code: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			list[int | float]	):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if len(lowerCAmelCase	) == 0:
  				raise ValueError('find_max() arg is an empty sequence'	)
  		__magic_name__ :					Any  			=  nums[0]
  		for x in nums:
  				if x > max_num:
  						__magic_name__ :					str  			=  x
  		return max_num
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod(verbose=True)
style_context: 
  
  
  '''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 _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any						,  _A	:		Optional[Any]    )						->				Optional[int]:
  			__magic_name__ :					Optional[int]  			=  list(tokenizer_class.max_model_input_sizes.keys()    )
  			for model_name in sa_models[:1]:
  				__magic_name__ :					List[str]  			=  tokenizer_class.from_pretrained(_A    )
  				self.assertIsNotNone(_A    )
  				self.assertIsInstance(_A						,  _A    )
  				self.assertIsInstance(_A						,  _A    )
  
  				for special_tok in tokenizer.all_special_tokens:
  					self.assertIsInstance(_A						,  _A    )
  					__magic_name__ :					Optional[Any]  			=  tokenizer.convert_tokens_to_ids(_A    )
  					self.assertIsInstance(_A						,  _A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		BatchEncoding						,  _A	:		Optional[Callable] = None    )						->				Tuple:
  			__magic_name__ :					Union[str, Any]  			=  pickle.dumps(_A    )
  			self.assertIsNotNone(_A    )
  
  			__magic_name__ :					Optional[Any]  			=  pickle.loads(_A    )
  
  			# 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(_A						,  _A    )    )
  				else:
  					self.assertEqual(_A						,  _A    )
  
  
  
  
  
  
  
  		@slow
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Optional[Any]:
  			self.check_tokenizer_from_pretrained(_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				List[Any]:
  			self.assertEqual(TensorType('tf'    )						,  TensorType.TENSORFLOW    )
  			self.assertEqual(TensorType('pt'    )						,  TensorType.PYTORCH    )
  			self.assertEqual(TensorType('np'    )						,  TensorType.NUMPY    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Optional[int]:
  			import numpy as np
  
  			__magic_name__ :					Dict  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					str  			=  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      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[str]:
  			import tensorflow as tf
  
  			def tf_array_equals(_A	:		str						,  _A	:		Union[str, Any]    ):
  				return tf.reduce_all(tf.equal(_A						,  _A    )    )
  
  			__magic_name__ :					str  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Optional[Any]  			=  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    )						,  _A    )
  
  			with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_r('Small example to encode'						,  return_tensors=TensorType.TENSORFLOW    )						,  _A    )
  
  
  
  
  
  
  
  		@require_torch
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		Any    )						->				List[str]:
  			import torch
  
  			__magic_name__ :					Union[str, Any]  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Optional[int]  			=  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      __lowerCAmelCase	(      self	:		List[str]    )						->				Dict:
  			__magic_name__ :					Optional[int]  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Dict  			=  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      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Optional[int]:
  			__magic_name__ :					Optional[int]  			=  BertTokenizerFast.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Tuple  			=  tokenizer_r(['Test', '\xad', 'test']						,  is_split_into_words=_A    )
  
  			self.assertEqual(encoded.word_to_tokens(0    )						,  TokenSpan(start=1						,  end=2    )    )
  			self.assertEqual(encoded.word_to_tokens(1    )						,  _A    )
  			self.assertEqual(encoded.word_to_tokens(2    )						,  TokenSpan(start=2						,  end=3    )    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				Optional[Any]:
  			__magic_name__ :					Any  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					Dict  			=  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:
  				__magic_name__ :					Any  			=  batch.convert_to_tensors(tensor_type='np'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					Optional[Any]  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					List[Any]  			=  batch.convert_to_tensors(tensor_type='np'						,  prepend_batch_axis=_A    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (1, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (1,)    )
  
  
  
  
  
  
  
  		@require_torch
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Any:
  			__magic_name__ :					Optional[int]  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					List[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:
  				__magic_name__ :					Union[str, Any]  			=  batch.convert_to_tensors(tensor_type='pt'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					Any  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					str  			=  batch.convert_to_tensors(tensor_type='pt'						,  prepend_batch_axis=_A    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (1, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (1,)    )
  
  
  
  
  
  
  
  		@require_tf
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				List[str]:
  			__magic_name__ :					str  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					List[str]  			=  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:
  				__magic_name__ :					Optional[int]  			=  batch.convert_to_tensors(tensor_type='tf'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					Union[str, Any]  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					Union[str, Any]  			=  batch.convert_to_tensors(tensor_type='tf'						,  prepend_batch_axis=_A    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (1, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (1,)    )
  
  
  
  
  
  
  
  		@require_flax
  		def      __lowerCAmelCase	(      self	:		str    )						->				Optional[Any]:
  			__magic_name__ :					List[Any]  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					int  			=  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:
  				__magic_name__ :					Optional[Any]  			=  batch.convert_to_tensors(tensor_type='jax'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					str  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					Dict  			=  batch.convert_to_tensors(tensor_type='jax'						,  prepend_batch_axis=_A    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (1, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (1,)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				int:
  			__magic_name__ :					int  			=  [{'input_ids': np.array([0, 1, 2]    )}, {'input_ids': np.array([0, 1, 2, 3]    )}]
  			__magic_name__ :					Dict  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  
  			__magic_name__ :					Optional[int]  			=  tokenizer.pad(_A						,  padding=_A    )
  			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]]    )
  			__magic_name__ :					Dict  			=  tokenizer.pad(_A						,  padding=_A						,  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      __lowerCAmelCase	(      self	:		List[Any]    )						->				str:
  			import torch
  
  			__magic_name__ :					List[str]  			=  [{'input_ids': torch.tensor([0, 1, 2]    )}, {'input_ids': torch.tensor([0, 1, 2, 3]    )}]
  			__magic_name__ :					List[str]  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  
  			__magic_name__ :					List[Any]  			=  tokenizer.pad(_A						,  padding=_A    )
  			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]]    )
  			__magic_name__ :					Union[str, Any]  			=  tokenizer.pad(_A						,  padding=_A						,  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      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
  			import tensorflow as tf
  
  			__magic_name__ :					Tuple  			=  [{'input_ids': tf.constant([0, 1, 2]    )}, {'input_ids': tf.constant([0, 1, 2, 3]    )}]
  			__magic_name__ :					Any  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  
  			__magic_name__ :					Any  			=  tokenizer.pad(_A						,  padding=_A    )
  			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]]    )
  			__magic_name__ :					Union[str, Any]  			=  tokenizer.pad(_A						,  padding=_A						,  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      __lowerCAmelCase	(      self	:		Any    )						->				str:
  			__magic_name__ :					str  			=  Tokenizer(WordPiece(unk_token='[UNK]'    )    )
  			PreTrainedTokenizerFast(tokenizer_object=_A    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Any:
  			__magic_name__ :					List[Any]  			=  Tokenizer(WordPiece(unk_token='[UNK]'    )    )
  			with tempfile.TemporaryDirectory() as tmpdirname:
  				bert_tokenizer.save(os.path.join(_A						,  'tokenizer.json'    )    )
  				PreTrainedTokenizerFast(tokenizer_file=os.path.join(_A						,  'tokenizer.json'    )    )