
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     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def __init__(    self						,  lowercase						,  lowercase=2						,  lowercase=56						,  lowercase=True						,  lowercase=True						,  lowercase=True						,  lowercase=True						,  lowercase=99						,  lowercase=32						,  lowercase=2						,  lowercase=2						,  lowercase=7						,  lowercase="gelu_new"						,  lowercase=0.1						,  lowercase=0.1						,  lowercase=512						,  lowercase=16						,  lowercase=2						,  lowercase=0.0_2						,  lowercase=4						,  lowercase="block_sparse"						,  lowercase=True						,  lowercase=False						,  lowercase=2						,  lowercase=3						,  )				->       List[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

					lowerCamelCase_						=			rescale_embeddings
					lowerCamelCase_						=			attention_type
					lowerCamelCase_						=			use_bias
					lowerCamelCase_						=			block_size
					lowerCamelCase_						=			num_random_blocks



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					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_						=			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=lowercase						,  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     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					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




@require_flax
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = (
	    (
	        FlaxBigBirdForCausalLM,
	        FlaxBigBirdModel,
	        FlaxBigBirdForPreTraining,
	        FlaxBigBirdForMaskedLM,
	        FlaxBigBirdForMultipleChoice,
	        FlaxBigBirdForQuestionAnswering,
	        FlaxBigBirdForSequenceClassification,
	        FlaxBigBirdForTokenClassification,
	    )
	    if is_flax_available()
	    else ()
	)

	lowerCAmelCase__         = False
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			FlaxBigBirdModelTester(self )



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



	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					super().test_from_pretrained_with_no_automatic_init()



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



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



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					for model_class_name in self.all_model_classes:
									lowerCamelCase_						=			model_class_name.from_pretrained("google/bigbird-roberta-base" )
									self.assertIsNotNone(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, 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     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__ ):
													lowerCamelCase_						=			self._prepare_for_class(lowercase						,  lowercase )
													lowerCamelCase_						=			model_class(lowercase )

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

													with self.subTest("JIT Enabled" ):
																	lowerCamelCase_						=			model_jitted(**lowercase ).to_tuple()

													with self.subTest("JIT Disabled" ):
																	with jax.disable_jit():
																					lowerCamelCase_						=			model_jitted(**lowercase ).to_tuple()

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase=1e-5						,  lowercase="outputs"						,  lowercase=None )				->       Optional[int]:
					# `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(lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )





'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Any			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      name
      __SCREAMING_SNAKE_CASE     =      value
      __SCREAMING_SNAKE_CASE     =      weight
 def __repr__(		self					:       str			)  -> Union[str, Any]:


      """simple docstring"""




      return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[Any]:


      """simple docstring"""




      return self.value
 def       UpperCAmelCase__		(		self					:       Any			)  -> str:


      """simple docstring"""




      return self.name
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Union[str, Any]:


      """simple docstring"""




      return self.weight


 def       UpperCAmelCase__		(		self					:       int			)  -> Tuple:


      """simple docstring"""




      return self.value / self.weight







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      []
     for i in range(len(a__						)						):
          menu.append(Things(name[i]      ,    value[i]      ,    weight[i]						)						)
     return menu







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      sorted(a__      ,    key=a__      ,    reverse=a__						)
     __SCREAMING_SNAKE_CASE     =      []
     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      0.0, 0.0
     for i in range(len(a__						)						):
          if (total_cost + items_copy[i].get_weight()) <= max_cost:
               result.append(items_copy[i]						)
               total_cost += items_copy[i].get_weight()
               total_value += items_copy[i].get_value()
     return (result, total_value)







def    a__       (  ):




     """simple docstring"""




if __name__ == "__main__":
  import doctest

  doctest.testmod()






