code: 
  
  
  import tempfile
  import unittest
  
  import numpy as np
  
  import transformers
  from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
  
  
  if is_flax_available():
   import jax
   import jax.numpy as jnp
  
   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel
  
  if is_torch_available():
   import torch
  
  
  
  
  
  class        lowercase_				:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase=14			,    _UpperCAmelCase=7			,    _UpperCAmelCase=True			,    _UpperCAmelCase=True			,    _UpperCAmelCase=False			,    _UpperCAmelCase=True			,    _UpperCAmelCase=99			,    _UpperCAmelCase=32			,    _UpperCAmelCase=4			,    _UpperCAmelCase=4			,    _UpperCAmelCase=[[["global", "local"], 2]]			,    _UpperCAmelCase=37			,    _UpperCAmelCase="gelu"			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=512			,    _UpperCAmelCase=7			,    _UpperCAmelCase=0.0_2			,    ):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		parent
               a_					=		batch_size
               a_					=		seq_length
               a_					=		is_training
               a_					=		use_input_mask
               a_					=		use_token_type_ids
               a_					=		use_labels
               a_					=		vocab_size
               a_					=		hidden_size
               a_					=		num_hidden_layers
               a_					=		num_attention_heads
               a_					=		attention_types
               a_					=		intermediate_size
               a_					=		hidden_act
               a_					=		hidden_dropout_prob
               a_					=		attention_probs_dropout_prob
               a_					=		max_position_embeddings
               a_					=		window_size
               a_					=		initializer_range
               a_					=		None
               a_					=		vocab_size - 1
               a_					=		vocab_size - 1
               a_					=		vocab_size - 1
  
  
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size		)
  
               a_					=		None
               if self.use_input_mask:
                     a_					=		random_attention_mask([self.batch_size, self.seq_length]		)
  
               a_					=		GPTNeoConfig(
                   vocab_size=self.vocab_size			,    hidden_size=self.hidden_size			,    num_layers=self.num_hidden_layers			,    num_heads=self.num_attention_heads			,    max_position_embeddings=self.max_position_embeddings			,    use_cache=_UpperCAmelCase			,    bos_token_id=self.bos_token_id			,    eos_token_id=self.eos_token_id			,    pad_token_id=self.pad_token_id			,    window_size=self.window_size			,    attention_types=self.attention_types			,    )
  
               return (config, input_ids, input_mask)
  
  
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		self.prepare_config_and_inputs()
               a_			,   a_			,   a_					=		config_and_inputs
               a_					=		{"""input_ids""": input_ids, """attention_mask""": attention_mask}
               return config, inputs_dict
  
  
         def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		20
               a_					=		model_class_name(_UpperCAmelCase		)
  
               a_					=		model.init_cache(input_ids.shape[0]			,    _UpperCAmelCase		)
               a_					=		jnp.ones((input_ids.shape[0], max_decoder_length)			,    dtype="""i4"""		)
  
               a_					=		jnp.broadcast_to(
                   jnp.arange(input_ids.shape[-1] - 1		)[None, :]			,    (input_ids.shape[0], input_ids.shape[-1] - 1)		)
               a_					=		model(
                   input_ids[:, :-1]			,    attention_mask=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    position_ids=_UpperCAmelCase			,    )
  
               a_					=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
               a_					=		model(
                   input_ids[:, -1:]			,    attention_mask=_UpperCAmelCase			,    past_key_values=outputs_cache.past_key_values			,    position_ids=_UpperCAmelCase			,    )
  
               a_					=		model(_UpperCAmelCase		)
  
               a_					=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
               self.parent.assertTrue(diff < 1e-3			,    msg=f"Max diff is {diff}"		)
  
  
  
  
  
  
         def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		20
               a_					=		model_class_name(_UpperCAmelCase		)
  
               a_					=		jnp.concatenate(
                   [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])		)]			,    axis=-1			,    )
  
               a_					=		model.init_cache(input_ids.shape[0]			,    _UpperCAmelCase		)
               a_					=		jnp.broadcast_to(
                   jnp.arange(input_ids.shape[-1] - 1		)[None, :]			,    (input_ids.shape[0], input_ids.shape[-1] - 1)		)
  
               a_					=		model(
                   input_ids[:, :-1]			,    attention_mask=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    position_ids=_UpperCAmelCase			,    )
               a_					=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
               a_					=		model(
                   input_ids[:, -1:]			,    past_key_values=outputs_cache.past_key_values			,    attention_mask=_UpperCAmelCase			,    position_ids=_UpperCAmelCase			,    )
  
               a_					=		model(_UpperCAmelCase			,    attention_mask=_UpperCAmelCase		)
  
               a_					=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
               self.parent.assertTrue(diff < 1e-3			,    msg=f"Max diff is {diff}"		)
  
  
  
  
  
  @require_flax
  class        lowercase_				( UpperCamelCase__			,UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         snake_case_        =		(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
         snake_case_        =		(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
  
  
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		FlaxGPTNeoModelTester(self		)
  
  
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               for model_class_name in self.all_model_classes:
                     a_			,   a_			,   a_					=		self.model_tester.prepare_config_and_inputs()
                     self.model_tester.check_use_cache_forward(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               for model_class_name in self.all_model_classes:
                     a_			,   a_			,   a_					=		self.model_tester.prepare_config_and_inputs()
                     self.model_tester.check_use_cache_forward_with_attn_mask(
                         _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
         @slow
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		GPTaTokenizer.from_pretrained("""gpt2"""			,    pad_token="""<|endoftext|>"""			,    padding_side="""left"""		)
               a_					=		tokenizer(["""Hello this is a long string""", """Hey"""]			,    return_tensors="""np"""			,    padding=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)
  
               a_					=		FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""		)
               a_					=		False
               a_					=		model.config.eos_token_id
  
               a_					=		jax.jit(model.generate		)
  
               a_					=		jit_generate(
                   inputs["""input_ids"""]			,    attention_mask=inputs["""attention_mask"""]			,    pad_token_id=tokenizer.pad_token_id		).sequences
  
               a_					=		tokenizer.batch_decode(_UpperCAmelCase			,    skip_special_tokens=_UpperCAmelCase		)
  
               a_					=		[
                   """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
                   """Hey, I'm a little late to the party. I'm going to""",
               ]
  
               self.assertListEqual(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  
         @is_pt_flax_cross_test
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_			,   a_					=		self.model_tester.prepare_config_and_inputs_for_common()
  
               for model_class in self.all_model_classes:
                     with self.subTest(model_class.__name__		):
                           # prepare inputs
                           a_					=		self._prepare_for_class(_UpperCAmelCase			,    _UpperCAmelCase		)
                           a_					=		{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}
  
                           # load corresponding PyTorch class
                           a_					=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                           a_					=		getattr(_UpperCAmelCase			,    _UpperCAmelCase		)
  
                           a_			,   a_					=		pt_inputs["""input_ids"""].shape
                           a_					=		np.random.randint(0			,    seq_length - 1			,    size=(batch_size,)		)
                           for batch_idx, start_index in enumerate(_UpperCAmelCase		):
                                 a_					=		0
                                 a_					=		1
                                 a_					=		0
                                 a_					=		1
                           a_					=		pt_model_class(_UpperCAmelCase		).eval()
                           a_					=		model_class(_UpperCAmelCase			,    dtype=jnp.floataa		)
  
                           a_					=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,    _UpperCAmelCase		)
                           a_					=		fx_state
  
                           with torch.no_grad():
                                 a_					=		pt_model(**_UpperCAmelCase		).to_tuple()
  
                           a_					=		fx_model(**_UpperCAmelCase		).to_tuple()
                           self.assertEqual(len(_UpperCAmelCase		)			,    len(_UpperCAmelCase		)			,    """Output lengths differ between Flax and PyTorch"""		)
                           for fx_output, pt_output in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
                                 self.assert_almost_equals(fx_output[:, -1]			,    pt_output[:, -1].numpy()			,    4e-2		)
  
                           with tempfile.TemporaryDirectory() as tmpdirname:
                                 pt_model.save_pretrained(_UpperCAmelCase		)
                                 a_					=		model_class.from_pretrained(_UpperCAmelCase			,    from_pt=_UpperCAmelCase		)
  
                           a_					=		fx_model_loaded(**_UpperCAmelCase		).to_tuple()
                           self.assertEqual(
                               len(_UpperCAmelCase		)			,    len(_UpperCAmelCase		)			,    """Output lengths differ between Flax and PyTorch"""		)
                           for fx_output_loaded, pt_output in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
                                 self.assert_almost_equals(fx_output_loaded[:, -1]			,    pt_output[:, -1].numpy()			,    4e-2		)
  
  
         @is_pt_flax_cross_test
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_			,   a_					=		self.model_tester.prepare_config_and_inputs_for_common()
               for model_class in self.all_model_classes:
                     with self.subTest(model_class.__name__		):
                           # prepare inputs
                           a_					=		self._prepare_for_class(_UpperCAmelCase			,    _UpperCAmelCase		)
                           a_					=		{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}
  
                           # load corresponding PyTorch class
                           a_					=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                           a_					=		getattr(_UpperCAmelCase			,    _UpperCAmelCase		)
  
                           a_					=		pt_model_class(_UpperCAmelCase		).eval()
                           a_					=		model_class(_UpperCAmelCase			,    dtype=jnp.floataa		)
  
                           a_					=		load_flax_weights_in_pytorch_model(_UpperCAmelCase			,    fx_model.params		)
                           a_			,   a_					=		pt_inputs["""input_ids"""].shape
                           a_					=		np.random.randint(0			,    seq_length - 1			,    size=(batch_size,)		)
                           for batch_idx, start_index in enumerate(_UpperCAmelCase		):
                                 a_					=		0
                                 a_					=		1
                                 a_					=		0
                                 a_					=		1
  
                           # make sure weights are tied in PyTorch
                           pt_model.tie_weights()
  
                           with torch.no_grad():
                                 a_					=		pt_model(**_UpperCAmelCase		).to_tuple()
  
                           a_					=		fx_model(**_UpperCAmelCase		).to_tuple()
                           self.assertEqual(len(_UpperCAmelCase		)			,    len(_UpperCAmelCase		)			,    """Output lengths differ between Flax and PyTorch"""		)
                           for fx_output, pt_output in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
                                 self.assert_almost_equals(fx_output[:, -1]			,    pt_output[:, -1].numpy()			,    4e-2		)
  
                           with tempfile.TemporaryDirectory() as tmpdirname:
                                 fx_model.save_pretrained(_UpperCAmelCase		)
                                 a_					=		pt_model_class.from_pretrained(_UpperCAmelCase			,    from_flax=_UpperCAmelCase		)
  
                           with torch.no_grad():
                                 a_					=		pt_model_loaded(**_UpperCAmelCase		).to_tuple()
  
                           self.assertEqual(
                               len(_UpperCAmelCase		)			,    len(_UpperCAmelCase		)			,    """Output lengths differ between Flax and PyTorch"""		)
                           for fx_output, pt_output in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
                                 self.assert_almost_equals(fx_output[:, -1]			,    pt_output[:, -1].numpy()			,    4e-2		)
  
  
  
  
  
  
         @slow
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               for model_class_name in self.all_model_classes:
                     a_					=		model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""		)
                     a_					=		model(np.ones((1, 1)		)		)
                     self.assertIsNotNone(_UpperCAmelCase		)
style_context: 
  
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_tokenizers, require_torch
  from transformers.utils import cached_property
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
  
  
  
  
  
  @require_tokenizers
  class        lowercase_				( UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		BartTokenizer
  							snake_case_        =		BartTokenizerFast
  							snake_case_        =		True
  							snake_case_        =		filter_roberta_detectors
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().setUp()
  													a_					=		[
  													    """l""",
  													    """o""",
  													    """w""",
  													    """e""",
  													    """r""",
  													    """s""",
  													    """t""",
  													    """i""",
  													    """d""",
  													    """n""",
  													    """\u0120""",
  													    """\u0120l""",
  													    """\u0120n""",
  													    """\u0120lo""",
  													    """\u0120low""",
  													    """er""",
  													    """\u0120lowest""",
  													    """\u0120newer""",
  													    """\u0120wider""",
  													    """<unk>""",
  													]
  													a_					=		dict(zip(_UpperCAmelCase			,    range(len(_UpperCAmelCase		)		)		)		)
  													a_					=		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
  													a_					=		{"""unk_token""": """<unk>"""}
  
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""]		)
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""merges_file"""]		)
  													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
  																			fp.write(json.dumps(_UpperCAmelCase		) + """\n"""		)
  													with open(self.merges_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
  																			fp.write("""\n""".join(_UpperCAmelCase		)		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													kwargs.update(self.special_tokens_map		)
  													return self.tokenizer_class.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													kwargs.update(self.special_tokens_map		)
  													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return "lower newer", "lower newer"
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return BartTokenizer.from_pretrained("""facebook/bart-large"""		)
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return BartTokenizerFast.from_pretrained("""facebook/bart-large"""		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
  													a_					=		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]
  
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    max_length=len(_UpperCAmelCase		)			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  																			self.assertEqual((2, 9)			,    batch.input_ids.shape		)
  																			self.assertEqual((2, 9)			,    batch.attention_mask.shape		)
  																			a_					=		batch.input_ids.tolist()[0]
  																			self.assertListEqual(_UpperCAmelCase			,    _UpperCAmelCase		)
  																			# Test that special tokens are reset
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			# check if input_ids are returned and no labels
  																			self.assertIn("""input_ids"""			,    _UpperCAmelCase		)
  																			self.assertIn("""attention_mask"""			,    _UpperCAmelCase		)
  																			self.assertNotIn("""labels"""			,    _UpperCAmelCase		)
  																			self.assertNotIn("""decoder_attention_mask"""			,    _UpperCAmelCase		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		[
  													    """Summary of the text.""",
  													    """Another summary.""",
  													]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    max_length=32			,    padding="""max_length"""			,    return_tensors="""pt"""		)
  																			self.assertEqual(32			,    targets["""input_ids"""].shape[1]		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(
  																			    ["""I am a small frog""" * 1_024, """I am a small frog"""]			,    padding=_UpperCAmelCase			,    truncation=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)
  																			self.assertEqual(batch.input_ids.shape			,    (2, 1_024)		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization."""]
  													a_					=		[
  													    """Summary of the text.""",
  													]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			a_					=		inputs["""input_ids"""]
  																			a_					=		targets["""input_ids"""]
  																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
  																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
  																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
  																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													pass
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  																			with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
  																									a_					=		self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
  																									a_					=		self.tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
  																									a_					=		"""A, <mask> AllenNLP sentence."""
  																									a_					=		tokenizer_r.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
  																									a_					=		tokenizer_p.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
  
  																									# token_type_ids should put 0 everywhere
  																									self.assertEqual(sum(tokens_r["""token_type_ids"""]		)			,    sum(tokens_p["""token_type_ids"""]		)		)
  
  																									# attention_mask should put 1 everywhere, so sum over length should be 1
  																									self.assertEqual(
  																									    sum(tokens_r["""attention_mask"""]		) / len(tokens_r["""attention_mask"""]		)			,    sum(tokens_p["""attention_mask"""]		) / len(tokens_p["""attention_mask"""]		)			,    )
  
  																									a_					=		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
  																									a_					=		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]		)
  
  																									# Rust correctly handles the space before the mask while python doesnt
  																									self.assertSequenceEqual(tokens_p["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
  																									self.assertSequenceEqual(tokens_r["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
  
  																									self.assertSequenceEqual(
  																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
  																									self.assertSequenceEqual(
  																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)