code: 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 				_lowercase						(    unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       List[str]         =  list(tokenizer_class.max_model_input_sizes.keys()   )
         for model_name in sa_models[:1]:
            lowerCAmelCase_:       Any         =  tokenizer_class.from_pretrained(lowerCamelCase__   )
            self.assertIsNotNone(lowerCamelCase__   )
            self.assertIsInstance(lowerCamelCase__					,	lowerCamelCase__   )
            self.assertIsInstance(lowerCamelCase__					,	lowerCamelCase__   )
  
            for special_tok in tokenizer.all_special_tokens:
               self.assertIsInstance(lowerCamelCase__					,	lowerCamelCase__   )
               lowerCAmelCase_:       Optional[int]         =  tokenizer.convert_tokens_to_ids(lowerCamelCase__   )
               self.assertIsInstance(lowerCamelCase__					,	lowerCamelCase__   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__ = None   ):
         lowerCAmelCase_:       Tuple         =  pickle.dumps(lowerCamelCase__   )
         self.assertIsNotNone(lowerCamelCase__   )
  
         lowerCAmelCase_:       Any         =  pickle.loads(lowerCamelCase__   )
  
         # 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(lowerCamelCase__					,	lowerCamelCase__   )   )
            else:
               self.assertEqual(lowerCamelCase__					,	lowerCamelCase__   )
  
  
  
  
      @slow
      def     _a						(						self   ):
         self.check_tokenizer_from_pretrained(lowerCamelCase__   )
  
  
  
  
      def     _a						(						self   ):
         self.assertEqual(TensorType("tf"   )					,	TensorType.TENSORFLOW   )
         self.assertEqual(TensorType("pt"   )					,	TensorType.PYTORCH   )
         self.assertEqual(TensorType("np"   )					,	TensorType.NUMPY   )
  
  
  
  
      @require_tokenizers
      def     _a						(						self   ):
         import numpy as np
  
         lowerCAmelCase_:       int         =  BertTokenizer.from_pretrained("bert-base-cased"   )
         lowerCAmelCase_:       Dict         =  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   ):
         import tensorflow as tf
  
         def tf_array_equals(lowerCamelCase__					,	lowerCamelCase__   ):
            return tf.reduce_all(tf.equal(lowerCamelCase__					,	lowerCamelCase__   )   )
  
         lowerCAmelCase_:       Optional[int]         =  BertTokenizer.from_pretrained("bert-base-cased"   )
         lowerCAmelCase_:       str         =  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   )					,	lowerCamelCase__   )
  
         with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"   ):
            self.assert_dump_and_restore(
                tokenizer_r("Small example to encode"					,	return_tensors=TensorType.TENSORFLOW   )					,	lowerCamelCase__   )
  
  
  
  
      @require_torch
      @require_tokenizers
      def     _a						(						self   ):
         import torch
  
         lowerCAmelCase_:       List[str]         =  BertTokenizer.from_pretrained("bert-base-cased"   )
         lowerCAmelCase_:       Dict         =  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   ):
         lowerCAmelCase_:       List[Any]         =  BertTokenizer.from_pretrained("bert-base-cased"   )
         lowerCAmelCase_:       List[str]         =  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   ):
         lowerCAmelCase_:       Tuple         =  BertTokenizerFast.from_pretrained("bert-base-cased"   )
         lowerCAmelCase_:       Any         =  tokenizer_r(["Test", "\xad", "test"]					,	is_split_into_words=lowerCamelCase__   )
  
         self.assertEqual(encoded.word_to_tokens(0   )					,	TokenSpan(start=1					,	end=2   )   )
         self.assertEqual(encoded.word_to_tokens(1   )					,	lowerCamelCase__   )
         self.assertEqual(encoded.word_to_tokens(2   )					,	TokenSpan(start=2					,	end=3   )   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Optional[int]         =  BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
         lowerCAmelCase_:       List[Any]         =  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_:       List[Any]         =  batch.convert_to_tensors(tensor_type="np"   )
         self.assertFalse(len(cs.err   )					,	msg=F'''should have no warning, but got {cs.err}'''   )
  
         lowerCAmelCase_:       List[Any]         =  BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
         lowerCAmelCase_:       Tuple         =  batch.convert_to_tensors(tensor_type="np"					,	prepend_batch_axis=lowerCamelCase__   )
         self.assertEqual(tensor_batch["inputs"].shape					,	(1, 3)   )
         self.assertEqual(tensor_batch["labels"].shape					,	(1,)   )
  
  
  
  
      @require_torch
      def     _a						(						self   ):
         lowerCAmelCase_:       Tuple         =  BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
         lowerCAmelCase_:       Union[str, Any]         =  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_:       Optional[int]         =  batch.convert_to_tensors(tensor_type="pt"   )
         self.assertFalse(len(cs.err   )					,	msg=F'''should have no warning, but got {cs.err}'''   )
  
         lowerCAmelCase_:       Union[str, Any]         =  BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
         lowerCAmelCase_:       Union[str, Any]         =  batch.convert_to_tensors(tensor_type="pt"					,	prepend_batch_axis=lowerCamelCase__   )
         self.assertEqual(tensor_batch["inputs"].shape					,	(1, 3)   )
         self.assertEqual(tensor_batch["labels"].shape					,	(1,)   )
  
  
  
  
      @require_tf
      def     _a						(						self   ):
         lowerCAmelCase_:       Optional[Any]         =  BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
         lowerCAmelCase_:       Optional[int]         =  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_:       List[str]         =  batch.convert_to_tensors(tensor_type="tf"   )
         self.assertFalse(len(cs.err   )					,	msg=F'''should have no warning, but got {cs.err}'''   )
  
         lowerCAmelCase_:       Any         =  BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
         lowerCAmelCase_:       Optional[int]         =  batch.convert_to_tensors(tensor_type="tf"					,	prepend_batch_axis=lowerCamelCase__   )
         self.assertEqual(tensor_batch["inputs"].shape					,	(1, 3)   )
         self.assertEqual(tensor_batch["labels"].shape					,	(1,)   )
  
  
  
  
      @require_flax
      def     _a						(						self   ):
         lowerCAmelCase_:       Optional[Any]         =  BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
         lowerCAmelCase_:       Any         =  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_:       Union[str, Any]         =  batch.convert_to_tensors(tensor_type="jax"   )
         self.assertFalse(len(cs.err   )					,	msg=F'''should have no warning, but got {cs.err}'''   )
  
         lowerCAmelCase_:       Union[str, Any]         =  BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
         lowerCAmelCase_:       List[Any]         =  batch.convert_to_tensors(tensor_type="jax"					,	prepend_batch_axis=lowerCamelCase__   )
         self.assertEqual(tensor_batch["inputs"].shape					,	(1, 3)   )
         self.assertEqual(tensor_batch["labels"].shape					,	(1,)   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       str         =  [{"input_ids": np.array([0, 1, 2]   )}, {"input_ids": np.array([0, 1, 2, 3]   )}]
         lowerCAmelCase_:       List[str]         =  BertTokenizer.from_pretrained("bert-base-cased"   )
  
         lowerCAmelCase_:       List[Any]         =  tokenizer.pad(lowerCamelCase__					,	padding=lowerCamelCase__   )
         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_:       int         =  tokenizer.pad(lowerCamelCase__					,	padding=lowerCamelCase__					,	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   ):
         import torch
  
         lowerCAmelCase_:       Tuple         =  [{"input_ids": torch.tensor([0, 1, 2]   )}, {"input_ids": torch.tensor([0, 1, 2, 3]   )}]
         lowerCAmelCase_:       int         =  BertTokenizer.from_pretrained("bert-base-cased"   )
  
         lowerCAmelCase_:       str         =  tokenizer.pad(lowerCamelCase__					,	padding=lowerCamelCase__   )
         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_:       Optional[Any]         =  tokenizer.pad(lowerCamelCase__					,	padding=lowerCamelCase__					,	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   ):
         import tensorflow as tf
  
         lowerCAmelCase_:       Any         =  [{"input_ids": tf.constant([0, 1, 2]   )}, {"input_ids": tf.constant([0, 1, 2, 3]   )}]
         lowerCAmelCase_:       Dict         =  BertTokenizer.from_pretrained("bert-base-cased"   )
  
         lowerCAmelCase_:       Dict         =  tokenizer.pad(lowerCamelCase__					,	padding=lowerCamelCase__   )
         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_:       str         =  tokenizer.pad(lowerCamelCase__					,	padding=lowerCamelCase__					,	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   ):
         lowerCAmelCase_:       Union[str, Any]         =  Tokenizer(WordPiece(unk_token="[UNK]"   )   )
         PreTrainedTokenizerFast(tokenizer_object=lowerCamelCase__   )
  
  
  
  
      @require_tokenizers
      def     _a						(						self   ):
         lowerCAmelCase_:       List[str]         =  Tokenizer(WordPiece(unk_token="[UNK]"   )   )
         with tempfile.TemporaryDirectory() as tmpdirname:
            bert_tokenizer.save(os.path.join(lowerCamelCase__					,	"tokenizer.json"   )   )
            PreTrainedTokenizerFast(tokenizer_file=os.path.join(lowerCamelCase__					,	"tokenizer.json"   )   )
style_context: from .balanced_parentheses import balanced_parentheses
  from .stack import Stack
  def 		snake_case__  (		lowercase				):
     return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(lowercase       ,							-1				)
  def 		snake_case__  (		lowercase				):
     if not balanced_parentheses(lowercase				):
        raise ValueError("Mismatched parentheses"				)
     lowerCAmelCase_:       Stack[str]         =  Stack()
     lowerCAmelCase_:       Union[str, Any]         =  []
     for char in expression_str:
        if char.isalpha() or char.isdigit():
           postfix.append(lowercase				)
        elif char == "(":
           stack.push(lowercase				)
        elif char == ")":
           while not stack.is_empty() and stack.peek() != "(":
              postfix.append(stack.pop()				)
           stack.pop()
        else:
           while not stack.is_empty() and precedence(lowercase				) <= precedence(stack.peek()				):
              postfix.append(stack.pop()				)
           stack.push(lowercase				)
     while not stack.is_empty():
        postfix.append(stack.pop()				)
     return " ".join(lowercase				)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
   a     :			int					    =						"""a+b*(c^d-e)^(f+g*h)-i"""
  
   print("""Infix to Postfix Notation demonstration:\n""")
   print("""Infix notation: """ + expression)
   print("""Postfix notation: """ + infix_to_postfix(expression))