'''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 						A   (   unittest.TestCase   ):





							def __init__(      self				:							Tuple     ,		__a				:							Dict     ,		__a				:							Optional[int]=2     ,		__a				:							List[Any]=5_6     ,		__a				:							int=True     ,		__a				:							Union[str, Any]=True     ,		__a				:							str=True     ,		__a				:							Optional[int]=True     ,		__a				:							Tuple=9_9     ,		__a				:							List[Any]=3_2     ,		__a				:							Optional[Any]=2     ,		__a				:							str=2     ,		__a				:							Any=7     ,		__a				:							Union[str, Any]="gelu_new"     ,		__a				:							str=0.1     ,		__a				:							Optional[Any]=0.1     ,		__a				:							List[str]=5_1_2     ,		__a				:							Union[str, Any]=1_6     ,		__a				:							Tuple=2     ,		__a				:							Any=0.0_2     ,		__a				:							List[str]=4     ,		__a				:							Union[str, Any]="block_sparse"     ,		__a				:							Optional[Any]=True     ,		__a				:							Any=False     ,		__a				:							List[Any]=2     ,		__a				:							Dict=3     ,		)							->	Tuple:
								__UpperCAmelCase							=			parent
								__UpperCAmelCase							=			batch_size
								__UpperCAmelCase							=			seq_length
								__UpperCAmelCase							=			is_training
								__UpperCAmelCase							=			use_attention_mask
								__UpperCAmelCase							=			use_token_type_ids
								__UpperCAmelCase							=			use_labels
								__UpperCAmelCase							=			vocab_size
								__UpperCAmelCase							=			hidden_size
								__UpperCAmelCase							=			num_hidden_layers
								__UpperCAmelCase							=			num_attention_heads
								__UpperCAmelCase							=			intermediate_size
								__UpperCAmelCase							=			hidden_act
								__UpperCAmelCase							=			hidden_dropout_prob
								__UpperCAmelCase							=			attention_probs_dropout_prob
								__UpperCAmelCase							=			max_position_embeddings
								__UpperCAmelCase							=			type_vocab_size
								__UpperCAmelCase							=			type_sequence_label_size
								__UpperCAmelCase							=			initializer_range
								__UpperCAmelCase							=			num_choices

								__UpperCAmelCase							=			rescale_embeddings
								__UpperCAmelCase							=			attention_type
								__UpperCAmelCase							=			use_bias
								__UpperCAmelCase							=			block_size
								__UpperCAmelCase							=			num_random_blocks





							def       snake_case__						(      self				:							Union[str, Any]	)							->	Dict:
								__UpperCAmelCase							=			ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size	)

								__UpperCAmelCase							=			None
								if self.use_attention_mask:
									__UpperCAmelCase							=			random_attention_mask([self.batch_size, self.seq_length]	)

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

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






@require_flax
class 						A   (   UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      (
							    (
							        FlaxBigBirdForCausalLM,
							        FlaxBigBirdModel,
							        FlaxBigBirdForPreTraining,
							        FlaxBigBirdForMaskedLM,
							        FlaxBigBirdForMultipleChoice,
							        FlaxBigBirdForQuestionAnswering,
							        FlaxBigBirdForSequenceClassification,
							        FlaxBigBirdForTokenClassification,
							    )
							    if is_flax_available()
							    else ()
							)

							a_        =      False
							a_        =      False





							def       snake_case__						(      self				:							Any	)							->	int:
								__UpperCAmelCase							=			FlaxBigBirdModelTester(self	)





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





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





							@slow
							def       snake_case__						(      self				:							List[str]	)							->	Union[str, Any]:
								for model_class_name in self.all_model_classes:
									__UpperCAmelCase							=			model_class_name.from_pretrained('''google/bigbird-roberta-base'''	)
									self.assertIsNotNone(__a	)





							def       snake_case__						(      self				:							List[Any]	)							->	Optional[int]:
								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				:							Tuple	)							->	Optional[Any]:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__	):
										__UpperCAmelCase							=			self._prepare_for_class(__a     ,		__a	)
										__UpperCAmelCase							=			model_class(__a	)

										@jax.jit
										def model_jitted(__a				:							Any     ,		__a				:							Any=None     ,		**__a				:							List[str]	):
											return model(input_ids=__a     ,		attention_mask=__a     ,		**__a	)

										with self.subTest('''JIT Enabled'''	):
											__UpperCAmelCase							=			model_jitted(**__a	).to_tuple()

										with self.subTest('''JIT Disabled'''	):
											with jax.disable_jit():
												__UpperCAmelCase							=			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       snake_case__						(      self				:							Optional[int]     ,		__a				:							Any     ,		__a				:							Dict     ,		__a				:							Optional[int]     ,		__a				:							Tuple=1e-5     ,		__a				:							Tuple="outputs"     ,		__a				:							List[str]=None	)							->	List[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'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)
