


'''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  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__=0.0_2 ,					snake_case__=4 ,					snake_case__="block_sparse" ,					snake_case__=True ,					snake_case__=False ,					snake_case__=2 ,					snake_case__=3 ,					):
		lowerCAmelCase					:    Optional[int]      							= parent
		lowerCAmelCase					:    Optional[Any]      							= batch_size
		lowerCAmelCase					:    Any      							= seq_length
		lowerCAmelCase					:    str      							= is_training
		lowerCAmelCase					:    List[Any]      							= use_attention_mask
		lowerCAmelCase					:    List[Any]      							= use_token_type_ids
		lowerCAmelCase					:    Optional[Any]      							= use_labels
		lowerCAmelCase					:    Any      							= vocab_size
		lowerCAmelCase					:    Tuple      							= hidden_size
		lowerCAmelCase					:    Dict      							= num_hidden_layers
		lowerCAmelCase					:    int      							= num_attention_heads
		lowerCAmelCase					:    Optional[Any]      							= intermediate_size
		lowerCAmelCase					:    Any      							= hidden_act
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    Dict      							= attention_probs_dropout_prob
		lowerCAmelCase					:    str      							= max_position_embeddings
		lowerCAmelCase					:    List[Any]      							= type_vocab_size
		lowerCAmelCase					:    Tuple      							= type_sequence_label_size
		lowerCAmelCase					:    List[Any]      							= initializer_range
		lowerCAmelCase					:    int      							= num_choices

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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size    )

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

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

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



@require_flax
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					int			      = (
	    (
	        FlaxBigBirdForCausalLM,
	        FlaxBigBirdModel,
	        FlaxBigBirdForPreTraining,
	        FlaxBigBirdForMaskedLM,
	        FlaxBigBirdForMultipleChoice,
	        FlaxBigBirdForQuestionAnswering,
	        FlaxBigBirdForSequenceClassification,
	        FlaxBigBirdForTokenClassification,
	    )
	    if is_flax_available()
	    else ()
	)

	_lowerCamelCase   :					Tuple			      = False
	_lowerCamelCase   :					str			      = False



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= FlaxBigBirdModelTester(self    )



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



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



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



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



	def 				lowercase     (					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 				lowercase     (					self    ):
		lowerCAmelCase							,   lowerCAmelCase					:    int      							= self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__    ):
				lowerCAmelCase					:    int      							= self._prepare_for_class(snake_case__ ,					snake_case__    )
				lowerCAmelCase					:    int      							= 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'    ):
					lowerCAmelCase					:    Tuple      							= model_jitted(**snake_case__    ).to_tuple()

				with self.subTest('JIT Disabled'    ):
					with jax.disable_jit():
						lowerCAmelCase					:    List[str]      							= 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 				lowercase     (					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__    )




'''simple docstring'''




import math
import sys

import cva
import numpy as np

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				float				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= math.sqrt(_A				)
	lowerCAmelCase					:    Union[str, Any]      							= 1 / (sigma * math.sqrt(2 * math.pi				))
	return cons * np.exp(-((img / sigma) ** 2) * 0.5				)

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				int					,   _A    :				int					,   _A    :				int				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= kernel_size // 2
	return img[x - half : x + half + 1, y - half : y + half + 1]

def 			__UpperCamelCase    (    _A    :				int					,   _A    :				float				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= np.zeros((kernel_size, kernel_size)				)
	for i in range(0					,   _A				):
		for j in range(0					,   _A				):
			lowerCAmelCase					:    Optional[int]      							= math.sqrt(
			    abs(i - kernel_size // 2				) ** 2 + abs(j - kernel_size // 2				) ** 2				)
	return vec_gaussian(_A					,   _A				)

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				float					,   _A    :				float					,   _A    :				int					,   )    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= np.zeros(img.shape				)
	lowerCAmelCase					:    int      							= get_gauss_kernel(_A					,   _A				)
	lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= img.shape
	for i in range(kernel_size // 2					,   size_x - kernel_size // 2				):
		for j in range(kernel_size // 2					,   size_y - kernel_size // 2				):
			lowerCAmelCase					:    int      							= get_slice(_A					,   _A					,   _A					,   _A				)
			lowerCAmelCase					:    Any      							= img_s - img_s[kernel_size // 2, kernel_size // 2]
			lowerCAmelCase					:    str      							= vec_gaussian(_A					,   _A				)
			lowerCAmelCase					:    Optional[int]      							= np.multiply(_A					,   _A				)
			lowerCAmelCase					:    str      							= np.multiply(_A					,   _A				)
			lowerCAmelCase					:    Union[str, Any]      							= np.sum(_A				) / np.sum(_A				)
			lowerCAmelCase					:    Tuple      							= val
	return imga

def 			__UpperCamelCase    (    _A    :				list				)    ->      tuple:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= args[1] if args[1:] else '../image_data/lena.jpg'
	lowerCAmelCase					:    Any      							= float(args[2]				) if args[2:] else 1.0
	lowerCAmelCase					:    Union[str, Any]      							= float(args[3]				) if args[3:] else 1.0
	if args[4:]:
		lowerCAmelCase					:    int      							= int(args[4]				)
		lowerCAmelCase					:    Optional[Any]      							= kernel_size + abs(kernel_size % 2 - 1				)
	else:
		lowerCAmelCase					:    Optional[int]      							= 5
	return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
				_lowerCAmelCase     ,      _lowerCAmelCase     ,      _lowerCAmelCase     ,      _lowerCAmelCase  :		Any		     =	parse_args(sys.argv)
				_lowerCAmelCase  :		str		     =	cva.imread(filename, 0)
				cva.imshow('input image', img)

				_lowerCAmelCase  :		Union[str, Any]		     =	img / 255
				_lowerCAmelCase  :		List[str]		     =	out.astype('float32')
				_lowerCAmelCase  :		Optional[int]		     =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
				_lowerCAmelCase  :		Union[str, Any]		     =	out * 255
				_lowerCAmelCase  :		Optional[Any]		     =	np.uinta(out)
				cva.imshow('output image', out)
				cva.waitKey(0)
				cva.destroyAllWindows()
