"""simple docstring"""





import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax

       from transformers.models.big_bird.modeling_flax_big_bird import (
           FlaxBigBirdForCausalLM,
           FlaxBigBirdForMaskedLM,
           FlaxBigBirdForMultipleChoice,
           FlaxBigBirdForPreTraining,
           FlaxBigBirdForQuestionAnswering,
           FlaxBigBirdForSequenceClassification,
           FlaxBigBirdForTokenClassification,
           FlaxBigBirdModel,
       )


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=2	,    a=5_6	,    a=True	,    a=True	,    a=True	,    a=True	,    a=9_9	,    a=3_2	,    a=2	,    a=2	,    a=7	,    a="gelu_new"	,    a=0.1	,    a=0.1	,    a=5_1_2	,    a=1_6	,    a=2	,    a=0.02	,    a=4	,    a="block_sparse"	,    a=True	,    a=False	,    a=2	,    a=3	,    ) ->      int:
             lowercase__    :	Tuple     					=			parent
             lowercase__    :	Tuple     					=			batch_size
             lowercase__    :	List[Any]     					=			seq_length
             lowercase__    :	Optional[Any]     					=			is_training
             lowercase__    :	str     					=			use_attention_mask
             lowercase__    :	Union[str, Any]     					=			use_token_type_ids
             lowercase__    :	Dict     					=			use_labels
             lowercase__    :	List[Any]     					=			vocab_size
             lowercase__    :	List[str]     					=			hidden_size
             lowercase__    :	Dict     					=			num_hidden_layers
             lowercase__    :	List[str]     					=			num_attention_heads
             lowercase__    :	Optional[Any]     					=			intermediate_size
             lowercase__    :	List[str]     					=			hidden_act
             lowercase__    :	List[Any]     					=			hidden_dropout_prob
             lowercase__    :	str     					=			attention_probs_dropout_prob
             lowercase__    :	Tuple     					=			max_position_embeddings
             lowercase__    :	List[Any]     					=			type_vocab_size
             lowercase__    :	Tuple     					=			type_sequence_label_size
             lowercase__    :	Dict     					=			initializer_range
             lowercase__    :	Optional[int]     					=			num_choices

             lowercase__    :	List[Any]     					=			rescale_embeddings
             lowercase__    :	Any     					=			attention_type
             lowercase__    :	Optional[int]     					=			use_bias
             lowercase__    :	Optional[Any]     					=			block_size
             lowercase__    :	Any     					=			num_random_blocks


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	str     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size							)

             lowercase__    :	List[str]     					=			None
             if self.use_attention_mask:
                    lowercase__    :	Dict     					=			random_attention_mask([self.batch_size, self.seq_length]							)

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

             lowercase__    :	List[Any]     					=			BigBirdConfig(
                 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=a	,    initializer_range=self.initializer_range	,    attention_type=self.attention_type	,    block_size=self.block_size	,    num_random_blocks=self.num_random_blocks	,    use_bias=self.use_bias	,    rescale_embeddings=self.rescale_embeddings	,    )

             return config, input_ids, token_type_ids, attention_mask


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	Dict     					=			config_and_inputs
             lowercase__    :	Optional[Any]     					=			{
                 'input_ids': input_ids,
                 'token_type_ids': token_type_ids,
                 'attention_mask': attention_mask,
             }
             return config, inputs_dict





@require_flax
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Any										=						(
          (
              FlaxBigBirdForCausalLM,
              FlaxBigBirdModel,
              FlaxBigBirdForPreTraining,
              FlaxBigBirdForMaskedLM,
              FlaxBigBirdForMultipleChoice,
              FlaxBigBirdForQuestionAnswering,
              FlaxBigBirdForSequenceClassification,
              FlaxBigBirdForTokenClassification,
          )
          if is_flax_available()
          else ()
      )

      lowerCamelCase__	:					Union[str, Any]										=						False
      lowerCamelCase__	:					Dict										=						False


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Dict     					=			FlaxBigBirdModelTester(self							)


      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             super().test_from_pretrained_save_pretrained()


      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             super().test_from_pretrained_with_no_automatic_init()


      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             super().test_no_automatic_init()


      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 	_UpperCAmelCase (     self							) ->      str:
             super().test_hidden_states_output()


      @slow
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             for model_class_name in self.all_model_classes:
                    lowercase__    :	int     					=			model_class_name.from_pretrained('google/bigbird-roberta-base'							)
                    self.assertIsNotNone(a							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             if self.test_attn_probs:
                    super().test_attention_outputs()


      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__					,   lowercase__    :	Optional[Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__							):
                           lowercase__    :	Optional[int]     					=			self._prepare_for_class(a	,    a							)
                           lowercase__    :	List[str]     					=			model_class(a							)

                           @jax.jit
                           def model_jitted(a	,    a=None	,    **a							):
                                  return model(input_ids=a	,    attention_mask=a	,    **a							)

                           with self.subTest('JIT Enabled'							):
                                  lowercase__    :	Optional[Any]     					=			model_jitted(**a							).to_tuple()

                           with self.subTest('JIT Disabled'							):
                                  with jax.disable_jit():
                                         lowercase__    :	List[str]     					=			model_jitted(**a							).to_tuple()

                           self.assertEqual(len(a							)	,    len(a							)							)
                           for jitted_output, output in zip(a	,    a							):
                                  self.assertEqual(jitted_output.shape	,    output.shape							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a=1e-5	,    a="outputs"	,    a=None							) ->      Optional[Any]:
             # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
             # an effort was done to return `attention_probs` (yet to be verified).
             if name.startswith('outputs.attentions'							):
                    return
             else:
                    super().check_pt_flax_outputs(a	,    a	,    a	,    a	,    a	,    a							)


"""simple docstring"""





from __future__ import annotations





def        a_  (   _lowerCAmelCase						:				float     ,				_lowerCAmelCase						:				float     ,				_lowerCAmelCase						:				float     ,				):





       '''simple docstring'''

       if (stress, tangential_force, area).count(0  ) != 1:
              raise ValueError('You cannot supply more or less than 2 values'  )
       elif stress < 0:
              raise ValueError('Stress cannot be negative'  )
       elif tangential_force < 0:
              raise ValueError('Tangential Force cannot be negative'  )
       elif area < 0:
              raise ValueError('Area cannot be negative'  )
       elif stress == 0:
              return (
                  "stress",
                  tangential_force / area,
              )
       elif tangential_force == 0:
              return (
                  "tangential_force",
                  stress * area,
              )
       else:
              return (
                  "area",
                  tangential_force / stress,
              )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

