

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  UpperCamelCase_      (	__UpperCAmelCase		,  unittest.TestCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   MPNetTokenizer
  SCREAMING_SNAKE_CASE__					:			str					    =   MPNetTokenizerFast
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   True
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   True



  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			str:
      super().setUp()

      SCREAMING_SNAKE_CASE__         =    [
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "[PAD]",
          "[MASK]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      SCREAMING_SNAKE_CASE__         =    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   _UpperCamelCase     (						self     :					Any  ,	A_     :					Dict						)       ->			int:
      SCREAMING_SNAKE_CASE__         =    "UNwant\u00E9d,running"
      SCREAMING_SNAKE_CASE__         =    "unwanted, running"
      return input_text, output_text



  def   _UpperCamelCase     (						self     :					str						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    self.tokenizer_class(self.vocab_file						)

      SCREAMING_SNAKE_CASE__         =    tokenizer.tokenize("UNwant\u00E9d,running"						)
      self.assertListEqual(UpperCAmelCase_  ,	["un", "##want", "##ed", ",", "runn", "##ing"]						)
      self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_						)  ,	[9, 6, 7, 12, 10, 11]						)







  @slow
  def   _UpperCamelCase     (						self     :					List[str]						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    self.tokenizer_class.from_pretrained("microsoft/mpnet-base"						)

      SCREAMING_SNAKE_CASE__         =    tokenizer.encode("sequence builders"  ,	add_special_tokens=UpperCAmelCase_						)
      SCREAMING_SNAKE_CASE__         =    tokenizer.encode("multi-sequence build"  ,	add_special_tokens=UpperCAmelCase_						)

      SCREAMING_SNAKE_CASE__         =    tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_						)
      SCREAMING_SNAKE_CASE__         =    tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_  ,	UpperCAmelCase_						)

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








from typing import TYPE_CHECKING

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


UpperCAmelCase_    ={"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =["""ViltFeatureExtractor"""]
		UpperCAmelCase_    =["""ViltImageProcessor"""]
		UpperCAmelCase_    =["""ViltProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =[
		    """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ViltForImageAndTextRetrieval""",
		    """ViltForImagesAndTextClassification""",
		    """ViltForTokenClassification""",
		    """ViltForMaskedLM""",
		    """ViltForQuestionAnswering""",
		    """ViltLayer""",
		    """ViltModel""",
		    """ViltPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

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



