

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class 				SCREAMING_SNAKE_CASE__ :
    __lowerCamelCase						:	str														=					PegasusConfig
    __lowerCamelCase						:	str														=					{}
    __lowerCamelCase						:	Union[str, Any]														=					"""gelu"""





    def __init__(				self						,       a						,       a=13						,       a=7						,       a=True						,       a=False						,       a=99						,       a=32						,       a=2						,       a=4						,       a=37						,       a=0.1						,       a=0.1						,       a=40						,       a=2						,       a=1						,       a=0						,       ):
         lowercase__   :       int				=	parent
         lowercase__   :       Optional[int]				=	batch_size
         lowercase__   :       Dict				=	seq_length
         lowercase__   :       str				=	is_training
         lowercase__   :       List[Any]				=	use_labels
         lowercase__   :       int				=	vocab_size
         lowercase__   :       Dict				=	hidden_size
         lowercase__   :       int				=	num_hidden_layers
         lowercase__   :       Tuple				=	num_attention_heads
         lowercase__   :       List[str]				=	intermediate_size

         lowercase__   :       Union[str, Any]				=	hidden_dropout_prob
         lowercase__   :       Optional[int]				=	attention_probs_dropout_prob
         lowercase__   :       int				=	max_position_embeddings
         lowercase__   :       Optional[int]				=	eos_token_id
         lowercase__   :       List[str]				=	pad_token_id
         lowercase__   :       Union[str, Any]				=	bos_token_id





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	ids_tensor([self.batch_size, self.seq_length - 1]						,       self.vocab_size)
         lowercase__   :       Dict				=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)						,       1)
         lowercase__   :       Optional[int]				=	tf.concat([input_ids, eos_tensor]						,       axis=1)

         lowercase__   :       Union[str, Any]				=	ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size)

         lowercase__   :       str				=	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						,       )
         lowercase__   :       Optional[Any]				=	prepare_pegasus_inputs_dict(a						,       a						,       a)
         return config, inputs_dict





    def 				snake_case_    (				self						,       a						,       a):
         lowercase__   :       List[str]				=	TFPegasusModel(config=a).get_decoder()
         lowercase__   :       List[str]				=	inputs_dict["input_ids"]

         lowercase__   :       Union[str, Any]				=	input_ids[:1, :]
         lowercase__   :       List[str]				=	inputs_dict["attention_mask"][:1, :]
         lowercase__   :       Union[str, Any]				=	inputs_dict["head_mask"]
         lowercase__   :       Optional[Any]				=	1

         # first forward pass
         lowercase__   :       List[str]				=	model(a						,       attention_mask=a						,       head_mask=a						,       use_cache=a)

         lowercase__   :       Optional[Any]				=	outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         lowercase__   :       str				=	ids_tensor((self.batch_size, 3)						,       config.vocab_size)
         lowercase__   :       int				=	tf.cast(ids_tensor((self.batch_size, 3)						,       2)						,       tf.inta)

         # append to next input_ids and
         lowercase__   :       List[str]				=	tf.concat([input_ids, next_tokens]						,       axis=-1)
         lowercase__   :       Optional[Any]				=	tf.concat([attention_mask, next_attn_mask]						,       axis=-1)

         lowercase__   :       Any				=	model(a						,       attention_mask=a)[0]
         lowercase__   :       str				=	model(a						,       attention_mask=a						,       past_key_values=a)[0]

         self.parent.assertEqual(next_tokens.shape[1]						,       output_from_past.shape[1])

         # select random slice
         lowercase__   :       int				=	int(ids_tensor((1,)						,       output_from_past.shape[-1]))
         lowercase__   :       Tuple				=	output_from_no_past[:, -3:, random_slice_idx]
         lowercase__   :       Tuple				=	output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(a						,       a						,       rtol=1e-3)



def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   List[str]=None     ,					SCREAMING_SNAKE_CASE_		:   List[Any]=None     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]=None     ,					SCREAMING_SNAKE_CASE_		:   List[str]=None     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]=None     ,					):



     '''simple docstring'''





     if attention_mask is None:
          lowercase__   :       Tuple				=	tf.cast(tf.math.not_equal(_lowerCamelCase     ,					config.pad_token_id      )     ,					tf.inta      )
     if decoder_attention_mask is None:
          lowercase__   :       str				=	tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape     ,					dtype=tf.inta      ),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,					config.pad_token_id      )     ,					tf.inta      ),
              ]     ,					axis=-1     ,					)
     if head_mask is None:
          lowercase__   :       Union[str, Any]				=	tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
     if decoder_head_mask is None:
          lowercase__   :       str				=	tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
     if cross_attn_head_mask is None:
          lowercase__   :       Optional[int]				=	tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }


@require_tf
class 				SCREAMING_SNAKE_CASE__ (a__		,	a__		,	unittest.TestCase					):
    __lowerCamelCase						:	Optional[int]														=					(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
    __lowerCamelCase						:	List[str]														=					(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    __lowerCamelCase						:	str														=					(
        {
            """conversational""": TFPegasusForConditionalGeneration,
            """feature-extraction""": TFPegasusModel,
            """summarization""": TFPegasusForConditionalGeneration,
            """text2text-generation""": TFPegasusForConditionalGeneration,
            """translation""": TFPegasusForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    __lowerCamelCase						:	str														=					True
    __lowerCamelCase						:	List[Any]														=					False
    __lowerCamelCase						:	Optional[int]														=					False





    def 				snake_case_    (				self):
         lowercase__   :       int				=	TFPegasusModelTester(self)
         lowercase__   :       str				=	ConfigTester(self						,       config_class=a)





    def 				snake_case_    (				self):
         self.config_tester.run_common_tests()





    def 				snake_case_    (				self):
         lowercase__   :       List[Any]				=	self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*a)



@require_sentencepiece
@require_tokenizers
@require_tf
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):
    __lowerCamelCase						:	Optional[Any]														=					[
        """ 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						:	str														=					[
        """California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
        """ reduce the risk of wildfires.""",
        """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
    ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
    __lowerCamelCase						:	Optional[Any]														=					"""google/pegasus-xsum"""





    @cached_property
    def 				snake_case_    (				self):
         return AutoTokenizer.from_pretrained(self.model_name)





    @cached_property
    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
         return model





    def 				snake_case_    (				self						,       **a):
         lowercase__   :       Optional[Any]				=	self.translate_src_text(**a)
         assert self.expected_text == generated_words





    def 				snake_case_    (				self						,       **a):
         lowercase__   :       Any				=	self.tokenizer(self.src_text						,       **a						,       padding=a						,       return_tensors='tf')
         lowercase__   :       List[Any]				=	self.model.generate(
             model_inputs.input_ids						,       attention_mask=model_inputs.attention_mask						,       num_beams=2						,       use_cache=a						,       )
         lowercase__   :       Tuple				=	self.tokenizer.batch_decode(generated_ids.numpy()						,       skip_special_tokens=a)
         return generated_words





    @slow
    def 				snake_case_    (				self):
         self._assert_generated_batch_equal_expected()




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int = 50_000_000      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	set()
     lowercase__   :       Any				=	int((limit - 24) ** (1 / 2)      )

     lowercase__   :       Any				=	set(range(3     ,					prime_square_limit + 1     ,					2      )      )
     primes.add(2      )
     for p in range(3     ,					prime_square_limit + 1     ,					2      ):
          if p not in primes:
               continue
          primes.difference_update(set(range(p * p     ,					prime_square_limit + 1     ,					SCREAMING_SNAKE_CASE_      )      )      )

     for primea in primes:
          lowercase__   :       Any				=	primea * primea
          for primea in primes:
               lowercase__   :       Optional[int]				=	primea * primea * primea
               if square + cube >= limit - 16:
                    break
               for primea in primes:
                    lowercase__   :       Dict				=	primea * primea * primea * primea
                    lowercase__   :       List[Any]				=	square + cube + tetr
                    if total >= limit:
                         break
                    ret.add(SCREAMING_SNAKE_CASE_      )

     return len(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
       print(F'''{solution() = }''')

