




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"""





       SCREAMING_SNAKE_CASE_      : Dict      =  PegasusConfig
       SCREAMING_SNAKE_CASE_      : Any      =  {}
       SCREAMING_SNAKE_CASE_      : Tuple      =  '''gelu'''







       def __init__(    self	:	List[Any]						,__A	:	int						,__A	:	Optional[int]=13						,__A	:	Dict=7						,__A	:	Tuple=True						,__A	:	List[str]=False						,__A	:	Dict=99						,__A	:	Any=32						,__A	:	Union[str, Any]=2						,__A	:	str=4						,__A	:	Optional[int]=37						,__A	:	List[Any]=0.1						,__A	:	List[str]=0.1						,__A	:	Optional[int]=40						,__A	:	Any=2						,__A	:	Optional[int]=1						,__A	:	int=0						,)						->					int:
         _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	:	Union[str, Any]  )						->					List[str]:
         _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(lowercase_						,lowercase_						,lowercase_  )
         return config, inputs_dict







       def    __UpperCAmelCase (    self	:	int						,__A	:	Any						,__A	:	Union[str, Any]  )						->					Union[str, Any]:
         _lowercase							= TFPegasusModel(config=lowercase_  ).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(lowercase_						,attention_mask=lowercase_						,head_mask=lowercase_						,use_cache=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(lowercase_						,attention_mask=lowercase_  )[0]
         _lowercase							= 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
         _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(lowercase_						,lowercase_						,rtol=1e-3  )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int			,							snake_case__  :List[Any]			,							snake_case__  :Optional[int]			,							snake_case__  :List[Any]=None			,							snake_case__  :Optional[Any]=None			,							snake_case__  :Union[str, Any]=None			,							snake_case__  :Tuple=None			,							snake_case__  :Optional[int]=None			,							)      ->						Dict:
  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 				A_				(      _UpperCAmelCase					,	_UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





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







       def    __UpperCAmelCase (    self	:	List[str]  )						->					str:
         _lowercase							= TFPegasusModelTester(self  )
         _lowercase							= ConfigTester(self						,config_class=lowercase_  )







       def    __UpperCAmelCase (    self	:	Dict  )						->					int:
         self.config_tester.run_common_tests()







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Dict:
         _lowercase							= 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 				A_				(      unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Optional[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!" ''',
       ]
       SCREAMING_SNAKE_CASE_      : Optional[int]      =  [
           '''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
       SCREAMING_SNAKE_CASE_      : Tuple      =  '''google/pegasus-xsum'''







       @cached_property
       def    __UpperCAmelCase (    self	:	Dict  )						->					Any:
         return AutoTokenizer.from_pretrained(self.model_name  )







       @cached_property
       def    __UpperCAmelCase (    self	:	List[str]  )						->					Dict:
         _lowercase							= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
         return model







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,**__A	:	Any  )						->					Tuple:
         _lowercase							= self.translate_src_text(**lowercase_  )
         assert self.expected_text == generated_words







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







       @slow
       def    __UpperCAmelCase (    self	:	Any  )						->					List[str]:
         self._assert_generated_batch_equal_expected()






from ....utils import logging


snake_case 				=       logging.get_logger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Union[str, Any]						,__A	:	int						,__A	:	List[Any]=None						,__A	:	List[str]=2048  )						->					List[Any]:
         _lowercase							= config.__dict__
         _lowercase							= modal_hidden_size
         if num_labels:
           _lowercase							= num_labels