


'''simple docstring'''



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	_lowercase      :      Optional[int]            =					"""platform"""
	import jax
	import jax.numpy as jnp
	import numpy as np

	from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel






@require_flax
class 	UpperCamelCase__:
				__magic_name__    :				List[Any]  		=	PegasusConfig
				__magic_name__    :				Any  		=	{}
				__magic_name__    :				Optional[Any]  		=	"gelu"




				def __init__(		self      :			Dict				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Dict=13				,   lowerCAmelCase      :			Optional[int]=7				,   lowerCAmelCase      :			Any=True				,   lowerCAmelCase      :			Optional[int]=False				,   lowerCAmelCase      :			str=99				,   lowerCAmelCase      :			str=32				,   lowerCAmelCase      :			Dict=5				,   lowerCAmelCase      :			str=4				,   lowerCAmelCase      :			Dict=37				,   lowerCAmelCase      :			Tuple=0.1				,   lowerCAmelCase      :			Union[str, Any]=0.1				,   lowerCAmelCase      :			Union[str, Any]=20				,   lowerCAmelCase      :			str=2				,   lowerCAmelCase      :			Optional[int]=1				,   lowerCAmelCase      :			List[str]=0				,   )->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     parent
										UpperCAmelCase					    =     batch_size
										UpperCAmelCase					    =     seq_length
										UpperCAmelCase					    =     is_training
										UpperCAmelCase					    =     use_labels
										UpperCAmelCase					    =     vocab_size
										UpperCAmelCase					    =     hidden_size
										UpperCAmelCase					    =     num_hidden_layers
										UpperCAmelCase					    =     num_attention_heads
										UpperCAmelCase					    =     intermediate_size

										UpperCAmelCase					    =     hidden_dropout_prob
										UpperCAmelCase					    =     attention_probs_dropout_prob
										UpperCAmelCase					    =     max_position_embeddings
										UpperCAmelCase					    =     eos_token_id
										UpperCAmelCase					    =     pad_token_id
										UpperCAmelCase					    =     bos_token_id




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

										"""simple docstring"""




										UpperCAmelCase					    =     ids_tensor([self.batch_size, self.seq_length - 1]				,   self.vocab_size				).clip(3				,   self.vocab_size				)
										UpperCAmelCase					    =     np.expand_dims(np.array([self.eos_token_id] * self.batch_size				)				,   1				)
										UpperCAmelCase					    =     np.concatenate([input_ids, eos_tensor]				,   axis=1				)

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

										UpperCAmelCase					    =     self.config_cls(
										    vocab_size=self.vocab_size				,   d_model=self.hidden_size				,   encoder_layers=self.num_hidden_layers				,   decoder_layers=self.num_hidden_layers				,   encoder_attention_heads=self.num_attention_heads				,   decoder_attention_heads=self.num_attention_heads				,   encoder_ffn_dim=self.intermediate_size				,   decoder_ffn_dim=self.intermediate_size				,   dropout=self.hidden_dropout_prob				,   attention_dropout=self.attention_probs_dropout_prob				,   max_position_embeddings=self.max_position_embeddings				,   eos_token_ids=[2]				,   bos_token_id=self.bos_token_id				,   pad_token_id=self.pad_token_id				,   decoder_start_token_id=self.pad_token_id				,   **self.config_updates				,   )
										UpperCAmelCase					    =     prepare_pegasus_inputs_dict(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				)
										return config, inputs_dict




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Tuple				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     20
										UpperCAmelCase					    =     model_class_name(lowerCAmelCase				)

										UpperCAmelCase					    =     model.encode(inputs_dict['''input_ids''']				)

										UpperCAmelCase    ,    UpperCAmelCase					    =     (
										    inputs_dict['''decoder_input_ids'''],
										    inputs_dict['''decoder_attention_mask'''],
										)

										UpperCAmelCase					    =     model.init_cache(decoder_input_ids.shape[0]				,   lowerCAmelCase				,   lowerCAmelCase				)
										UpperCAmelCase					    =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,   dtype='''i4'''				)

										UpperCAmelCase					    =     jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]				,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,   )
										UpperCAmelCase					    =     model.decode(
										    decoder_input_ids[:, :-1]				,   lowerCAmelCase				,   decoder_attention_mask=lowerCAmelCase				,   past_key_values=lowerCAmelCase				,   decoder_position_ids=lowerCAmelCase				,   )

										UpperCAmelCase					    =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,   dtype='''i4'''				)
										UpperCAmelCase					    =     model.decode(
										    decoder_input_ids[:, -1:]				,   lowerCAmelCase				,   decoder_attention_mask=lowerCAmelCase				,   past_key_values=outputs_cache.past_key_values				,   decoder_position_ids=lowerCAmelCase				,   )

										UpperCAmelCase					    =     model.decode(lowerCAmelCase				,   lowerCAmelCase				)

										UpperCAmelCase					    =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
										self.parent.assertTrue(diff < 1E-3				,   msg=F"""Max diff is {diff}"""				)





				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Optional[int]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     20
										UpperCAmelCase					    =     model_class_name(lowerCAmelCase				)

										UpperCAmelCase					    =     model.encode(inputs_dict['''input_ids''']				)

										UpperCAmelCase    ,    UpperCAmelCase					    =     (
										    inputs_dict['''decoder_input_ids'''],
										    inputs_dict['''decoder_attention_mask'''],
										)

										UpperCAmelCase					    =     jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
										    ]				,   axis=-1				,   )

										UpperCAmelCase					    =     model.init_cache(decoder_input_ids.shape[0]				,   lowerCAmelCase				,   lowerCAmelCase				)
										UpperCAmelCase					    =     jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]				,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,   )

										UpperCAmelCase					    =     model.decode(
										    decoder_input_ids[:, :-1]				,   lowerCAmelCase				,   decoder_attention_mask=lowerCAmelCase				,   past_key_values=lowerCAmelCase				,   decoder_position_ids=lowerCAmelCase				,   )
										UpperCAmelCase					    =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,   dtype='''i4'''				)
										UpperCAmelCase					    =     model.decode(
										    decoder_input_ids[:, -1:]				,   lowerCAmelCase				,   past_key_values=outputs_cache.past_key_values				,   decoder_attention_mask=lowerCAmelCase				,   decoder_position_ids=lowerCAmelCase				,   )

										UpperCAmelCase					    =     model.decode(lowerCAmelCase				,   lowerCAmelCase				,   decoder_attention_mask=lowerCAmelCase				)

										UpperCAmelCase					    =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
										self.parent.assertTrue(diff < 1E-3				,   msg=F"""Max diff is {diff}"""				)







def 				lowerCamelCase__				(	A       :      Optional[Any]				,       A       :      Dict				,       A       :      int				,       A       :      List[Any]=None				,       A       :      str=None				,       ):




						'''simple docstring'''






						if attention_mask is None:
												UpperCAmelCase					    =     np.not_equal(A				,       config.pad_token_id					).astype(np.inta					)
						if decoder_attention_mask is None:
												UpperCAmelCase					    =     np.concatenate(
												    [
												        np.ones(decoder_input_ids[:, :1].shape				,       dtype=np.inta					),
												        np.not_equal(decoder_input_ids[:, 1:]				,       config.pad_token_id					).astype(np.inta					),
												    ]				,       axis=-1				,       )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						}






@require_flax
class 	UpperCamelCase__(       lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				int  		=	(
				    (
				        FlaxPegasusForConditionalGeneration,
				        FlaxPegasusModel,
				    )
				    if is_flax_available()
				    else ()
				)
				__magic_name__    :				Any  		=	(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
				__magic_name__    :				List[str]  		=	True
				__magic_name__    :				Union[str, Any]  		=	False
				__magic_name__    :				str  		=	False
				__magic_name__    :				List[str]  		=	False




				def 					a__(		self      :			List[Any]				)->					int:

										"""simple docstring"""




										UpperCAmelCase					    =     FlaxPegasusModelTester(self				)
										UpperCAmelCase					    =     ConfigTester(self				,   config_class=lowerCAmelCase				)




				def 					a__(		self      :			Optional[Any]				)->					List[str]:

										"""simple docstring"""




										self.config_tester.run_common_tests()




				def 					a__(		self      :			Tuple				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				)




				def 					a__(		self      :			Any				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				)




				def 					a__(		self      :			Tuple				)->					Optional[Any]:

										"""simple docstring"""




										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(lowerCAmelCase				,   lowerCAmelCase				)
																						UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																						@jax.jit
																						def encode_jitted(lowerCAmelCase      :			int				,   lowerCAmelCase      :			Dict=None				,   **lowerCAmelCase      :			Tuple				):
																												return model.encode(input_ids=lowerCAmelCase				,   attention_mask=lowerCAmelCase				)

																						with self.subTest('''JIT Enabled'''				):
																												UpperCAmelCase					    =     encode_jitted(**lowerCAmelCase				).to_tuple()

																						with self.subTest('''JIT Disabled'''				):
																												with jax.disable_jit():
																																		UpperCAmelCase					    =     encode_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      :			List[str]				)->					Any:

										"""simple docstring"""




										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					    =     model_class(lowerCAmelCase				)
																						UpperCAmelCase					    =     model.encode(inputs_dict['''input_ids''']				,   inputs_dict['''attention_mask''']				)

																						UpperCAmelCase					    =     {
																						    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
																						    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
																						    '''encoder_outputs''': encoder_outputs,
																						}

																						@jax.jit
																						def decode_jitted(lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Any				):
																												return model.decode(
																												    decoder_input_ids=lowerCAmelCase				,   decoder_attention_mask=lowerCAmelCase				,   encoder_outputs=lowerCAmelCase				,   )

																						with self.subTest('''JIT Enabled'''				):
																												UpperCAmelCase					    =     decode_jitted(**lowerCAmelCase				).to_tuple()

																						with self.subTest('''JIT Disabled'''				):
																												with jax.disable_jit():
																																		UpperCAmelCase					    =     decode_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				)




				@slow
				def 					a__(		self      :			Dict				)->					Union[str, Any]:

										"""simple docstring"""




										for model_class_name in self.all_model_classes:
																UpperCAmelCase					    =     model_class_name.from_pretrained('''google/pegasus-large'''				,   from_pt=lowerCAmelCase				)
																UpperCAmelCase					    =     np.ones((1, 1)				)
																UpperCAmelCase					    =     model(lowerCAmelCase				)
																self.assertIsNotNone(lowerCAmelCase				)





				@slow
				def 					a__(		self      :			Tuple				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''				)
										UpperCAmelCase					    =     PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''				)

										UpperCAmelCase					    =     [
										    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
										    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
										]

										UpperCAmelCase					    =     [
										    '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
										    '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
										]

										UpperCAmelCase					    =     tokenizer(lowerCAmelCase				,   return_tensors='''np'''				,   truncation=lowerCAmelCase				,   max_length=512				,   padding=lowerCAmelCase				)
										UpperCAmelCase					    =     model.generate(**lowerCAmelCase				,   num_beams=2				).sequences
										UpperCAmelCase					    =     tokenizer.batch_decode(lowerCAmelCase				,   skip_special_tokens=lowerCAmelCase				)
										assert tgt_text == decoded









'''simple docstring'''



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


def 				lowerCamelCase__				(	A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int					):




						'''simple docstring'''






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

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

																		# degree to radiant
																		UpperCAmelCase					    =     theta / 1_80 * np.pi
																		UpperCAmelCase					    =     np.cos(_theta					)
																		UpperCAmelCase					    =     np.sin(_theta					)

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

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

																		# fill kernel
																		UpperCAmelCase					    =     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
	_lowercase      :      Tuple            =					imread("""../image_data/lena.jpg""")
	# turn image in gray scale value
	_lowercase      :      int            =					cvtColor(img, COLOR_BGR2GRAY)

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

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

	waitKey(0)





