




'''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    :
      __magic_name__  :       Any   							=					PegasusConfig
      __magic_name__  :       Tuple   							=					{}
      __magic_name__  :       List[str]   							=					'''gelu'''






      def __init__( self   :  Optional[Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  str=13						,						lowercase__   :  Union[str, Any]=7						,						lowercase__   :  Any=True						,						lowercase__   :  Any=False						,						lowercase__   :  Tuple=99						,						lowercase__   :  List[Any]=32						,						lowercase__   :  Dict=2						,						lowercase__   :  int=4						,						lowercase__   :  Optional[Any]=37						,						lowercase__   :  Optional[int]=0.1						,						lowercase__   :  Optional[int]=0.1						,						lowercase__   :  Union[str, Any]=40						,						lowercase__   :  Optional[Any]=2						,						lowercase__   :  str=1						,						lowercase__   :  Union[str, Any]=0						,						):


           '''simple docstring'''

           a_     :							List[str]     						=  parent
           a_     :							List[Any]     						=  batch_size
           a_     :							str     						=  seq_length
           a_     :							Optional[int]     						=  is_training
           a_     :							List[Any]     						=  use_labels
           a_     :							Any     						=  vocab_size
           a_     :							Dict     						=  hidden_size
           a_     :							Any     						=  num_hidden_layers
           a_     :							int     						=  num_attention_heads
           a_     :							Optional[int]     						=  intermediate_size

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






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

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

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

           a_     :							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						,						)
           a_     :							List[Any]     						=  prepare_pegasus_inputs_dict(lowercase__						,						lowercase__						,						lowercase__       )
           return config, inputs_dict






      def 							lowercase_						( self   :  Any						,						lowercase__   :  Tuple						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           a_     :							Tuple     						=  TFPegasusModel(config=lowercase__       ).get_decoder()
           a_     :							Union[str, Any]     						=  inputs_dict["""input_ids"""]

           a_     :							Optional[Any]     						=  input_ids[:1, :]
           a_     :							Dict     						=  inputs_dict["""attention_mask"""][:1, :]
           a_     :							str     						=  inputs_dict["""head_mask"""]
           a_     :							str     						=  1

           # first forward pass
           a_     :							List[Any]     						=  model(lowercase__						,						attention_mask=lowercase__						,						head_mask=lowercase__						,						use_cache=lowercase__       )

           a_						,     a_     :							Optional[int]     						=  outputs.to_tuple()

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

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

           a_     :							Tuple     						=  model(lowercase__						,						attention_mask=lowercase__       )[0]
           a_     :							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
           a_     :							str     						=  int(ids_tensor((1,)						,						output_from_past.shape[-1]       )       )
           a_     :							List[Any]     						=  output_from_no_past[:, -3:, random_slice_idx]
           a_     :							str     						=  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     (      UpperCamelCase__    :					Any ,    UpperCamelCase__    :					Tuple ,    UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					List[Any]=None ,    UpperCamelCase__    :					Dict=None ,    UpperCamelCase__    :					List[str]=None ,    UpperCamelCase__    :					List[Any]=None ,    UpperCamelCase__    :					Optional[Any]=None ,    ):




     """simple docstring"""


     if attention_mask is None:
          a_     :							str     						=  tf.cast(tf.math.not_equal(UpperCamelCase__ ,    config.pad_token_id				) ,    tf.inta				)
     if decoder_attention_mask is None:
          a_     :							Dict     						=  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_     :							int     						=  tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
     if decoder_head_mask is None:
          a_     :							Union[str, Any]     						=  tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
     if cross_attn_head_mask is None:
          a_     :							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      SCREAMING_SNAKE_CASE    (						snake_case_							,      snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Any   							=					(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
      __magic_name__  :       Tuple   							=					(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
      __magic_name__  :       Optional[Any]   							=					(
          {
              '''conversational''': TFPegasusForConditionalGeneration,
              '''feature-extraction''': TFPegasusModel,
              '''summarization''': TFPegasusForConditionalGeneration,
              '''text2text-generation''': TFPegasusForConditionalGeneration,
              '''translation''': TFPegasusForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      __magic_name__  :       Union[str, Any]   							=					True
      __magic_name__  :       Dict   							=					False
      __magic_name__  :       str   							=					False






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Any     						=  TFPegasusModelTester(self       )
           a_     :							Any     						=  ConfigTester(self						,						config_class=lowercase__       )






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           self.config_tester.run_common_tests()






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							str     						=  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				):
      __magic_name__  :       Any   							=					[
          ''' 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!" ''',
      ]
      __magic_name__  :       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
      __magic_name__  :       Optional[int]   							=					'''google/pegasus-xsum'''






      @cached_property
      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           return AutoTokenizer.from_pretrained(self.model_name       )






      @cached_property
      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Dict     						=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
           return model






      def 							lowercase_						( self   :  Union[str, Any]						,						**lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							int     						=  self.translate_src_text(**lowercase__       )
           assert self.expected_text == generated_words






      def 							lowercase_						( self   :  int						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							str     						=  self.tokenizer(self.src_text						,						**lowercase__						,						padding=lowercase__						,						return_tensors="""tf"""       )
           a_     :							int     						=  self.model.generate(
               model_inputs.input_ids						,						attention_mask=model_inputs.attention_mask						,						num_beams=2						,						use_cache=lowercase__						,						)
           a_     :							Optional[Any]     						=  self.tokenizer.batch_decode(generated_ids.numpy()						,						skip_special_tokens=lowercase__       )
           return generated_words






      @slow
      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






'''simple docstring'''


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class      SCREAMING_SNAKE_CASE    (						tf.keras.layers.Layer				):






      def __init__( self   :  List[Any]						,						lowercase__   :  Dict[str, int]						,						lowercase__   :  List[str]						,						lowercase__   :  int = None						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           super().__init__()
           a_     :							Optional[Any]     						=  pad_token_id
           a_     :							str     						=  max_length
           a_     :							List[str]     						=  vocab
           a_     :							Union[str, Any]     						=  merges
           a_     :							Union[str, Any]     						=  BytePairTokenizer(lowercase__						,						lowercase__						,						sequence_length=lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  List[Any]						,						lowercase__   :  GPTaTokenizer						,						*lowercase__   :  Optional[Any]						,						**lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							str     						=  [""" """.join(lowercase__       ) for m in tokenizer.bpe_ranks.keys()]
           a_     :							Any     						=  tokenizer.get_vocab()
           return cls(lowercase__						,						lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Union[str, os.PathLike]						,						*lowercase__   :  str						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  GPTaTokenizer.from_pretrained(lowercase__						,						*lowercase__						,						**lowercase__       )
           return cls.from_tokenizer(lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           return cls(**lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.tf_tokenizer(lowercase__       )
           a_     :							int     						=  tf.ones_like(lowercase__       )

           if self.pad_token_id is not None:
                # pad the tokens up to max length
                a_     :							int     						=  max_length if max_length is not None else self.max_length

                if max_length is not None:
                     a_						,     a_     :							int     						=  pad_model_inputs(
                         lowercase__						,						max_seq_length=lowercase__						,						pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}
