



import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
@require_tokenizers
class lowerCAmelCase_       (   unittest.TestCase			):







	'''simple docstring'''




	A_       :     List[Any]									=		EsmTokenizer

	def 				_A			(	self       ):



				'''simple docstring'''


				super().setUp()
				a__					=  tempfile.mkdtemp()
				# fmt: off
				a__					=  ["""<cls>""", """<pad>""", """<eos>""", """<unk>""", """L""", """A""", """G""", """V""", """S""", """E""", """R""", """T""", """I""", """D""", """P""", """K""", """Q""", """N""", """F""", """Y""", """M""", """H""", """W""", """C""", """X""", """B""", """U""", """Z""", """O""", """.""", """-""", """<null_1>""", """<mask>"""]  # noqa: E501
				# fmt: on
				a__					=  os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""vocab_file"""]       )
				with open(self.vocab_file   ,       """w"""   ,       encoding="""utf-8"""       ) as vocab_writer:
							vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )

	def 				_A			(	self   ,       **lowerCamelCase       ):



				'''simple docstring'''


				return [self.get_tokenizer(**lowerCamelCase__       )]

	def 				_A			(	self   ,       **lowerCamelCase       ):



				'''simple docstring'''


				return self.tokenizer_class.from_pretrained(self.tmpdirname   ,       **lowerCamelCase__       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.tokenizer_class(self.vocab_file       )

				a__					=  tokenizer.tokenize("""LAGVS"""       )
				self.assertListEqual(lowerCamelCase__   ,       ["""L""", """A""", """G""", """V""", """S"""]       )
				self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__       )   ,       [4, 5, 6, 7, 8]       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.tokenizer_class(self.vocab_file       )

				a__					=  """LAGVS"""
				self.assertListEqual(tokenizer.encode(lowerCamelCase__       )   ,       [0, 4, 5, 6, 7, 8, 2]       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.tokenizer_class(self.vocab_file       )

				a__					=  ["""LAGVS""", """WCB"""]
				a__					=  tokenizer(lowerCamelCase__   ,       padding=lowerCamelCase__       )["""input_ids"""]

				self.assertListEqual(lowerCamelCase__   ,       [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.tokenizer_class(self.vocab_file       )

				a__					=  ["""LAGVS""", """WCB"""]
				a__					=  tokenizer(lowerCamelCase__   ,       padding=lowerCamelCase__       )["""input_ids"""]

				self.assertListEqual(lowerCamelCase__   ,       [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]       )







	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_tokenizers(fast=lowerCamelCase__       )
				for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'       ):
										a__					=  """<unk>"""
										a__					=  """<mask>"""

										a__					=  tokenizer.tokenize(lowerCamelCase__       )
										a__					=  tokenizer.tokenize(lowerCamelCase__       )

										self.assertEqual(len(lowerCamelCase__       )   ,       1       )
										self.assertEqual(len(lowerCamelCase__       )   ,       1       )
										self.assertEqual(token_a[0]   ,       lowerCamelCase__       )
										self.assertEqual(token_a[0]   ,       lowerCamelCase__       )








from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_		  =			{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_		  =			["""MarkupLMTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_		  =			[
          """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MarkupLMForQuestionAnswering""",
          """MarkupLMForSequenceClassification""",
          """MarkupLMForTokenClassification""",
          """MarkupLMModel""",
          """MarkupLMPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
      from .feature_extraction_markuplm import MarkupLMFeatureExtractor
      from .processing_markuplm import MarkupLMProcessor
      from .tokenization_markuplm import MarkupLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_markuplm_fast import MarkupLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_markuplm import (
                MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarkupLMForQuestionAnswering,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMModel,
                MarkupLMPreTrainedModel,
            )


else:
      import sys

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


