




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(		lowercase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			Union[str, Any]							=     MPNetTokenizer
     __UpperCAmelCase						:			str							=     MPNetTokenizerFast
     __UpperCAmelCase						:			Any							=     True
     __UpperCAmelCase						:			int							=     True

     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Optional[int]:






         """simple docstring"""
         super().setUp()

         a__  			=							[
             """[UNK]""",
             """[CLS]""",
             """[SEP]""",
             """[PAD]""",
             """[MASK]""",
             """want""",
             """##want""",
             """##ed""",
             """wa""",
             """un""",
             """runn""",
             """##ing""",
             """,""",
             """low""",
             """lowest""",
         ]
         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 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							int     )      ->      List[Any]:






         """simple docstring"""
         a__  			=							"""UNwant\u00E9d,running"""
         a__  			=							"""unwanted, running"""
         return input_text, output_text

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							self.tokenizer_class(self.vocab_file     )

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






     @slow
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Optional[int]:






         """simple docstring"""
         a__  			=							self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""     )

         a__  			=							tokenizer.encode("""sequence builders"""    ,					add_special_tokens=A     )
         a__  			=							tokenizer.encode("""multi-sequence build"""    ,					add_special_tokens=A     )

         a__  			=							tokenizer.build_inputs_with_special_tokens(A     )
         a__  			=							tokenizer.build_inputs_with_special_tokens(A    ,					A     )

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












# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_    :       int       =							{
    '''agents''': ['''Agent''', '''AzureOpenAiAgent''', '''HfAgent''', '''LocalAgent''', '''OpenAiAgent'''],
    '''base''': ['''PipelineTool''', '''RemoteTool''', '''Tool''', '''launch_gradio_demo''', '''load_tool'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_    :       int       =							['''DocumentQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''ImageCaptioningTool''']
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							['''ImageQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       str       =							['''ImageSegmentationTool''']
 SCREAMING_SNAKE_CASE_    :       Any       =							['''SpeechToTextTool''']
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							['''TextClassificationTool''']
 SCREAMING_SNAKE_CASE_    :       List[Any]       =							['''TextQuestionAnsweringTool''']
 SCREAMING_SNAKE_CASE_    :       Dict       =							['''TextSummarizationTool''']
 SCREAMING_SNAKE_CASE_    :       Optional[int]       =							['''TextToSpeechTool''']
 SCREAMING_SNAKE_CASE_    :       Dict       =							['''TranslationTool''']

if TYPE_CHECKING:
 from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
 from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .document_question_answering import DocumentQuestionAnsweringTool
  from .image_captioning import ImageCaptioningTool
  from .image_question_answering import ImageQuestionAnsweringTool
  from .image_segmentation import ImageSegmentationTool
  from .speech_to_text import SpeechToTextTool
  from .text_classification import TextClassificationTool
  from .text_question_answering import TextQuestionAnsweringTool
  from .text_summarization import TextSummarizationTool
  from .text_to_speech import TextToSpeechTool
  from .translation import TranslationTool
else:
 import sys

 SCREAMING_SNAKE_CASE_    :       Optional[Any]       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






