





'''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    lowerCAmelCase_		(		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			,     )    ->							Union[str, Any]:
     _lowerCAmelCase				=      parent
     _lowerCAmelCase				=      batch_size
     _lowerCAmelCase				=      seq_length
     _lowerCAmelCase				=      is_training
     _lowerCAmelCase				=      use_attention_mask
     _lowerCAmelCase				=      use_token_type_ids
     _lowerCAmelCase				=      use_labels
     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      hidden_size
     _lowerCAmelCase				=      num_hidden_layers
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      intermediate_size
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      hidden_dropout_prob
     _lowerCAmelCase				=      attention_probs_dropout_prob
     _lowerCAmelCase				=      max_position_embeddings
     _lowerCAmelCase				=      type_vocab_size
     _lowerCAmelCase				=      type_sequence_label_size
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      num_choices




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size		)

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

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

     _lowerCAmelCase				=      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=_lowerCAmelCase			,     initializer_range=self.initializer_range			,     )

     return config, input_ids, token_type_ids, attention_mask




 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      config_and_inputs
     _lowerCAmelCase				=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
     return config, inputs_dict





 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      config_and_inputs

     _lowerCAmelCase				=      True
     _lowerCAmelCase				=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
     _lowerCAmelCase				=      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_		(		__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							List[str]         =					True

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




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      FlaxRobertaModelTester(self		)





 @slow
 def 							_snake_case   (	self		)    ->							Dict:
     for model_class_name in self.all_model_classes:
         _lowerCAmelCase				=      model_class_name.from_pretrained("roberta-base"			,     from_pt=_lowerCAmelCase		)
         _lowerCAmelCase				=      model(np.ones((1, 1)		)		)
         self.assertIsNotNone(_lowerCAmelCase		)






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "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

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


