





'''simple docstring'''

import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class  a_					(    snake_case_				,   unittest.TestCase	):

    '''simple docstring'''
    UpperCamelCase			   =			BertweetTokenizer
    UpperCamelCase			   =			False


    def       snake_case_(		self	)		->       str:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        _SCREAMING_SNAKE_CASE     =      ["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
        _SCREAMING_SNAKE_CASE     =      dict(zip(A			,		range(len(A	)	)	)	)
        _SCREAMING_SNAKE_CASE     =      ["""#version: 0.2""", """a m</w>"""]
        _SCREAMING_SNAKE_CASE     =      {"""unk_token""": """<unk>"""}

        _SCREAMING_SNAKE_CASE     =      os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES["""vocab_file"""]	)
        _SCREAMING_SNAKE_CASE     =      os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES["""merges_file"""]	)
        with open(self.vocab_file			,		"""w"""			,		encoding="""utf-8"""	) as fp:
            for token in vocab_tokens:
                fp.write(f'{token} {vocab_tokens[token]}\n'	)
        with open(self.merges_file			,		"""w"""			,		encoding="""utf-8"""	) as fp:
            fp.write("""\n""".join(A	)	)


    def       snake_case_(		self			,		**A	)		->       str:
        kwargs.update(self.special_tokens_map	)
        return BertweetTokenizer.from_pretrained(self.tmpdirname			,		**A	)


    def       snake_case_(		self			,		A	)		->       Dict:
        _SCREAMING_SNAKE_CASE     =      """I am VinAI Research"""
        _SCREAMING_SNAKE_CASE     =      """I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
        return input_text, output_text





    def       snake_case_(		self	)		->       Dict:
        _SCREAMING_SNAKE_CASE     =      BertweetTokenizer(self.vocab_file			,		self.merges_file			,		**self.special_tokens_map	)
        _SCREAMING_SNAKE_CASE     =      """I am VinAI Research"""
        _SCREAMING_SNAKE_CASE     =      """I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
        _SCREAMING_SNAKE_CASE     =      tokenizer.tokenize(A	)
        self.assertListEqual(A			,		A	)

        _SCREAMING_SNAKE_CASE     =      tokens + [tokenizer.unk_token]

        _SCREAMING_SNAKE_CASE     =      [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A	)			,		A	)











'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase_         =			{
    """configuration_esm""": ["""ESM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """EsmConfig"""],
    """tokenization_esm""": ["""EsmTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EsmForMaskedLM""",
          """EsmForSequenceClassification""",
          """EsmForTokenClassification""",
          """EsmModel""",
          """EsmPreTrainedModel""",
      ]
      lowercase_         =			["""EsmForProteinFolding""", """EsmFoldPreTrainedModel"""]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEsmForMaskedLM""",
          """TFEsmForSequenceClassification""",
          """TFEsmForTokenClassification""",
          """TFEsmModel""",
          """TFEsmPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
      from .tokenization_esm import EsmTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_esm import (
                ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                EsmForMaskedLM,
                EsmForSequenceClassification,
                EsmForTokenClassification,
                EsmModel,
                EsmPreTrainedModel,
            )
            from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_esm import (
                TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEsmForMaskedLM,
                TFEsmForSequenceClassification,
                TFEsmForTokenClassification,
                TFEsmModel,
                TFEsmPreTrainedModel,
            )


else:
      import sys

      lowercase_         =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)



