
'''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 					A							(				unittest.TestCase		):





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=13		,     SCREAMING_SNAKE_CASE=7		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=99		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=37		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=512		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=4		,     )					->							Any:






         """simple docstring"""






         A			:  List[Any]   =		parent
         A			:  List[str]   =		batch_size
         A			:  Dict   =		seq_length
         A			:  Optional[Any]   =		is_training
         A			:  Optional[int]   =		use_attention_mask
         A			:  Tuple   =		use_token_type_ids
         A			:  str   =		use_labels
         A			:  Union[str, Any]   =		vocab_size
         A			:  List[str]   =		hidden_size
         A			:  Tuple   =		num_hidden_layers
         A			:  List[str]   =		num_attention_heads
         A			:  int   =		intermediate_size
         A			:  Dict   =		hidden_act
         A			:  str   =		hidden_dropout_prob
         A			:  Union[str, Any]   =		attention_probs_dropout_prob
         A			:  Dict   =		max_position_embeddings
         A			:  Optional[Any]   =		type_vocab_size
         A			:  List[str]   =		type_sequence_label_size
         A			:  str   =		initializer_range
         A			:  List[str]   =		num_choices





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A			:  Any   =		ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )

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

         A			:  Any   =		None
         if self.use_token_type_ids:
             A			:  Union[str, Any]   =		ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size      )

         A			:  Optional[Any]   =		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=SCREAMING_SNAKE_CASE		,     initializer_range=self.initializer_range		,     )

         return config, input_ids, token_type_ids, attention_mask





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






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





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






         """simple docstring"""






         A			:  List[str]   =		self.prepare_config_and_inputs()
         A,    A,    A,    A			:  Any   =		config_and_inputs

         A			:  str   =		True
         A			:  Optional[int]   =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
         A			:  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 					A							(				__snake_case       ,     unittest.TestCase		):
     __magic_name__          =						True

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





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Tuple   =		FlaxRobertaModelTester(self      )





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






         """simple docstring"""






         for model_class_name in self.all_model_classes:
             A			:  List[str]   =		model_class_name.from_pretrained('''roberta-base'''		,     from_pt=SCREAMING_SNAKE_CASE      )
             A			:  Any   =		model(np.ones((1, 1)      )      )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE      )






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase     :							Tuple       						=     {
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase     :							int       						=     [
          'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LukeForEntityClassification',
          'LukeForEntityPairClassification',
          'LukeForEntitySpanClassification',
          'LukeForMultipleChoice',
          'LukeForQuestionAnswering',
          'LukeForSequenceClassification',
          'LukeForTokenClassification',
          'LukeForMaskedLM',
          'LukeModel',
          'LukePreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
      from .tokenization_luke import LukeTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_luke import (
                LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
                LukeForEntityClassification,
                LukeForEntityPairClassification,
                LukeForEntitySpanClassification,
                LukeForMaskedLM,
                LukeForMultipleChoice,
                LukeForQuestionAnswering,
                LukeForSequenceClassification,
                LukeForTokenClassification,
                LukeModel,
                LukePreTrainedModel,
            )

else:
      import sys

      lowercase     :							Dict       						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




