



'''simple docstring'''






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 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






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







	def      A	(						self			:     List[str]		)			->		List[str]:
								UpperCAmelCase :       Any 							=		ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size		)

								UpperCAmelCase :       Optional[int] 							=		None
								if self.use_attention_mask:
															UpperCAmelCase :       Tuple 							=		random_attention_mask([self.batch_size, self.seq_length]		)

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

								UpperCAmelCase :       str 							=		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=__snake_case		,			initializer_range=self.initializer_range		,			)

								return config, input_ids, token_type_ids, attention_mask







	def      A	(						self			:     Optional[Any]		)			->		Optional[int]:
								UpperCAmelCase :       Optional[int] 							=		self.prepare_config_and_inputs()
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       int 							=		config_and_inputs
								UpperCAmelCase :       int 							=		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict







	def      A	(						self			:     Optional[int]		)			->		Union[str, Any]:
								UpperCAmelCase :       Union[str, Any] 							=		self.prepare_config_and_inputs()
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       int 							=		config_and_inputs

								UpperCAmelCase :       Optional[Any] 							=		True
								UpperCAmelCase :       int 							=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
								UpperCAmelCase :       List[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 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       True

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







	def      A	(						self			:     Optional[Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       str 							=		FlaxRobertaModelTester(self		)







	@slow
	def      A	(						self			:     Optional[Any]		)			->		int:
								for model_class_name in self.all_model_classes:
															UpperCAmelCase :       Optional[Any] 							=		model_class_name.from_pretrained('''roberta-base'''		,			from_pt=__snake_case		)
															UpperCAmelCase :       Tuple 							=		model(np.ones((1, 1)		)		)
															self.assertIsNotNone(__snake_case		)








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__UpperCamelCase  :      str							      =		{
    '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      str							      =		[
							    '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LongT5EncoderModel''',
							    '''LongT5ForConditionalGeneration''',
							    '''LongT5Model''',
							    '''LongT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''FlaxLongT5ForConditionalGeneration''',
							    '''FlaxLongT5Model''',
							    '''FlaxLongT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_longta import (
														    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LongTaEncoderModel,
														    LongTaForConditionalGeneration,
														    LongTaModel,
														    LongTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_longta import (
														    FlaxLongTaForConditionalGeneration,
														    FlaxLongTaModel,
														    FlaxLongTaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      int							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




