



"""simple docstring"""
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   List[Any]						=						MvpTokenizer
   __lowerCAmelCase  :   Any						=						MvpTokenizerFast
   __lowerCAmelCase  :   Union[str, Any]						=						True
   __lowerCAmelCase  :   Optional[int]						=						filter_roberta_detectors

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    super().setUp()
    UpperCAmelCase	:					List[Any]	=	[
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """\u0120""",
        """\u0120l""",
        """\u0120n""",
        """\u0120lo""",
        """\u0120low""",
        """er""",
        """\u0120lowest""",
        """\u0120newer""",
        """\u0120wider""",
        """<unk>""",
    ]
    UpperCAmelCase	:					int	=	dict(zip(_SCREAMING_SNAKE_CASE      ,     range(len(_SCREAMING_SNAKE_CASE						)						)						)						)
    UpperCAmelCase	:					Optional[Any]	=	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
    UpperCAmelCase	:					Optional[Any]	=	{"""unk_token""": """<unk>"""}

    UpperCAmelCase	:					int	=	os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
    UpperCAmelCase	:					str	=	os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES["""merges_file"""]						)
    with open(self.vocab_file      ,     """w"""      ,     encoding="""utf-8"""						) as fp:
     fp.write(json.dumps(_SCREAMING_SNAKE_CASE						) + """\n"""						)
    with open(self.merges_file      ,     """w"""      ,     encoding="""utf-8"""						) as fp:
     fp.write("""\n""".join(_SCREAMING_SNAKE_CASE						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     **_SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    kwargs.update(self.special_tokens_map						)
    return self.tokenizer_class.from_pretrained(self.tmpdirname      ,     **_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     **_SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    kwargs.update(self.special_tokens_map						)
    return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,     **_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    return "lower newer", "lower newer"

   @cached_property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""						)

   @cached_property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""						)

   @require_torch
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    UpperCAmelCase	:					Optional[int]	=	[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase	:					Dict	=	tokenizer(_SCREAMING_SNAKE_CASE      ,     max_length=len(_SCREAMING_SNAKE_CASE						)      ,     padding=_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

     self.assertEqual((2, 9)      ,     batch.input_ids.shape						)
     self.assertEqual((2, 9)      ,     batch.attention_mask.shape						)
     UpperCAmelCase	:					List[Any]	=	batch.input_ids.tolist()[0]
     self.assertListEqual(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
     # Test that special tokens are reset

   @require_torch
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase	:					Optional[Any]	=	tokenizer(_SCREAMING_SNAKE_CASE      ,     padding=_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
     # check if input_ids are returned and no labels
     self.assertIn("""input_ids"""      ,     _SCREAMING_SNAKE_CASE						)
     self.assertIn("""attention_mask"""      ,     _SCREAMING_SNAKE_CASE						)
     self.assertNotIn("""labels"""      ,     _SCREAMING_SNAKE_CASE						)
     self.assertNotIn("""decoder_attention_mask"""      ,     _SCREAMING_SNAKE_CASE						)

   @require_torch
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	[
        """Summary of the text.""",
        """Another summary.""",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase	:					str	=	tokenizer(text_target=_SCREAMING_SNAKE_CASE      ,     max_length=32      ,     padding="""max_length"""      ,     return_tensors="""pt"""						)
     self.assertEqual(32      ,     targets["""input_ids"""].shape[1]						)

   @require_torch
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase	:					List[Any]	=	tokenizer(
         ["""I am a small frog""" * 1024, """I am a small frog"""]      ,     padding=_SCREAMING_SNAKE_CASE      ,     truncation=_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
     self.assertEqual(batch.input_ids.shape      ,     (2, 1024)						)

   @require_torch
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	["""A long paragraph for summarization."""]
    UpperCAmelCase	:					Any	=	[
        """Summary of the text.""",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     UpperCAmelCase	:					List[str]	=	tokenizer(_SCREAMING_SNAKE_CASE      ,     text_target=_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
     UpperCAmelCase	:					Optional[Any]	=	inputs["""input_ids"""]
     UpperCAmelCase	:					Optional[Any]	=	inputs["""labels"""]
     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()						)
     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()						)
     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()						)
     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    pass




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
      UpperCAmelCase	:					Any	=	self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					Union[str, Any]	=	self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					Union[str, Any]	=	"""A, <mask> AllenNLP sentence."""
      UpperCAmelCase	:					str	=	tokenizer_r.encode_plus(_SCREAMING_SNAKE_CASE      ,     add_special_tokens=_SCREAMING_SNAKE_CASE      ,     return_token_type_ids=_SCREAMING_SNAKE_CASE						)
      UpperCAmelCase	:					str	=	tokenizer_p.encode_plus(_SCREAMING_SNAKE_CASE      ,     add_special_tokens=_SCREAMING_SNAKE_CASE      ,     return_token_type_ids=_SCREAMING_SNAKE_CASE						)

      # token_type_ids should put 0 everywhere
      self.assertEqual(sum(tokens_r["""token_type_ids"""]						)      ,     sum(tokens_p["""token_type_ids"""]						)						)

      # attention_mask should put 1 everywhere, so sum over length should be 1
      self.assertEqual(
          sum(tokens_r["""attention_mask"""]						) / len(tokens_r["""attention_mask"""]						)      ,     sum(tokens_p["""attention_mask"""]						) / len(tokens_p["""attention_mask"""]						)      ,     )

      UpperCAmelCase	:					Any	=	tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]						)
      UpperCAmelCase	:					Dict	=	tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]						)

      # Rust correctly handles the space before the mask while python doesnt
      self.assertSequenceEqual(tokens_p["""input_ids"""]      ,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]						)
      self.assertSequenceEqual(tokens_r["""input_ids"""]      ,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]						)

      self.assertSequenceEqual(
          _SCREAMING_SNAKE_CASE      ,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)
      self.assertSequenceEqual(
          _SCREAMING_SNAKE_CASE      ,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)





"""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:   str								=		logging.get_logger(__name__)

A:   List[Any]								=		{"vocab_file": "vocab.txt"}

A:   List[str]								=		{
    "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:   Dict								=		{
    "facebook/esm2_t6_8M_UR50D": 1_0_2_4,
    "facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}

def       _snake_case						(						UpperCamelCase						:		int						):
 with open(UpperCamelCase			,		"""r"""						) as f:
  UpperCAmelCase	:					int	=	f.read().splitlines()
  return [l.strip() for l in lines]



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Union[str, Any]						=						VOCAB_FILES_NAMES
   __lowerCAmelCase  :   Dict						=						PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase  :   List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase  :   str						=						['input_ids', 'attention_mask']

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE="<unk>"      ,     _SCREAMING_SNAKE_CASE="<cls>"      ,     _SCREAMING_SNAKE_CASE="<pad>"      ,     _SCREAMING_SNAKE_CASE="<mask>"      ,     _SCREAMING_SNAKE_CASE="<eos>"      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Any:





    '''simple docstring'''




    super().__init__(**_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	load_vocab_file(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	dict(enumerate(self.all_tokens						)						)
    UpperCAmelCase	:					List[str]	=	{tok: ind for ind, tok in enumerate(self.all_tokens						)}
    UpperCAmelCase	:					Any	=	unk_token
    UpperCAmelCase	:					str	=	cls_token
    UpperCAmelCase	:					int	=	pad_token
    UpperCAmelCase	:					Tuple	=	mask_token
    UpperCAmelCase	:					str	=	eos_token
    UpperCAmelCase	:					List[str]	=	self.all_tokens
    self._create_trie(self.unique_no_split_tokens						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,     self.unk_token						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,     self._token_to_id.get(self.unk_token						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    return text.split()

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=False						)  ->      Union[str, Any]:





    '''simple docstring'''




    return len(self._id_to_token						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    return {token: i for i, token in enumerate(self.all_tokens						)}

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,     self._token_to_id.get(self.unk_token						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,     self.unk_token						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	[self.cls_token_id]
    UpperCAmelCase	:					Tuple	=	[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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = False						)  ->      List[int]:





    '''simple docstring'''




    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]
    UpperCAmelCase	:					str	=	[1] + ([0] * len(_SCREAMING_SNAKE_CASE						)) + [1]
    if token_ids_a is not None:
     mask += [0] * len(_SCREAMING_SNAKE_CASE						) + [1]
    return mask

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	os.path.join(_SCREAMING_SNAKE_CASE      ,     (filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""						)
    with open(_SCREAMING_SNAKE_CASE      ,     """w"""						) as f:
     f.write("""\n""".join(self.all_tokens						)						)
    return (vocab_file,)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.get_vocab_size(with_added_tokens=_SCREAMING_SNAKE_CASE						)




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = False						)  ->      int:





    '''simple docstring'''




    return super()._add_tokens(_SCREAMING_SNAKE_CASE      ,     special_tokens=_SCREAMING_SNAKE_CASE						)
