


import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.roberta.modeling_flax_roberta import (
							    FlaxRobertaForCausalLM,
							    FlaxRobertaForMaskedLM,
							    FlaxRobertaForMultipleChoice,
							    FlaxRobertaForQuestionAnswering,
							    FlaxRobertaForSequenceClassification,
							    FlaxRobertaForTokenClassification,
							    FlaxRobertaModel,
							)
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					str,						lowerCamelCase		:					Union[str, Any]=13,						lowerCamelCase		:					List[str]=7,						lowerCamelCase		:					List[str]=True,						lowerCamelCase		:					int=True,						lowerCamelCase		:					Dict=True,						lowerCamelCase		:					str=True,						lowerCamelCase		:					Dict=99,						lowerCamelCase		:					Optional[int]=32,						lowerCamelCase		:					str=5,						lowerCamelCase		:					Optional[Any]=4,						lowerCamelCase		:					int=37,						lowerCamelCase		:					List[str]="gelu",						lowerCamelCase		:					Union[str, Any]=0.1,						lowerCamelCase		:					str=0.1,						lowerCamelCase		:					str=512,						lowerCamelCase		:					Union[str, Any]=16,						lowerCamelCase		:					Tuple=2,						lowerCamelCase		:					Union[str, Any]=0.02,						lowerCamelCase		:					Tuple=4,						):

								'''simple docstring'''
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    seq_length
								lowercase__     				=    is_training
								lowercase__     				=    use_attention_mask
								lowercase__     				=    use_token_type_ids
								lowercase__     				=    use_labels
								lowercase__     				=    vocab_size
								lowercase__     				=    hidden_size
								lowercase__     				=    num_hidden_layers
								lowercase__     				=    num_attention_heads
								lowercase__     				=    intermediate_size
								lowercase__     				=    hidden_act
								lowercase__     				=    hidden_dropout_prob
								lowercase__     				=    attention_probs_dropout_prob
								lowercase__     				=    max_position_embeddings
								lowercase__     				=    type_vocab_size
								lowercase__     				=    type_sequence_label_size
								lowercase__     				=    initializer_range
								lowercase__     				=    num_choices




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    ids_tensor([self.batch_size, self.seq_length],						self.vocab_size						)

								lowercase__     				=    None
								if self.use_attention_mask:
												lowercase__     				=    random_attention_mask([self.batch_size, self.seq_length]						)

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

								lowercase__     				=    RobertaConfig(
								    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,						is_decoder=UpperCamelCase__,						initializer_range=self.initializer_range,						)

								return config, input_ids, token_type_ids, attention_mask




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.prepare_config_and_inputs()
								lowercase__     				=    config_and_inputs
								lowercase__     				=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.prepare_config_and_inputs()
								lowercase__     				=    config_and_inputs

								lowercase__     				=    True
								lowercase__     				=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
								lowercase__     				=    ids_tensor([self.batch_size, self.seq_length],						vocab_size=2						)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)



@require_flax
class    _UpperCAmelCase      ( UpperCamelCase_	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= True

				lowercase__       							= (
				    (
				        FlaxRobertaModel,
				        FlaxRobertaForCausalLM,
				        FlaxRobertaForMaskedLM,
				        FlaxRobertaForSequenceClassification,
				        FlaxRobertaForTokenClassification,
				        FlaxRobertaForMultipleChoice,
				        FlaxRobertaForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    FlaxRobertaModelTester(self						)




				@slow
				def        lowercase__     (  self		:					int						):

								'''simple docstring'''





								for model_class_name in self.all_model_classes:
												lowercase__     				=    model_class_name.from_pretrained('''roberta-base''',						from_pt=UpperCamelCase__						)
												lowercase__     				=    model(np.ones((1, 1)						)						)
												self.assertIsNotNone(UpperCamelCase__						)












from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= DistilBertTokenizer
				lowercase__       							= DistilBertTokenizerFast
				lowercase__       							= True




				@slow
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''						)

								lowercase__     				=    tokenizer.encode('''sequence builders''',						add_special_tokens=lowerCamelCase						)
								lowercase__     				=    tokenizer.encode('''multi-sequence build''',						add_special_tokens=lowerCamelCase						)

								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase						)
								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase,						lowerCamelCase						)

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]




