

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
      __a    :List[str]      =			'platform'
      import jax
      import jax.numpy as jnp
      import numpy as np

      from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class   _a					:




      """simple docstring"""

      _lowerCamelCase       :			List[str]      		=       PegasusConfig
      _lowerCamelCase       :			Any      		=       {}
      _lowerCamelCase       :			Optional[Any]      		=       'gelu'





      def __init__(   self							:				Any						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				str=13						,			UpperCAmelCase							:				Optional[int]=7						,			UpperCAmelCase							:				Union[str, Any]=True						,			UpperCAmelCase							:				Union[str, Any]=False						,			UpperCAmelCase							:				List[str]=99						,			UpperCAmelCase							:				Optional[int]=32						,			UpperCAmelCase							:				Union[str, Any]=5						,			UpperCAmelCase							:				List[str]=4						,			UpperCAmelCase							:				List[Any]=37						,			UpperCAmelCase							:				Optional[int]=0.1						,			UpperCAmelCase							:				Union[str, Any]=0.1						,			UpperCAmelCase							:				Any=20						,			UpperCAmelCase							:				List[Any]=2						,			UpperCAmelCase							:				Any=1						,			UpperCAmelCase							:				Union[str, Any]=0						,			):
            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 		__A	(   self							:				Union[str, Any]	):
            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(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase	)
            return config, inputs_dict





      def 		__A	(   self							:				List[str]						,			UpperCAmelCase							:				Any						,			UpperCAmelCase							:				List[str]						,			UpperCAmelCase							:				Optional[int]	):
            A_						  =				20
            A_						  =				model_class_name(UpperCAmelCase	)

            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]						,			UpperCAmelCase						,			UpperCAmelCase	)
            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]						,			UpperCAmelCase						,			decoder_attention_mask=UpperCAmelCase						,			past_key_values=UpperCAmelCase						,			decoder_position_ids=UpperCAmelCase						,			)

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

            A_						  =				model.decode(UpperCAmelCase						,			UpperCAmelCase	)

            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 		__A	(   self							:				Any						,			UpperCAmelCase							:				Tuple						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				Optional[Any]	):
            A_						  =				20
            A_						  =				model_class_name(UpperCAmelCase	)

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

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

            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       __snake_case						( __UpperCamelCase	: Tuple				,__UpperCamelCase	: Dict				,__UpperCamelCase	: List[Any]				,__UpperCamelCase	: List[str]=None				,__UpperCamelCase	: int=None				,):



      """simple docstring"""

      if attention_mask is None:
            A_						  =				np.not_equal(__UpperCamelCase				,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   _a					(			snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			List[Any]      		=       (
          (
              FlaxPegasusForConditionalGeneration,
              FlaxPegasusModel,
          )
          if is_flax_available()
          else ()
      )
      _lowerCamelCase       :			Tuple      		=       (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
      _lowerCamelCase       :			List[str]      		=       True
      _lowerCamelCase       :			List[str]      		=       False
      _lowerCamelCase       :			Tuple      		=       False
      _lowerCamelCase       :			Union[str, Any]      		=       False





      def 		__A	(   self							:				Optional[int]	):
            A_						  =				FlaxPegasusModelTester(self	)
            A_						  =				ConfigTester(self						,			config_class=UpperCAmelCase	)





      def 		__A	(   self							:				Dict	):
            self.config_tester.run_common_tests()





      def 		__A	(   self							:				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(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				List[str]	):
            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(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Union[str, 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_						  =				self._prepare_for_class(UpperCAmelCase						,			UpperCAmelCase	)
                        A_						  =				model_class(UpperCAmelCase	)

                        @jax.jit
                        def encode_jitted(UpperCAmelCase							:				Tuple						,			UpperCAmelCase							:				Optional[Any]=None						,			**UpperCAmelCase							:				str	):
                              return model.encode(input_ids=UpperCAmelCase						,			attention_mask=UpperCAmelCase	)

                        with self.subTest("JIT Enabled"	):
                              A_						  =				encode_jitted(**UpperCAmelCase	).to_tuple()

                        with self.subTest("JIT Disabled"	):
                              with jax.disable_jit():
                                    A_						  =				encode_jitted(**UpperCAmelCase	).to_tuple()

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





      def 		__A	(   self							:				Optional[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(UpperCAmelCase	)
                        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(UpperCAmelCase							:				Any						,			UpperCAmelCase							:				Optional[Any]						,			UpperCAmelCase							:				Optional[int]	):
                              return model.decode(
                                  decoder_input_ids=UpperCAmelCase						,			decoder_attention_mask=UpperCAmelCase						,			encoder_outputs=UpperCAmelCase						,			)

                        with self.subTest("JIT Enabled"	):
                              A_						  =				decode_jitted(**UpperCAmelCase	).to_tuple()

                        with self.subTest("JIT Disabled"	):
                              with jax.disable_jit():
                                    A_						  =				decode_jitted(**UpperCAmelCase	).to_tuple()

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





      @slow
      def 		__A	(   self							:				int	):
            for model_class_name in self.all_model_classes:
                  A_						  =				model_class_name.from_pretrained("google/pegasus-large"						,			from_pt=UpperCAmelCase	)
                  A_						  =				np.ones((1, 1)	)
                  A_						  =				model(UpperCAmelCase	)
                  self.assertIsNotNone(UpperCAmelCase	)







      @slow
      def 		__A	(   self							:				Optional[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(UpperCAmelCase						,			return_tensors="np"						,			truncation=UpperCAmelCase						,			max_length=512						,			padding=UpperCAmelCase	)
            A_						  =				model.generate(**UpperCAmelCase						,			num_beams=2	).sequences
            A_						  =				tokenizer.batch_decode(UpperCAmelCase						,			skip_special_tokens=UpperCAmelCase	)
            assert tgt_text == decoded




"""simple docstring"""







import math
import sys

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						if number != int(lowerCamelCase_ ):
												raise ValueError("the value of input must be a natural number" )
						if number < 0:
												raise ValueError("the value of input must not be a negative number" )
						if number == 0:
												return 1
						snake_case   :			List[str]						    = [-1] * (number + 1)
						snake_case   :			str						    = 0
						for i in range(1     ,		number + 1 ):
												snake_case   :			Tuple						    = sys.maxsize
												snake_case   :			Optional[Any]						    = int(math.sqrt(lowerCamelCase_ ) )
												for j in range(1     ,		root + 1 ):
																		snake_case   :			List[str]						    = 1 + answers[i - (j**2)]
																		snake_case   :			List[str]						    = min(lowerCamelCase_     ,		lowerCamelCase_ )
												snake_case   :			Dict						    = answer
						return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



