
"""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


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


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

_A     :			Tuple  						=			5_00_03
_A     :			Optional[Any]  						=			5_00_02

@require_sentencepiece
@require_tokenizers
class 						a__		( a_,     unittest.TestCase		):
   __lowerCAmelCase				=							PLBartTokenizer
   __lowerCAmelCase				=							None
   __lowerCAmelCase				=							False
   def      __magic_name__     (     self							):
          super().setUp()

          # We have a SentencePiece fixture for testing
          lowercase       :							Union[str, Any]									=				PLBartTokenizer(_a      ,       language_codes="base"      ,       keep_accents=_a							)
          tokenizer.save_pretrained(self.tmpdirname							)
   def      __magic_name__     (     self							):
          lowercase       :							str									=				PLBartTokenizer(_a      ,       language_codes="base"      ,       keep_accents=_a							)

          lowercase       :							List[Any]									=				tokenizer.tokenize("This is a test"							)
          self.assertListEqual(_a      ,       ["▁This", "▁is", "▁a", "▁t", "est"]							)

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

          lowercase       :							Any									=				tokenizer.tokenize("I was born in 92000, and this is falsé."							)
          self.assertListEqual(
              _a      ,       [
                  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       :							Tuple									=				tokenizer.convert_tokens_to_ids(_a							)
          self.assertListEqual(
              _a      ,       [
                  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(_a							)
          self.assertListEqual(
              _a      ,       [
                  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       :							Optional[Any]									=				tokenizer.vocab_size
          lowercase       :							Optional[int]									=				[tokenizer.convert_ids_to_tokens(_a							) for x in range(end - 4      ,       _a							)]

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

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

   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				PLBartTokenizer(_a      ,       language_codes="multi"      ,       keep_accents=_a							)

          lowercase       :							List[Any]									=				tokenizer.tokenize("This is a test"							)
          self.assertListEqual(_a      ,       ["▁This", "▁is", "▁a", "▁t", "est"]							)

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

          lowercase       :							List[Any]									=				tokenizer.tokenize("I was born in 92000, and this is falsé."							)
          self.assertListEqual(
              _a      ,       [
                  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       :							Tuple									=				tokenizer.convert_tokens_to_ids(_a							)
          self.assertListEqual(
              _a      ,       [
                  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(_a							)
          self.assertListEqual(
              _a      ,       [
                  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       :							Dict									=				tokenizer.vocab_size
          lowercase       :							Any									=				[tokenizer.convert_ids_to_tokens(_a							) for x in range(end - 7      ,       _a							)]

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


@require_torch
@require_sentencepiece
@require_tokenizers
class 						a__		( unittest.TestCase		):
   __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,
       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      __magic_name__     (     cls							):
          lowercase       :							PLBartTokenizer									=				PLBartTokenizer.from_pretrained(
              cls.checkpoint_name      ,       language_codes="base"      ,       src_lang="python"      ,       tgt_lang="en_XX"							)
          lowercase       :							Dict									=				1
          return cls
   def      __magic_name__     (     self							):
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]      ,       50_001							)
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]      ,       50_002							)
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]      ,       50_003							)
   def      __magic_name__     (     self							):
          lowercase       :							int									=				self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
          self.assertListEqual(self.expected_src_tokens      ,       _a							)
   def      __magic_name__     (     self							):
          self.assertIn(_a      ,       self.tokenizer.all_special_ids							)
          lowercase       :							Tuple									=				[EN_CODE, 9_037, 33_442, 57, 752, 153, 14, 56, 18, 9, 2]
          lowercase       :							List[Any]									=				self.tokenizer.decode(_a      ,       skip_special_tokens=_a							)
          lowercase       :							Dict									=				self.tokenizer.decode(generated_ids[1:]      ,       skip_special_tokens=_a							)
          self.assertEqual(_a      ,       _a							)
          self.assertNotIn(self.tokenizer.eos_token      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
          self.assertIsInstance(src_text[0]      ,       _a							)
          lowercase       :							int									=				10
          lowercase       :							Optional[int]									=				self.tokenizer(_a      ,       max_length=_a      ,       truncation=_a							).input_ids[0]
          self.assertEqual(ids[-2]      ,       2							)
          self.assertEqual(ids[-1]      ,       _a							)
          self.assertEqual(len(_a							)      ,       _a							)
   def      __magic_name__     (     self							):
          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]							)      ,       [50_004, 50_001]							)
   def      __magic_name__     (     self							):
          lowercase       :							str									=				tempfile.mkdtemp()
          lowercase       :							Optional[int]									=				self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(_a							)
          lowercase       :							str									=				PLBartTokenizer.from_pretrained(_a							)
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,       _a							)
   @require_torch
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.tokenizer(self.src_text      ,       text_target=self.tgt_text      ,       padding=_a      ,       return_tensors="pt"							)
          lowercase       :							Any									=				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]      ,       _a							)
          self.assertEqual(batch.decoder_input_ids[1][-1]      ,       2							)
          self.assertEqual(batch.labels[1][-2:].tolist()      ,       [2, EN_CODE]							)
   @require_torch
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				self.tokenizer(
              self.src_text      ,       text_target=self.tgt_text      ,       padding=_a      ,       truncation=_a      ,       max_length=len(self.expected_src_tokens							)      ,       return_tensors="pt"      ,       )
          lowercase       :							Tuple									=				shift_tokens_right(batch["labels"]      ,       self.tokenizer.pad_token_id							)

          self.assertIsInstance(_a      ,       _a							)

          self.assertEqual((2, 26)      ,       batch.input_ids.shape							)
          self.assertEqual((2, 26)      ,       batch.attention_mask.shape							)
          lowercase       :							str									=				batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens      ,       _a							)
          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      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.tokenizer(self.src_text      ,       padding=_a      ,       truncation=_a      ,       max_length=3      ,       return_tensors="pt"							)
          lowercase       :							Union[str, Any]									=				self.tokenizer(
              text_target=self.tgt_text      ,       padding=_a      ,       truncation=_a      ,       max_length=10      ,       return_tensors="pt"							)
          lowercase       :							List[str]									=				targets["input_ids"]
          lowercase       :							int									=				shift_tokens_right(_a      ,       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      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				self.tokenizer._build_translation_inputs(
              "A test"      ,       return_tensors="pt"      ,       src_lang="en_XX"      ,       tgt_lang="java"							)

          self.assertEqual(
              nested_simplify(_a							)      ,       {
                  # A, test, EOS, en_XX
                  "input_ids": [[150, 242, 2, 50_003]],
                  "attention_mask": [[1, 1, 1, 1]],
                  # java
                  "forced_bos_token_id": 50_001,
              }      ,       )




"""simple docstring"""




import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


_A     :			Tuple  						=			logging.get_logger(__name__)

_A     :			Dict  						=			{"""vocab_file""": """vocab.txt"""}

_A     :			Optional[Any]  						=			{
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

_A     :			Optional[Any]  						=			{
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}


def     __magic_name__      ( __snake_case							:			Tuple    )					->							str:
       with open(__snake_case	,					"r"    ) as f:
              lowercase       :							Dict									=				f.read().splitlines()
              return [l.strip() for l in lines]

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   def __init__(     self      ,       _a      ,       _a="<unk>"      ,       _a="<cls>"      ,       _a="<pad>"      ,       _a="<mask>"      ,       _a="<eos>"      ,       **_a      ,       ):
          super().__init__(**_a							)
          lowercase       :							Union[str, Any]									=				load_vocab_file(_a							)
          lowercase       :							Union[str, Any]									=				dict(enumerate(self.all_tokens							)							)
          lowercase       :							Any									=				{tok: ind for ind, tok in enumerate(self.all_tokens							)}
          lowercase       :							Dict									=				unk_token
          lowercase       :							Optional[int]									=				cls_token
          lowercase       :							Optional[int]									=				pad_token
          lowercase       :							Any									=				mask_token
          lowercase       :							List[str]									=				eos_token
          lowercase       :							Tuple									=				self.all_tokens
          self._create_trie(self.unique_no_split_tokens							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a      ,       **_a							):
          return text.split()
   def      __magic_name__     (     self      ,       _a=False							):
          return len(self._id_to_token							)
   def      __magic_name__     (     self							):
          return {token: i for i, token in enumerate(self.all_tokens							)}
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Optional[int]									=				[self.cls_token_id]
          lowercase       :							Dict									=				[self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
                 if self.eos_token_id is None:
                        return cls + token_ids_a
                 else:
                        return cls + token_ids_a + sep
          elif self.eos_token_id is None:
                 raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"							)
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
   def      __magic_name__     (     self      ,       _a      ,       _a = None      ,       _a = False							):
          if already_has_special_tokens:
                 if token_ids_a is not None:
                        raise ValueError(
                            "You should not supply a second sequence if the provided sequence of "
                            "ids is already formatted with special tokens for the model."							)

                 return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
          lowercase       :							Union[str, Any]									=				[1] + ([0] * len(_a							)) + [1]
          if token_ids_a is not None:
                 mask += [0] * len(_a							) + [1]
          return mask
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Optional[Any]									=				os.path.join(_a      ,       (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"							)
          with open(_a      ,       "w"							) as f:
                 f.write("\n".join(self.all_tokens							)							)
          return (vocab_file,)
   @property
   def      __magic_name__     (     self							):
          return self.get_vocab_size(with_added_tokens=_a							)

   def      __magic_name__     (     self      ,       _a      ,       _a = False							):
          return super()._add_tokens(_a      ,       special_tokens=_a							)


