

"""simple docstring"""


import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
  from transformers import WhisperFeatureExtractor

if is_torch_available():
  import torch

UpperCAmelCase													=random.Random()



def   _A					(	_a	:						List[Any]     ,   _a	:						List[Any]=1.0     ,   _a	:						int=None     ,   _a	:						Tuple=None				):



      """simple docstring"""






      if rng is None:
            A				  =		global_rng

      A				  =		[]
      for batch_idx in range(shape[0]				):
            values.append([]				)
            for _ in range(shape[1]				):
                  values[-1].append(rng.random() * scale				)

      return values





@require_torch
@require_torchaudio
class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_=7 ,lowerCamelCase_=4_0_0 ,lowerCamelCase_=2_0_0_0 ,lowerCamelCase_=1_0 ,lowerCamelCase_=1_6_0 ,lowerCamelCase_=8 ,lowerCamelCase_=0.0 ,lowerCamelCase_=4_0_0_0 ,lowerCamelCase_=False ,lowerCamelCase_=True ,)    ->      Any:
          A				  =		parent
          A				  =		batch_size
          A				  =		min_seq_length
          A				  =		max_seq_length
          A				  =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          A				  =		padding_value
          A				  =		sampling_rate
          A				  =		return_attention_mask
          A				  =		do_normalize
          A				  =		feature_size
          A				  =		chunk_length
          A				  =		hop_length




    def UpperCamelCase__		( self    )    ->      str:
          return {
              "feature_size": self.feature_size,
              "hop_length": self.hop_length,
              "chunk_length": self.chunk_length,
              "padding_value": self.padding_value,
              "sampling_rate": self.sampling_rate,
              "return_attention_mask": self.return_attention_mask,
              "do_normalize": self.do_normalize,
          }




    def UpperCamelCase__		( self ,lowerCamelCase_=False ,lowerCamelCase_=False    )    ->      int:
          def _flatten(lowerCamelCase_    ):
                return list(itertools.chain(*lowerCamelCase_    )    )

          if equal_length:
                A				  =		[floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
          else:
                # make sure that inputs increase in size
                A				  =		[
                    floats_list((x, self.feature_size)    )
                    for x in range(self.min_seq_length ,self.max_seq_length ,self.seq_length_diff    )
                ]
          if numpify:
                A				  =		[np.asarray(lowerCamelCase_    ) for x in speech_inputs]
          return speech_inputs





@require_torch
@require_torchaudio
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   WhisperFeatureExtractor if is_speech_available() else None




    def UpperCamelCase__		( self    )    ->      Union[str, Any]:
          A				  =		WhisperFeatureExtractionTester(self    )




    def UpperCamelCase__		( self    )    ->      List[Any]:
          A				  =		self.feature_extraction_class(**self.feat_extract_dict    )

          with tempfile.TemporaryDirectory() as tmpdirname:
                A				  =		feat_extract_first.save_pretrained(lowerCamelCase_    )[0]
                check_json_file_has_correct_format(lowerCamelCase_    )
                A				  =		self.feature_extraction_class.from_pretrained(lowerCamelCase_    )

          A				  =		feat_extract_first.to_dict()
          A				  =		feat_extract_second.to_dict()
          A				  =		feat_extract_first.mel_filters
          A				  =		feat_extract_second.mel_filters
          self.assertTrue(np.allclose(lowerCamelCase_ ,lowerCamelCase_    )    )
          self.assertEqual(lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		self.feature_extraction_class(**self.feat_extract_dict    )

          with tempfile.TemporaryDirectory() as tmpdirname:
                A				  =		os.path.join(lowerCamelCase_ ,"""feat_extract.json"""    )
                feat_extract_first.to_json_file(lowerCamelCase_    )
                A				  =		self.feature_extraction_class.from_json_file(lowerCamelCase_    )

          A				  =		feat_extract_first.to_dict()
          A				  =		feat_extract_second.to_dict()
          A				  =		feat_extract_first.mel_filters
          A				  =		feat_extract_second.mel_filters
          self.assertTrue(np.allclose(lowerCamelCase_ ,lowerCamelCase_    )    )
          self.assertEqual(lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          # Tests that all call wrap to encode_plus and batch_encode_plus
          A				  =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
          # create three inputs of length 800, 1000, and 1200
          A				  =		[floats_list((1, x)    )[0] for x in range(8_0_0 ,1_4_0_0 ,2_0_0    )]
          A				  =		[np.asarray(lowerCamelCase_    ) for speech_input in speech_inputs]

          # Test feature size
          A				  =		feature_extractor(lowerCamelCase_ ,padding="""max_length""" ,return_tensors="""np"""    ).input_features
          self.assertTrue(input_features.ndim == 3    )
          self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames    )
          self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size    )

          # Test not batched input
          A				  =		feature_extractor(speech_inputs[0] ,return_tensors="""np"""    ).input_features
          A				  =		feature_extractor(np_speech_inputs[0] ,return_tensors="""np"""    ).input_features
          self.assertTrue(np.allclose(lowerCamelCase_ ,lowerCamelCase_ ,atol=1E-3    )    )

          # Test batched
          A				  =		feature_extractor(lowerCamelCase_ ,return_tensors="""np"""    ).input_features
          A				  =		feature_extractor(lowerCamelCase_ ,return_tensors="""np"""    ).input_features
          for enc_seq_a, enc_seq_a in zip(lowerCamelCase_ ,lowerCamelCase_    ):
                self.assertTrue(np.allclose(lowerCamelCase_ ,lowerCamelCase_ ,atol=1E-3    )    )

          # Test 2-D numpy arrays are batched.
          A				  =		[floats_list((1, x)    )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
          A				  =		np.asarray(lowerCamelCase_    )
          A				  =		feature_extractor(lowerCamelCase_ ,return_tensors="""np"""    ).input_features
          A				  =		feature_extractor(lowerCamelCase_ ,return_tensors="""np"""    ).input_features
          for enc_seq_a, enc_seq_a in zip(lowerCamelCase_ ,lowerCamelCase_    ):
                self.assertTrue(np.allclose(lowerCamelCase_ ,lowerCamelCase_ ,atol=1E-3    )    )

          # Test truncation required
          A				  =		[floats_list((1, x)    )[0] for x in range(2_0_0 ,(feature_extractor.n_samples + 5_0_0) ,2_0_0    )]
          A				  =		[np.asarray(lowerCamelCase_    ) for speech_input in speech_inputs]

          A				  =		[x[: feature_extractor.n_samples] for x in speech_inputs]
          A				  =		[np.asarray(lowerCamelCase_    ) for speech_input in speech_inputs_truncated]

          A				  =		feature_extractor(lowerCamelCase_ ,return_tensors="""np"""    ).input_features
          A				  =		feature_extractor(lowerCamelCase_ ,return_tensors="""np"""    ).input_features
          for enc_seq_a, enc_seq_a in zip(lowerCamelCase_ ,lowerCamelCase_    ):
                self.assertTrue(np.allclose(lowerCamelCase_ ,lowerCamelCase_ ,atol=1E-3    )    )




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          import torch

          A				  =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
          A				  =		np.random.rand(1_0_0 ,3_2    ).astype(np.floataa    )
          A				  =		np_speech_inputs.tolist()

          for inputs in [py_speech_inputs, np_speech_inputs]:
                A				  =		feature_extractor.pad([{"""input_features""": inputs}] ,return_tensors="""np"""    )
                self.assertTrue(np_processed.input_features.dtype == np.floataa    )
                A				  =		feature_extractor.pad([{"""input_features""": inputs}] ,return_tensors="""pt"""    )
                self.assertTrue(pt_processed.input_features.dtype == torch.floataa    )




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      int:
          A				  =		load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,"""clean""" ,split="""validation"""    )
          # automatic decoding with librispeech
          A				  =		ds.sort("""id"""    ).select(range(lowerCamelCase_    )    )[:num_samples]["""audio"""]

          return [x["array"] for x in speech_samples]




    def UpperCamelCase__		( self    )    ->      List[str]:
          # fmt: off
          A				  =		torch.tensor(
              [
                  0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
                  0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
                  0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
                  -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
              ]    )
          # fmt: on

          A				  =		self._load_datasamples(1    )
          A				  =		WhisperFeatureExtractor()
          A				  =		feature_extractor(lowerCamelCase_ ,return_tensors="""pt"""    ).input_features
          self.assertEqual(input_features.shape ,(1, 8_0, 3_0_0_0)    )
          self.assertTrue(torch.allclose(input_features[0, 0, :3_0] ,lowerCamelCase_ ,atol=1E-4    )    )




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
          A				  =		self._load_datasamples(1    )[0]
          A				  =		((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
          A				  =		feat_extract.zero_mean_unit_var_norm([audio] ,attention_mask=lowerCamelCase_    )[0]

          self.assertTrue(np.all(np.mean(lowerCamelCase_    ) < 1E-3    )    )
          self.assertTrue(np.all(np.abs(np.var(lowerCamelCase_    ) - 1    ) < 1E-3    )    )







"""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


UpperCAmelCase													=get_tests_dir("fixtures/test_sentencepiece.model")


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

UpperCAmelCase													=50_003
UpperCAmelCase													=50_002





@require_sentencepiece
@require_tokenizers
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   PLBartTokenizer
    _lowerCamelCase					=   None
    _lowerCamelCase					=   False




    def UpperCamelCase__		( self    )    ->      Tuple:
          super().setUp()

          # We have a SentencePiece fixture for testing
          A				  =		PLBartTokenizer(lowerCamelCase_ ,language_codes="""base""" ,keep_accents=lowerCamelCase_    )
          tokenizer.save_pretrained(self.tmpdirname    )




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		PLBartTokenizer(lowerCamelCase_ ,language_codes="""base""" ,keep_accents=lowerCamelCase_    )

          A				  =		tokenizer.tokenize("""This is a test"""    )
          self.assertListEqual(lowerCamelCase_ ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(lowerCamelCase_    ) ,[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] ,)

          A				  =		tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
          self.assertListEqual(
              lowerCamelCase_ ,[
                  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""",
                  """.""",
              ] ,)
          A				  =		tokenizer.convert_tokens_to_ids(lowerCamelCase_    )
          self.assertListEqual(
              lowerCamelCase_ ,[
                  value + tokenizer.fairseq_offset
                  for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
              ] ,)

          A				  =		tokenizer.convert_ids_to_tokens(lowerCamelCase_    )
          self.assertListEqual(
              lowerCamelCase_ ,[
                  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>""",
                  """.""",
              ] ,)

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

          self.assertListEqual(lowerCamelCase_ ,["""__java__""", """__python__""", """__en_XX__""", """<mask>"""]    )

          A				  =		"""java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
          A				  =		tokenizer(lowerCamelCase_    ).input_ids
          self.assertEqual(
              tokenizer.decode(lowerCamelCase_ ,skip_special_tokens=lowerCamelCase_ ,clean_up_tokenization_spaces=lowerCamelCase_    ) ,lowerCamelCase_ ,)




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		PLBartTokenizer(lowerCamelCase_ ,language_codes="""multi""" ,keep_accents=lowerCamelCase_    )

          A				  =		tokenizer.tokenize("""This is a test"""    )
          self.assertListEqual(lowerCamelCase_ ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(lowerCamelCase_    ) ,[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] ,)

          A				  =		tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
          self.assertListEqual(
              lowerCamelCase_ ,[
                  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""",
                  """.""",
              ] ,)
          A				  =		tokenizer.convert_tokens_to_ids(lowerCamelCase_    )
          self.assertListEqual(
              lowerCamelCase_ ,[
                  value + tokenizer.fairseq_offset
                  for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
              ] ,)

          A				  =		tokenizer.convert_ids_to_tokens(lowerCamelCase_    )
          self.assertListEqual(
              lowerCamelCase_ ,[
                  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>""",
                  """.""",
              ] ,)
          A				  =		tokenizer.vocab_size
          A				  =		[tokenizer.convert_ids_to_tokens(lowerCamelCase_    ) for x in range(end - 7 ,lowerCamelCase_    )]

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





@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					=   [
        134,
        5452,
        33460,
        33441,
        33463,
        33465,
        33463,
        33449,
        988,
        20,
        33456,
        19,
        33456,
        771,
        39,
        4258,
        889,
        3318,
        33441,
        33463,
        33465,
        33463,
        33449,
        2471,
        2,
        PYTHON_CODE,
    ]




    @classmethod
    def UpperCamelCase__		( cls    )    ->      List[str]:
          A				  =		PLBartTokenizer.from_pretrained(
              cls.checkpoint_name ,language_codes="""base""" ,src_lang="""python""" ,tgt_lang="""en_XX"""    )
          A				  =		1
          return cls




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__java__"""] ,5_0_0_0_1    )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__python__"""] ,5_0_0_0_2    )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__en_XX__"""] ,5_0_0_0_3    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		self.tokenizer.batch_encode_plus(self.src_text    ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertIn(lowerCamelCase_ ,self.tokenizer.all_special_ids    )
          A				  =		[EN_CODE, 9_0_3_7, 3_3_4_4_2, 5_7, 7_5_2, 1_5_3, 1_4, 5_6, 1_8, 9, 2]
          A				  =		self.tokenizer.decode(lowerCamelCase_ ,skip_special_tokens=lowerCamelCase_    )
          A				  =		self.tokenizer.decode(generated_ids[1:] ,skip_special_tokens=lowerCamelCase_    )
          self.assertEqual(lowerCamelCase_ ,lowerCamelCase_    )
          self.assertNotIn(self.tokenizer.eos_token ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      List[str]:
          A				  =		["""def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""" * 2_0]
          self.assertIsInstance(src_text[0] ,lowerCamelCase_    )
          A				  =		1_0
          A				  =		self.tokenizer(lowerCamelCase_ ,max_length=lowerCamelCase_ ,truncation=lowerCamelCase_    ).input_ids[0]
          self.assertEqual(ids[-2] ,2    )
          self.assertEqual(ids[-1] ,lowerCamelCase_    )
          self.assertEqual(len(lowerCamelCase_    ) ,lowerCamelCase_    )




    def UpperCamelCase__		( self    )    ->      Any:
          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """__java__"""]    ) ,[5_0_0_0_4, 5_0_0_0_1]    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		tempfile.mkdtemp()
          A				  =		self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(lowerCamelCase_    )
          A				  =		PLBartTokenizer.from_pretrained(lowerCamelCase_    )
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,lowerCamelCase_    )




    @require_torch
    def UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		self.tokenizer(self.src_text ,text_target=self.tgt_text ,padding=lowerCamelCase_ ,return_tensors="""pt"""    )
          A				  =		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] ,lowerCamelCase_    )
          self.assertEqual(batch.decoder_input_ids[1][-1] ,2    )
          self.assertEqual(batch.labels[1][-2:].tolist() ,[2, EN_CODE]    )




    @require_torch
    def UpperCamelCase__		( self    )    ->      str:
          A				  =		self.tokenizer(
              self.src_text ,text_target=self.tgt_text ,padding=lowerCamelCase_ ,truncation=lowerCamelCase_ ,max_length=len(self.expected_src_tokens    ) ,return_tensors="""pt""" ,)
          A				  =		shift_tokens_right(batch["""labels"""] ,self.tokenizer.pad_token_id    )

          self.assertIsInstance(lowerCamelCase_ ,lowerCamelCase_    )

          self.assertEqual((2, 2_6) ,batch.input_ids.shape    )
          self.assertEqual((2, 2_6) ,batch.attention_mask.shape    )
          A				  =		batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens ,lowerCamelCase_    )
          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 UpperCamelCase__		( self    )    ->      Tuple:
          A				  =		self.tokenizer(self.src_text ,padding=lowerCamelCase_ ,truncation=lowerCamelCase_ ,max_length=3 ,return_tensors="""pt"""    )
          A				  =		self.tokenizer(
              text_target=self.tgt_text ,padding=lowerCamelCase_ ,truncation=lowerCamelCase_ ,max_length=1_0 ,return_tensors="""pt"""    )
          A				  =		targets["""input_ids"""]
          A				  =		shift_tokens_right(lowerCamelCase_ ,self.tokenizer.pad_token_id    )

          self.assertEqual(batch.input_ids.shape[1] ,3    )
          self.assertEqual(batch.decoder_input_ids.shape[1] ,1_0    )




    @require_torch
    def UpperCamelCase__		( self    )    ->      List[Any]:
          A				  =		self.tokenizer._build_translation_inputs(
              """A test""" ,return_tensors="""pt""" ,src_lang="""en_XX""" ,tgt_lang="""java"""    )

          self.assertEqual(
              nested_simplify(lowerCamelCase_    ) ,{
                  # A, test, EOS, en_XX
                  """input_ids""": [[1_5_0, 2_4_2, 2, 5_0_0_0_3]],
                  """attention_mask""": [[1, 1, 1, 1]],
                  # java
                  """forced_bos_token_id""": 5_0_0_0_1,
              } ,)




