




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 				lowerCAmelCase_       (					unittest.TestCase							):






				def __init__(			self      ,snake_case__      ,snake_case__=2      ,snake_case__=56      ,snake_case__=True      ,snake_case__=True      ,snake_case__=True      ,snake_case__=True      ,snake_case__=99      ,snake_case__=32      ,snake_case__=2      ,snake_case__=2      ,snake_case__=7      ,snake_case__="gelu_new"      ,snake_case__=0.1      ,snake_case__=0.1      ,snake_case__=512      ,snake_case__=16      ,snake_case__=2      ,snake_case__=0.02      ,snake_case__=4      ,snake_case__="block_sparse"      ,snake_case__=True      ,snake_case__=False      ,snake_case__=2      ,snake_case__=3      ,):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      parent
							SCREAMING_SNAKE_CASE_   : int					  =      batch_size
							SCREAMING_SNAKE_CASE_   : str					  =      seq_length
							SCREAMING_SNAKE_CASE_   : Tuple					  =      is_training
							SCREAMING_SNAKE_CASE_   : int					  =      use_attention_mask
							SCREAMING_SNAKE_CASE_   : int					  =      use_token_type_ids
							SCREAMING_SNAKE_CASE_   : int					  =      use_labels
							SCREAMING_SNAKE_CASE_   : str					  =      vocab_size
							SCREAMING_SNAKE_CASE_   : List[str]					  =      hidden_size
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      num_hidden_layers
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      num_attention_heads
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      intermediate_size
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      hidden_act
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      hidden_dropout_prob
							SCREAMING_SNAKE_CASE_   : str					  =      attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      max_position_embeddings
							SCREAMING_SNAKE_CASE_   : int					  =      type_vocab_size
							SCREAMING_SNAKE_CASE_   : Tuple					  =      type_sequence_label_size
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      num_choices

							SCREAMING_SNAKE_CASE_   : Any					  =      rescale_embeddings
							SCREAMING_SNAKE_CASE_   : int					  =      attention_type
							SCREAMING_SNAKE_CASE_   : Any					  =      use_bias
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      block_size
							SCREAMING_SNAKE_CASE_   : Dict					  =      num_random_blocks






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size )

							SCREAMING_SNAKE_CASE_   : int					  =      None
							if self.use_attention_mask:
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      random_attention_mask([self.batch_size, self.seq_length] )

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

							SCREAMING_SNAKE_CASE_   : int					  =      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=snake_case__      ,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 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      config_and_inputs
							SCREAMING_SNAKE_CASE_   : int					  =      {
							    'input_ids': input_ids,
							    'token_type_ids': token_type_ids,
							    'attention_mask': attention_mask,
							}
							return config, inputs_dict







@require_flax
class 				lowerCAmelCase_       (					lowerCamelCase_						,     unittest.TestCase							):
				__a     :       int						=     (
				    (
				        FlaxBigBirdForCausalLM,
				        FlaxBigBirdModel,
				        FlaxBigBirdForPreTraining,
				        FlaxBigBirdForMaskedLM,
				        FlaxBigBirdForMultipleChoice,
				        FlaxBigBirdForQuestionAnswering,
				        FlaxBigBirdForSequenceClassification,
				        FlaxBigBirdForTokenClassification,
				    )
				    if is_flax_available()
				    else ()
				)

				__a     :       List[Any]						=     False
				__a     :       Any						=     False






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      FlaxBigBirdModelTester(self )






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							super().test_from_pretrained_save_pretrained()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							super().test_from_pretrained_with_no_automatic_init()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							super().test_no_automatic_init()






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






				@slow
				def 		snake_case	(			self ):
							for model_class_name in self.all_model_classes:
										SCREAMING_SNAKE_CASE_   : Dict					  =      model_class_name.from_pretrained('google/bigbird-roberta-base' )
										self.assertIsNotNone(snake_case__ )






				def 		snake_case	(			self ):
							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 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__ ):
													SCREAMING_SNAKE_CASE_   : str					  =      self._prepare_for_class(snake_case__      ,snake_case__ )
													SCREAMING_SNAKE_CASE_   : Dict					  =      model_class(snake_case__ )

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

													with self.subTest('JIT Enabled' ):
																SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model_jitted(**snake_case__ ).to_tuple()

													with self.subTest('JIT Disabled' ):
																with jax.disable_jit():
																			SCREAMING_SNAKE_CASE_   : Any					  =      model_jitted(**snake_case__ ).to_tuple()

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






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__=1E-5      ,snake_case__="outputs"      ,snake_case__=None ):
							# `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(snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ )









import os
import sys
import unittest


UpperCamelCase__ :   Optional[Any]						=							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCamelCase__ :   Tuple						=							os.path.join(git_repo_path, '''src''', '''transformers''')

UpperCamelCase__ :   List[Any]						=							'''
{0} = None
'''

UpperCamelCase__ :   str						=							'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


UpperCamelCase__ :   List[Any]						=							'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''







class 				lowerCAmelCase_       (					unittest.TestCase							):






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")' )
							self.assertIsNone(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      find_backend('    if not is_tokenizers_available():' )
							self.assertEqual(snake_case__      ,'tokenizers' )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      find_backend('    if not is_tensorflow_text_available():' )
							self.assertEqual(snake_case__      ,'tensorflow_text' )

							SCREAMING_SNAKE_CASE_   : int					  =      find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tokenizers' )

							SCREAMING_SNAKE_CASE_   : str					  =      find_backend(
							    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tensorflow_text' )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      find_backend(
							    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tokenizers_and_vision' )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn('torch'      ,snake_case__ )
							self.assertIn('tensorflow_text'      ,snake_case__ )
							self.assertIn('sentencepiece_and_tokenizers'      ,snake_case__ )

							# Likewise, we can't assert on the exact content of a key
							self.assertIn('BertModel'      ,objects['torch'] )
							self.assertIn('TFBertModel'      ,objects['tf'] )
							self.assertIn('FlaxBertModel'      ,objects['flax'] )
							self.assertIn('BertModel'      ,objects['torch'] )
							self.assertIn('TFBertTokenizer'      ,objects['tensorflow_text'] )
							self.assertIn('convert_slow_tokenizer'      ,objects['sentencepiece_and_tokenizers'] )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      create_dummy_object('CONSTANT'      ,'\'torch\'' )
							self.assertEqual(snake_case__      ,'\nCONSTANT = None\n' )

							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_object('function'      ,'\'torch\'' )
							self.assertEqual(
							    snake_case__      ,'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n' )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_object('FakeClass'      ,'\'torch\'' )
							self.assertEqual(snake_case__      ,snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
							self.assertEqual(dummy_files['torch']      ,snake_case__ )



