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 						UpperCAmelCase__   (      unittest.TestCase		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]	,      UpperCamelCase       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			list(tokenizer_class.max_model_input_sizes.keys()		)
              for model_name in sa_models[:1]:
                     _lowercase					:				Optional[Any]           =			tokenizer_class.from_pretrained(UpperCamelCase		)
                     self.assertIsNotNone(UpperCamelCase		)
                     self.assertIsInstance(UpperCamelCase	,      UpperCamelCase		)
                     self.assertIsInstance(UpperCamelCase	,      UpperCamelCase		)
  
                     for special_tok in tokenizer.all_special_tokens:
                            self.assertIsInstance(UpperCamelCase	,      UpperCamelCase		)
                            _lowercase					:				int           =			tokenizer.convert_tokens_to_ids(UpperCamelCase		)
                            self.assertIsInstance(UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]	,      UpperCamelCase       :       BatchEncoding	,      UpperCamelCase       :       Optional[Callable] = None		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Tuple           =			pickle.dumps(UpperCamelCase		)
              self.assertIsNotNone(UpperCamelCase		)
  
              _lowercase					:				int           =			pickle.loads(UpperCamelCase		)
  
              # 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(UpperCamelCase	,      UpperCamelCase		)		)
                     else:
                            self.assertEqual(UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       @slow
       def   lowerCAmelCase_   ( self       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              self.check_tokenizer_from_pretrained(UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              self.assertEqual(TensorType('''tf'''		)	,      TensorType.TENSORFLOW		)
              self.assertEqual(TensorType('''pt'''		)	,      TensorType.PYTORCH		)
              self.assertEqual(TensorType('''np'''		)	,      TensorType.NUMPY		)
  
  
  
  
       @require_tokenizers
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              import numpy as np
  
              _lowercase					:				Dict           =			BertTokenizer.from_pretrained('''bert-base-cased'''		)
              _lowercase					:				List[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       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              import tensorflow as tf
  
              def tf_array_equals(UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       List[Any]		):
                     return tf.reduce_all(tf.equal(UpperCamelCase	,      UpperCamelCase		)		)
  
              _lowercase					:				Union[str, Any]           =			BertTokenizer.from_pretrained('''bert-base-cased'''		)
              _lowercase					:				Dict           =			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		)	,      UpperCamelCase		)
  
              with self.subTest('''BatchEncoding (Rust, return_tensors=TENSORFLOW)'''		):
                     self.assert_dump_and_restore(
                         tokenizer_r('''Small example to encode'''	,      return_tensors=TensorType.TENSORFLOW		)	,      UpperCamelCase		)
  
  
  
  
       @require_torch
       @require_tokenizers
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              import torch
  
              _lowercase					:				Union[str, Any]           =			BertTokenizer.from_pretrained('''bert-base-cased'''		)
              _lowercase					:				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       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			BertTokenizer.from_pretrained('''bert-base-cased'''		)
              _lowercase					:				Tuple           =			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]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[str]           =			BertTokenizerFast.from_pretrained('''bert-base-cased'''		)
              _lowercase					:				Union[str, Any]           =			tokenizer_r(['''Test''', '''\xad''', '''test''']	,      is_split_into_words=UpperCamelCase		)
  
              self.assertEqual(encoded.word_to_tokens(0		)	,      TokenSpan(start=1	,      end=2		)		)
              self.assertEqual(encoded.word_to_tokens(1		)	,      UpperCamelCase		)
              self.assertEqual(encoded.word_to_tokens(2		)	,      TokenSpan(start=2	,      end=3		)		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}		)
              _lowercase					:				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:
                     _lowercase					:				Union[str, Any]           =			batch.convert_to_tensors(tensor_type='''np'''		)
              self.assertFalse(len(cs.err		)	,      msg=F'should have no warning, but got {cs.err}'		)
  
              _lowercase					:				Tuple           =			BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}		)
              _lowercase					:				str           =			batch.convert_to_tensors(tensor_type='''np'''	,      prepend_batch_axis=UpperCamelCase		)
              self.assertEqual(tensor_batch['''inputs'''].shape	,      (1, 3)		)
              self.assertEqual(tensor_batch['''labels'''].shape	,      (1,)		)
  
  
  
  
       @require_torch
       def   lowerCAmelCase_   ( self       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[Any]           =			BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}		)
              _lowercase					:				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:
                     _lowercase					:				str           =			batch.convert_to_tensors(tensor_type='''pt'''		)
              self.assertFalse(len(cs.err		)	,      msg=F'should have no warning, but got {cs.err}'		)
  
              _lowercase					:				str           =			BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}		)
              _lowercase					:				Tuple           =			batch.convert_to_tensors(tensor_type='''pt'''	,      prepend_batch_axis=UpperCamelCase		)
              self.assertEqual(tensor_batch['''inputs'''].shape	,      (1, 3)		)
              self.assertEqual(tensor_batch['''labels'''].shape	,      (1,)		)
  
  
  
  
       @require_tf
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Tuple           =			BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}		)
              _lowercase					:				Any           =			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:
                     _lowercase					:				Optional[int]           =			batch.convert_to_tensors(tensor_type='''tf'''		)
              self.assertFalse(len(cs.err		)	,      msg=F'should have no warning, but got {cs.err}'		)
  
              _lowercase					:				str           =			BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}		)
              _lowercase					:				Optional[Any]           =			batch.convert_to_tensors(tensor_type='''tf'''	,      prepend_batch_axis=UpperCamelCase		)
              self.assertEqual(tensor_batch['''inputs'''].shape	,      (1, 3)		)
              self.assertEqual(tensor_batch['''labels'''].shape	,      (1,)		)
  
  
  
  
       @require_flax
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Dict           =			BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}		)
              _lowercase					:				Union[str, 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:
                     _lowercase					:				List[Any]           =			batch.convert_to_tensors(tensor_type='''jax'''		)
              self.assertFalse(len(cs.err		)	,      msg=F'should have no warning, but got {cs.err}'		)
  
              _lowercase					:				int           =			BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}		)
              _lowercase					:				str           =			batch.convert_to_tensors(tensor_type='''jax'''	,      prepend_batch_axis=UpperCamelCase		)
              self.assertEqual(tensor_batch['''inputs'''].shape	,      (1, 3)		)
              self.assertEqual(tensor_batch['''labels'''].shape	,      (1,)		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			[{'''input_ids''': np.array([0, 1, 2]		)}, {'''input_ids''': np.array([0, 1, 2, 3]		)}]
              _lowercase					:				Optional[Any]           =			BertTokenizer.from_pretrained('''bert-base-cased'''		)
  
              _lowercase					:				List[Any]           =			tokenizer.pad(UpperCamelCase	,      padding=UpperCamelCase		)
              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]]		)
              _lowercase					:				Any           =			tokenizer.pad(UpperCamelCase	,      padding=UpperCamelCase	,      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       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              import torch
  
              _lowercase					:				Union[str, Any]           =			[{'''input_ids''': torch.tensor([0, 1, 2]		)}, {'''input_ids''': torch.tensor([0, 1, 2, 3]		)}]
              _lowercase					:				Dict           =			BertTokenizer.from_pretrained('''bert-base-cased'''		)
  
              _lowercase					:				Tuple           =			tokenizer.pad(UpperCamelCase	,      padding=UpperCamelCase		)
              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]]		)
              _lowercase					:				Optional[int]           =			tokenizer.pad(UpperCamelCase	,      padding=UpperCamelCase	,      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       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              import tensorflow as tf
  
              _lowercase					:				List[Any]           =			[{'''input_ids''': tf.constant([0, 1, 2]		)}, {'''input_ids''': tf.constant([0, 1, 2, 3]		)}]
              _lowercase					:				Tuple           =			BertTokenizer.from_pretrained('''bert-base-cased'''		)
  
              _lowercase					:				Optional[Any]           =			tokenizer.pad(UpperCamelCase	,      padding=UpperCamelCase		)
              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]]		)
              _lowercase					:				int           =			tokenizer.pad(UpperCamelCase	,      padding=UpperCamelCase	,      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       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				int           =			Tokenizer(WordPiece(unk_token='''[UNK]'''		)		)
              PreTrainedTokenizerFast(tokenizer_object=UpperCamelCase		)
  
  
  
  
       @require_tokenizers
       def   lowerCAmelCase_   ( self       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			Tokenizer(WordPiece(unk_token='''[UNK]'''		)		)
              with tempfile.TemporaryDirectory() as tmpdirname:
                     bert_tokenizer.save(os.path.join(UpperCamelCase	,      '''tokenizer.json'''		)		)
                     PreTrainedTokenizerFast(tokenizer_file=os.path.join(UpperCamelCase	,      '''tokenizer.json'''		)		)
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  UpperCamelCase__				       =			{
      'configuration_realm': ['REALM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RealmConfig'],
      'tokenization_realm': ['RealmTokenizer'],
  }
  
  try:
     if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     UpperCamelCase__				       =			['RealmTokenizerFast']
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     UpperCamelCase__				       =			[
         'REALM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'RealmEmbedder',
         'RealmForOpenQA',
         'RealmKnowledgeAugEncoder',
         'RealmPreTrainedModel',
         'RealmReader',
         'RealmScorer',
         'load_tf_weights_in_realm',
     ]
     UpperCamelCase__				       =			['RealmRetriever']
  
  
  if TYPE_CHECKING:
     from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
     from .tokenization_realm import RealmTokenizer
  
     try:
        if not is_tokenizers_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .tokenization_realm import RealmTokenizerFast
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_realm import (
            REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RealmEmbedder,
            RealmForOpenQA,
            RealmKnowledgeAugEncoder,
            RealmPreTrainedModel,
            RealmReader,
            RealmScorer,
            load_tf_weights_in_realm,
        )
        from .retrieval_realm import RealmRetriever
  
  
  else:
     import sys
  
     UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)