



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 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





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

									return config, input_ids, token_type_ids, attention_mask





			def 		snake_case__			(				self	:       List[Any]   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				self.prepare_config_and_inputs()
									__lowercase	,			__lowercase	,			__lowercase	,			__lowercase					=				config_and_inputs
									__lowercase					=				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
									return config, inputs_dict







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







									__lowercase					=				self.prepare_config_and_inputs()
									__lowercase	,			__lowercase	,			__lowercase	,			__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 			_lowerCAmelCase   ( _UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  int      =			True

			lowercase__      :  Optional[int]      =			(
			    (
			        FlaxRobertaModel,
			        FlaxRobertaForCausalLM,
			        FlaxRobertaForMaskedLM,
			        FlaxRobertaForSequenceClassification,
			        FlaxRobertaForTokenClassification,
			        FlaxRobertaForMultipleChoice,
			        FlaxRobertaForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)





			def 		snake_case__			(				self	:       int   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				FlaxRobertaModelTester(self   )







			@slow
			def 		snake_case__			(				self	:       Optional[Any]   )						->					Dict:
									"""simple docstring"""







									for model_class_name in self.all_model_classes:
															__lowercase					=				model_class_name.from_pretrained("""roberta-base"""			,     from_pt=lowercase   )
															__lowercase					=				model(np.ones((1, 1)   )   )
															self.assertIsNotNone(lowercase   )








from typing import TYPE_CHECKING

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


UpperCamelCase__ =						{
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "CTRLForSequenceClassification",
			    "CTRLLMHeadModel",
			    "CTRLModel",
			    "CTRLPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFCTRLForSequenceClassification",
			    "TFCTRLLMHeadModel",
			    "TFCTRLModel",
			    "TFCTRLPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
			from .tokenization_ctrl import CTRLTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ctrl import (
						    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CTRLForSequenceClassification,
						    CTRLLMHeadModel,
						    CTRLModel,
						    CTRLPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_ctrl import (
						    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCTRLForSequenceClassification,
						    TFCTRLLMHeadModel,
						    TFCTRLModel,
						    TFCTRLPreTrainedModel,
						)

else:
			import sys

			UpperCamelCase__ =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





