"""simple docstring"""







import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
     from tokenizers import Tokenizer
     from tokenizers.models import WordPiece






class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					list(tokenizer_class.max_model_input_sizes.keys()	)
      for model_name in sa_models[:1]:
         __lowerCamelCase    		=					tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)
         self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
         self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

         for special_tok in tokenizer.all_special_tokens:
            self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
            __lowerCamelCase    		=					tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	)
            self.assertIsInstance(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None	):


      '''simple docstring'''

      __lowerCamelCase    		=					pickle.dumps(_SCREAMING_SNAKE_CASE	)
      self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					pickle.loads(_SCREAMING_SNAKE_CASE	)

      # Ensure is_fast is correctly restored
      self.assertEqual(be_restored.is_fast ,				be_original.is_fast	)

      # Ensure encodings are potentially correctly restored
      if be_original.is_fast:
         self.assertIsNotNone(be_restored.encodings	)
      else:
         self.assertIsNone(be_restored.encodings	)

      # Ensure the keys are the same
      for original_v, restored_v in zip(be_original.values() ,				be_restored.values()	):
         if equal_op:
            self.assertTrue(equal_op(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)
         else:
            self.assertEqual(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      self.check_tokenizer_from_pretrained(_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      self.assertEqual(TensorType('tf'	) ,				TensorType.TENSORFLOW	)
      self.assertEqual(TensorType('pt'	) ,				TensorType.PYTORCH	)
      self.assertEqual(TensorType('np'	) ,				TensorType.NUMPY	)







   @require_tokenizers
   def    _A				(	self	):


      '''simple docstring'''

      import numpy as np

      __lowerCamelCase    		=					BertTokenizer.from_pretrained('bert-base-cased'	)
      __lowerCamelCase    		=					BertTokenizerFast.from_pretrained('bert-base-cased'	)

      # Python no tensor
      with self.subTest('BatchEncoding (Python, return_tensors=None)'	):
         self.assert_dump_and_restore(tokenizer_p('Small example to encode'	)	)

      with self.subTest('BatchEncoding (Python, return_tensors=NUMPY)'	):
         self.assert_dump_and_restore(
             tokenizer_p('Small example to encode' ,				return_tensors=TensorType.NUMPY	) ,				np.array_equal	)

      with self.subTest('BatchEncoding (Rust, return_tensors=None)'	):
         self.assert_dump_and_restore(tokenizer_r('Small example to encode'	)	)

      with self.subTest('BatchEncoding (Rust, return_tensors=NUMPY)'	):
         self.assert_dump_and_restore(
             tokenizer_r('Small example to encode' ,				return_tensors=TensorType.NUMPY	) ,				np.array_equal	)







   @require_tf
   @require_tokenizers
   def    _A				(	self	):


      '''simple docstring'''

      import tensorflow as tf

      def tf_array_equals(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):
         return tf.reduce_all(tf.equal(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)

      __lowerCamelCase    		=					BertTokenizer.from_pretrained('bert-base-cased'	)
      __lowerCamelCase    		=					BertTokenizerFast.from_pretrained('bert-base-cased'	)

      with self.subTest('BatchEncoding (Python, return_tensors=TENSORFLOW)'	):
         self.assert_dump_and_restore(
             tokenizer_p('Small example to encode' ,				return_tensors=TensorType.TENSORFLOW	) ,				_SCREAMING_SNAKE_CASE	)

      with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'	):
         self.assert_dump_and_restore(
             tokenizer_r('Small example to encode' ,				return_tensors=TensorType.TENSORFLOW	) ,				_SCREAMING_SNAKE_CASE	)







   @require_torch
   @require_tokenizers
   def    _A				(	self	):


      '''simple docstring'''

      import torch

      __lowerCamelCase    		=					BertTokenizer.from_pretrained('bert-base-cased'	)
      __lowerCamelCase    		=					BertTokenizerFast.from_pretrained('bert-base-cased'	)

      with self.subTest('BatchEncoding (Python, return_tensors=PYTORCH)'	):
         self.assert_dump_and_restore(
             tokenizer_p('Small example to encode' ,				return_tensors=TensorType.PYTORCH	) ,				torch.equal	)

      with self.subTest('BatchEncoding (Rust, return_tensors=PYTORCH)'	):
         self.assert_dump_and_restore(
             tokenizer_r('Small example to encode' ,				return_tensors=TensorType.PYTORCH	) ,				torch.equal	)







   @require_tokenizers
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					BertTokenizer.from_pretrained('bert-base-cased'	)
      __lowerCamelCase    		=					BertTokenizerFast.from_pretrained('bert-base-cased'	)

      with self.subTest('Python Tokenizer'	):
         self.assertFalse(tokenizer_p('Small example to_encode'	).is_fast	)

      with self.subTest('Rust Tokenizer'	):
         self.assertTrue(tokenizer_r('Small example to_encode'	).is_fast	)







   @require_tokenizers
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					BertTokenizerFast.from_pretrained('bert-base-cased'	)
      __lowerCamelCase    		=					tokenizer_r(['Test', '\xad', 'test'] ,				is_split_into_words=_SCREAMING_SNAKE_CASE	)

      self.assertEqual(encoded.word_to_tokens(0	) ,				TokenSpan(start=1 ,				end=2	)	)
      self.assertEqual(encoded.word_to_tokens(1	) ,				_SCREAMING_SNAKE_CASE	)
      self.assertEqual(encoded.word_to_tokens(2	) ,				TokenSpan(start=2 ,				end=3	)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}	)
      __lowerCamelCase    		=					batch.convert_to_tensors(tensor_type='np'	)
      self.assertEqual(tensor_batch['inputs'].shape ,				(2, 3)	)
      self.assertEqual(tensor_batch['labels'].shape ,				(2,)	)
      # test converting the converted
      with CaptureStderr() as cs:
         __lowerCamelCase    		=					batch.convert_to_tensors(tensor_type='np'	)
      self.assertFalse(len(cs.err	) ,				msg=F'''should have no warning, but got {cs.err}'''	)

      __lowerCamelCase    		=					BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}	)
      __lowerCamelCase    		=					batch.convert_to_tensors(tensor_type='np' ,				prepend_batch_axis=_SCREAMING_SNAKE_CASE	)
      self.assertEqual(tensor_batch['inputs'].shape ,				(1, 3)	)
      self.assertEqual(tensor_batch['labels'].shape ,				(1,)	)







   @require_torch
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}	)
      __lowerCamelCase    		=					batch.convert_to_tensors(tensor_type='pt'	)
      self.assertEqual(tensor_batch['inputs'].shape ,				(2, 3)	)
      self.assertEqual(tensor_batch['labels'].shape ,				(2,)	)
      # test converting the converted
      with CaptureStderr() as cs:
         __lowerCamelCase    		=					batch.convert_to_tensors(tensor_type='pt'	)
      self.assertFalse(len(cs.err	) ,				msg=F'''should have no warning, but got {cs.err}'''	)

      __lowerCamelCase    		=					BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}	)
      __lowerCamelCase    		=					batch.convert_to_tensors(tensor_type='pt' ,				prepend_batch_axis=_SCREAMING_SNAKE_CASE	)
      self.assertEqual(tensor_batch['inputs'].shape ,				(1, 3)	)
      self.assertEqual(tensor_batch['labels'].shape ,				(1,)	)







   @require_tf
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}	)
      __lowerCamelCase    		=					batch.convert_to_tensors(tensor_type='tf'	)
      self.assertEqual(tensor_batch['inputs'].shape ,				(2, 3)	)
      self.assertEqual(tensor_batch['labels'].shape ,				(2,)	)
      # test converting the converted
      with CaptureStderr() as cs:
         __lowerCamelCase    		=					batch.convert_to_tensors(tensor_type='tf'	)
      self.assertFalse(len(cs.err	) ,				msg=F'''should have no warning, but got {cs.err}'''	)

      __lowerCamelCase    		=					BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}	)
      __lowerCamelCase    		=					batch.convert_to_tensors(tensor_type='tf' ,				prepend_batch_axis=_SCREAMING_SNAKE_CASE	)
      self.assertEqual(tensor_batch['inputs'].shape ,				(1, 3)	)
      self.assertEqual(tensor_batch['labels'].shape ,				(1,)	)







   @require_flax
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}	)
      __lowerCamelCase    		=					batch.convert_to_tensors(tensor_type='jax'	)
      self.assertEqual(tensor_batch['inputs'].shape ,				(2, 3)	)
      self.assertEqual(tensor_batch['labels'].shape ,				(2,)	)
      # test converting the converted
      with CaptureStderr() as cs:
         __lowerCamelCase    		=					batch.convert_to_tensors(tensor_type='jax'	)
      self.assertFalse(len(cs.err	) ,				msg=F'''should have no warning, but got {cs.err}'''	)

      __lowerCamelCase    		=					BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}	)
      __lowerCamelCase    		=					batch.convert_to_tensors(tensor_type='jax' ,				prepend_batch_axis=_SCREAMING_SNAKE_CASE	)
      self.assertEqual(tensor_batch['inputs'].shape ,				(1, 3)	)
      self.assertEqual(tensor_batch['labels'].shape ,				(1,)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					[{'input_ids': np.array([0, 1, 2]	)}, {'input_ids': np.array([0, 1, 2, 3]	)}]
      __lowerCamelCase    		=					BertTokenizer.from_pretrained('bert-base-cased'	)

      __lowerCamelCase    		=					tokenizer.pad(_SCREAMING_SNAKE_CASE ,				padding=_SCREAMING_SNAKE_CASE	)
      self.assertTrue(isinstance(batch['input_ids'] ,				np.ndarray	)	)
      self.assertEqual(batch['input_ids'].tolist() ,				[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)
      __lowerCamelCase    		=					tokenizer.pad(_SCREAMING_SNAKE_CASE ,				padding=_SCREAMING_SNAKE_CASE ,				return_tensors='np'	)
      self.assertTrue(isinstance(batch['input_ids'] ,				np.ndarray	)	)
      self.assertEqual(batch['input_ids'].tolist() ,				[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)







   @require_torch
   def    _A				(	self	):


      '''simple docstring'''

      import torch

      __lowerCamelCase    		=					[{'input_ids': torch.tensor([0, 1, 2]	)}, {'input_ids': torch.tensor([0, 1, 2, 3]	)}]
      __lowerCamelCase    		=					BertTokenizer.from_pretrained('bert-base-cased'	)

      __lowerCamelCase    		=					tokenizer.pad(_SCREAMING_SNAKE_CASE ,				padding=_SCREAMING_SNAKE_CASE	)
      self.assertTrue(isinstance(batch['input_ids'] ,				torch.Tensor	)	)
      self.assertEqual(batch['input_ids'].tolist() ,				[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)
      __lowerCamelCase    		=					tokenizer.pad(_SCREAMING_SNAKE_CASE ,				padding=_SCREAMING_SNAKE_CASE ,				return_tensors='pt'	)
      self.assertTrue(isinstance(batch['input_ids'] ,				torch.Tensor	)	)
      self.assertEqual(batch['input_ids'].tolist() ,				[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)







   @require_tf
   def    _A				(	self	):


      '''simple docstring'''

      import tensorflow as tf

      __lowerCamelCase    		=					[{'input_ids': tf.constant([0, 1, 2]	)}, {'input_ids': tf.constant([0, 1, 2, 3]	)}]
      __lowerCamelCase    		=					BertTokenizer.from_pretrained('bert-base-cased'	)

      __lowerCamelCase    		=					tokenizer.pad(_SCREAMING_SNAKE_CASE ,				padding=_SCREAMING_SNAKE_CASE	)
      self.assertTrue(isinstance(batch['input_ids'] ,				tf.Tensor	)	)
      self.assertEqual(batch['input_ids'].numpy().tolist() ,				[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)
      __lowerCamelCase    		=					tokenizer.pad(_SCREAMING_SNAKE_CASE ,				padding=_SCREAMING_SNAKE_CASE ,				return_tensors='tf'	)
      self.assertTrue(isinstance(batch['input_ids'] ,				tf.Tensor	)	)
      self.assertEqual(batch['input_ids'].numpy().tolist() ,				[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)







   @require_tokenizers
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					Tokenizer(WordPiece(unk_token='[UNK]'	)	)
      PreTrainedTokenizerFast(tokenizer_object=_SCREAMING_SNAKE_CASE	)







   @require_tokenizers
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					Tokenizer(WordPiece(unk_token='[UNK]'	)	)
      with tempfile.TemporaryDirectory() as tmpdirname:
         bert_tokenizer.save(os.path.join(_SCREAMING_SNAKE_CASE ,				'tokenizer.json'	)	)
         PreTrainedTokenizerFast(tokenizer_file=os.path.join(_SCREAMING_SNAKE_CASE ,				'tokenizer.json'	)	)











'''simple docstring'''



def 							__UpperCAmelCase				(				UpperCamelCase__				:int			)		->						Any:
				snake_case__      :   Dict      =    len(UpperCamelCase__			)
				for _ in range(
				    0   ,				int(((no_of_elements - 1) / 2) + 1			)			):  # we don't need to traverse to end of list as
								for j in range(0   ,				no_of_elements - 1			):
												if (
												    lst[j + 1] < lst[j]
												):  # applying bubble sort algorithm from left to right (or forwards)
																snake_case__      :   int      =    lst[j + 1]
																snake_case__      :   Tuple      =    lst[j]
																snake_case__      :   Tuple      =    temp
												if (
												    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
												):  # applying bubble sort algorithm from right to left (or backwards)
																snake_case__      :   Optional[Any]      =    lst[no_of_elements - 1 - j]
																snake_case__      :   Tuple      =    lst[no_of_elements - 2 - j]
																snake_case__      :   Any      =    temp
				return lst


if __name__ == "__main__":
				print("enter the list to be sorted")
				_lowercase       :						Any						=[int(x) for x in input().split()]  # inputing elements of the list in one line
				_lowercase       :						Tuple						=double_sort(lst)
				print("the sorted list is")
				print(sorted_lst)




