






'''simple docstring'''
from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a_					=	TypeVar('T')





def 	_a(					UpperCamelCase__			:  int							):




						'''simple docstring'''






						return (position - 1) // 2





def 	_a(					UpperCamelCase__			:  int							):




						'''simple docstring'''






						return (2 * position) + 1





def 	_a(					UpperCamelCase__			:  int							):




						'''simple docstring'''






						return (2 * position) + 2






class        __SCREAMING_SNAKE_CASE (     Generic[T]			):




	def __init__( self		:							List[str] )		->  None:
							SCREAMING_SNAKE_CASE__       :			list[tuple[T, int]]													=[]
							SCREAMING_SNAKE_CASE__       :			dict[T, int]													={}
							SCREAMING_SNAKE_CASE__       :			int													=0




	def __len__( self		:							List[Any] )		->  int:
							return self.elements




	def __repr__( self		:							Union[str, Any] )		->  str:
							return str(self.heap )




	def        __magic_name__		( self		:							Optional[Any] )		->  bool:
							# Check if the priority queue is empty
							return self.elements == 0




	def        __magic_name__		( self		:							List[str]   , __lowercase		:							T   , __lowercase		:							int )		->  None:
							# Add an element with given priority to the queue
							self.heap.append((elem, weight) )
							SCREAMING_SNAKE_CASE__       :			Dict													=self.elements
							self.elements += 1
							self._bubble_up(__lowercase )




	def        __magic_name__		( self		:							Optional[Any] )		->  T:
							# Remove and return the element with lowest weight (highest priority)
							if self.elements > 1:
													self._swap_nodes(0   , self.elements - 1 )
							SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.heap.pop()
							del self.position_map[elem]
							self.elements -= 1
							if self.elements > 0:
													SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.heap[0]
													self._bubble_down(__lowercase )
							return elem




	def        __magic_name__		( self		:							int   , __lowercase		:							T   , __lowercase		:							int )		->  None:
							# Update the weight of the given key
							SCREAMING_SNAKE_CASE__       :			Dict													=self.position_map[elem]
							SCREAMING_SNAKE_CASE__       :			List[Any]													=(elem, weight)
							if position > 0:
													SCREAMING_SNAKE_CASE__       :			int													=get_parent_position(__lowercase )
													SCREAMING_SNAKE_CASE__       :			Optional[Any]													=self.heap[parent_position]
													if parent_weight > weight:
																			self._bubble_up(__lowercase )
													else:
																			self._bubble_down(__lowercase )
							else:
													self._bubble_down(__lowercase )




	def        __magic_name__		( self		:							Tuple   , __lowercase		:							T )		->  None:
							# Place a node at the proper position (upward movement) [to be used internally
							# only]
							SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.position_map[elem]
							if curr_pos == 0:
													return None
							SCREAMING_SNAKE_CASE__       :			int													=get_parent_position(__lowercase )
							SCREAMING_SNAKE_CASE__       :			Dict													=self.heap[curr_pos]
							SCREAMING_SNAKE_CASE__       :			Any													=self.heap[parent_position]
							if parent_weight > weight:
													self._swap_nodes(__lowercase   , __lowercase )
													return self._bubble_up(__lowercase )
							return None




	def        __magic_name__		( self		:							str   , __lowercase		:							T )		->  None:
							# Place a node at the proper position (downward movement) [to be used
							# internally only]
							SCREAMING_SNAKE_CASE__       :			Any													=self.position_map[elem]
							SCREAMING_SNAKE_CASE__       :			str													=self.heap[curr_pos]
							SCREAMING_SNAKE_CASE__       :			Any													=get_child_left_position(__lowercase )
							SCREAMING_SNAKE_CASE__       :			Optional[Any]													=get_child_right_position(__lowercase )
							if child_left_position < self.elements and child_right_position < self.elements:
													SCREAMING_SNAKE_CASE__       :			int													=self.heap[child_left_position]
													SCREAMING_SNAKE_CASE__       :			List[Any]													=self.heap[child_right_position]
													if child_right_weight < child_left_weight and child_right_weight < weight:
																			self._swap_nodes(__lowercase   , __lowercase )
																			return self._bubble_down(__lowercase )
							if child_left_position < self.elements:
													SCREAMING_SNAKE_CASE__       :			List[Any]													=self.heap[child_left_position]
													if child_left_weight < weight:
																			self._swap_nodes(__lowercase   , __lowercase )
																			return self._bubble_down(__lowercase )
							else:
													return None
							if child_right_position < self.elements:
													SCREAMING_SNAKE_CASE__       :			Tuple													=self.heap[child_right_position]
													if child_right_weight < weight:
																			self._swap_nodes(__lowercase   , __lowercase )
																			return self._bubble_down(__lowercase )
							return None




	def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							int   , __lowercase		:							int )		->  None:
							# Swap the nodes at the given positions
							SCREAMING_SNAKE_CASE__       :			List[Any]													=self.heap[nodea_pos][0]
							SCREAMING_SNAKE_CASE__       :			Tuple													=self.heap[nodea_pos][0]
							SCREAMING_SNAKE_CASE__       :			List[Any]													=(
							    self.heap[nodea_pos],
							    self.heap[nodea_pos],
							)
							SCREAMING_SNAKE_CASE__       :			Optional[int]													=nodea_pos
							SCREAMING_SNAKE_CASE__       :			List[str]													=nodea_pos






class        __SCREAMING_SNAKE_CASE (     Generic[T]			):




	def __init__( self		:							Any )		->  None:
							SCREAMING_SNAKE_CASE__       :			dict[T, dict[T, int]]													={}
							SCREAMING_SNAKE_CASE__       :			int													=0




	def __repr__( self		:							Dict )		->  str:
							return str(self.connections )




	def __len__( self		:							Optional[Any] )		->  int:
							return self.nodes




	def        __magic_name__		( self		:							str   , __lowercase		:							T )		->  None:
							# Add a node in the graph if it is not in the graph
							if node not in self.connections:
													SCREAMING_SNAKE_CASE__       :			str													={}
													self.nodes += 1




	def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							T   , __lowercase		:							T   , __lowercase		:							int )		->  None:
							# Add an edge between 2 nodes in the graph
							self.add_node(__lowercase )
							self.add_node(__lowercase )
							SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=weight
							SCREAMING_SNAKE_CASE__       :			int													=weight





def 	_a(					UpperCamelCase__			:  GraphUndirectedWeighted[T],		):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__       :			dict[T, int]													={node: maxsize for node in graph.connections}
						SCREAMING_SNAKE_CASE__       :			dict[T, T | None]													={node: None for node in graph.connections}

						SCREAMING_SNAKE_CASE__       :			MinPriorityQueue[T]													=MinPriorityQueue()
						for node, weight in dist.items():
												priority_queue.push(UpperCamelCase__,		UpperCamelCase__							)

						if priority_queue.is_empty():
												return dist, parent

						# initialization
						SCREAMING_SNAKE_CASE__       :			Optional[int]													=priority_queue.extract_min()
						SCREAMING_SNAKE_CASE__       :			List[Any]													=0
						for neighbour in graph.connections[node]:
												if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																		SCREAMING_SNAKE_CASE__       :			Dict													=dist[node] + graph.connections[node][neighbour]
																		priority_queue.update_key(UpperCamelCase__,		dist[neighbour]							)
																		SCREAMING_SNAKE_CASE__       :			Optional[Any]													=node

    # running prim's algorithm
						while not priority_queue.is_empty():
												SCREAMING_SNAKE_CASE__       :			Tuple													=priority_queue.extract_min()
												for neighbour in graph.connections[node]:
																		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																								SCREAMING_SNAKE_CASE__       :			Optional[Any]													=dist[node] + graph.connections[node][neighbour]
																								priority_queue.update_key(UpperCamelCase__,		dist[neighbour]							)
																								SCREAMING_SNAKE_CASE__       :			int													=node
						return dist, parent







'''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        __SCREAMING_SNAKE_CASE :
 snake_case_												=							PegasusConfig
 snake_case_												=							{}
 snake_case_												=							"""gelu"""




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

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




 def        __magic_name__		( self		:							Any )		->  str:
       SCREAMING_SNAKE_CASE__       :			List[str]													=ids_tensor([self.batch_size, self.seq_length - 1]   , self.vocab_size )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )   , 1 )
       SCREAMING_SNAKE_CASE__       :			Any													=tf.concat([input_ids, eos_tensor]   , axis=1 )

       SCREAMING_SNAKE_CASE__       :			Dict													=ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=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   , )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=prepare_pegasus_inputs_dict(__lowercase   , __lowercase   , __lowercase )
       return config, inputs_dict




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							List[str]   , __lowercase		:							Optional[int] )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=TFPegasusModel(config=__lowercase ).get_decoder()
       SCREAMING_SNAKE_CASE__       :			List[str]													=inputs_dict['''input_ids''']

       SCREAMING_SNAKE_CASE__       :			Tuple													=input_ids[:1, :]
       SCREAMING_SNAKE_CASE__       :			Tuple													=inputs_dict['''attention_mask'''][:1, :]
       SCREAMING_SNAKE_CASE__       :			Tuple													=inputs_dict['''head_mask''']
       SCREAMING_SNAKE_CASE__       :			List[str]													=1

       # first forward pass
       SCREAMING_SNAKE_CASE__       :			Any													=model(__lowercase   , attention_mask=__lowercase   , head_mask=__lowercase   , use_cache=__lowercase )

       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Tuple													=outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE__       :			str													=ids_tensor((self.batch_size, 3)   , config.vocab_size )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=tf.cast(ids_tensor((self.batch_size, 3)   , 2 )   , tf.inta )

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

       SCREAMING_SNAKE_CASE__       :			int													=model(__lowercase   , attention_mask=__lowercase )[0]
       SCREAMING_SNAKE_CASE__       :			Any													=model(__lowercase   , attention_mask=__lowercase   , past_key_values=__lowercase )[0]

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

       # select random slice
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=int(ids_tensor((1,)   , output_from_past.shape[-1] ) )
       SCREAMING_SNAKE_CASE__       :			Any													=output_from_no_past[:, -3:, random_slice_idx]
       SCREAMING_SNAKE_CASE__       :			List[str]													=output_from_past[:, :, random_slice_idx]

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





def 	_a(					UpperCamelCase__			:  Any,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  Optional[Any]=None,		UpperCamelCase__			:  Optional[Any]=None,		UpperCamelCase__			:  Union[str, Any]=None,		UpperCamelCase__			:  Any=None,		UpperCamelCase__			:  Optional[Any]=None,		):




      '''simple docstring'''






      if attention_mask is None:
            SCREAMING_SNAKE_CASE__       :			str													=tf.cast(tf.math.not_equal(UpperCamelCase__,		config.pad_token_id							),		tf.inta							)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE__       :			Any													=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:
            SCREAMING_SNAKE_CASE__       :			int													=tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE__       :			List[str]													=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 (     lowerCamelCase		,			lowerCamelCase		,			unittest.TestCase			):
 snake_case_												=							(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
 snake_case_												=							(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
 snake_case_												=							(
     {
         """conversational""": TFPegasusForConditionalGeneration,
         """feature-extraction""": TFPegasusModel,
         """summarization""": TFPegasusForConditionalGeneration,
         """text2text-generation""": TFPegasusForConditionalGeneration,
         """translation""": TFPegasusForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 snake_case_												=							True
 snake_case_												=							False
 snake_case_												=							False




 def        __magic_name__		( self		:							Union[str, Any] )		->  str:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=TFPegasusModelTester(self )
       SCREAMING_SNAKE_CASE__       :			Dict													=ConfigTester(self   , config_class=__lowercase )




 def        __magic_name__		( self		:							int )		->  Any:
       self.config_tester.run_common_tests()




 def        __magic_name__		( self		:							Optional[Any] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			Tuple													=self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*__lowercase )






@require_sentencepiece
@require_tokenizers
@require_tf
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):
 snake_case_												=							[
     """ 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!\" """,
 ]
 snake_case_												=							[
     """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
 snake_case_												=							"""google/pegasus-xsum"""




 @cached_property
 def        __magic_name__		( self		:							Optional[int] )		->  Tuple:
       return AutoTokenizer.from_pretrained(self.model_name )




 @cached_property
 def        __magic_name__		( self		:							List[Any] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
       return model




 def        __magic_name__		( self		:							List[str]   , **__lowercase		:							Any )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.translate_src_text(**__lowercase )
       assert self.expected_text == generated_words




 def        __magic_name__		( self		:							Optional[Any]   , **__lowercase		:							List[str] )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.tokenizer(self.src_text   , **__lowercase   , padding=__lowercase   , return_tensors='''tf''' )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.model.generate(
           model_inputs.input_ids   , attention_mask=model_inputs.attention_mask   , num_beams=2   , use_cache=__lowercase   , )
       SCREAMING_SNAKE_CASE__       :			Any													=self.tokenizer.batch_decode(generated_ids.numpy()   , skip_special_tokens=__lowercase )
       return generated_words




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