



'''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        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







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




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Optional[Any]       		= ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)

          snake_case__    :       Tuple       		= None
          if self.use_attention_mask:
                snake_case__    :       Any       		= random_attention_mask([self.batch_size, self.seq_length]		)

          snake_case__    :       Optional[Any]       		= None
          if self.use_token_type_ids:
                snake_case__    :       List[str]       		= ids_tensor([self.batch_size, self.seq_length]					,	self.type_vocab_size		)

          snake_case__    :       Tuple       		= 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 lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       List[Any]       		= self.prepare_config_and_inputs()
          snake_case__		,							snake_case__		,							snake_case__		,							snake_case__    :       Tuple       		= config_and_inputs
          snake_case__    :       Optional[int]       		= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
          return config, inputs_dict




    def lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       List[Any]       		= self.prepare_config_and_inputs()
          snake_case__		,							snake_case__		,							snake_case__		,							snake_case__    :       Tuple       		= config_and_inputs

          snake_case__    :       Dict       		= True
          snake_case__    :       Tuple       		= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
          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        UpperCAmelCase_			(			_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						True

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




    def lowerCamelCase			(       self   :	int		):
          snake_case__    :       str       		= FlaxRobertaModelTester(self		)




    @slow
    def lowerCamelCase			(       self   :	str		):
          for model_class_name in self.all_model_classes:
                snake_case__    :       Optional[Any]       		= model_class_name.from_pretrained("""roberta-base"""					,	from_pt=snake_case_		)
                snake_case__    :       Optional[int]       		= model(np.ones((1, 1)		)		)
                self.assertIsNotNone(snake_case_		)





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a  			=						{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BigBirdPegasusForCausalLM",
           "BigBirdPegasusForConditionalGeneration",
           "BigBirdPegasusForQuestionAnswering",
           "BigBirdPegasusForSequenceClassification",
           "BigBirdPegasusModel",
           "BigBirdPegasusPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bigbird_pegasus import (
           BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BigBirdPegasusConfig,
           BigBirdPegasusOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bigbird_pegasus import (
                  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BigBirdPegasusForCausalLM,
                  BigBirdPegasusForConditionalGeneration,
                  BigBirdPegasusForQuestionAnswering,
                  BigBirdPegasusForSequenceClassification,
                  BigBirdPegasusModel,
                  BigBirdPegasusPreTrainedModel,
              )


else:
       import sys

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