
"""simple docstring"""






import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin




def __magic_name__  (							_lowerCamelCase:		List[Any]=2,				_lowerCamelCase:		List[Any]=True,				_lowerCamelCase:		Union[str, Any]=False )				->	List[Any]:

 '''simple docstring'''




 lowerCAmelCase				       =     Accelerator(step_scheduler_with_optimizer=_lowerCamelCase,				split_batches=_lowerCamelCase )
 lowerCAmelCase				       =     torch.nn.Linear(2,				4 )
 lowerCAmelCase				       =     torch.optim.AdamW(model.parameters(),				lr=1.0 )
 lowerCAmelCase				       =     torch.optim.lr_scheduler.OneCycleLR(_lowerCamelCase,				max_lr=0.01,				steps_per_epoch=2,				epochs=1 )
 lowerCAmelCase       ,					lowerCAmelCase       ,					lowerCAmelCase				       =     accelerator.prepare(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase )

 # Optimizer has stepped
 scheduler.step()
 if step_scheduler_with_optimizer or (num_processes == 1):
  assert (
      scheduler.scheduler.last_epoch == num_processes
  ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
 else:
  assert (
      scheduler.scheduler.last_epoch != num_processes
  ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""




def __magic_name__  (							_lowerCamelCase:		List[Any]=2,				_lowerCamelCase:		Union[str, Any]=True,				_lowerCamelCase:		Optional[int]=False )				->	Optional[int]:

 '''simple docstring'''




 lowerCAmelCase				       =     Accelerator(step_scheduler_with_optimizer=_lowerCamelCase,				split_batches=_lowerCamelCase )
 lowerCAmelCase				       =     torch.nn.Linear(2,				4 )
 lowerCAmelCase				       =     torch.optim.AdamW(model.parameters(),				lr=1.0 )
 lowerCAmelCase				       =     torch.optim.lr_scheduler.LambdaLR(_lowerCamelCase,				lr_lambda=lambda _lowerCamelCase : 1 - n / 10 )
 lowerCAmelCase       ,					lowerCAmelCase       ,					lowerCAmelCase				       =     accelerator.prepare(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase )

 # Optimizer has stepped
 lowerCAmelCase				       =     False
 scheduler.step()
 lowerCAmelCase				       =     1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

 # Optimizer has not stepped
 lowerCAmelCase				       =     True
 scheduler.step()
 if not step_scheduler_with_optimizer:
  lowerCAmelCase				       =     1 - 2 / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""




def __magic_name__  (							_lowerCamelCase:		int = 2 )				->	Dict:

 '''simple docstring'''




 from transformers import get_linear_schedule_with_warmup

 lowerCAmelCase				       =     [1, 2, 4]
 for num_steps in steps:
  lowerCAmelCase				       =     GradientAccumulationPlugin(num_steps=_lowerCamelCase,				adjust_scheduler=num_steps > 1 )
  lowerCAmelCase				       =     Accelerator(gradient_accumulation_plugin=_lowerCamelCase )
  lowerCAmelCase				       =     torch.nn.Linear(2,				4 )
  lowerCAmelCase				       =     torch.optim.AdamW(model.parameters(),				lr=10.0 )
  lowerCAmelCase				       =     get_linear_schedule_with_warmup(optimizer=_lowerCamelCase,				num_warmup_steps=0,				num_training_steps=20 )

  lowerCAmelCase       ,					lowerCAmelCase       ,					lowerCAmelCase				       =     accelerator.prepare(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase )

  for i in range(10 * num_steps ):
   with accelerator.accumulate(_lowerCamelCase ):
    optimizer.step()
    scheduler.step()

   if i == (10 * num_steps - 2):
    assert (
        scheduler.get_last_lr()[0] != 0
    ), F"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
  assert (
      scheduler.get_last_lr()[0] == 0
  ), F"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
  GradientState._reset_state()



@require_cpu
class  lowercase				(   unittest.TestCase		):






   def 				UpperCAmelCase       (self     :  Tuple )					->  Union[str, Any]:





    """simple docstring"""


    debug_launcher(partial(SCREAMING_SNAKE_CASE_			,num_processes=1 )			,num_processes=1 )
    debug_launcher(partial(SCREAMING_SNAKE_CASE_			,num_processes=1			,split_batches=SCREAMING_SNAKE_CASE_ )			,num_processes=1 )






   def 				UpperCAmelCase       (self     :  str )					->  Dict:





    """simple docstring"""


    debug_launcher(partial(SCREAMING_SNAKE_CASE_			,num_processes=1 )			,num_processes=1 )
    debug_launcher(partial(SCREAMING_SNAKE_CASE_			,num_processes=1			,split_batches=SCREAMING_SNAKE_CASE_ )			,num_processes=1 )






   def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  List[str]:





    """simple docstring"""


    debug_launcher(partial(SCREAMING_SNAKE_CASE_			,num_processes=1			,step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_ )			,num_processes=1 )






   def 				UpperCAmelCase       (self     :  Dict )					->  List[Any]:





    """simple docstring"""


    debug_launcher(partial(SCREAMING_SNAKE_CASE_			,num_processes=1			,step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_ )			,num_processes=1 )






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





    """simple docstring"""


    AcceleratorState._reset_state(SCREAMING_SNAKE_CASE_ )
    debug_launcher(SCREAMING_SNAKE_CASE_ )
    debug_launcher(partial(SCREAMING_SNAKE_CASE_			,num_processes=1			,split_batches=SCREAMING_SNAKE_CASE_ )			,num_processes=1 )






   def 				UpperCAmelCase       (self     :  Tuple )					->  List[str]:





    """simple docstring"""


    AcceleratorState._reset_state(SCREAMING_SNAKE_CASE_ )
    debug_launcher(SCREAMING_SNAKE_CASE_ )
    debug_launcher(partial(SCREAMING_SNAKE_CASE_			,num_processes=1			,split_batches=SCREAMING_SNAKE_CASE_ )			,num_processes=1 )






   def 				UpperCAmelCase       (self     :  Tuple )					->  Optional[int]:





    """simple docstring"""


    AcceleratorState._reset_state(SCREAMING_SNAKE_CASE_ )
    debug_launcher(partial(SCREAMING_SNAKE_CASE_			,step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_ ) )






   def 				UpperCAmelCase       (self     :  Optional[int] )					->  str:





    """simple docstring"""


    AcceleratorState._reset_state(SCREAMING_SNAKE_CASE_ )
    debug_launcher(partial(SCREAMING_SNAKE_CASE_			,step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_ ) )






   @require_huggingface_suite
   def 				UpperCAmelCase       (self     :  int )					->  Tuple:





    """simple docstring"""


    AcceleratorState._reset_state(SCREAMING_SNAKE_CASE_ )
    debug_launcher(partial(SCREAMING_SNAKE_CASE_			,num_processes=1 ) )
    debug_launcher(SCREAMING_SNAKE_CASE_ )




"""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  lowercase				(   unittest.TestCase		):






			def 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  Dict )					->  int:





				"""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 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  BatchEncoding			,SCREAMING_SNAKE_CASE_     :  Optional[Callable] = None )					->  Dict:





				"""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 				UpperCAmelCase       (self     :  List[str] )					->  int:





				"""simple docstring"""


				self.check_tokenizer_from_pretrained(SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  str )					->  Dict:





				"""simple docstring"""


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






			@require_tokenizers
			def 				UpperCAmelCase       (self     :  int )					->  Tuple:





				"""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 				UpperCAmelCase       (self     :  str )					->  int:





				"""simple docstring"""


				import tensorflow as tf

				def tf_array_equals(SCREAMING_SNAKE_CASE_     :  Optional[Any]			,SCREAMING_SNAKE_CASE_     :  List[Any] ):
					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 				UpperCAmelCase       (self     :  Optional[Any] )					->  Union[str, Any]:





				"""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 				UpperCAmelCase       (self     :  str )					->  Tuple:





				"""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 				UpperCAmelCase       (self     :  Any )					->  Dict:





				"""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 				UpperCAmelCase       (self     :  List[Any] )					->  int:





				"""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 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Optional[int]:





				"""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 				UpperCAmelCase       (self     :  List[Any] )					->  List[Any]:





				"""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 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Optional[int]:





				"""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 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Optional[Any]:





				"""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 				UpperCAmelCase       (self     :  str )					->  Tuple:





				"""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 				UpperCAmelCase       (self     :  Tuple )					->  Any:





				"""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 				UpperCAmelCase       (self     :  Union[str, Any] )					->  List[str]:





				"""simple docstring"""


				lowerCAmelCase				       =     Tokenizer(WordPiece(unk_token='''[UNK]''' ) )
				PreTrainedTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE_ )






			@require_tokenizers
			def 				UpperCAmelCase       (self     :  Tuple )					->  Optional[Any]:





				"""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''' ) )


