


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 						A__							(		unittest.TestCase  ):




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


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size  )

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

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

										_SCREAMING_SNAKE_CASE			  =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 							__UpperCamelCase				(       self					:      int  )							->     Union[str, Any]:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE			  =config_and_inputs

										_SCREAMING_SNAKE_CASE			  =True
										_SCREAMING_SNAKE_CASE			  =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
										_SCREAMING_SNAKE_CASE			  =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 						A__							(		_UpperCamelCase   ,     unittest.TestCase  ):
						UpperCAmelCase           =						True

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




						def 							__UpperCamelCase				(       self					:      Any  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =FlaxRobertaModelTester(self  )




						@slow
						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Any:


										"""simple docstring"""





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



from typing import TYPE_CHECKING

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


snake_case_						:			List[Any]													=							{
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			str													=							[
							    '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GraphormerForGraphClassification''',
							    '''GraphormerModel''',
							    '''GraphormerPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							snake_case_						:			Union[str, Any]													=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)