






import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_           = get_tests_dir('fixtures/test_sentencepiece_no_bos.model')
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase (				_UpperCAmelCase			,       unittest.TestCase ):







		"""simple docstring"""



		snake_case    =    PegasusTokenizer
		snake_case    =    PegasusTokenizerFast
		snake_case    =    True
		snake_case    =    True
		def       A(  self		:       Any		)    ->   List[str]:


						'''simple docstring'''
						super().setUp()

						# We have a SentencePiece fixture for testing
						A	 =      PegasusTokenizer(lowerCamelCase_		)
						tokenizer.save_pretrained(self.tmpdirname		)
		@cached_property
		def       A(  self		:       Tuple		)    ->   Union[str, Any]:


						'''simple docstring'''
						return PegasusTokenizer.from_pretrained('google/pegasus-large'		)
		def       A(  self		:       int						,**_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   PegasusTokenizer:


						'''simple docstring'''
						return PegasusTokenizer.from_pretrained(self.tmpdirname						,**lowerCamelCase_		)
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						return ("This is a test", "This is a test")
		def       A(  self		:       Optional[int]		)    ->   Dict:


						'''simple docstring'''
						A	 =      '''</s>'''
						A	 =      1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_		)						,lowerCamelCase_		)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_		)						,lowerCamelCase_		)
		def       A(  self		:       Any		)    ->   List[str]:


						'''simple docstring'''
						A	 =      list(self.get_tokenizer().get_vocab().keys()		)

						self.assertEqual(vocab_keys[0]						,'<pad>'		)
						self.assertEqual(vocab_keys[1]						,'</s>'		)
						self.assertEqual(vocab_keys[-1]						,'v'		)
						self.assertEqual(len(lowerCamelCase_		)						,1_1_0_3		)
		def       A(  self		:       Dict		)    ->   int:


						'''simple docstring'''
						self.assertEqual(self.get_tokenizer().vocab_size						,1_1_0_3		)
		def       A(  self		:       Any		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
						A	 =      self.tokenizer_class.from_pretrained(self.tmpdirname		)
						A	 =      (
						    '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
						    ''' </s> <pad> <pad> <pad>'''
						)
						A	 =      rust_tokenizer([raw_input_str]						,return_tensors=lowerCamelCase_						,add_special_tokens=lowerCamelCase_		).input_ids[0]
						A	 =      py_tokenizer([raw_input_str]						,return_tensors=lowerCamelCase_						,add_special_tokens=lowerCamelCase_		).input_ids[0]
						self.assertListEqual(lowerCamelCase_						,lowerCamelCase_		)
		def       A(  self		:       Dict		)    ->   Dict:


						'''simple docstring'''
						A	 =      self._large_tokenizer
						# <mask_1> masks whole sentence while <mask_2> masks single word
						A	 =      '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
						A	 =      [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
						A	 =      tokenizer([raw_input_str]						,return_tensors=lowerCamelCase_		).input_ids[0]
						self.assertListEqual(lowerCamelCase_						,lowerCamelCase_		)
		def       A(  self		:       List[str]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      self._large_tokenizer
						# The tracebacks for the following asserts are **better** without messages or self.assertEqual
						assert tokenizer.vocab_size == 9_6_1_0_3
						assert tokenizer.pad_token_id == 0
						assert tokenizer.eos_token_id == 1
						assert tokenizer.offset == 1_0_3
						assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
						assert tokenizer.unk_token == "<unk>"
						assert tokenizer.model_max_length == 1_0_2_4
						A	 =      '''To ensure a smooth flow of bank resolutions.'''
						A	 =      [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
						A	 =      tokenizer([raw_input_str]						,return_tensors=lowerCamelCase_		).input_ids[0]
						self.assertListEqual(lowerCamelCase_						,lowerCamelCase_		)
						assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
		@require_torch
		def       A(  self		:       Union[str, Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      ['''This is going to be way too long.''' * 1_5_0, '''short example''']
						A	 =      ['''not super long but more than 5 tokens''', '''tiny''']
						A	 =      self._large_tokenizer(lowerCamelCase_						,padding=lowerCamelCase_						,truncation=lowerCamelCase_						,return_tensors='pt'		)
						A	 =      self._large_tokenizer(
						    text_target=lowerCamelCase_						,max_length=5						,padding=lowerCamelCase_						,truncation=lowerCamelCase_						,return_tensors='pt'		)

						assert batch.input_ids.shape == (2, 1_0_2_4)
						assert batch.attention_mask.shape == (2, 1_0_2_4)
						assert targets["input_ids"].shape == (2, 5)
						assert len(lowerCamelCase_		) == 2  # input_ids, attention_mask.







		@slow
		def       A(  self		:       Optional[Any]		)    ->   str:


						'''simple docstring'''
						A	 =      {'''input_ids''': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=lowerCamelCase_						,model_name='google/bigbird-pegasus-large-arxiv'						,revision='ba85d0851d708441f91440d509690f1ab6353415'						,)


@require_sentencepiece
@require_tokenizers
class      UpperCamelCase (				_UpperCAmelCase			,       unittest.TestCase ):







		"""simple docstring"""



		snake_case    =    PegasusTokenizer
		snake_case    =    PegasusTokenizerFast
		snake_case    =    True
		snake_case    =    True
		def       A(  self		:       List[Any]		)    ->   Optional[int]:


						'''simple docstring'''
						super().setUp()

						# We have a SentencePiece fixture for testing
						A	 =      PegasusTokenizer(lowerCamelCase_						,offset=0						,mask_token_sent=lowerCamelCase_						,mask_token='[MASK]'		)
						tokenizer.save_pretrained(self.tmpdirname		)
		@cached_property
		def       A(  self		:       Dict		)    ->   Optional[int]:


						'''simple docstring'''
						return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'		)
		def       A(  self		:       Any						,**_SCREAMING_SNAKE_CASE		:       int		)    ->   PegasusTokenizer:


						'''simple docstring'''
						return PegasusTokenizer.from_pretrained(self.tmpdirname						,**lowerCamelCase_		)
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       str		)    ->   Tuple:


						'''simple docstring'''
						return ("This is a test", "This is a test")
		def       A(  self		:       Any		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
						A	 =      self.tokenizer_class.from_pretrained(self.tmpdirname		)
						A	 =      (
						    '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
						    ''' <pad> <pad> <pad>'''
						)
						A	 =      rust_tokenizer([raw_input_str]						,return_tensors=lowerCamelCase_						,add_special_tokens=lowerCamelCase_		).input_ids[0]
						A	 =      py_tokenizer([raw_input_str]						,return_tensors=lowerCamelCase_						,add_special_tokens=lowerCamelCase_		).input_ids[0]
						self.assertListEqual(lowerCamelCase_						,lowerCamelCase_		)
		@require_torch
		def       A(  self		:       List[str]		)    ->   int:


						'''simple docstring'''
						A	 =      ['''This is going to be way too long.''' * 1_0_0_0, '''short example''']
						A	 =      ['''not super long but more than 5 tokens''', '''tiny''']
						A	 =      self._large_tokenizer(lowerCamelCase_						,padding=lowerCamelCase_						,truncation=lowerCamelCase_						,return_tensors='pt'		)
						A	 =      self._large_tokenizer(
						    text_target=lowerCamelCase_						,max_length=5						,padding=lowerCamelCase_						,truncation=lowerCamelCase_						,return_tensors='pt'		)

						assert batch.input_ids.shape == (2, 4_0_9_6)
						assert batch.attention_mask.shape == (2, 4_0_9_6)
						assert targets["input_ids"].shape == (2, 5)
						assert len(lowerCamelCase_		) == 2  # input_ids, attention_mask.







		def       A(  self		:       Optional[int]		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      (
						    '''This is an example string that is used to test the original TF implementation against the HF'''
						    ''' implementation'''
						)

						A	 =      self._large_tokenizer(lowerCamelCase_		).input_ids

						self.assertListEqual(
						    lowerCamelCase_						,[1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]						,)













import math







def      snake_case		(							UpperCAmelCase	:       list,						UpperCAmelCase	:       int   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
				A	 =      0
				while arr[min(UpperCAmelCase,						UpperCAmelCase   ) - 1] < x:
								A	 =      step
								step += int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
								if prev >= n:
												return -1

				while arr[prev] < x:
								A	 =      prev + 1
								if prev == min(UpperCAmelCase,						UpperCAmelCase   ):
												return -1
				if arr[prev] == x:
								return prev
				return -1


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_           = [int(item) for item in user_input.split(',')]
					lowerCAmelCase_           = int(input('Enter the number to be searched:\n'))
					lowerCAmelCase_           = jump_search(arr, x)
					if res == -1:
										print('Number not found!')
					else:
										print(f'''Number {x} is at index {res}''')





