


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
						snake_case							:       Any     			=      '''platform'''
						import jax
						import jax.numpy as jnp
						import numpy as np

						from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel







@require_flax
class        snake_case_   :
					UpperCAmelCase__					:    int          =			PegasusConfig
					UpperCAmelCase__					:    List[Any]          =			{}
					UpperCAmelCase__					:    Tuple          =			'''gelu'''
					def __init__(      self   :Optional[int]			,__snake_case   :Optional[Any]			,__snake_case   :Optional[int]=13			,__snake_case   :Optional[int]=7			,__snake_case   :str=True			,__snake_case   :List[Any]=False			,__snake_case   :Dict=99			,__snake_case   :List[Any]=32			,__snake_case   :List[str]=5			,__snake_case   :List[Any]=4			,__snake_case   :Optional[Any]=37			,__snake_case   :Optional[int]=0.1			,__snake_case   :str=0.1			,__snake_case   :Any=20			,__snake_case   :Optional[int]=2			,__snake_case   :Optional[int]=1			,__snake_case   :Tuple=0			,)					->    Any:
												a__             =     parent
												a__             =     batch_size
												a__             =     seq_length
												a__             =     is_training
												a__             =     use_labels
												a__             =     vocab_size
												a__             =     hidden_size
												a__             =     num_hidden_layers
												a__             =     num_attention_heads
												a__             =     intermediate_size

												a__             =     hidden_dropout_prob
												a__             =     attention_probs_dropout_prob
												a__             =     max_position_embeddings
												a__             =     eos_token_id
												a__             =     pad_token_id
												a__             =     bos_token_id
					def  lowerCamelCase__(      self   :List[Any]      )					->    List[str]:
												a__             =     ids_tensor([self.batch_size, self.seq_length - 1]			,self.vocab_size      ).clip(3			,self.vocab_size      )
												a__             =     np.expand_dims(np.array([self.eos_token_id] * self.batch_size      )			,1      )
												a__             =     np.concatenate([input_ids, eos_tensor]			,axis=1      )

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

												a__             =     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			,)
												a__             =     prepare_pegasus_inputs_dict(__snake_case			,__snake_case			,__snake_case      )
												return config, inputs_dict
					def  lowerCamelCase__(      self   :int			,__snake_case   :Any			,__snake_case   :Optional[int]			,__snake_case   :Any      )					->    List[Any]:
												a__             =     20
												a__             =     model_class_name(__snake_case      )

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

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

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

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

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

												a__             =     model.decode(__snake_case			,__snake_case      )

												a__             =     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__(      self   :Dict			,__snake_case   :int			,__snake_case   :List[str]			,__snake_case   :str      )					->    Tuple:
												a__             =     20
												a__             =     model_class_name(__snake_case      )

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

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

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

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

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

												a__             =     model.decode(__snake_case			,__snake_case			,decoder_attention_mask=__snake_case      )

												a__             =     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 							__lowercase   ( __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				List[str]					,    __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				Optional[int]=None					,    __lowerCAmelCase							:				Optional[Any]=None					,    ):
							if attention_mask is None:
														a__             =     np.not_equal(__lowerCAmelCase					,    config.pad_token_id   ).astype(np.inta   )
							if decoder_attention_mask is None:
														a__             =     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        snake_case_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Dict          =			(
					    (
					        FlaxPegasusForConditionalGeneration,
					        FlaxPegasusModel,
					    )
					    if is_flax_available()
					    else ()
					)
					UpperCAmelCase__					:    Union[str, Any]          =			(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
					UpperCAmelCase__					:    int          =			True
					UpperCAmelCase__					:    Dict          =			False
					UpperCAmelCase__					:    str          =			False
					UpperCAmelCase__					:    Tuple          =			False
					def  lowerCamelCase__(      self   :Dict      )					->    Any:
												a__             =     FlaxPegasusModelTester(self      )
												a__             =     ConfigTester(self			,config_class=__snake_case      )
					def  lowerCamelCase__(      self   :Dict      )					->    int:
												self.config_tester.run_common_tests()
					def  lowerCamelCase__(      self   :Tuple      )					->    Any:
												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(__snake_case			,__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Union[str, Any]:
												a__      ,					a__             =     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(__snake_case			,__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Optional[int]:
												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__      ):
																										a__             =     self._prepare_for_class(__snake_case			,__snake_case      )
																										a__             =     model_class(__snake_case      )

																										@jax.jit
																										def encode_jitted(__snake_case   :Dict			,__snake_case   :Union[str, Any]=None			,**__snake_case   :List[str]      ):
																																	return model.encode(input_ids=__snake_case			,attention_mask=__snake_case      )

																										with self.subTest('JIT Enabled'      ):
																																	a__             =     encode_jitted(**__snake_case      ).to_tuple()

																										with self.subTest('JIT Disabled'      ):
																																	with jax.disable_jit():
																																								a__             =     encode_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  lowerCamelCase__(      self   :List[Any]      )					->    List[Any]:
												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()

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

																										a__             =     {
																										    '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(__snake_case   :Tuple			,__snake_case   :Tuple			,__snake_case   :int      ):
																																	return model.decode(
																																	    decoder_input_ids=__snake_case			,decoder_attention_mask=__snake_case			,encoder_outputs=__snake_case			,)

																										with self.subTest('JIT Enabled'      ):
																																	a__             =     decode_jitted(**__snake_case      ).to_tuple()

																										with self.subTest('JIT Disabled'      ):
																																	with jax.disable_jit():
																																								a__             =     decode_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      )
					@slow
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    List[str]:
												for model_class_name in self.all_model_classes:
																			a__             =     model_class_name.from_pretrained('google/pegasus-large'			,from_pt=__snake_case      )
																			a__             =     np.ones((1, 1)      )
																			a__             =     model(__snake_case      )
																			self.assertIsNotNone(__snake_case      )



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

												a__             =     [
												    ' 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!" ',
												]

												a__             =     [
												    '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.',
												]

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








import math






def 							__lowercase   ( __lowerCAmelCase							:				int   ):
							a__             =     [True] * n
							a__             =     False
							a__             =     False
							a__             =     True

							for i in range(3					,    int(n**0.5 + 1   )					,    2   ):
														a__             =     i * 2
														while index < n:
																					a__             =     False
																					a__             =     index + i

							a__             =     [2]

							for i in range(3					,    __lowerCAmelCase					,    2   ):
														if is_prime[i]:
																					primes.append(__lowerCAmelCase   )

							return primes






def 							__lowercase   ( __lowerCAmelCase							:				int = 9_9_9_9_6_6_6_6_3_3_3_3   ):
							a__             =     math.floor(math.sqrt(__lowerCAmelCase   )   ) + 1_0_0
							a__             =     prime_sieve(__lowerCAmelCase   )

							a__             =     0
							a__             =     0
							a__             =     primes[prime_index]

							while (last_prime**2) <= limit:
														a__             =     primes[prime_index + 1]

														a__             =     last_prime**2
														a__             =     next_prime**2

														# Get numbers divisible by lps(current)
														a__             =     lower_bound + last_prime
														while upper_bound > current <= limit:
																					matches_sum += current
																					current += last_prime

														# Reset the upper_bound
														while (upper_bound - next_prime) > limit:
																					upper_bound -= next_prime

														# Add the numbers divisible by ups(current)
														a__             =     upper_bound - next_prime
														while current > lower_bound:
																					matches_sum += current
																					current -= next_prime

														# Remove the numbers divisible by both ups and lps
														a__             =     0
														while upper_bound > current <= limit:
																					if current <= lower_bound:
																												# Increment the current number
																												current += last_prime * next_prime
																												continue

																					if current > limit:
																												break

																					# Remove twice since it was added by both ups and lps
																					matches_sum -= current * 2

																					# Increment the current number
																					current += last_prime * next_prime

														# Setup for next pair
														a__             =     next_prime
														prime_index += 1

							return matches_sum


if __name__ == "__main__":
						print(solution())




