






'''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							:


			'''simple docstring'''





			__a							:							Any					     =					PegasusConfig
			__a							:							Optional[Any]					     =					{}
			__a							:							Any					     =					"gelu"






			def __init__(   self							:						Optional[int] ,      lowercase							:						List[Any] ,      lowercase							:						Optional[Any]=1_3 ,      lowercase							:						Any=7 ,      lowercase							:						str=True ,      lowercase							:						Any=False ,      lowercase							:						int=9_9 ,      lowercase							:						Dict=3_2 ,      lowercase							:						str=2 ,      lowercase							:						Optional[Any]=4 ,      lowercase							:						Dict=3_7 ,      lowercase							:						Any=0.1 ,      lowercase							:						Union[str, Any]=0.1 ,      lowercase							:						Tuple=4_0 ,      lowercase							:						List[Any]=2 ,      lowercase							:						int=1 ,      lowercase							:						int=0 ,      )							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = parent
									UpperCamelCase__         = batch_size
									UpperCamelCase__         = seq_length
									UpperCamelCase__         = is_training
									UpperCamelCase__         = use_labels
									UpperCamelCase__         = vocab_size
									UpperCamelCase__         = hidden_size
									UpperCamelCase__         = num_hidden_layers
									UpperCamelCase__         = num_attention_heads
									UpperCamelCase__         = intermediate_size

									UpperCamelCase__         = hidden_dropout_prob
									UpperCamelCase__         = attention_probs_dropout_prob
									UpperCamelCase__         = max_position_embeddings
									UpperCamelCase__         = eos_token_id
									UpperCamelCase__         = pad_token_id
									UpperCamelCase__         = bos_token_id






			def 				A      (   self							:						Any			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = ids_tensor([self.batch_size, self.seq_length - 1] ,      self.vocab_size			)
									UpperCamelCase__         = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			) ,      1			)
									UpperCamelCase__         = tf.concat([input_ids, eos_tensor] ,      axis=1			)

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

									UpperCamelCase__         = 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 ,      )
									UpperCamelCase__         = prepare_pegasus_inputs_dict(lowercase ,      lowercase ,      lowercase			)
									return config, inputs_dict







			def 				A      (   self							:						Any ,      lowercase							:						Union[str, Any] ,      lowercase							:						Optional[Any]			)							->    Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__         = TFPegasusModel(config=lowercase			).get_decoder()
									UpperCamelCase__         = inputs_dict["""input_ids"""]

									UpperCamelCase__         = input_ids[:1, :]
									UpperCamelCase__         = inputs_dict["""attention_mask"""][:1, :]
									UpperCamelCase__         = inputs_dict["""head_mask"""]
									UpperCamelCase__         = 1

									# first forward pass
									UpperCamelCase__         = model(lowercase ,      attention_mask=lowercase ,      head_mask=lowercase ,      use_cache=lowercase			)

									UpperCamelCase__    ,   UpperCamelCase__         = outputs.to_tuple()

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

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

									UpperCamelCase__         = model(lowercase ,      attention_mask=lowercase			)[0]
									UpperCamelCase__         = 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
									UpperCamelCase__         = int(ids_tensor((1,) ,      output_from_past.shape[-1]			)			)
									UpperCamelCase__         = output_from_no_past[:, -3:, random_slice_idx]
									UpperCamelCase__         = output_from_past[:, :, random_slice_idx]

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







def 	__magic_name__( _A						,      _A						,      _A						,      _A=None						,      _A=None						,      _A=None						,      _A=None						,      _A=None						,      ):
						'''simple docstring'''





						if attention_mask is None:
												UpperCamelCase__         = tf.cast(tf.math.not_equal(_A						,      config.pad_token_id					)						,      tf.inta					)
						if decoder_attention_mask is None:
												UpperCamelCase__         = 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:
												UpperCamelCase__         = tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
						if decoder_head_mask is None:
												UpperCamelCase__         = tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
						if cross_attn_head_mask is None:
												UpperCamelCase__         = 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							(		SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,unittest.TestCase	):


			'''simple docstring'''





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






			def 				A      (   self							:						Union[str, Any]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = TFPegasusModelTester(self			)
									UpperCamelCase__         = ConfigTester(self ,      config_class=lowercase			)






			def 				A      (   self							:						Any			)							->    Union[str, Any]:

									'''simple docstring'''







									self.config_tester.run_common_tests()







			def 				A      (   self							:						Optional[Any]			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = 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	):


			'''simple docstring'''





			__a							:							Tuple					     =					[
			    " 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							:							List[Any]					     =					[
			    "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
			__a							:							Dict					     =					"google/pegasus-xsum"






			@cached_property
			def 				A      (   self							:						Any			)							->    int:

									'''simple docstring'''







									return AutoTokenizer.from_pretrained(self.model_name			)






			@cached_property
			def 				A      (   self							:						Optional[int]			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
									return model






			def 				A      (   self							:						Any ,      **lowercase							:						Optional[Any]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = self.translate_src_text(**lowercase			)
									assert self.expected_text == generated_words






			def 				A      (   self							:						Optional[Any] ,      **lowercase							:						Tuple			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = self.tokenizer(self.src_text ,      **lowercase ,      padding=lowercase ,      return_tensors="""tf"""			)
									UpperCamelCase__         = self.model.generate(
									    model_inputs.input_ids ,      attention_mask=model_inputs.attention_mask ,      num_beams=2 ,      use_cache=lowercase ,      )
									UpperCamelCase__         = self.tokenizer.batch_decode(generated_ids.numpy() ,      skip_special_tokens=lowercase			)
									return generated_words







			@slow
			def 				A      (   self							:						List[Any]			)							->    Optional[int]:

									'''simple docstring'''







									self._assert_generated_batch_equal_expected()








'''simple docstring'''


from __future__ import annotations

from typing import Any
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						Union[str, Any] ,      lowercase							:						int			)							->    None:

									'''simple docstring'''







									UpperCamelCase__         = num_of_nodes
									UpperCamelCase__         = []
									UpperCamelCase__         = {}






			def 				A      (   self							:						Optional[Any] ,      lowercase							:						int ,      lowercase							:						int ,      lowercase							:						int			)							->    None:

									'''simple docstring'''







									self.m_edges.append([u_node, v_node, weight]			)






			def 				A      (   self							:						str ,      lowercase							:						int			)							->    int:

									'''simple docstring'''







									if self.m_component[u_node] == u_node:
															return u_node
									return self.find_component(self.m_component[u_node]			)






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						int			)							->    None:

									'''simple docstring'''







									if self.m_component[u_node] != u_node:
															for k in self.m_component:
																					UpperCamelCase__         = self.find_component(lowercase			)






			def 				A      (   self							:						Tuple ,      lowercase							:						list[int] ,      lowercase							:						int ,      lowercase							:						int			)							->    None:

									'''simple docstring'''







									if component_size[u_node] <= component_size[v_node]:
															UpperCamelCase__         = v_node
															component_size[v_node] += component_size[u_node]
															self.set_component(lowercase			)

									elif component_size[u_node] >= component_size[v_node]:
															UpperCamelCase__         = self.find_component(lowercase			)
															component_size[u_node] += component_size[v_node]
															self.set_component(lowercase			)







			def 				A      (   self							:						int			)							->    None:

									'''simple docstring'''







									UpperCamelCase__         = []
									UpperCamelCase__         = 0

									UpperCamelCase__         = [-1] * self.m_num_of_nodes

									# A list of components (initialized to all of the nodes)
									for node in range(self.m_num_of_nodes			):
															self.m_component.update({node: node}			)
															component_size.append(1			)

									UpperCamelCase__         = self.m_num_of_nodes

									while num_of_components > 1:
															for edge in self.m_edges:
																					UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = edge

																					UpperCamelCase__         = self.m_component[u]
																					UpperCamelCase__         = self.m_component[v]

																					if u_component != v_component:

																											for component in (u_component, v_component):
																																	if (
																																	    minimum_weight_edge[component] == -1
																																	    or minimum_weight_edge[component][2] > w
																																	):
																																							UpperCamelCase__         = [u, v, w]

															for edge in minimum_weight_edge:
																					if isinstance(lowercase ,      lowercase			):
																											UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = edge

																											UpperCamelCase__         = self.m_component[u]
																											UpperCamelCase__         = self.m_component[v]

																											if u_component != v_component:
																																	mst_weight += w
																																	self.union(lowercase ,      lowercase ,      lowercase			)
																																	print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n"			)
																																	num_of_components -= 1

															UpperCamelCase__         = [-1] * self.m_num_of_nodes
									print(f"The total weight of the minimal spanning tree is: {mst_weight}"			)







def 	__magic_name__( ):
						'''simple docstring'''







if __name__ == "__main__":
					import doctest

					doctest.testmod()
