
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 							snake_case_ (				unittest.TestCase    ):





			def __init__(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase=13      ,						__lowerCAmelCase=7      ,						__lowerCAmelCase=True      ,						__lowerCAmelCase=True      ,						__lowerCAmelCase=True      ,						__lowerCAmelCase=True      ,						__lowerCAmelCase=99      ,						__lowerCAmelCase=32      ,						__lowerCAmelCase=5      ,						__lowerCAmelCase=4      ,						__lowerCAmelCase=37      ,						__lowerCAmelCase="gelu"      ,						__lowerCAmelCase=0.1      ,						__lowerCAmelCase=0.1      ,						__lowerCAmelCase=512      ,						__lowerCAmelCase=16      ,						__lowerCAmelCase=2      ,						__lowerCAmelCase=0.02      ,						__lowerCAmelCase=4      ,						):
					SCREAMING_SNAKE_CASE_					:	int		  =			parent
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			batch_size
					SCREAMING_SNAKE_CASE_					:	Any		  =			seq_length
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			is_training
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			use_attention_mask
					SCREAMING_SNAKE_CASE_					:	Any		  =			use_token_type_ids
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			use_labels
					SCREAMING_SNAKE_CASE_					:	Dict		  =			vocab_size
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			hidden_size
					SCREAMING_SNAKE_CASE_					:	int		  =			num_hidden_layers
					SCREAMING_SNAKE_CASE_					:	str		  =			num_attention_heads
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			intermediate_size
					SCREAMING_SNAKE_CASE_					:	str		  =			hidden_act
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			hidden_dropout_prob
					SCREAMING_SNAKE_CASE_					:	Any		  =			attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			max_position_embeddings
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			type_vocab_size
					SCREAMING_SNAKE_CASE_					:	str		  =			type_sequence_label_size
					SCREAMING_SNAKE_CASE_					:	str		  =			initializer_range
					SCREAMING_SNAKE_CASE_					:	str		  =			num_choices





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size      )

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

					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			None
					if self.use_token_type_ids:
							SCREAMING_SNAKE_CASE_					:	int		  =			ids_tensor([self.batch_size, self.seq_length]      ,						self.type_vocab_size      )

					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			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 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			self.prepare_config_and_inputs()
					SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	List[str]		  =			config_and_inputs
					SCREAMING_SNAKE_CASE_					:	int		  =			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
					return config, inputs_dict






			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			self.prepare_config_and_inputs()
					SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			config_and_inputs

					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			True
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
					SCREAMING_SNAKE_CASE_					:	str		  =			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 							snake_case_ (				A__   ,  unittest.TestCase    ):
			__lowerCamelCase							:     Union[str, Any]						=			True

			__lowerCamelCase							:     str						=			(
			    (
			        FlaxRobertaModel,
			        FlaxRobertaForCausalLM,
			        FlaxRobertaForMaskedLM,
			        FlaxRobertaForSequenceClassification,
			        FlaxRobertaForTokenClassification,
			        FlaxRobertaForMultipleChoice,
			        FlaxRobertaForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Dict		  =			FlaxRobertaModelTester(self      )






			@slow
			def 						__A						(		self      ):
					for model_class_name in self.all_model_classes:
							SCREAMING_SNAKE_CASE_					:	Tuple		  =			model_class_name.from_pretrained('roberta-base'      ,						from_pt=_UpperCamelCase      )
							SCREAMING_SNAKE_CASE_					:	List[str]		  =			model(np.ones((1, 1)      )      )
							self.assertIsNotNone(_UpperCamelCase      )













from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=A__    )
class 			__UpperCamelCase    (							A__    ):
     __A	:  str							  =   field(default="""language-modeling"""	, metadata={"""include_in_asdict_even_if_is_default""": True}    )

     __A	:  ClassVar[Features]							  =   Features({"""text""": Value("""string"""    )}    )
     __A	:  ClassVar[Features]							  =   Features({}    )
     __A	:  str							  =   "text"





     @property
     def 		UpperCamelCase(     self					):
        return {self.text_column: "text"}