




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 						lowercase							:
     _a							 =						PegasusConfig
     _a							 =						{}
     _a							 =						"gelu"

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

           _A							:     Dict			=						hidden_dropout_prob
           _A							:     Dict			=						attention_probs_dropout_prob
           _A							:     Union[str, Any]			=						max_position_embeddings
           _A							:     int			=						eos_token_id
           _A							:     Dict			=						pad_token_id
           _A							:     List[str]			=						bos_token_id

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

           _A							:     Optional[int]			=						ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size				)

           _A							:     Optional[Any]			=						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		,     )
           _A							:     List[Any]			=						prepare_pegasus_inputs_dict(_a		,     _a		,     _a				)
           return config, inputs_dict



     def 					a__	(  self		,     _a		,     _a				)						->							Optional[Any]:
           _A							:     Any			=						TFPegasusModel(config=_a				).get_decoder()
           _A							:     Union[str, Any]			=						inputs_dict["""input_ids"""]

           _A							:     Union[str, Any]			=						input_ids[:1, :]
           _A							:     List[str]			=						inputs_dict["""attention_mask"""][:1, :]
           _A							:     Any			=						inputs_dict["""head_mask"""]
           _A							:     Any			=						1

           # first forward pass
           _A							:     int			=						model(_a		,     attention_mask=_a		,     head_mask=_a		,     use_cache=_a				)

           _A			,   _A							:     Union[str, Any]			=						outputs.to_tuple()

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

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

           _A							:     Any			=						model(_a		,     attention_mask=_a				)[0]
           _A							:     List[Any]			=						model(_a		,     attention_mask=_a		,     past_key_values=_a				)[0]

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

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

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







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

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Tuple			=						TFPegasusModelTester(self				)
           _A							:     List[Any]			=						ConfigTester(self		,     config_class=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           self.config_tester.run_common_tests()



     def 					a__	(  self				)						->							Any:
           _A							:     Union[str, Any]			=						self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*_a				)







@require_sentencepiece
@require_tokenizers
@require_tf
class 						lowercase							(   unittest.TestCase		):
     _a							 =						[
         " 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							 =						[
         "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							 =						"google/pegasus-xsum"

     @cached_property
     def 					a__	(  self				)						->							List[Any]:
           return AutoTokenizer.from_pretrained(self.model_name				)

     @cached_property
     def 					a__	(  self				)						->							List[Any]:
           _A							:     str			=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
           return model

     def 					a__	(  self		,     **_a				)						->							Tuple:
           _A							:     Dict			=						self.translate_src_text(**_a				)
           assert self.expected_text == generated_words

     def 					a__	(  self		,     **_a				)						->							Any:
           _A							:     str			=						self.tokenizer(self.src_text		,     **_a		,     padding=_a		,     return_tensors="""tf"""				)
           _A							:     Tuple			=						self.model.generate(
               model_inputs.input_ids		,     attention_mask=model_inputs.attention_mask		,     num_beams=2		,     use_cache=_a		,     )
           _A							:     List[str]			=						self.tokenizer.batch_decode(generated_ids.numpy()		,     skip_special_tokens=_a				)
           return generated_words



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





def 				UpperCAmelCase_					(							__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      =0

 # if input_string is "aba" than new_input_string become "a|b|a"
 _lowercase							      =''''''
 _lowercase							      =''''''

 # append each character + "|" in new_string for range(0, length-1)
 for i in input_string[: len(__snake_case						) - 1]:
  new_input_string += i + "|"
 # append last character
 new_input_string += input_string[-1]

 # we will store the starting and ending of previous furthest ending palindromic
 # substring
 _lowercase      ,    _lowercase							      =0, 0

 # length[i] shows the length of palindromic substring with center i
 _lowercase							      =[1 for i in range(len(__snake_case						)						)]

 # for each character in new_string find corresponding palindromic string
 _lowercase							      =0
 for j in range(len(__snake_case						)						):
  _lowercase							      =1 if j > r else min(length[l + r - j] // 2     ,						r - j + 1						)
  while (
      j - k >= 0
      and j + k < len(__snake_case						)
      and new_input_string[k + j] == new_input_string[j - k]
  ):
   k += 1

  _lowercase							      =2 * k - 1

  # does this string is ending after the previously explored end (that is r) ?
  # if yes the update the new r to the last index of this
  if j + k - 1 > r:
   _lowercase							      =j - k + 1  # noqa: E741
   _lowercase							      =j + k - 1

  # update max_length and start position
  if max_length < length[j]:
   _lowercase							      =length[j]
   _lowercase							      =j

    # create that string
 _lowercase							      =new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
 for i in s:
  if i != "|":
   output_string += i

 return output_string


if __name__ == "__main__":
  import doctest

  doctest.testmod()

