

"""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        _lowercase					:
     _lowerCamelCase     =       PegasusConfig
     _lowerCamelCase     =       {}
     _lowerCamelCase     =       '''gelu'''







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_=13 ,						UpperCamelCase_=7 ,						UpperCamelCase_=True ,						UpperCamelCase_=False ,						UpperCamelCase_=99 ,						UpperCamelCase_=32 ,						UpperCamelCase_=2 ,						UpperCamelCase_=4 ,						UpperCamelCase_=37 ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=40 ,						UpperCamelCase_=2 ,						UpperCamelCase_=1 ,						UpperCamelCase_=0 ,						):
      __magic_name__     =      parent
      __magic_name__     =      batch_size
      __magic_name__     =      seq_length
      __magic_name__     =      is_training
      __magic_name__     =      use_labels
      __magic_name__     =      vocab_size
      __magic_name__     =      hidden_size
      __magic_name__     =      num_hidden_layers
      __magic_name__     =      num_attention_heads
      __magic_name__     =      intermediate_size

      __magic_name__     =      hidden_dropout_prob
      __magic_name__     =      attention_probs_dropout_prob
      __magic_name__     =      max_position_embeddings
      __magic_name__     =      eos_token_id
      __magic_name__     =      pad_token_id
      __magic_name__     =      bos_token_id







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

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

      __magic_name__     =      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 ,						)
      __magic_name__     =      prepare_pegasus_inputs_dict(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
      return config, inputs_dict







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      TFPegasusModel(config=UpperCamelCase_       ).get_decoder()
      __magic_name__     =      inputs_dict["input_ids"]

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

      # first forward pass
      __magic_name__     =      model(UpperCamelCase_ ,						attention_mask=UpperCamelCase_ ,						head_mask=UpperCamelCase_ ,						use_cache=UpperCamelCase_       )

      __magic_name__     =      outputs.to_tuple()

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

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

      __magic_name__     =      model(UpperCamelCase_ ,						attention_mask=UpperCamelCase_       )[0]
      __magic_name__     =      model(UpperCamelCase_ ,						attention_mask=UpperCamelCase_ ,						past_key_values=UpperCamelCase_       )[0]

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

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

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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase=None      ,   __UpperCamelCase=None      ,   __UpperCamelCase=None      ,   __UpperCamelCase=None      ,   __UpperCamelCase=None      ,   ) ->      Tuple:


 if attention_mask is None:
  __magic_name__     =      tf.cast(tf.math.not_equal(__snake_case      ,   config.pad_token_id						)      ,   tf.inta						)
 if decoder_attention_mask is None:
  __magic_name__     =      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:
  __magic_name__     =      tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
 if decoder_head_mask is None:
  __magic_name__     =      tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
 if cross_attn_head_mask is None:
  __magic_name__     =      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					(     _A   , _A   , unittest.TestCase     ):
     _lowerCamelCase     =       (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
     _lowerCamelCase     =       (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
     _lowerCamelCase     =       (
         {
             '''conversational''': TFPegasusForConditionalGeneration,
             '''feature-extraction''': TFPegasusModel,
             '''summarization''': TFPegasusForConditionalGeneration,
             '''text2text-generation''': TFPegasusForConditionalGeneration,
             '''translation''': TFPegasusForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     _lowerCamelCase     =       True
     _lowerCamelCase     =       False
     _lowerCamelCase     =       False







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      TFPegasusModelTester(self       )
      __magic_name__     =      ConfigTester(self ,						config_class=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      self.config_tester.run_common_tests()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_       )


@require_sentencepiece
@require_tokenizers
@require_tf
class        _lowercase					(     unittest.TestCase     ):
     _lowerCamelCase     =       [
         ''' 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!" ''',
     ]
     _lowerCamelCase     =       [
         '''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
     _lowerCamelCase     =       '''google/pegasus-xsum'''







     @cached_property
     def 	lowerCAmelCase__    (      self       ):
      return AutoTokenizer.from_pretrained(self.model_name       )







     @cached_property
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
      return model







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      __magic_name__     =      self.translate_src_text(**UpperCamelCase_       )
      assert self.expected_text == generated_words







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      __magic_name__     =      self.tokenizer(self.src_text ,						**UpperCamelCase_ ,						padding=UpperCamelCase_ ,						return_tensors='''tf'''       )
      __magic_name__     =      self.model.generate(
          model_inputs.input_ids ,						attention_mask=model_inputs.attention_mask ,						num_beams=2 ,						use_cache=UpperCamelCase_ ,						)
      __magic_name__     =      self.tokenizer.batch_decode(generated_ids.numpy() ,						skip_special_tokens=UpperCamelCase_       )
      return generated_words







     @slow
     def 	lowerCAmelCase__    (      self       ):
      self._assert_generated_batch_equal_expected()




"""simple docstring"""






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       FunnelTokenizer
     _lowerCamelCase     =       FunnelTokenizerFast
     _lowerCamelCase     =       True
     _lowerCamelCase     =       True







     def 	lowerCAmelCase__    (      self       ):
      super().setUp()

      __magic_name__     =      [
          '''<unk>''',
          '''<cls>''',
          '''<sep>''',
          '''want''',
          '''##want''',
          '''##ed''',
          '''wa''',
          '''un''',
          '''runn''',
          '''##ing''',
          ''',''',
          '''low''',
          '''lowest''',
      ]
      __magic_name__     =      os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']       )
      with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
       vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return FunnelTokenizer.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return FunnelTokenizerFast.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      '''UNwant\u00E9d,running'''
      __magic_name__     =      '''unwanted, running'''
      return input_text, output_text







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.tokenizer_class(self.vocab_file       )

      __magic_name__     =      tokenizer.tokenize('''UNwant\u00E9d,running'''       )
      self.assertListEqual(UpperCamelCase_ ,						['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']       )
      self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_       ) ,						[7, 4, 5, 10, 8, 9]       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_tokenizers(do_lower_case=UpperCamelCase_       )
      for tokenizer in tokenizers:
       __magic_name__     =      tokenizer('''UNwant\u00E9d,running'''       )
       __magic_name__     =      len(inputs['''input_ids''']       ) - 1
       self.assertListEqual(inputs['''token_type_ids'''] ,						[2] + [0] * sentence_len       )

       __magic_name__     =      tokenizer('''UNwant\u00E9d,running''' ,						'''UNwant\u00E9d,running'''       )
       self.assertListEqual(inputs['''token_type_ids'''] ,						[2] + [0] * sentence_len + [1] * sentence_len       )

