'''simple docstring'''
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__  :      Dict        =					get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
  from transformers.models.plbart.modeling_plbart import shift_tokens_right

lowercase__  :      Any        =					5_0003
lowercase__  :      Any        =					5_0002







@require_sentencepiece
@require_tokenizers
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	PLBartTokenizer
  lowerCAmelCase__										=	None
  lowerCAmelCase__										=	False






  def  lowerCAmelCase__ (      self			:       str  )				->List[str]:
        super().setUp()

        # We have a SentencePiece fixture for testing
        UpperCAmelCase_            =  PLBartTokenizer(UpperCAmelCase__  ,							language_codes='''base'''  ,							keep_accents=UpperCAmelCase__  )
        tokenizer.save_pretrained(self.tmpdirname  )






  def  lowerCAmelCase__ (      self			:       int  )				->Optional[int]:
        UpperCAmelCase_            =  PLBartTokenizer(UpperCAmelCase__  ,							language_codes='''base'''  ,							keep_accents=UpperCAmelCase__  )

        UpperCAmelCase_            =  tokenizer.tokenize('''This is a test'''  )
        self.assertListEqual(UpperCAmelCase__  ,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']  )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(UpperCAmelCase__  )  ,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,							)

        UpperCAmelCase_            =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''  )
        self.assertListEqual(
            UpperCAmelCase__  ,							[
                SPIECE_UNDERLINE + '''I''',
                SPIECE_UNDERLINE + '''was''',
                SPIECE_UNDERLINE + '''b''',
                '''or''',
                '''n''',
                SPIECE_UNDERLINE + '''in''',
                SPIECE_UNDERLINE + '''''',
                '''9''',
                '''2''',
                '''0''',
                '''0''',
                '''0''',
                ''',''',
                SPIECE_UNDERLINE + '''and''',
                SPIECE_UNDERLINE + '''this''',
                SPIECE_UNDERLINE + '''is''',
                SPIECE_UNDERLINE + '''f''',
                '''al''',
                '''s''',
                '''é''',
                '''.''',
            ]  ,							)
        UpperCAmelCase_            =  tokenizer.convert_tokens_to_ids(UpperCAmelCase__  )
        self.assertListEqual(
            UpperCAmelCase__  ,							[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]  ,							)

        UpperCAmelCase_            =  tokenizer.convert_ids_to_tokens(UpperCAmelCase__  )
        self.assertListEqual(
            UpperCAmelCase__  ,							[
                SPIECE_UNDERLINE + '''I''',
                SPIECE_UNDERLINE + '''was''',
                SPIECE_UNDERLINE + '''b''',
                '''or''',
                '''n''',
                SPIECE_UNDERLINE + '''in''',
                SPIECE_UNDERLINE + '''''',
                '''<unk>''',
                '''2''',
                '''0''',
                '''0''',
                '''0''',
                ''',''',
                SPIECE_UNDERLINE + '''and''',
                SPIECE_UNDERLINE + '''this''',
                SPIECE_UNDERLINE + '''is''',
                SPIECE_UNDERLINE + '''f''',
                '''al''',
                '''s''',
                '''<unk>''',
                '''.''',
            ]  ,							)

        UpperCAmelCase_            =  tokenizer.vocab_size
        UpperCAmelCase_            =  [tokenizer.convert_ids_to_tokens(UpperCAmelCase__  ) for x in range(end - 4  ,							UpperCAmelCase__  )]

        self.assertListEqual(UpperCAmelCase__  ,							['''__java__''', '''__python__''', '''__en_XX__''', '''<mask>''']  )

        UpperCAmelCase_            =  '''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
        UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ).input_ids
        self.assertEqual(
            tokenizer.decode(UpperCAmelCase__  ,							skip_special_tokens=UpperCAmelCase__  ,							clean_up_tokenization_spaces=UpperCAmelCase__  )  ,							UpperCAmelCase__  ,							)






  def  lowerCAmelCase__ (      self			:       str  )				->Union[str, Any]:
        UpperCAmelCase_            =  PLBartTokenizer(UpperCAmelCase__  ,							language_codes='''multi'''  ,							keep_accents=UpperCAmelCase__  )

        UpperCAmelCase_            =  tokenizer.tokenize('''This is a test'''  )
        self.assertListEqual(UpperCAmelCase__  ,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']  )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(UpperCAmelCase__  )  ,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,							)

        UpperCAmelCase_            =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''  )
        self.assertListEqual(
            UpperCAmelCase__  ,							[
                SPIECE_UNDERLINE + '''I''',
                SPIECE_UNDERLINE + '''was''',
                SPIECE_UNDERLINE + '''b''',
                '''or''',
                '''n''',
                SPIECE_UNDERLINE + '''in''',
                SPIECE_UNDERLINE + '''''',
                '''9''',
                '''2''',
                '''0''',
                '''0''',
                '''0''',
                ''',''',
                SPIECE_UNDERLINE + '''and''',
                SPIECE_UNDERLINE + '''this''',
                SPIECE_UNDERLINE + '''is''',
                SPIECE_UNDERLINE + '''f''',
                '''al''',
                '''s''',
                '''é''',
                '''.''',
            ]  ,							)
        UpperCAmelCase_            =  tokenizer.convert_tokens_to_ids(UpperCAmelCase__  )
        self.assertListEqual(
            UpperCAmelCase__  ,							[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]  ,							)

        UpperCAmelCase_            =  tokenizer.convert_ids_to_tokens(UpperCAmelCase__  )
        self.assertListEqual(
            UpperCAmelCase__  ,							[
                SPIECE_UNDERLINE + '''I''',
                SPIECE_UNDERLINE + '''was''',
                SPIECE_UNDERLINE + '''b''',
                '''or''',
                '''n''',
                SPIECE_UNDERLINE + '''in''',
                SPIECE_UNDERLINE + '''''',
                '''<unk>''',
                '''2''',
                '''0''',
                '''0''',
                '''0''',
                ''',''',
                SPIECE_UNDERLINE + '''and''',
                SPIECE_UNDERLINE + '''this''',
                SPIECE_UNDERLINE + '''is''',
                SPIECE_UNDERLINE + '''f''',
                '''al''',
                '''s''',
                '''<unk>''',
                '''.''',
            ]  ,							)
        UpperCAmelCase_            =  tokenizer.vocab_size
        UpperCAmelCase_            =  [tokenizer.convert_ids_to_tokens(UpperCAmelCase__  ) for x in range(end - 7  ,							UpperCAmelCase__  )]

        self.assertListEqual(
            UpperCAmelCase__  ,							['''__java__''', '''__python__''', '''__en_XX__''', '''__javascript__''', '''__php__''', '''__ruby__''', '''__go__''']  )
        UpperCAmelCase_            =  '''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
        UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ).input_ids
        self.assertEqual(
            tokenizer.decode(UpperCAmelCase__  ,							skip_special_tokens=UpperCAmelCase__  ,							clean_up_tokenization_spaces=UpperCAmelCase__  )  ,							UpperCAmelCase__  ,							)







@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''uclanlp/plbart-python-en_XX'''
  lowerCAmelCase__										=	[
      '''def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])''',
      '''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''',
  ]
  lowerCAmelCase__										=	[
      '''Returns the maximum value of a b c.''',
      '''Sums the values of a b c.''',
  ]
  lowerCAmelCase__										=	[
      1_34,
      54_52,
      3_34_60,
      3_34_41,
      3_34_63,
      3_34_65,
      3_34_63,
      3_34_49,
      9_88,
      20,
      3_34_56,
      19,
      3_34_56,
      7_71,
      39,
      42_58,
      8_89,
      33_18,
      3_34_41,
      3_34_63,
      3_34_65,
      3_34_63,
      3_34_49,
      24_71,
      2,
      PYTHON_CODE,
  ]






  @classmethod
  def  lowerCAmelCase__ (      cls			:       Union[str, Any]  )				->int:
        UpperCAmelCase_            =  PLBartTokenizer.from_pretrained(
            cls.checkpoint_name  ,							language_codes='''base'''  ,							src_lang='''python'''  ,							tgt_lang='''en_XX'''  )
        UpperCAmelCase_            =  1
        return cls






  def  lowerCAmelCase__ (      self			:       int  )				->Tuple:
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__java__''']  ,							5_0001  )
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__python__''']  ,							5_0002  )
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__en_XX__''']  ,							5_0003  )






  def  lowerCAmelCase__ (      self			:       List[str]  )				->List[str]:
        UpperCAmelCase_            =  self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
        self.assertListEqual(self.expected_src_tokens  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Union[str, Any]:
        self.assertIn(UpperCAmelCase__  ,							self.tokenizer.all_special_ids  )
        UpperCAmelCase_            =  [EN_CODE, 9037, 3_3442, 57, 752, 153, 14, 56, 18, 9, 2]
        UpperCAmelCase_            =  self.tokenizer.decode(UpperCAmelCase__  ,							skip_special_tokens=UpperCAmelCase__  )
        UpperCAmelCase_            =  self.tokenizer.decode(generated_ids[1:]  ,							skip_special_tokens=UpperCAmelCase__  )
        self.assertEqual(UpperCAmelCase__  ,							UpperCAmelCase__  )
        self.assertNotIn(self.tokenizer.eos_token  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        UpperCAmelCase_            =  ['''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''' * 20]
        self.assertIsInstance(src_text[0]  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  10
        UpperCAmelCase_            =  self.tokenizer(UpperCAmelCase__  ,							max_length=UpperCAmelCase__  ,							truncation=UpperCAmelCase__  ).input_ids[0]
        self.assertEqual(ids[-2]  ,							2  )
        self.assertEqual(ids[-1]  ,							UpperCAmelCase__  )
        self.assertEqual(len(UpperCAmelCase__  )  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[int]:
        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''__java__''']  )  ,							[5_0004, 5_0001]  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->Union[str, Any]:
        UpperCAmelCase_            =  tempfile.mkdtemp()
        UpperCAmelCase_            =  self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(UpperCAmelCase__  )
        UpperCAmelCase_            =  PLBartTokenizer.from_pretrained(UpperCAmelCase__  )
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,							UpperCAmelCase__  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[Any]:
        UpperCAmelCase_            =  self.tokenizer(self.src_text  ,							text_target=self.tgt_text  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
        UpperCAmelCase_            =  shift_tokens_right(batch['''labels''']  ,							self.tokenizer.pad_token_id  )

        # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
        self.assertEqual(batch.input_ids[1][-2:].tolist()  ,							[2, PYTHON_CODE]  )
        self.assertEqual(batch.decoder_input_ids[1][0]  ,							UpperCAmelCase__  )
        self.assertEqual(batch.decoder_input_ids[1][-1]  ,							2  )
        self.assertEqual(batch.labels[1][-2:].tolist()  ,							[2, EN_CODE]  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Union[str, Any]:
        UpperCAmelCase_            =  self.tokenizer(
            self.src_text  ,							text_target=self.tgt_text  ,							padding=UpperCAmelCase__  ,							truncation=UpperCAmelCase__  ,							max_length=len(self.expected_src_tokens  )  ,							return_tensors='''pt'''  ,							)
        UpperCAmelCase_            =  shift_tokens_right(batch['''labels''']  ,							self.tokenizer.pad_token_id  )

        self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )

        self.assertEqual((2, 26)  ,							batch.input_ids.shape  )
        self.assertEqual((2, 26)  ,							batch.attention_mask.shape  )
        UpperCAmelCase_            =  batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens  ,							UpperCAmelCase__  )
        self.assertEqual(2  ,							batch.decoder_input_ids[0, -1]  )  # EOS
        # Test that special tokens are reset
        self.assertEqual(self.tokenizer.prefix_tokens  ,							[]  )
        self.assertEqual(self.tokenizer.suffix_tokens  ,							[self.tokenizer.eos_token_id, PYTHON_CODE]  )






  def  lowerCAmelCase__ (      self			:       Optional[Any]  )				->Union[str, Any]:
        UpperCAmelCase_            =  self.tokenizer(self.src_text  ,							padding=UpperCAmelCase__  ,							truncation=UpperCAmelCase__  ,							max_length=3  ,							return_tensors='''pt'''  )
        UpperCAmelCase_            =  self.tokenizer(
            text_target=self.tgt_text  ,							padding=UpperCAmelCase__  ,							truncation=UpperCAmelCase__  ,							max_length=10  ,							return_tensors='''pt'''  )
        UpperCAmelCase_            =  targets['''input_ids''']
        UpperCAmelCase_            =  shift_tokens_right(UpperCAmelCase__  ,							self.tokenizer.pad_token_id  )

        self.assertEqual(batch.input_ids.shape[1]  ,							3  )
        self.assertEqual(batch.decoder_input_ids.shape[1]  ,							10  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Dict  )				->int:
        UpperCAmelCase_            =  self.tokenizer._build_translation_inputs(
            '''A test'''  ,							return_tensors='''pt'''  ,							src_lang='''en_XX'''  ,							tgt_lang='''java'''  )

        self.assertEqual(
            nested_simplify(UpperCAmelCase__  )  ,							{
                # A, test, EOS, en_XX
                '''input_ids''': [[150, 242, 2, 5_0003]],
                '''attention_mask''': [[1, 1, 1, 1]],
                # java
                '''forced_bos_token_id''': 5_0001,
            }  ,							)






'''simple docstring'''
from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          AutoTokenizer,
          TFAutoModelForCausalLM,
          TFAutoModelForSeqaSeqLM,
          TFAutoModelForSpeechSeqaSeq,
          TFAutoModelForVisionaSeq,
          TFBartForConditionalGeneration,
          TFLogitsProcessorList,
          TFMinLengthLogitsProcessor,
          tf_top_k_top_p_filtering,
      )

if is_tensorflow_text_available():
      import tensorflow_text as text







@require_tf
class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       Dict  )				->List[str]:
        UpperCAmelCase_            =  tf.convert_to_tensor(
            [
                [
                    8.222_0991,  # 3rd highest value; idx. 0
                    -0.562_0044,
                    5.2322_9752,
                    4.038_6393,
                    -6.879_8378,
                    -0.5478_5802,
                    -3.201_2153,
                    2.9277_7176,
                    1.8817_1953,
                    7.3534_1276,  # 5th highest value; idx. 9
                    8.4320_7833,  # 2nd highest value; idx. 10
                    -9.8571_1836,
                    -5.9620_9236,
                    -1.1303_9161,
                    -7.111_5294,
                    -0.836_9633,
                    -5.318_6408,
                    7.0642_7407,
                    0.8136_9344,
                    -0.8202_3817,
                    -5.917_9796,
                    0.5881_3443,
                    -6.9977_8438,
                    4.7155_1189,
                    -0.1877_1637,
                    7.4402_0759,  # 4th highest value; idx. 25
                    9.3845_0987,  # 1st highest value; idx. 26
                    2.1266_2941,
                    -9.3256_2038,
                    2.3565_2522,
                ],  # cummulative prob of 5 highest values <= 0.6
                [
                    0.5842_5518,
                    4.5313_9238,
                    -5.5751_0464,
                    -6.2803_0699,
                    -7.1952_9503,
                    -4.0212_2551,
                    1.3933_7037,
                    -6.0670_7057,
                    1.5948_0517,
                    -9.64_3119,
                    0.0390_7799,
                    0.6723_1762,
                    -8.8820_6726,
                    6.2711_5922,  # 4th highest value; idx. 13
                    2.2852_0723,
                    4.8276_7506,
                    4.3042_1368,
                    8.827_5313,  # 2nd highest value; idx. 17
                    5.4402_9958,  # 5th highest value; idx. 18
                    -4.473_5794,
                    7.3857_9536,  # 3rd highest value; idx. 20
                    -2.9105_1663,
                    2.6194_6077,
                    -2.567_4762,
                    -9.4895_9302,
                    -4.0292_2645,
                    -1.3541_6918,
                    9.6770_2323,  # 1st highest value; idx. 27
                    -5.8947_8553,
                    1.8537_0467,
                ],  # cummulative prob of 5 highest values <= 0.6
            ]  ,							dtype=tf.floataa  ,							)

        UpperCAmelCase_            =  tf.convert_to_tensor(
            [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]  ,							dtype=tf.intaa  ,							)  # expected non filtered idx as noted above

        UpperCAmelCase_            =  tf.convert_to_tensor(
            [8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023]  ,							dtype=tf.floataa  ,							)  # expected non filtered values as noted above

        UpperCAmelCase_            =  tf_top_k_top_p_filtering(UpperCAmelCase__  ,							top_k=10  ,							top_p=0.6  ,							min_tokens_to_keep=4  )

        UpperCAmelCase_            =  output[output != -float('''inf'''  )]
        UpperCAmelCase_            =  tf.cast(
            tf.where(tf.not_equal(UpperCAmelCase__  ,							tf.constant(-float('''inf'''  )  ,							dtype=tf.floataa  )  )  )  ,							dtype=tf.intaa  ,							)

        tf.debugging.assert_near(UpperCAmelCase__  ,							UpperCAmelCase__  ,							rtol=1e-12  )
        tf.debugging.assert_equal(UpperCAmelCase__  ,							UpperCAmelCase__  )







@require_tf
class lowerCamelCase							(	unittest.TestCase     ,		lowerCamelCase					):




  '''simple docstring'''



  if is_tf_available():
    lowerCAmelCase__										=	{
        '''AutoModelForCausalLM''': TFAutoModelForCausalLM,
        '''AutoModelForSpeechSeq2Seq''': TFAutoModelForSpeechSeqaSeq,
        '''AutoModelForSeq2SeqLM''': TFAutoModelForSeqaSeqLM,
        '''AutoModelForVision2Seq''': TFAutoModelForVisionaSeq,
        '''LogitsProcessorList''': TFLogitsProcessorList,
        '''MinLengthLogitsProcessor''': TFMinLengthLogitsProcessor,
        '''create_tensor_fn''': tf.convert_to_tensor,
        '''floats_tensor''': floats_tensor,
        '''return_tensors''': '''tf''',
    }






  @slow
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[Any]:
        # TF-only test: tf.saved_model export
        UpperCAmelCase_            =  TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''  )
        UpperCAmelCase_            =  2
        UpperCAmelCase_            =  2







        class lowerCamelCase							(	tf.Module					):




          '''simple docstring'''



          def __init__(      self			:       List[str]  ,							UpperCAmelCase__			:       List[str]  )				->Dict:
                super(UpperCAmelCase__  ,							self  ).__init__()
                UpperCAmelCase_            =  model






          @tf.function(
              input_signature=(
                  tf.TensorSpec((None, input_length)  ,							tf.intaa  ,							name='''input_ids'''  ),
                  tf.TensorSpec((None, input_length)  ,							tf.intaa  ,							name='''attention_mask'''  ),
              )  ,							jit_compile=UpperCAmelCase__  ,							)
          def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       List[str]  )				->int:
                UpperCAmelCase_            =  self.model.generate(
                    input_ids=UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							max_new_tokens=UpperCAmelCase__  ,							return_dict_in_generate=UpperCAmelCase__  ,							)
                return {"sequences": outputs["sequences"]}






        UpperCAmelCase_            =  [[2, 0], [102, 103]]
        UpperCAmelCase_            =  [[1, 0], [1, 1]]
        UpperCAmelCase_            =  DummyModel(model=UpperCAmelCase__  )
        with tempfile.TemporaryDirectory() as tmp_dir:
              tf.saved_model.save(UpperCAmelCase__  ,							UpperCAmelCase__  ,							signatures={'''serving_default''': dummy_model.serving}  )
              UpperCAmelCase_            =  tf.saved_model.load(UpperCAmelCase__  ).signatures['''serving_default''']
              for batch_size in range(1  ,							len(UpperCAmelCase__  ) + 1  ):
                    UpperCAmelCase_            =  {
                        '''input_ids''': tf.constant(dummy_input_ids[:batch_size]  ),
                        '''attention_mask''': tf.constant(dummy_attention_masks[:batch_size]  ),
                    }
                    UpperCAmelCase_            =  serving_func(**UpperCAmelCase__  )['''sequences''']
                    UpperCAmelCase_            =  test_model.generate(**UpperCAmelCase__  ,							max_new_tokens=UpperCAmelCase__  )
                    tf.debugging.assert_equal(UpperCAmelCase__  ,							UpperCAmelCase__  )






  @slow
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[int]:
        # TF-only test: tf.saved_model export
        UpperCAmelCase_            =  TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''  )
        UpperCAmelCase_            =  1
        UpperCAmelCase_            =  2







        class lowerCamelCase							(	tf.Module					):




          '''simple docstring'''



          def __init__(      self			:       int  ,							UpperCAmelCase__			:       int  )				->List[str]:
                super(UpperCAmelCase__  ,							self  ).__init__()
                UpperCAmelCase_            =  model






          @tf.function(
              input_signature=(
                  tf.TensorSpec((batch_size, None)  ,							tf.intaa  ,							name='''input_ids'''  ),
                  tf.TensorSpec((batch_size, None)  ,							tf.intaa  ,							name='''attention_mask'''  ),
              )  ,							jit_compile=UpperCAmelCase__  ,							)
          def  lowerCAmelCase__ (      self			:       Dict  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Optional[Any]  )				->int:
                UpperCAmelCase_            =  self.model.generate(
                    input_ids=UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							max_new_tokens=UpperCAmelCase__  ,							return_dict_in_generate=UpperCAmelCase__  ,							)
                return {"sequences": outputs["sequences"]}






        UpperCAmelCase_            =  [[2], [102, 103]]
        UpperCAmelCase_            =  [[1], [1, 1]]
        UpperCAmelCase_            =  DummyModel(model=UpperCAmelCase__  )
        with tempfile.TemporaryDirectory() as tmp_dir:
              tf.saved_model.save(UpperCAmelCase__  ,							UpperCAmelCase__  ,							signatures={'''serving_default''': dummy_model.serving}  )
              UpperCAmelCase_            =  tf.saved_model.load(UpperCAmelCase__  ).signatures['''serving_default''']
              for input_row in range(len(UpperCAmelCase__  )  ):
                    UpperCAmelCase_            =  {
                        '''input_ids''': tf.constant([dummy_input_ids[input_row]]  ),
                        '''attention_mask''': tf.constant([dummy_attention_masks[input_row]]  ),
                    }
                    UpperCAmelCase_            =  serving_func(**UpperCAmelCase__  )['''sequences''']
                    UpperCAmelCase_            =  test_model.generate(**UpperCAmelCase__  ,							max_new_tokens=UpperCAmelCase__  )
                    tf.debugging.assert_equal(UpperCAmelCase__  ,							UpperCAmelCase__  )






  @slow
  @require_tensorflow_text
  def  lowerCAmelCase__ (      self			:       Optional[Any]  )				->int:
        # TF-only test: tf.saved_model export
        with tempfile.TemporaryDirectory() as tmp_dir:
              # file needed to load the TF tokenizer
              hf_hub_download(repo_id='''google/flan-t5-small'''  ,							filename='''spiece.model'''  ,							local_dir=UpperCAmelCase__  )







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




                '''simple docstring'''



                def __init__(      self			:       List[str]  )				->Any:
                      super().__init__()
                      UpperCAmelCase_            =  text.SentencepieceTokenizer(
                          model=tf.io.gfile.GFile(os.path.join(UpperCAmelCase__  ,							'''spiece.model'''  )  ,							'''rb'''  ).read()  )
                      UpperCAmelCase_            =  TFAutoModelForSeqaSeqLM.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )






                def  lowerCAmelCase__ (      self			:       Any  ,							UpperCAmelCase__			:       Union[str, Any]  ,							*UpperCAmelCase__			:       Union[str, Any]  ,							**UpperCAmelCase__			:       str  )				->List[str]:
                      UpperCAmelCase_            =  self.tokenizer.tokenize(UpperCAmelCase__  )
                      UpperCAmelCase_      ,		UpperCAmelCase_            =  text.pad_model_inputs(
                          UpperCAmelCase__  ,							max_seq_length=64  ,							pad_value=self.model.config.pad_token_id  )
                      UpperCAmelCase_            =  self.model.generate(input_ids=UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  )
                      return self.tokenizer.detokenize(UpperCAmelCase__  )






              UpperCAmelCase_            =  CompleteSentenceTransformer()
              UpperCAmelCase_            =  tf.keras.layers.Input(shape=(1,)  ,							dtype=tf.string  ,							name='''inputs'''  )
              UpperCAmelCase_            =  complete_model(UpperCAmelCase__  )
              UpperCAmelCase_            =  tf.keras.Model(UpperCAmelCase__  ,							UpperCAmelCase__  )
              keras_model.save(UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->Tuple:
        # Has PT equivalent: this test relies on random sampling
        UpperCAmelCase_            =  {
            '''do_sample''': True,
            '''num_beams''': 1,
            '''top_p''': 0.7,
            '''top_k''': 10,
            '''temperature''': 0.7,
        }
        UpperCAmelCase_            =  14

        UpperCAmelCase_            =  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''  )
        UpperCAmelCase_            =  '''Hello, my dog is cute and'''
        UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							return_tensors='''tf'''  )
        UpperCAmelCase_            =  TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''  )

        UpperCAmelCase_            =  638
        # forces the generation to happen on CPU, to avoid GPU-related quirks
        with tf.device(''':/CPU:0'''  ):
              tf.random.set_seed(0  )
              UpperCAmelCase_            =  model.generate(**UpperCAmelCase__  ,							eos_token_id=UpperCAmelCase__  ,							**UpperCAmelCase__  )
        self.assertTrue(expectation == len(generated_tokens[0]  )  )

        UpperCAmelCase_            =  [638, 198]
        with tf.device(''':/CPU:0'''  ):
              tf.random.set_seed(0  )
              UpperCAmelCase_            =  model.generate(**UpperCAmelCase__  ,							eos_token_id=UpperCAmelCase__  ,							**UpperCAmelCase__  )
        self.assertTrue(expectation == len(generated_tokens[0]  )  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->List[str]:
        # Has PT equivalent: ample use of framework-specific code
        UpperCAmelCase_            =  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bart'''  )
        UpperCAmelCase_            =  '''Hugging Face is a technology company based in New York and Paris.'''
        UpperCAmelCase_            =  bart_tokenizer(UpperCAmelCase__  ,							return_tensors='''tf'''  ).input_ids
        UpperCAmelCase_            =  TFBartForConditionalGeneration.from_pretrained('''hf-internal-testing/tiny-random-bart'''  )
        UpperCAmelCase_            =  bart_model.generate(UpperCAmelCase__  ).numpy()







        class lowerCamelCase							(	lowerCamelCase					):




          '''simple docstring'''



          def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       List[Any]=None  ,							**UpperCAmelCase__			:       int  )				->List[str]:
                return super().call(UpperCAmelCase__  ,							**UpperCAmelCase__  )






        UpperCAmelCase_            =  FakeBart.from_pretrained('''hf-internal-testing/tiny-random-bart'''  )
        UpperCAmelCase_            =  bart_model.generate(UpperCAmelCase__  ,							foo='''bar'''  ).numpy()
        self.assertTrue(np.array_equal(UpperCAmelCase__  ,							UpperCAmelCase__  )  )







        class lowerCamelCase							(	bart_model.model.encoder.__class__					):




          '''simple docstring'''



          def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       List[str]  ,							**UpperCAmelCase__			:       Dict  )				->Any:
                return super().call(UpperCAmelCase__  ,							**UpperCAmelCase__  )






        UpperCAmelCase_            =  FakeEncoder(bart_model.config  ,							bart_model.model.shared  )
        UpperCAmelCase_            =  fake_encoder

        # Normal generation still works (the output will be different because the encoder weights are different)
        UpperCAmelCase_            =  bart_model.generate(UpperCAmelCase__  ).numpy()
        with self.assertRaises(UpperCAmelCase__  ):
              # FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
              bart_model.generate(UpperCAmelCase__  ,							foo='''bar'''  )





