





from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFRoFormerForCausalLM,
       TFRoFormerForMaskedLM,
       TFRoFormerForMultipleChoice,
       TFRoFormerForQuestionAnswering,
       TFRoFormerForSequenceClassification,
       TFRoFormerForTokenClassification,
       TFRoFormerModel,
   )
   from transformers.models.roformer.modeling_tf_roformer import (
       TFRoFormerSelfAttention,
       TFRoFormerSinusoidalPositionalEmbedding,
   )
class     __lowerCAmelCase					:






       """simple docstring"""





       def __init__(							self       :		int	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Optional[int]=13	,			lowerCAmelCase__       :		int=7	,			lowerCAmelCase__       :		Optional[Any]=True	,			lowerCAmelCase__       :		Optional[Any]=True	,			lowerCAmelCase__       :		Optional[int]=True	,			lowerCAmelCase__       :		str=True	,			lowerCAmelCase__       :		Tuple=99	,			lowerCAmelCase__       :		Optional[int]=32	,			lowerCAmelCase__       :		str=2	,			lowerCAmelCase__       :		Optional[Any]=4	,			lowerCAmelCase__       :		Optional[int]=37	,			lowerCAmelCase__       :		str="gelu"	,			lowerCAmelCase__       :		Dict=0.1	,			lowerCAmelCase__       :		Any=0.1	,			lowerCAmelCase__       :		Dict=512	,			lowerCAmelCase__       :		int=16	,			lowerCAmelCase__       :		List[Any]=2	,			lowerCAmelCase__       :		Optional[Any]=0.02	,			lowerCAmelCase__       :		List[str]=3	,			lowerCAmelCase__       :		int=4	,			lowerCAmelCase__       :		Optional[Any]=None	,			)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	parent
            _UpperCamelCase       =	13
            _UpperCamelCase       =	7
            _UpperCamelCase       =	True
            _UpperCamelCase       =	True
            _UpperCamelCase       =	True
            _UpperCamelCase       =	True
            _UpperCamelCase       =	99
            _UpperCamelCase       =	32
            _UpperCamelCase       =	2
            _UpperCamelCase       =	4
            _UpperCamelCase       =	37
            _UpperCamelCase       =	'''gelu'''
            _UpperCamelCase       =	0.1
            _UpperCamelCase       =	0.1
            _UpperCamelCase       =	512
            _UpperCamelCase       =	16
            _UpperCamelCase       =	2
            _UpperCamelCase       =	0.02
            _UpperCamelCase       =	3
            _UpperCamelCase       =	4
            _UpperCamelCase       =	None





       def snake_case__						(							self       :		Dict					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	ids_tensor([self.batch_size, self.seq_length]	,			self.vocab_size					)

            _UpperCamelCase       =	None
            if self.use_input_mask:
                 _UpperCamelCase       =	random_attention_mask([self.batch_size, self.seq_length]					)

            _UpperCamelCase       =	None
            if self.use_token_type_ids:
                 _UpperCamelCase       =	ids_tensor([self.batch_size, self.seq_length]	,			self.type_vocab_size					)

            _UpperCamelCase       =	None
            _UpperCamelCase       =	None
            _UpperCamelCase       =	None
            if self.use_labels:
                 _UpperCamelCase       =	ids_tensor([self.batch_size]	,			self.type_sequence_label_size					)
                 _UpperCamelCase       =	ids_tensor([self.batch_size, self.seq_length]	,			self.num_labels					)
                 _UpperCamelCase       =	ids_tensor([self.batch_size]	,			self.num_choices					)

            _UpperCamelCase       =	RoFormerConfig(
                vocab_size=self.vocab_size	,			hidden_size=self.hidden_size	,			num_hidden_layers=self.num_hidden_layers	,			num_attention_heads=self.num_attention_heads	,			intermediate_size=self.intermediate_size	,			hidden_act=self.hidden_act	,			hidden_dropout_prob=self.hidden_dropout_prob	,			attention_probs_dropout_prob=self.attention_probs_dropout_prob	,			max_position_embeddings=self.max_position_embeddings	,			type_vocab_size=self.type_vocab_size	,			initializer_range=self.initializer_range	,			return_dict=lowerCAmelCase__	,			)

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





       def snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Union[str, Any]					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	TFRoFormerModel(config=lowerCAmelCase__					)
            _UpperCamelCase       =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

            _UpperCamelCase       =	[input_ids, input_mask]
            _UpperCamelCase       =	model(lowerCAmelCase__					)

            _UpperCamelCase       =	model(lowerCAmelCase__					)

            self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.seq_length, self.hidden_size)					)





       def snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		List[str]					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	True
            _UpperCamelCase       =	TFRoFormerForCausalLM(config=lowerCAmelCase__					)
            _UpperCamelCase       =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }
            _UpperCamelCase       =	model(lowerCAmelCase__					)['''logits''']
            self.parent.assertListEqual(
                list(prediction_scores.numpy().shape					)	,			[self.batch_size, self.seq_length, self.vocab_size]					)





       def snake_case__						(							self       :		str	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		int					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	TFRoFormerForMaskedLM(config=lowerCAmelCase__					)
            _UpperCamelCase       =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }
            _UpperCamelCase       =	model(lowerCAmelCase__					)
            self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.seq_length, self.vocab_size)					)





       def snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Dict					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	self.num_labels
            _UpperCamelCase       =	TFRoFormerForSequenceClassification(config=lowerCAmelCase__					)
            _UpperCamelCase       =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }

            _UpperCamelCase       =	model(lowerCAmelCase__					)
            self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels)					)





       def snake_case__						(							self       :		int	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Union[str, Any]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	self.num_choices
            _UpperCamelCase       =	TFRoFormerForMultipleChoice(config=lowerCAmelCase__					)
            _UpperCamelCase       =	tf.tile(tf.expand_dims(lowerCAmelCase__	,			1					)	,			(1, self.num_choices, 1)					)
            _UpperCamelCase       =	tf.tile(tf.expand_dims(lowerCAmelCase__	,			1					)	,			(1, self.num_choices, 1)					)
            _UpperCamelCase       =	tf.tile(tf.expand_dims(lowerCAmelCase__	,			1					)	,			(1, self.num_choices, 1)					)
            _UpperCamelCase       =	{
                '''input_ids''': multiple_choice_inputs_ids,
                '''attention_mask''': multiple_choice_input_mask,
                '''token_type_ids''': multiple_choice_token_type_ids,
            }
            _UpperCamelCase       =	model(lowerCAmelCase__					)
            self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_choices)					)





       def snake_case__						(							self       :		str	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		Optional[Any]					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	self.num_labels
            _UpperCamelCase       =	TFRoFormerForTokenClassification(config=lowerCAmelCase__					)
            _UpperCamelCase       =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }
            _UpperCamelCase       =	model(lowerCAmelCase__					)
            self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.seq_length, self.num_labels)					)





       def snake_case__						(							self       :		List[str]	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		List[str]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	TFRoFormerForQuestionAnswering(config=lowerCAmelCase__					)
            _UpperCamelCase       =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }

            _UpperCamelCase       =	model(lowerCAmelCase__					)
            self.parent.assertEqual(result.start_logits.shape	,			(self.batch_size, self.seq_length)					)
            self.parent.assertEqual(result.end_logits.shape	,			(self.batch_size, self.seq_length)					)







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

            '''simple docstring'''




            _UpperCamelCase       =	self.prepare_config_and_inputs()
            (
                (
                _UpperCamelCase    
            )   ,    (
                _UpperCamelCase    
            )   ,    (
                _UpperCamelCase    
            )   ,    (
                _UpperCamelCase    
            )   ,    (
                _UpperCamelCase    
            )   ,    (
                _UpperCamelCase    
            )   ,    (
                _UpperCamelCase    
            )   ,        
            )       =	config_and_inputs
            _UpperCamelCase       =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
            return config, inputs_dict

@require_tf
class     __lowerCAmelCase					(   __magic_name__						,						__magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					List[str]						    =   (
           (
               TFRoFormerModel,
               TFRoFormerForCausalLM,
               TFRoFormerForMaskedLM,
               TFRoFormerForQuestionAnswering,
               TFRoFormerForSequenceClassification,
               TFRoFormerForTokenClassification,
               TFRoFormerForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       _snake_case     :					Any						    =   (
           {
               'feature-extraction': TFRoFormerModel,
               'fill-mask': TFRoFormerForMaskedLM,
               'question-answering': TFRoFormerForQuestionAnswering,
               'text-classification': TFRoFormerForSequenceClassification,
               'text-generation': TFRoFormerForCausalLM,
               'token-classification': TFRoFormerForTokenClassification,
               'zero-shot': TFRoFormerForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       _snake_case     :					Optional[int]						    =   False
       _snake_case     :					Optional[Any]						    =   False





       def snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		List[str]					)   ->					str:

            '''simple docstring'''




            if pipeline_test_casse_name == "TextGenerationPipelineTests":
                 return True

            return False





       def snake_case__						(							self       :		List[str]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	TFRoFormerModelTester(self					)
            _UpperCamelCase       =	ConfigTester(self	,			config_class=lowerCAmelCase__	,			hidden_size=37					)





       def snake_case__						(							self       :		Optional[int]					)   ->					Any:

            '''simple docstring'''




            self.config_tester.run_common_tests()





       def snake_case__						(							self       :		str					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_lm_head(*lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__					)





       def snake_case__						(							self       :		List[Any]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__					)





       def snake_case__						(							self       :		Union[str, Any]					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__					)





       def snake_case__						(							self       :		List[str]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__					)







       @slow
       def snake_case__						(							self       :		Optional[int]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''					)
            self.assertIsNotNone(lowerCAmelCase__					)

@require_tf
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       @slow
       def snake_case__						(							self       :		List[str]					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''					)
            _UpperCamelCase       =	tf.constant([[0, 1, 2, 3, 4, 5]]					)
            _UpperCamelCase       =	model(lowerCAmelCase__					)[0]

            # TODO Replace vocab size
            _UpperCamelCase       =	50000

            _UpperCamelCase       =	[1, 6, vocab_size]
            self.assertEqual(output.shape	,			lowerCAmelCase__					)

            print(output[:, :3, :3]					)

            # TODO Replace values below with what was printed above.
            _UpperCamelCase       =	tf.constant(
                [
                    [
                        [-0.12053341, -1.0264901, 0.29221946],
                        [-1.5133783, 0.197433, 0.15190607],
                        [-5.0135403, -3.900256, -0.84038764],
                    ]
                ]					)
            tf.debugging.assert_near(output[:, :3, :3]	,			lowerCAmelCase__	,			atol=1e-4					)

@require_tf
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   1e-4





       def snake_case__						(							self       :		str					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	tf.constant([[4, 10]]					)
            _UpperCamelCase       =	TFRoFormerSinusoidalPositionalEmbedding(num_positions=6	,			embedding_dim=6					)

            _UpperCamelCase       =	emba(input_ids.shape					)
            _UpperCamelCase       =	tf.constant(
                [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]					)

            tf.debugging.assert_near(lowerCAmelCase__	,			lowerCAmelCase__	,			atol=self.tolerance					)







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

            '''simple docstring'''




            _UpperCamelCase       =	tf.constant(
                [
                    [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
                    [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
                    [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
                ]					)
            _UpperCamelCase       =	TFRoFormerSinusoidalPositionalEmbedding(num_positions=512	,			embedding_dim=512					)
            emba([2, 16, 512]					)
            _UpperCamelCase       =	emba.weight[:3, :5]

            tf.debugging.assert_near(lowerCAmelCase__	,			lowerCAmelCase__	,			atol=self.tolerance					)

@require_tf
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Any						    =   1e-4





       def snake_case__						(							self       :		List[str]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	tf.reshape(tf.range(2 * 12 * 16 * 64	,			dtype=tf.floataa					)	,			shape=(2, 12, 16, 64)					) / 100

            _UpperCamelCase       =	-tf.reshape(tf.range(2 * 12 * 16 * 64	,			dtype=tf.floataa					)	,			shape=(2, 12, 16, 64)					) / 100

            _UpperCamelCase       =	TFRoFormerSinusoidalPositionalEmbedding(num_positions=32	,			embedding_dim=64					)
            _UpperCamelCase       =	embed_positions([2, 16, 768]					)[None, None, :, :]

            _UpperCamelCase   ,    _UpperCamelCase       =	TFRoFormerSelfAttention.apply_rotary_position_embeddings(
                lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tf.constant(
                [
                    [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
                    [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
                    [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
                    [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
                    [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
                    [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
                ]					)
            _UpperCamelCase       =	tf.constant(
                [
                    [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
                    [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
                    [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
                    [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
                    [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
                    [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
                ]					)

            tf.debugging.assert_near(query_layer[0, 0, :6, :8]	,			lowerCAmelCase__	,			atol=self.tolerance					)
            tf.debugging.assert_near(key_layer[0, 0, :6, :8]	,			lowerCAmelCase__	,			atol=self.tolerance					)










'''simple docstring'''

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__						:      Optional[Any]	=    get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__						:      Any	=    get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__						:      Tuple	=    'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   CamembertTokenizer
       _snake_case     :					str						    =   CamembertTokenizerFast
       _snake_case     :					int						    =   True
       _snake_case     :					List[str]						    =   True





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            super().setUp()

            # We have a SentencePiece fixture for testing
            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)





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

            '''simple docstring'''




            _UpperCamelCase       =	'''<pad>'''
            _UpperCamelCase       =	1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__					)	,			lowerCAmelCase__					)
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__					)	,			lowerCAmelCase__					)





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

            '''simple docstring'''




            _UpperCamelCase       =	list(self.get_tokenizer().get_vocab().keys()					)

            self.assertEqual(vocab_keys[0]	,			'''<s>NOTUSED'''					)
            self.assertEqual(vocab_keys[1]	,			'''<pad>'''					)
            self.assertEqual(vocab_keys[-1]	,			'''<mask>'''					)
            self.assertEqual(len(lowerCAmelCase__					)	,			1004					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size	,			1005					)





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)
            _UpperCamelCase       =	CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            # <unk> tokens are not the same for `rust` than for `slow`.
            # Because spm gives back raw token instead of `unk` in EncodeAsPieces
            # tokens = tokenizer.tokenize(sequence)
            _UpperCamelCase       =	tokenizer.convert_ids_to_tokens(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





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

            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            _UpperCamelCase       =	self.get_tokenizer()
            _UpperCamelCase       =	self.get_rust_tokenizer()

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.tokenize(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	self.get_rust_tokenizer()
            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)







       @slow
       def snake_case__						(							self       :		Any					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	{'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
            # fmt: on

            # camembert is a french model. So we also use french texts.
            _UpperCamelCase       =	[
                '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
                '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
                '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
                '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
                '''telles que la traduction et la synthèse de texte.''',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=lowerCAmelCase__	,			model_name='''camembert-base'''	,			revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''	,			sequences=lowerCAmelCase__	,			)



