


'''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
		__UpperCAmelCase					       =							'''platform'''
		import jax
		import jax.numpy as jnp
		import numpy as np

		from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class 	a__       :





							'''simple docstring'''






							lowercase__    :     Tuple					=   PegasusConfig
							lowercase__    :     Tuple					=   {}
							lowercase__    :     int					=   "gelu"







							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=13      ,					lowerCamelCase_=7      ,					lowerCamelCase_=True      ,					lowerCamelCase_=False      ,					lowerCamelCase_=99      ,					lowerCamelCase_=32      ,					lowerCamelCase_=5      ,					lowerCamelCase_=4      ,					lowerCamelCase_=37      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=20      ,					lowerCamelCase_=2      ,					lowerCamelCase_=1      ,					lowerCamelCase_=0      ,					) ->							str:
														lowerCAmelCase__      =			parent
														lowerCAmelCase__      =			batch_size
														lowerCAmelCase__      =			seq_length
														lowerCAmelCase__      =			is_training
														lowerCAmelCase__      =			use_labels
														lowerCAmelCase__      =			vocab_size
														lowerCAmelCase__      =			hidden_size
														lowerCAmelCase__      =			num_hidden_layers
														lowerCAmelCase__      =			num_attention_heads
														lowerCAmelCase__      =			intermediate_size

														lowerCAmelCase__      =			hidden_dropout_prob
														lowerCAmelCase__      =			attention_probs_dropout_prob
														lowerCAmelCase__      =			max_position_embeddings
														lowerCAmelCase__      =			eos_token_id
														lowerCAmelCase__      =			pad_token_id
														lowerCAmelCase__      =			bos_token_id







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														lowerCAmelCase__      =			ids_tensor([self.batch_size, self.seq_length - 1]      ,					self.vocab_size     ).clip(3      ,					self.vocab_size     )
														lowerCAmelCase__      =			np.expand_dims(np.array([self.eos_token_id] * self.batch_size     )      ,					1     )
														lowerCAmelCase__      =			np.concatenate([input_ids, eos_tensor]      ,					axis=1     )

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

														lowerCAmelCase__      =			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      ,					)
														lowerCAmelCase__      =			prepare_pegasus_inputs_dict(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     )
														return config, inputs_dict







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							str:
														lowerCAmelCase__      =			20
														lowerCAmelCase__      =			model_class_name(lowerCamelCase_     )

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

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

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

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

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

														lowerCAmelCase__      =			model.decode(lowerCamelCase_      ,					lowerCamelCase_     )

														lowerCAmelCase__      =			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 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Optional[int]:
														lowerCAmelCase__      =			20
														lowerCAmelCase__      =			model_class_name(lowerCamelCase_     )

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

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

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

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

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

														lowerCAmelCase__      =			model.decode(lowerCamelCase_      ,					lowerCamelCase_      ,					decoder_attention_mask=lowerCamelCase_     )

														lowerCAmelCase__      =			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       _snake_case     (  A				,					A				,					A				,					A=None				,					A=None				,					)		->					Tuple:
							if attention_mask is None:
														lowerCAmelCase__      =			np.not_equal(A				,					config.pad_token_id ).astype(np.inta )
							if decoder_attention_mask is None:
														lowerCAmelCase__      =			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 	a__       (							a__       ,       unittest.TestCase						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   (
							    (
							        FlaxPegasusForConditionalGeneration,
							        FlaxPegasusModel,
							    )
							    if is_flax_available()
							    else ()
							)
							lowercase__    :     str					=   (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
							lowercase__    :     Optional[int]					=   True
							lowercase__    :     List[Any]					=   False
							lowercase__    :     Union[str, Any]					=   False
							lowercase__    :     Any					=   False







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			FlaxPegasusModelTester(self     )
														lowerCAmelCase__      =			ConfigTester(self      ,					config_class=lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														self.config_tester.run_common_tests()







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__     ):
																												lowerCAmelCase__      =			self._prepare_for_class(lowerCamelCase_      ,					lowerCamelCase_     )
																												lowerCAmelCase__      =			model_class(lowerCamelCase_     )

																												@jax.jit
																												def encode_jitted(lowerCamelCase_      ,					lowerCamelCase_=None      ,					**lowerCamelCase_     ):
																																			return model.encode(input_ids=lowerCamelCase_      ,					attention_mask=lowerCamelCase_     )

																												with self.subTest('''JIT Enabled'''     ):
																																			lowerCAmelCase__      =			encode_jitted(**lowerCamelCase_     ).to_tuple()

																												with self.subTest('''JIT Disabled'''     ):
																																			with jax.disable_jit():
																																										lowerCAmelCase__      =			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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__     ):
																												lowerCAmelCase__      =			model_class(lowerCamelCase_     )
																												lowerCAmelCase__      =			model.encode(inputs_dict['''input_ids''']      ,					inputs_dict['''attention_mask''']     )

																												lowerCAmelCase__      =			{
																												    '''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_      ,					lowerCamelCase_      ,					lowerCamelCase_     ):
																																			return model.decode(
																																			    decoder_input_ids=lowerCamelCase_      ,					decoder_attention_mask=lowerCamelCase_      ,					encoder_outputs=lowerCamelCase_      ,					)

																												with self.subTest('''JIT Enabled'''     ):
																																			lowerCAmelCase__      =			decode_jitted(**lowerCamelCase_     ).to_tuple()

																												with self.subTest('''JIT Disabled'''     ):
																																			with jax.disable_jit():
																																										lowerCAmelCase__      =			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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														for model_class_name in self.all_model_classes:
																					lowerCAmelCase__      =			model_class_name.from_pretrained('''google/pegasus-large'''      ,					from_pt=lowerCamelCase_     )
																					lowerCAmelCase__      =			np.ones((1, 1)     )
																					lowerCAmelCase__      =			model(lowerCamelCase_     )
																					self.assertIsNotNone(lowerCamelCase_     )







							@slow
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														lowerCAmelCase__      =			FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''     )
														lowerCAmelCase__      =			PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''     )

														lowerCAmelCase__      =			[
														    ''' 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!" ''',
														]

														lowerCAmelCase__      =			[
														    '''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.''',
														]

														lowerCAmelCase__      =			tokenizer(lowerCamelCase_      ,					return_tensors='''np'''      ,					truncation=lowerCamelCase_      ,					max_length=5_12      ,					padding=lowerCamelCase_     )
														lowerCAmelCase__      =			model.generate(**lowerCamelCase_      ,					num_beams=2     ).sequences
														lowerCAmelCase__      =			tokenizer.batch_decode(lowerCamelCase_      ,					skip_special_tokens=lowerCamelCase_     )
														assert tgt_text == decoded



'''simple docstring'''






def       _snake_case     (  A				,					A )		->					bool:
							lowerCAmelCase__      =			len(A ) + 1
							lowerCAmelCase__      =			len(A ) + 1

							# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
							# length i of input_string matches with prefix string of length j of
							# given pattern.
							# "dp" stands for dynamic programming.
							lowerCAmelCase__      =			[[0 for i in range(A )] for j in range(A )]

							# since string of zero length match pattern of zero length
							lowerCAmelCase__      =			1

							# since pattern of zero length will never match with string of non-zero length
							for i in range(1				,					A ):
														lowerCAmelCase__      =			0

							# since string of zero length will match with pattern where there
							# is at least one * alternatively
							for j in range(1				,					A ):
														lowerCAmelCase__      =			dp[0][j - 2] if pattern[j - 1] == '''*''' else 0

							# now using bottom-up approach to find for all remaining lengths
							for i in range(1				,					A ):
														for j in range(1				,					A ):
																					if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																												lowerCAmelCase__      =			dp[i - 1][j - 1]

																					elif pattern[j - 1] == "*":
																												if dp[i][j - 2] == 1:
																																			lowerCAmelCase__      =			1
																												elif pattern[j - 2] in (input_string[i - 1], "."):
																																			lowerCAmelCase__      =			dp[i - 1][j]
																												else:
																																			lowerCAmelCase__      =			0
																					else:
																												lowerCAmelCase__      =			0

							return bool(dp[-1][-1] )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# inputing the strings
		# input_string = input("input a string :")
		# pattern = input("input a pattern :")

		__UpperCAmelCase					       =							'''aab'''
		__UpperCAmelCase					       =							'''c*a*b'''

		# using function to check whether given string matches the given pattern
		if match_pattern(input_string, pattern):
				print(f"""{input_string} matches the given pattern {pattern}""")
		else:
				print(f"""{input_string} does not match with the given pattern {pattern}""")