



'''simple docstring'''



from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, 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 (
							    TFConvBertForMaskedLM,
							    TFConvBertForMultipleChoice,
							    TFConvBertForQuestionAnswering,
							    TFConvBertForSequenceClassification,
							    TFConvBertForTokenClassification,
							    TFConvBertModel,
							)
class 	lowerCamelCase_							:







						def __init__(  self   :      Optional[int]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      Optional[Any]=13				,    lowerCAmelCase__   :      Tuple=7				,    lowerCAmelCase__   :      Union[str, Any]=True				,    lowerCAmelCase__   :      Optional[int]=True				,    lowerCAmelCase__   :      int=True				,    lowerCAmelCase__   :      Any=True				,    lowerCAmelCase__   :      int=99				,    lowerCAmelCase__   :      Tuple=32				,    lowerCAmelCase__   :      str=2				,    lowerCAmelCase__   :      Optional[int]=4				,    lowerCAmelCase__   :      Optional[int]=37				,    lowerCAmelCase__   :      List[Any]="gelu"				,    lowerCAmelCase__   :      Tuple=0.1				,    lowerCAmelCase__   :      Union[str, Any]=0.1				,    lowerCAmelCase__   :      int=5_12				,    lowerCAmelCase__   :      Union[str, Any]=16				,    lowerCAmelCase__   :      Dict=2				,    lowerCAmelCase__   :      int=0.02				,    lowerCAmelCase__   :      Any=3				,    lowerCAmelCase__   :      Dict=4				,    lowerCAmelCase__   :      Union[str, Any]=None				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			parent
													SCREAMING_SNAKE_CASE					:      List[str]     					=			13
													SCREAMING_SNAKE_CASE					:      Dict     					=			7
													SCREAMING_SNAKE_CASE					:      str     					=			True
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			True
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			True
													SCREAMING_SNAKE_CASE					:      List[str]     					=			True
													SCREAMING_SNAKE_CASE					:      List[str]     					=			99
													SCREAMING_SNAKE_CASE					:      int     					=			3_84
													SCREAMING_SNAKE_CASE					:      Dict     					=			2
													SCREAMING_SNAKE_CASE					:      List[str]     					=			4
													SCREAMING_SNAKE_CASE					:      Any     					=			37
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''gelu'''
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0.1
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			0.1
													SCREAMING_SNAKE_CASE					:      List[str]     					=			5_12
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			16
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			2
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			0.02
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			3
													SCREAMING_SNAKE_CASE					:      List[str]     					=			4
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			1_28
													SCREAMING_SNAKE_CASE					:      int     					=			2
													SCREAMING_SNAKE_CASE					:      int     					=			9
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			1
													SCREAMING_SNAKE_CASE					:      str     					=			None







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			None
													if self.use_input_mask:
																				SCREAMING_SNAKE_CASE					:      str     					=			random_attention_mask([self.batch_size, self.seq_length]				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			None
													if self.use_token_type_ids:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.type_vocab_size				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			None
													SCREAMING_SNAKE_CASE					:      int     					=			None
													SCREAMING_SNAKE_CASE					:      Tuple     					=			None
													if self.use_labels:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			ids_tensor([self.batch_size]				,    self.type_sequence_label_size				)
																				SCREAMING_SNAKE_CASE					:      int     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels				)
																				SCREAMING_SNAKE_CASE					:      int     					=			ids_tensor([self.batch_size]				,    self.num_choices				)

													SCREAMING_SNAKE_CASE					:      str     					=			ConvBertConfig(
													    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 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			TFConvBertModel(config=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Any     					=			{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			[input_ids, input_mask]
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			TFConvBertForMaskedLM(config=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			{
													    '''input_ids''': input_ids,
													    '''attention_mask''': input_mask,
													    '''token_type_ids''': token_type_ids,
													}
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model(lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)				)







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      int     					=			TFConvBertForSequenceClassification(config=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			{
													    '''input_ids''': input_ids,
													    '''attention_mask''': input_mask,
													    '''token_type_ids''': token_type_ids,
													}

													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)				)







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.num_choices
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			TFConvBertForMultipleChoice(config=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tf.tile(tf.expand_dims(lowerCAmelCase__				,    1				)				,    (1, self.num_choices, 1)				)
													SCREAMING_SNAKE_CASE					:      str     					=			tf.tile(tf.expand_dims(lowerCAmelCase__				,    1				)				,    (1, self.num_choices, 1)				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			tf.tile(tf.expand_dims(lowerCAmelCase__				,    1				)				,    (1, self.num_choices, 1)				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			{
													    '''input_ids''': multiple_choice_inputs_ids,
													    '''attention_mask''': multiple_choice_input_mask,
													    '''token_type_ids''': multiple_choice_token_type_ids,
													}
													SCREAMING_SNAKE_CASE					:      Dict     					=			model(lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_choices)				)







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			TFConvBertForTokenClassification(config=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{
													    '''input_ids''': input_ids,
													    '''attention_mask''': input_mask,
													    '''token_type_ids''': token_type_ids,
													}
													SCREAMING_SNAKE_CASE					:      str     					=			model(lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.num_labels)				)







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			TFConvBertForQuestionAnswering(config=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			{
													    '''input_ids''': input_ids,
													    '''attention_mask''': input_mask,
													    '''token_type_ids''': token_type_ids,
													}

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			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 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.prepare_config_and_inputs()
													(
													    (
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,
													)					:      Optional[Any]     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      int     					=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
													return config, inputs_dict


@require_tf
class 	lowerCamelCase_							(					snake_case_    ,				snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   List[str]	     =				(
						    (
						        TFConvBertModel,
						        TFConvBertForMaskedLM,
						        TFConvBertForQuestionAnswering,
						        TFConvBertForSequenceClassification,
						        TFConvBertForTokenClassification,
						        TFConvBertForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowerCAmelCase     :   Any	     =				(
						    {
						        'feature-extraction': TFConvBertModel,
						        'fill-mask': TFConvBertForMaskedLM,
						        'question-answering': TFConvBertForQuestionAnswering,
						        'text-classification': TFConvBertForSequenceClassification,
						        'token-classification': TFConvBertForTokenClassification,
						        'zero-shot': TFConvBertForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_lowerCAmelCase     :   Optional[Any]	     =				False
						_lowerCAmelCase     :   Optional[Any]	     =				False
						_lowerCAmelCase     :   str	     =				False







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			TFConvBertModelTester(self				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ConfigTester(self				,    config_class=lowerCAmelCase__				,    hidden_size=37				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													self.config_tester.run_common_tests()







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			True
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			True

													if hasattr(lowerCAmelCase__				,    '''use_cache'''				):
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			True

													SCREAMING_SNAKE_CASE					:      Dict     					=			getattr(self.model_tester				,    '''encoder_seq_length'''				,    self.model_tester.seq_length				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			getattr(self.model_tester				,    '''key_length'''				,    lowerCAmelCase__				)

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      int     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			model_class(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			len(model(lowerCAmelCase__				)				)

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											model.save_pretrained(lowerCAmelCase__				,    saved_model=lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      int     					=			os.path.join(lowerCAmelCase__				,    '''saved_model'''				,    '''1'''				)
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			tf.keras.models.load_model(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Any     					=			model(lowerCAmelCase__				)

																											if self.is_encoder_decoder:
																																		SCREAMING_SNAKE_CASE					:      List[str]     					=			outputs['''encoder_hidden_states''']
																																		SCREAMING_SNAKE_CASE					:      List[str]     					=			outputs['''encoder_attentions''']
																											else:
																																		SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			outputs['''hidden_states''']
																																		SCREAMING_SNAKE_CASE					:      Dict     					=			outputs['''attentions''']

																											self.assertEqual(len(lowerCAmelCase__				)				,    lowerCAmelCase__				)

																											SCREAMING_SNAKE_CASE					:      Dict     					=			getattr(
																											    self.model_tester				,    '''expected_num_hidden_layers'''				,    self.model_tester.num_hidden_layers + 1				)

																											self.assertEqual(len(lowerCAmelCase__				)				,    lowerCAmelCase__				)
																											self.assertListEqual(
																											    list(output_hidden_states[0].shape[-2:]				)				,    [self.model_tester.seq_length, self.model_tester.hidden_size]				,    )

																											self.assertEqual(len(lowerCAmelCase__				)				,    self.model_tester.num_hidden_layers				)
																											self.assertListEqual(
																											    list(output_attentions[0].shape[-3:]				)				,    [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]				,    )







						@slow
						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''				)
													self.assertIsNotNone(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			True
													SCREAMING_SNAKE_CASE					:      List[str]     					=			getattr(self.model_tester				,    '''decoder_seq_length'''				,    self.model_tester.seq_length				)
													SCREAMING_SNAKE_CASE					:      int     					=			getattr(self.model_tester				,    '''encoder_seq_length'''				,    self.model_tester.seq_length				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			getattr(self.model_tester				,    '''key_length'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Any     					=			getattr(self.model_tester				,    '''key_length'''				,    lowerCAmelCase__				)

													def check_decoder_attentions_output(lowerCAmelCase__   :      str				):
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			len(lowerCAmelCase__				)
																				self.assertEqual(out_len % 2				,    0				)
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			outputs.decoder_attentions
																				self.assertEqual(len(lowerCAmelCase__				)				,    self.model_tester.num_hidden_layers				)
																				self.assertListEqual(
																				    list(decoder_attentions[0].shape[-3:]				)				,    [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]				,    )

													def check_encoder_attentions_output(lowerCAmelCase__   :      Any				):
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			[
																				    t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
																				]
																				self.assertEqual(len(lowerCAmelCase__				)				,    self.model_tester.num_hidden_layers				)
																				self.assertListEqual(
																				    list(attentions[0].shape[-3:]				)				,    [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]				,    )

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      int     					=			True
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			False
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			model_class(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)				)
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			len(lowerCAmelCase__				)
																				self.assertEqual(config.output_hidden_states				,    lowerCAmelCase__				)
																				check_encoder_attentions_output(lowerCAmelCase__				)

																				if self.is_encoder_decoder:
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model_class(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)				)
																											self.assertEqual(config.output_hidden_states				,    lowerCAmelCase__				)
																											check_decoder_attentions_output(lowerCAmelCase__				)

																				# Check that output attentions can also be changed via the config
																				del inputs_dict["output_attentions"]
																				SCREAMING_SNAKE_CASE					:      str     					=			True
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model_class(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)				)
																				self.assertEqual(config.output_hidden_states				,    lowerCAmelCase__				)
																				check_encoder_attentions_output(lowerCAmelCase__				)

																				# Check attention is always last and order is fine
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			True
																				SCREAMING_SNAKE_CASE					:      Dict     					=			True
																				SCREAMING_SNAKE_CASE					:      str     					=			model_class(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			model(self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)				)

																				self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)				,    len(lowerCAmelCase__				)				)
																				self.assertEqual(model.config.output_hidden_states				,    lowerCAmelCase__				)
																				check_encoder_attentions_output(lowerCAmelCase__				)


@require_tf
class 	lowerCamelCase_							(					unittest.TestCase			):







						@slow
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''				)
													SCREAMING_SNAKE_CASE					:      Any     					=			tf.constant([[0, 1, 2, 3, 4, 5]]				)
													SCREAMING_SNAKE_CASE					:      Any     					=			model(lowerCAmelCase__				)[0]

													SCREAMING_SNAKE_CASE					:      str     					=			[1, 6, 7_68]
													self.assertEqual(output.shape				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			tf.constant(
													    [
													        [
													            [-0.0347_5493, -0.468_6034, -0.3063_8832],
													            [0.2263_7248, -0.2698_8646, -0.742_3424],
													            [0.1032_4868, -0.4501_3508, -0.5828_0784],
													        ]
													    ]				)
													tf.debugging.assert_near(output[:, :3, :3]				,    lowerCAmelCase__				,    atol=1e-4				)









'''simple docstring'''



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_     :       int								=						get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_							(					snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Optional[int]	     =				XLMRobertaTokenizer
						_lowerCAmelCase     :   Optional[Any]	     =				XLMRobertaTokenizerFast
						_lowerCAmelCase     :   List[str]	     =				True
						_lowerCAmelCase     :   Union[str, Any]	     =				True







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													super().setUp()

													# We have a SentencePiece fixture for testing
													SCREAMING_SNAKE_CASE					:      str     					=			XLMRobertaTokenizer(lowerCAmelCase__				,    keep_accents=lowerCAmelCase__				)
													tokenizer.save_pretrained(self.tmpdirname				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''<pad>'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			1

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







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			list(self.get_tokenizer().get_vocab().keys()				)

													self.assertEqual(vocab_keys[0]				,    '''<s>'''				)
													self.assertEqual(vocab_keys[1]				,    '''<pad>'''				)
													self.assertEqual(vocab_keys[-1]				,    '''<mask>'''				)
													self.assertEqual(len(lowerCAmelCase__				)				,    10_02				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													self.assertEqual(self.get_tokenizer().vocab_size				,    10_02				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			XLMRobertaTokenizer(lowerCAmelCase__				,    keep_accents=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.tokenize('''This is a test'''				)
													self.assertListEqual(lowerCAmelCase__				,    ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

													self.assertListEqual(
													    tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)				,    [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]				,    )

													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        SPIECE_UNDERLINE + '''I''',
													        SPIECE_UNDERLINE + '''was''',
													        SPIECE_UNDERLINE + '''b''',
													        '''or''',
													        '''n''',
													        SPIECE_UNDERLINE + '''in''',
													        SPIECE_UNDERLINE + '''''',
													        '''9''',
													        '''2''',
													        '''0''',
													        '''0''',
													        '''0''',
													        ''',''',
													        SPIECE_UNDERLINE + '''and''',
													        SPIECE_UNDERLINE + '''this''',
													        SPIECE_UNDERLINE + '''is''',
													        SPIECE_UNDERLINE + '''f''',
													        '''al''',
													        '''s''',
													        '''é''',
													        '''.''',
													    ]				,    )
													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        value + tokenizer.fairseq_offset
													        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
													        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
													    ]				,    )

													SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        SPIECE_UNDERLINE + '''I''',
													        SPIECE_UNDERLINE + '''was''',
													        SPIECE_UNDERLINE + '''b''',
													        '''or''',
													        '''n''',
													        SPIECE_UNDERLINE + '''in''',
													        SPIECE_UNDERLINE + '''''',
													        '''<unk>''',
													        '''2''',
													        '''0''',
													        '''0''',
													        '''0''',
													        ''',''',
													        SPIECE_UNDERLINE + '''and''',
													        SPIECE_UNDERLINE + '''this''',
													        SPIECE_UNDERLINE + '''is''',
													        SPIECE_UNDERLINE + '''f''',
													        '''al''',
													        '''s''',
													        '''<unk>''',
													        '''.''',
													    ]				,    )







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													if not self.test_slow_tokenizer:
																				# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																				return

													SCREAMING_SNAKE_CASE					:      Tuple     					=			(self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																											SCREAMING_SNAKE_CASE					:      int     					=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			self.tokenizer_class.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)

																											SCREAMING_SNAKE_CASE					:      int     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# Checks it save with the same files + the tokenizer.json file for the fast one
																											self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
																											self.assertSequenceEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      int     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(lowerCAmelCase__				,    lowerCAmelCase__				)				)
																																		# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																																		# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																											shutil.rmtree(lowerCAmelCase__				)

																											# Save tokenizer rust, legacy_format=True
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				,    legacy_format=lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# Checks it save with the same files
																											self.assertSequenceEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      str     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(lowerCAmelCase__				,    lowerCAmelCase__				)				)

																											shutil.rmtree(lowerCAmelCase__				)

																											# Save tokenizer rust, legacy_format=False
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				,    legacy_format=lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# Checks it saved the tokenizer.json file
																											self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(lowerCAmelCase__				,    lowerCAmelCase__				)				)

																											shutil.rmtree(lowerCAmelCase__				)







						@cached_property
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													with tempfile.NamedTemporaryFile() as f:
																				shutil.copyfile(lowerCAmelCase__				,    f.name				)
																				SCREAMING_SNAKE_CASE					:      str     					=			XLMRobertaTokenizer(f.name				,    keep_accents=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      str     					=			pickle.dumps(lowerCAmelCase__				)
													pickle.loads(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													if not self.test_rust_tokenizer:
																				return

													SCREAMING_SNAKE_CASE					:      int     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      int     					=			self.get_rust_tokenizer()

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''I was born in 92000, and this is falsé.'''

													SCREAMING_SNAKE_CASE					:      int     					=			tokenizer.tokenize(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			rust_tokenizer.tokenize(lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer.encode(lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			rust_tokenizer.encode(lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      str     					=			self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.encode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			rust_tokenizer.encode(lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''Hello World!'''
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[0, 3_53_78, 66_61, 38, 2]
													# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
													# xlmr.eval()
													# xlmr.encode(symbols)

													self.assertListEqual(lowerCAmelCase__				,    self.big_tokenizer.encode(lowerCAmelCase__				)				)







						@slow
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			(
													    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
													    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
													)
													SCREAMING_SNAKE_CASE					:      Any     					=			[
													    0,
													    32_93,
													    83,
													    10,
													    45_52,
													    49_89,
													    79_86,
													    6_78,
													    10,
													    59_15,
													    1_11,
													    17_94_59,
													    12_48_50,
													    4,
													    60_44,
													    2_37,
													    12,
													    6,
													    5,
													    6,
													    4,
													    67_80,
													    7_05,
													    15,
													    13_88,
													    44,
													    3_78,
													    1_01_14,
													    7_11,
													    1_52,
													    20,
													    6,
													    5,
													    2_23_76,
													    6_42,
													    12_21,
													    1_51_90,
													    3_41_53,
													    4_50,
													    56_08,
													    9_59,
													    11_19,
													    5_77_02,
													    1_36,
													    1_86,
													    47,
													    10_98,
													    2_93_67,
													    47,
													    # 4426, # What fairseq tokenizes from "<unk>": "_<"
													    # 3678, # What fairseq tokenizes from "<unk>": "unk"
													    # 2740, # What fairseq tokenizes from "<unk>": ">"
													    3,  # What we tokenize from "<unk>": "<unk>"
													    6,  # Residue from the tokenization: an extra sentencepiece underline
													    4,
													    60_44,
													    2_37,
													    62_84,
													    5_09_01,
													    5_28,
													    31,
													    90,
													    34,
													    9_27,
													    2,
													]
													# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
													# xlmr.eval()
													# xlmr.encode(symbols)

													self.assertListEqual(lowerCAmelCase__				,    self.big_tokenizer.encode(lowerCAmelCase__				)				)







						@slow
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													# fmt: off
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			{'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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, 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]], '''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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
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													self.tokenizer_integration_test_util(
													    expected_encoding=lowerCAmelCase__				,    model_name='''xlm-roberta-base'''				,    revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''				,    )




