



"""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__       :
    lowercase__          =      PegasusConfig
    lowercase__          =      {}
    lowercase__          =      "gelu"

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







      """simple docstring"""







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

      lowercase_	    =       hidden_dropout_prob
      lowercase_	    =       attention_probs_dropout_prob
      lowercase_	    =       max_position_embeddings
      lowercase_	    =       eos_token_id
      lowercase_	    =       pad_token_id
      lowercase_	    =       bos_token_id

    def     _UpperCAmelCase		( self	: int):







      """simple docstring"""







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

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

      lowercase_	    =       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_	    =       prepare_pegasus_inputs_dict(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__)
      return config, inputs_dict




    def     _UpperCAmelCase		( self	: Dict       ,     lowerCAmelCase_	: str       ,     lowerCAmelCase_	: Any):







      """simple docstring"""







      lowercase_	    =       TFPegasusModel(config=lowerCamelCase__).get_decoder()
      lowercase_	    =       inputs_dict["""input_ids"""]

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

      # first forward pass
      lowercase_	    =       model(lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     head_mask=lowerCamelCase__       ,     use_cache=lowerCamelCase__)

      lowercase_    ,			lowercase_	    =       outputs.to_tuple()

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

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

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

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







def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase=None       ,     __lowerCAmelCase=None       ,     __lowerCAmelCase=None       ,     __lowerCAmelCase=None       ,     __lowerCAmelCase=None       ,     )       ->  int:







  '''simple docstring'''


  if attention_mask is None:
    lowercase_	    =       tf.cast(tf.math.not_equal(UpperCamelCase__       ,     config.pad_token_id							)       ,     tf.inta							)
  if decoder_attention_mask is None:
    lowercase_	    =       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_	    =       tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
  if decoder_head_mask is None:
    lowercase_	    =       tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
  if cross_attn_head_mask is None:
    lowercase_	    =       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__       (							__UpperCAmelCase					,     __UpperCAmelCase					,     unittest.TestCase      ):
    lowercase__          =      (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
    lowercase__          =      (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    lowercase__          =      (
        {
            "conversational": TFPegasusForConditionalGeneration,
            "feature-extraction": TFPegasusModel,
            "summarization": TFPegasusForConditionalGeneration,
            "text2text-generation": TFPegasusForConditionalGeneration,
            "translation": TFPegasusForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    lowercase__          =      True
    lowercase__          =      False
    lowercase__          =      False

    def     _UpperCAmelCase		( self	: Union[str, Any]):







      """simple docstring"""







      lowercase_	    =       TFPegasusModelTester(self)
      lowercase_	    =       ConfigTester(self       ,     config_class=lowerCamelCase__)

    def     _UpperCAmelCase		( self	: str):







      """simple docstring"""







      self.config_tester.run_common_tests()




    def     _UpperCAmelCase		( self	: int):







      """simple docstring"""







      lowercase_	    =       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       SCREAMING_SNAKE_CASE__       (							unittest.TestCase      ):
    lowercase__          =      [
        " 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__          =      [
        "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__          =      "google/pegasus-xsum"

    @cached_property
    def     _UpperCAmelCase		( self	: int):







      """simple docstring"""







      return AutoTokenizer.from_pretrained(self.model_name)

    @cached_property
    def     _UpperCAmelCase		( self	: str):







      """simple docstring"""







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

    def     _UpperCAmelCase		( self	: Union[str, Any]       ,     **lowerCAmelCase_	: List[Any]):







      """simple docstring"""







      lowercase_	    =       self.translate_src_text(**lowerCamelCase__)
      assert self.expected_text == generated_words

    def     _UpperCAmelCase		( self	: Optional[int]       ,     **lowerCAmelCase_	: List[Any]):







      """simple docstring"""







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




    @slow
    def     _UpperCAmelCase		( self	: int):







      """simple docstring"""







      self._assert_generated_batch_equal_expected()







class     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    0
							__lowerCamelCase		       =    0
							__lowerCamelCase		       =    {}


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							if vertex not in self.adjacency:
											__lowerCamelCase		       =    {}
											self.num_vertices += 1


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      str:



							'''simple docstring'''
							self.add_vertex(lowerCamelCase__							)
							self.add_vertex(lowerCamelCase__							)

							if head == tail:
											return

							__lowerCamelCase		       =    weight
							__lowerCamelCase		       =    weight


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.get_edges()
							for edge in edges:
											__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    edge
											edges.remove((tail, head, weight)							)
							for i in range(len(lowerCamelCase__							)							):
											__lowerCamelCase		       =    list(edges[i]							)

							edges.sort(key=lambda lowerCamelCase__							: e[2]							)
							for i in range(len(lowerCamelCase__							) - 1							):
											if edges[i][2] >= edges[i + 1][2]:
															__lowerCamelCase		       =    edges[i][2] + 1
							for edge in edges:
											__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    edge
											__lowerCamelCase		       =    weight
											__lowerCamelCase		       =    weight


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



							'''simple docstring'''
							__lowerCamelCase		       =    ''
							for tail in self.adjacency:
											for head in self.adjacency[tail]:
															__lowerCamelCase		       =    self.adjacency[head][tail]
															string += f"""{head} -> {tail} == {weight}\n"""
							return string.rstrip('\n'							)


			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    []
							for tail in self.adjacency:
											for head in self.adjacency[tail]:
															output.append((tail, head, self.adjacency[head][tail])							)
							return output


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							return self.adjacency.keys()


			@staticmethod
			def       lowercase_    (      lowerCamelCase__=None			,						lowerCamelCase__=None							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    Graph()
							if vertices is None:
											__lowerCamelCase		       =    []
							if edges is None:
											__lowerCamelCase		       =    []
							for vertex in vertices:
											g.add_vertex(lowerCamelCase__							)
							for edge in edges:
											g.add_edge(*lowerCamelCase__							)
							return g







			class     __lowerCAmelCase :

						"""simple docstring"""







						def __init__(      self							)    ->      Tuple:



										'''simple docstring'''
										__lowerCamelCase		       =    {}
										__lowerCamelCase		       =    {}


						def __len__(      self							)    ->      Tuple:



										'''simple docstring'''
										return len(self.parent							)


						def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[Any]:



										'''simple docstring'''
										if item in self.parent:
														return self.find(lowerCamelCase__							)

										__lowerCamelCase		       =    item
										__lowerCamelCase		       =    0
										return item


						def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[str]:



										'''simple docstring'''
										if item not in self.parent:
														return self.make_set(lowerCamelCase__							)
										if item != self.parent[item]:
														__lowerCamelCase		       =    self.find(self.parent[item]							)
										return self.parent[item]






						def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      int:



										'''simple docstring'''
										__lowerCamelCase		       =    self.find(lowerCamelCase__							)
										__lowerCamelCase		       =    self.find(lowerCamelCase__							)

										if roota == roota:
														return roota

										if self.rank[roota] > self.rank[roota]:
														__lowerCamelCase		       =    roota
														return roota

										if self.rank[roota] < self.rank[roota]:
														__lowerCamelCase		       =    roota
														return roota

										if self.rank[roota] == self.rank[roota]:
														self.rank[roota] += 1
														__lowerCamelCase		       =    roota
														return roota
										return None




			@staticmethod
			def       lowercase_    (      lowerCamelCase__							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    graph.num_vertices

							__lowerCamelCase		       =    Graph.UnionFind()
							__lowerCamelCase		       =    []
							while num_components > 1:
											__lowerCamelCase		       =    {}
											for vertex in graph.get_vertices():
															__lowerCamelCase		       =    -1

											__lowerCamelCase		       =    graph.get_edges()
											for edge in edges:
															__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    edge
															edges.remove((tail, head, weight)							)
											for edge in edges:
															__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    edge
															__lowerCamelCase		       =    union_find.find(lowerCamelCase__							)
															__lowerCamelCase		       =    union_find.find(lowerCamelCase__							)
															if seta != seta:
																			if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																							__lowerCamelCase		       =    [head, tail, weight]

																			if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																							__lowerCamelCase		       =    [head, tail, weight]
											for vertex in cheap_edge:
															if cheap_edge[vertex] != -1:
																			__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    cheap_edge[vertex]
																			if union_find.find(lowerCamelCase__							) != union_find.find(lowerCamelCase__							):
																							union_find.union(lowerCamelCase__			,						lowerCamelCase__							)
																							mst_edges.append(cheap_edge[vertex]							)
																							__lowerCamelCase		       =    num_components - 1
							__lowerCamelCase		       =    Graph.build(edges=lowerCamelCase__							)
							return mst


