






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        __UpperCamelCase    (     unittest.TestCase   ):







	def __init__(  self      :							Tuple       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Optional[int]=2       ,				_lowerCAmelCase      :							Optional[Any]=56       ,				_lowerCAmelCase      :							int=True       ,				_lowerCAmelCase      :							Optional[int]=True       ,				_lowerCAmelCase      :							str=True       ,				_lowerCAmelCase      :							Optional[int]=True       ,				_lowerCAmelCase      :							Any=99       ,				_lowerCAmelCase      :							List[str]=32       ,				_lowerCAmelCase      :							Any=2       ,				_lowerCAmelCase      :							int=2       ,				_lowerCAmelCase      :							Dict=7       ,				_lowerCAmelCase      :							Tuple="gelu_new"       ,				_lowerCAmelCase      :							Any=0.1       ,				_lowerCAmelCase      :							Any=0.1       ,				_lowerCAmelCase      :							List[str]=512       ,				_lowerCAmelCase      :							int=16       ,				_lowerCAmelCase      :							int=2       ,				_lowerCAmelCase      :							Any=0.02       ,				_lowerCAmelCase      :							Dict=4       ,				_lowerCAmelCase      :							List[str]="block_sparse"       ,				_lowerCAmelCase      :							Tuple=True       ,				_lowerCAmelCase      :							Dict=False       ,				_lowerCAmelCase      :							Dict=2       ,				_lowerCAmelCase      :							Optional[int]=3       ,				)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase					       =  parent
		__lowercase					       =  batch_size
		__lowercase					       =  seq_length
		__lowercase					       =  is_training
		__lowercase					       =  use_attention_mask
		__lowercase					       =  use_token_type_ids
		__lowercase					       =  use_labels
		__lowercase					       =  vocab_size
		__lowercase					       =  hidden_size
		__lowercase					       =  num_hidden_layers
		__lowercase					       =  num_attention_heads
		__lowercase					       =  intermediate_size
		__lowercase					       =  hidden_act
		__lowercase					       =  hidden_dropout_prob
		__lowercase					       =  attention_probs_dropout_prob
		__lowercase					       =  max_position_embeddings
		__lowercase					       =  type_vocab_size
		__lowercase					       =  type_sequence_label_size
		__lowercase					       =  initializer_range
		__lowercase					       =  num_choices

		__lowercase					       =  rescale_embeddings
		__lowercase					       =  attention_type
		__lowercase					       =  use_bias
		__lowercase					       =  block_size
		__lowercase					       =  num_random_blocks







	def  _a   (  self      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase					       =  ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size			)

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

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

		__lowercase					       =  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=_lowerCAmelCase       ,				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      :							Optional[Any]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase					       =  self.prepare_config_and_inputs()
		__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase					       =  config_and_inputs
		__lowercase					       =  {
		    """input_ids""": input_ids,
		    """token_type_ids""": token_type_ids,
		    """attention_mask""": attention_mask,
		}
		return config, inputs_dict


@require_flax
class        __UpperCamelCase    (     __UpperCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:int   =       (
	    (
	        FlaxBigBirdForCausalLM,
	        FlaxBigBirdModel,
	        FlaxBigBirdForPreTraining,
	        FlaxBigBirdForMaskedLM,
	        FlaxBigBirdForMultipleChoice,
	        FlaxBigBirdForQuestionAnswering,
	        FlaxBigBirdForSequenceClassification,
	        FlaxBigBirdForTokenClassification,
	    )
	    if is_flax_available()
	    else ()
	)

	__snake_case	:str   =       False
	__snake_case	:Union[str, Any]   =       False







	def  _a   (  self      :							List[str]			)		-> List[str]:
		"""simple docstring"""







		__lowercase					       =  FlaxBigBirdModelTester(self			)







	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def  _a   (  self      :							Union[str, Any]			)		-> Optional[int]:
		"""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      :							int			)		-> 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      :							Tuple			)		-> Union[str, Any]:
		"""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      :							str			)		-> int:
		"""simple docstring"""







		super().test_hidden_states_output()







	@slow
	def  _a   (  self      :							Tuple			)		-> Dict:
		"""simple docstring"""







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







	def  _a   (  self      :							Dict			)		-> List[Any]:
		"""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      :							Optional[Any]			)		-> int:
		"""simple docstring"""







		__lowercase       ,						__lowercase					       =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__			):
				__lowercase					       =  self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)
				__lowercase					       =  model_class(_lowerCAmelCase			)

				@jax.jit
				def model_jitted(_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							Optional[int]=None       ,				**_lowerCAmelCase      :							List[Any]			):
					return model(input_ids=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase       ,				**_lowerCAmelCase			)

				with self.subTest("""JIT Enabled"""			):
					__lowercase					       =  model_jitted(**_lowerCAmelCase			).to_tuple()

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

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







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Optional[Any]=1e-5       ,				_lowerCAmelCase      :							Tuple="outputs"       ,				_lowerCAmelCase      :							Any=None			)		-> int:
		"""simple docstring"""







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









import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if (ksize % 2) == 0:
		__lowercase             =  ksize + 1
	__lowercase             =  np.zeros((ksize, ksize)				,  dtype=np.floataa					)

	# each value
	for y in range(lowerCamelCase					):
		for x in range(lowerCamelCase					):
			# distance from center
			__lowercase             =  x - ksize // 2
			__lowercase             =  y - ksize // 2

			# degree to radiant
			__lowercase             =  theta / 180 * np.pi
			__lowercase             =  np.cos(_theta					)
			__lowercase             =  np.sin(_theta					)

			# get kernel x
			__lowercase             =  cos_theta * px + sin_theta * py

			# get kernel y
			__lowercase             =  -sin_theta * px + cos_theta * py

			# fill kernel
			__lowercase             =  np.exp(
			    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)					) * np.cos(2 * np.pi * _x / lambd + psi					)

	return gabor


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# read original image
						__UpperCamelCase      :      List[Any]     						=						imread("""../image_data/lena.jpg""")
						# turn image in gray scale value
						__UpperCamelCase      :      Union[str, Any]     						=						cvtColor(img, COLOR_BGR2GRAY)

						# Apply multiple Kernel to detect edges
						__UpperCamelCase      :      Union[str, Any]     						=						np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 120, 150]:
												__UpperCamelCase      :      Tuple     						=						gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						__UpperCamelCase      :      List[str]     						=						out / out.max() * 255
						__UpperCamelCase      :      List[str]     						=						out.astype(np.uinta)

						imshow("""Original""", gray)
						imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

						waitKey(0)

