


'''simple docstring'''






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 		a__    :





							'''simple docstring'''






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







							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_=13 ,   lowerCamelCase_=7 ,   lowerCamelCase_=True ,   lowerCamelCase_=False ,   lowerCamelCase_=99 ,   lowerCamelCase_=32 ,   lowerCamelCase_=2 ,   lowerCamelCase_=4 ,   lowerCamelCase_=37 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=40 ,   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       )
														lowerCAmelCase__	       =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       ) ,   1       )
														lowerCAmelCase__	       =     tf.concat([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_       )	->							str:
														lowerCAmelCase__	       =     TFPegasusModel(config=lowerCamelCase_       ).get_decoder()
														lowerCAmelCase__	       =     inputs_dict['''input_ids''']

														lowerCAmelCase__	       =     input_ids[:1, :]
														lowerCAmelCase__	       =     inputs_dict['''attention_mask'''][:1, :]
														lowerCAmelCase__	       =     inputs_dict['''head_mask''']
														lowerCAmelCase__	       =     1

														# first forward pass
														lowerCAmelCase__	       =     model(lowerCamelCase_ ,   attention_mask=lowerCamelCase_ ,   head_mask=lowerCamelCase_ ,   use_cache=lowerCamelCase_       )

														lowerCAmelCase__       , lowerCAmelCase__	       =     outputs.to_tuple()

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

														# append to next input_ids and
														lowerCAmelCase__	       =     tf.concat([input_ids, next_tokens] ,   axis=-1       )
														lowerCAmelCase__	       =     tf.concat([attention_mask, next_attn_mask] ,   axis=-1       )

														lowerCAmelCase__	       =     model(lowerCamelCase_ ,   attention_mask=lowerCamelCase_       )[0]
														lowerCAmelCase__	       =     model(lowerCamelCase_ ,   attention_mask=lowerCamelCase_ ,   past_key_values=lowerCamelCase_       )[0]

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

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

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


def  _snake_case       (					A			,   A			,   A			,   A=None			,   A=None			,   A=None			,   A=None			,   A=None			,   )						->   str:
							if attention_mask is None:
														lowerCAmelCase__	       =     tf.cast(tf.math.not_equal(A			,   config.pad_token_id	)			,   tf.inta	)
							if decoder_attention_mask is None:
														lowerCAmelCase__	       =     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:
														lowerCAmelCase__	       =     tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
							if decoder_head_mask is None:
														lowerCAmelCase__	       =     tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
							if cross_attn_head_mask is None:
														lowerCAmelCase__	       =     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 		a__    (							a__    ,    a__    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       List[str]     						=				(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
							lowercase__      :       Tuple     						=				(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
							lowercase__      :       Dict     						=				(
							    {
							        "conversational": TFPegasusForConditionalGeneration,
							        "feature-extraction": TFPegasusModel,
							        "summarization": TFPegasusForConditionalGeneration,
							        "text2text-generation": TFPegasusForConditionalGeneration,
							        "translation": TFPegasusForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowercase__      :       Optional[Any]     						=				True
							lowercase__      :       List[Any]     						=				False
							lowercase__      :       Any     						=				False







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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														self.config_tester.run_common_tests()







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




@require_sentencepiece
@require_tokenizers
@require_tf
class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       int     						=				[
							    " 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!\" ",
							]
							lowercase__      :       Dict     						=				[
							    "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
							lowercase__      :       Union[str, Any]     						=				"google/pegasus-xsum"







							@cached_property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														return AutoTokenizer.from_pretrained(self.model_name       )







							@cached_property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__	       =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
														return model







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							Any:
														lowerCAmelCase__	       =     self.translate_src_text(**lowerCamelCase_       )
														assert self.expected_text == generated_words







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							str:
														lowerCAmelCase__	       =     self.tokenizer(self.src_text ,   **lowerCamelCase_ ,   padding=lowerCamelCase_ ,   return_tensors='''tf'''       )
														lowerCAmelCase__	       =     self.model.generate(
														    model_inputs.input_ids ,   attention_mask=model_inputs.attention_mask ,   num_beams=2 ,   use_cache=lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     self.tokenizer.batch_decode(generated_ids.numpy() ,   skip_special_tokens=lowerCamelCase_       )
														return generated_words







							@slow
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														self._assert_generated_batch_equal_expected()



'''simple docstring'''






from __future__ import annotations


def  _snake_case       (					A			,   A	)						->   float:
							lowerCAmelCase__	       =     sorted(numsa + numsa	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     divmod(len(A	)			,   2	)
							if mod == 1:
														return all_numbers[div]
							else:
														return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		__UpperCAmelCase       =							[float(x) for x in input('''Enter the elements of first array: ''').split()]
		__UpperCAmelCase       =							[float(x) for x in input('''Enter the elements of second array: ''').split()]
		print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")