






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

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 						a_ (      _a    ,		unittest.TestCase					):







      '''simple docstring'''

      lowerCamelCase__			:							Union[str, Any]					=      HerbertTokenizer
      lowerCamelCase__			:							Any					=      HerbertTokenizerFast
      lowerCamelCase__			:							str					=      True




      def      a__				(self   ):

       '''simple docstring'''



       super().setUp()

       # Use a simpler test file without japanese/chinese characters
       with open(f'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt''',	encoding='utf-8'   ) as f_data:
        lowerCamelCase__	:     Dict								=		f_data.read().replace('\n\n',	'\n'   ).strip()

       lowerCamelCase__	:     Any								=		[
           """<s>""",
           """</s>""",
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """w</w>""",
           """r</w>""",
           """t</w>""",
           """lo""",
           """low""",
           """er</w>""",
           """low</w>""",
           """lowest</w>""",
           """newer</w>""",
           """wider</w>""",
           """,</w>""",
           """<unk>""",
       ]
       lowerCamelCase__	:     Optional[int]								=		dict(zip(_SCREAMING_SNAKE_CASE,	range(len(_SCREAMING_SNAKE_CASE   )   )   )   )
       lowerCamelCase__	:     List[Any]								=		["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

       lowerCamelCase__	:     Optional[Any]								=		os.path.join(self.tmpdirname,	VOCAB_FILES_NAMES['vocab_file']   )
       lowerCamelCase__	:     str								=		os.path.join(self.tmpdirname,	VOCAB_FILES_NAMES['merges_file']   )
       with open(self.vocab_file,	'w'   ) as fp:
        fp.write(json.dumps(_SCREAMING_SNAKE_CASE   )   )
       with open(self.merges_file,	'w'   ) as fp:
        fp.write('\n'.join(_SCREAMING_SNAKE_CASE   )   )




      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		"""lower newer"""
       lowerCamelCase__	:     List[Any]								=		"""lower newer"""
       return input_text, output_text




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		self.tokenizer_class(vocab_file=self.vocab_file,	merges_file=self.merges_file   )

       lowerCamelCase__	:     Tuple								=		"""lower"""
       lowerCamelCase__	:     List[Any]								=		["""low""", """er</w>"""]
       lowerCamelCase__	:     Dict								=		tokenizer.tokenize(_SCREAMING_SNAKE_CASE   )
       self.assertListEqual(_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE   )

       lowerCamelCase__	:     int								=		tokens + ["""<unk>"""]
       lowerCamelCase__	:     List[Any]								=		[1_6, 1_7, 2_3]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   ),	_SCREAMING_SNAKE_CASE   )




      def      a__				(self   ):

       '''simple docstring'''



       if not self.test_rust_tokenizer:
        return

       lowerCamelCase__	:     Tuple								=		self.get_tokenizer()
       lowerCamelCase__	:     Optional[Any]								=		self.get_rust_tokenizer()

       lowerCamelCase__	:     List[Any]								=		"""lower,newer"""

       lowerCamelCase__	:     Tuple								=		tokenizer.tokenize(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase__	:     Optional[Any]								=		rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE   )
       self.assertListEqual(_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE   )

       lowerCamelCase__	:     Optional[Any]								=		tokenizer.encode(_SCREAMING_SNAKE_CASE,	add_special_tokens=_SCREAMING_SNAKE_CASE   )
       lowerCamelCase__	:     Optional[Any]								=		rust_tokenizer.encode(_SCREAMING_SNAKE_CASE,	add_special_tokens=_SCREAMING_SNAKE_CASE   )
       self.assertListEqual(_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE   )

       lowerCamelCase__	:     Any								=		self.get_rust_tokenizer()
       lowerCamelCase__	:     Tuple								=		tokenizer.encode(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase__	:     Dict								=		rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   )
       self.assertListEqual(_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE   )





      @slow
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Tuple								=		self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'   )

       lowerCamelCase__	:     Dict								=		tokenizer.encode('konstruowanie sekwencji',	add_special_tokens=_SCREAMING_SNAKE_CASE   )
       lowerCamelCase__	:     Union[str, Any]								=		tokenizer.encode('konstruowanie wielu sekwencji',	add_special_tokens=_SCREAMING_SNAKE_CASE   )

       lowerCamelCase__	:     Any								=		tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase__	:     int								=		tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE   )

       assert encoded_sentence == [0] + text + [2]
       assert encoded_pair == [0] + text + [2] + text_a + [2]



'''simple docstring'''


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)




