


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        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A=2		,      A=56		,      A=True		,      A=True		,      A=True		,      A=True		,      A=99		,      A=32		,      A=2		,      A=2		,      A=7		,      A="gelu_new"		,      A=0.1		,      A=0.1		,      A=5_12		,      A=16		,      A=2		,      A=0.02		,      A=4		,      A="block_sparse"		,      A=True		,      A=False		,      A=2		,      A=3		,      )  -> str:







					'''simple docstring'''







					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 					__A   ( self	)  -> int:







					'''simple docstring'''







					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=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 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					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        __lowercase       (							a_			,							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[int]			       =		(
		    (
		        FlaxBigBirdForCausalLM,
		        FlaxBigBirdModel,
		        FlaxBigBirdForPreTraining,
		        FlaxBigBirdForMaskedLM,
		        FlaxBigBirdForMultipleChoice,
		        FlaxBigBirdForQuestionAnswering,
		        FlaxBigBirdForSequenceClassification,
		        FlaxBigBirdForTokenClassification,
		    )
		    if is_flax_available()
		    else ()
		)

		UpperCamelCase     :					List[str]			       =		False
		UpperCamelCase     :					Optional[Any]			       =		False







		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase    		=  FlaxBigBirdModelTester(self	)







		@slow
		# copied from `test_modeling_flax_common` because it takes much longer than other models
		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					super().test_from_pretrained_save_pretrained()







		@slow
		# copied from `test_modeling_flax_common` because it takes much longer than other models
		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







					super().test_from_pretrained_with_no_automatic_init()







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







					'''simple docstring'''







					super().test_no_automatic_init()







		@slow
		# copied from `test_modeling_flax_common` because it takes much longer than other models
		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					super().test_hidden_states_output()







		@slow
		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







					for model_class_name in self.all_model_classes:
								lowerCamelCase    		=  model_class_name.from_pretrained("""google/bigbird-roberta-base"""	)
								self.assertIsNotNone(A	)







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					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 					__A   ( self	)  -> List[str]:







					'''simple docstring'''







					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(A		,      A	)
											lowerCamelCase    		=  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"""	):
														lowerCamelCase    		=  model_jitted(**A	).to_tuple()

											with self.subTest("""JIT Disabled"""	):
														with jax.disable_jit():
																	lowerCamelCase    		=  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 					__A   ( self		,      A		,      A		,      A		,      A=1e-5		,      A="outputs"		,      A=None	)  -> List[str]:







					'''simple docstring'''







					if name.startswith("""outputs.attentions"""	):
								return
					else:
								super().check_pt_flax_outputs(A		,      A		,      A		,      A		,      A		,      A	)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase   :    str     =		logging.get_logger(__name__)

UpperCAmelCase   :    List[Any]     =		{
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[Any]			       =		"vit_mae"







		def __init__( self		,      A=7_68		,      A=12		,      A=12		,      A=30_72		,      A="gelu"		,      A=0.0		,      A=0.0		,      A=0.02		,      A=1e-1_2		,      A=2_24		,      A=16		,      A=3		,      A=True		,      A=16		,      A=5_12		,      A=8		,      A=20_48		,      A=0.75		,      A=False		,      **A		,      )  -> Dict:







					'''simple docstring'''







					super().__init__(**A	)

					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    		=  initializer_range
					lowerCamelCase    		=  layer_norm_eps
					lowerCamelCase    		=  image_size
					lowerCamelCase    		=  patch_size
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  qkv_bias
					lowerCamelCase    		=  decoder_num_attention_heads
					lowerCamelCase    		=  decoder_hidden_size
					lowerCamelCase    		=  decoder_num_hidden_layers
					lowerCamelCase    		=  decoder_intermediate_size
					lowerCamelCase    		=  mask_ratio
					lowerCamelCase    		=  norm_pix_loss

