			print(
			    '''next_greatest_element_slow():''',
			    timeit('''next_greatest_element_slow(arr)''', setup=setup),
			)
			print(
			    '''next_greatest_element_fast():''',
			    timeit('''next_greatest_element_fast(arr)''', setup=setup),
			)
			print(
			    '''     next_greatest_element():''',
			    timeit('''next_greatest_element(arr)''', setup=setup),
			)