

'''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


_snake_case         =  get_tests_dir('fixtures/test_sentencepiece.model')


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

_snake_case         =  50_003
_snake_case         =  50_002


@require_sentencepiece
@require_tokenizers
class        a__  (							lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							PLBartTokenizer
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							None
   _SCREAMING_SNAKE_CASE   :					int     	=							False


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      super().setUp()

      # We have a SentencePiece fixture for testing
      _lowercase      :				Optional[Any]      					=       PLBartTokenizer(_UpperCamelCase      ,    language_codes="base"      ,    keep_accents=_UpperCamelCase  )
      tokenizer.save_pretrained(self.tmpdirname  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Any      					=       PLBartTokenizer(_UpperCamelCase      ,    language_codes="base"      ,    keep_accents=_UpperCamelCase  )

      _lowercase      :				int      					=       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]]      ,    )

      _lowercase      :				List[str]      					=       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",
              ".",
          ]      ,    )
      _lowercase      :				Dict      					=       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]
          ]      ,    )

      _lowercase      :				List[str]      					=       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>",
              ".",
          ]      ,    )

      _lowercase      :				int      					=       tokenizer.vocab_size
      _lowercase      :				int      					=       [tokenizer.convert_ids_to_tokens(_UpperCamelCase  ) for x in range(end - 4      ,    _UpperCamelCase  )]

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

      _lowercase      :				Optional[Any]      					=       "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
      _lowercase      :				Union[str, Any]      					=       tokenizer(_UpperCamelCase  ).input_ids
      self.assertEqual(
          tokenizer.decode(_UpperCamelCase      ,    skip_special_tokens=_UpperCamelCase      ,    clean_up_tokenization_spaces=_UpperCamelCase  )      ,    _UpperCamelCase      ,    )






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       PLBartTokenizer(_UpperCamelCase      ,    language_codes="multi"      ,    keep_accents=_UpperCamelCase  )

      _lowercase      :				List[Any]      					=       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]]      ,    )

      _lowercase      :				Tuple      					=       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",
              ".",
          ]      ,    )
      _lowercase      :				Any      					=       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]
          ]      ,    )

      _lowercase      :				Any      					=       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>",
              ".",
          ]      ,    )
      _lowercase      :				Any      					=       tokenizer.vocab_size
      _lowercase      :				List[str]      					=       [tokenizer.convert_ids_to_tokens(_UpperCamelCase  ) for x in range(end - 7      ,    _UpperCamelCase  )]

      self.assertListEqual(
          _UpperCamelCase      ,    ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]  )
      _lowercase      :				List[Any]      					=       "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
      _lowercase      :				Optional[Any]      					=       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        a__  (							unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							'uclanlp/plbart-python-en_XX'
   _SCREAMING_SNAKE_CASE   :					Optional[Any]     	=							[
       'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
       'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
   ]
   _SCREAMING_SNAKE_CASE   :					Dict     	=							[
       'Returns the maximum value of a b c.',
       'Sums the values of a b c.',
   ]
   _SCREAMING_SNAKE_CASE   :					Union[str, Any]     	=							[
       134,
       5452,
       3_3460,
       3_3441,
       3_3463,
       3_3465,
       3_3463,
       3_3449,
       988,
       20,
       3_3456,
       19,
       3_3456,
       771,
       39,
       4258,
       889,
       3318,
       3_3441,
       3_3463,
       3_3465,
       3_3463,
       3_3449,
       2471,
       2,
       PYTHON_CODE,
   ]


   @classmethod
   def 						_lowerCamelCase       (			cls  ):





      """simple docstring"""





      _lowercase      :				PLBartTokenizer      					=       PLBartTokenizer.from_pretrained(
          cls.checkpoint_name      ,    language_codes="base"      ,    src_lang="python"      ,    tgt_lang="en_XX"  )
      _lowercase      :				int      					=       1
      return cls


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]      ,    50001  )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]      ,    50002  )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]      ,    50003  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
      self.assertListEqual(self.expected_src_tokens      ,    _UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self.assertIn(_UpperCamelCase      ,    self.tokenizer.all_special_ids  )
      _lowercase      :				Tuple      					=       [EN_CODE, 9037, 33442, 57, 752, 153, 14, 56, 18, 9, 2]
      _lowercase      :				List[str]      					=       self.tokenizer.decode(_UpperCamelCase      ,    skip_special_tokens=_UpperCamelCase  )
      _lowercase      :				Optional[int]      					=       self.tokenizer.decode(generated_ids[1:]      ,    skip_special_tokens=_UpperCamelCase  )
      self.assertEqual(_UpperCamelCase      ,    _UpperCamelCase  )
      self.assertNotIn(self.tokenizer.eos_token      ,    _UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				str      					=       ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
      self.assertIsInstance(src_text[0]      ,    _UpperCamelCase  )
      _lowercase      :				List[Any]      					=       10
      _lowercase      :				List[str]      					=       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  ):





      """simple docstring"""





      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]  )      ,    [50004, 50001]  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       tempfile.mkdtemp()
      _lowercase      :				Tuple      					=       self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(_UpperCamelCase  )
      _lowercase      :				int      					=       PLBartTokenizer.from_pretrained(_UpperCamelCase  )
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,    _UpperCamelCase  )


   @require_torch
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       self.tokenizer(self.src_text      ,    text_target=self.tgt_text      ,    padding=_UpperCamelCase      ,    return_tensors="pt"  )
      _lowercase      :				Optional[int]      					=       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  ):





      """simple docstring"""





      _lowercase      :				Any      					=       self.tokenizer(
          self.src_text      ,    text_target=self.tgt_text      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=len(self.expected_src_tokens  )      ,    return_tensors="pt"      ,    )
      _lowercase      :				Any      					=       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  )
      _lowercase      :				int      					=       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  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       self.tokenizer(self.src_text      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=3      ,    return_tensors="pt"  )
      _lowercase      :				Union[str, Any]      					=       self.tokenizer(
          text_target=self.tgt_text      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=10      ,    return_tensors="pt"  )
      _lowercase      :				Union[str, Any]      					=       targets["input_ids"]
      _lowercase      :				Optional[int]      					=       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  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       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, 50003]],
              "attention_mask": [[1, 1, 1, 1]],
              # java
              "forced_bos_token_id": 50001,
          }      ,    )









'''simple docstring'''
import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'BlipImageProcessor'
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'AutoTokenizer'


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )

      # add QFormer tokenizer
      _lowercase      :				List[Any]      					=       qformer_tokenizer


   def __call__(			self      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 0      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = True      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      if images is None and text is None:
         raise ValueError("You have to specify at least images or text."  )

      _lowercase      :				str      					=       BatchFeature()

      if text is not None:
         _lowercase      :				Dict      					=       self.tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         encoding.update(_UpperCamelCase  )
         _lowercase      :				Dict      					=       self.qformer_tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         _lowercase      :				Union[str, Any]      					=       qformer_text_encoding.pop("input_ids"  )
         _lowercase      :				List[Any]      					=       qformer_text_encoding.pop("attention_mask"  )

      if images is not None:
         _lowercase      :				List[Any]      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase  )
         encoding.update(_UpperCamelCase  )

      return encoding


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.tokenizer.model_input_names
      _lowercase      :				Optional[Any]      					=       self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if os.path.isfile(_UpperCamelCase  ):
         raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''  )
      os.makedirs(_UpperCamelCase      ,    exist_ok=_UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       os.path.join(_UpperCamelCase      ,    "qformer_tokenizer"  )
      self.qformer_tokenizer.save_pretrained(_UpperCamelCase  )
      return super().save_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )






   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       AutoTokenizer.from_pretrained(_UpperCamelCase      ,    subfolder="qformer_tokenizer"  )
      _lowercase      :				Optional[Any]      					=       cls._get_arguments_from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )
      args.append(_UpperCamelCase  )
      return cls(*_UpperCamelCase  )






