


"""simple docstring"""





import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 		snake_case_(       a__							,					unittest.TestCase			):
     __UpperCamelCase  =					PhobertTokenizer
     __UpperCamelCase  =					False






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           lowerCAmelCase					:	str       =   ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
           lowerCAmelCase					:	Dict       =   dict(zip(UpperCamelCase_					,				range(len(UpperCamelCase_				)				)				)				)
           lowerCAmelCase					:	Tuple       =   ['''#version: 0.2''', '''l à</w>''']
           lowerCAmelCase					:	Any       =   {'''unk_token''': '''<unk>'''}

           lowerCAmelCase					:	int       =   os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''vocab_file''']				)
           lowerCAmelCase					:	Dict       =   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(UpperCamelCase_				)				)






     def      lowerCamelCase__     (	self  :					List[str]					,				**UpperCamelCase_  :					Tuple				):
           kwargs.update(self.special_tokens_map				)
           return PhobertTokenizer.from_pretrained(self.tmpdirname					,				**UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					Any				):
           lowerCAmelCase					:	Union[str, Any]       =   '''Tôi là VinAI Research'''
           lowerCAmelCase					:	str       =   '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
           return input_text, output_text






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           lowerCAmelCase					:	Optional[int]       =   PhobertTokenizer(self.vocab_file					,				self.merges_file					,				**self.special_tokens_map				)
           lowerCAmelCase					:	int       =   '''Tôi là VinAI Research'''
           lowerCAmelCase					:	Any       =   '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
           lowerCAmelCase					:	Tuple       =   tokenizer.tokenize(UpperCamelCase_				)
           print(UpperCamelCase_				)
           self.assertListEqual(UpperCamelCase_					,				UpperCamelCase_				)

           lowerCAmelCase					:	Dict       =   tokens + [tokenizer.unk_token]

           lowerCAmelCase					:	Tuple       =   [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_				)					,				UpperCamelCase_				)







"""simple docstring"""





# 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class 		snake_case_(       a__			):
     __UpperCamelCase  =					'''philschmid/bart-large-cnn-samsum'''
     __UpperCamelCase  =					(
         '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
         '''and returns a summary of the text.'''
     )
     __UpperCamelCase  =					'''summarizer'''
     __UpperCamelCase  =					AutoTokenizer
     __UpperCamelCase  =					AutoModelForSeqaSeqLM

     __UpperCamelCase  =					['''text''']
     __UpperCamelCase  =					['''text''']






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					int				):
           return self.pre_processor(UpperCamelCase_					,				return_tensors='''pt'''					,				truncation=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str				):
           return self.model.generate(**UpperCamelCase_				)[0]






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Tuple				):
           return self.pre_processor.decode(UpperCamelCase_					,				skip_special_tokens=UpperCamelCase_					,				clean_up_tokenization_spaces=UpperCamelCase_				)


