code: 
  
  
  
  
  '''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
  
  
  _lowerCamelCase							:		Dict										=						'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				"""google/flan-t5-base"""
  					lowercase__					=				(
  					    """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
  					    """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
  					)
  					lowercase__					=				"""text_qa"""
  					lowercase__					=				AutoTokenizer
  					lowercase__					=				AutoModelForSeqaSeqLM
  
  					lowercase__					=				["""text""", """text"""]
  					lowercase__					=				["""text"""]
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase				):
  							_snake_case			 = QA_PROMPT.format(text=__UpperCamelCase,						question=__UpperCamelCase				)
  							return self.pre_processor(__UpperCamelCase,						return_tensors="pt"				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = self.model.generate(**__UpperCamelCase				)
  
  							_snake_case ,					_snake_case			 = inputs["input_ids"].shape
  							_snake_case			 = output_ids.shape[0]
  
  							return output_ids.reshape(__UpperCamelCase,						out_b // in_b,						*output_ids.shape[1:]				)[0][0]
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							return self.pre_processor.decode(__UpperCamelCase,						skip_special_tokens=__UpperCamelCase,						clean_up_tokenization_spaces=__UpperCamelCase				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import json
  import os
  import unittest
  
  from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
      VOCAB_FILES_NAMES,
      GPTNeoXJapaneseTokenizer,
  )
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class   lowerCamelCase_							( UpperCAmelCase_, unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				GPTNeoXJapaneseTokenizer
  					lowercase__					=				False
  					lowercase__					=				{"""do_clean_text""": False, """add_prefix_space""": False}
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							super().setUp()
  
  							_snake_case			 = [
  							    "こん",
  							    "こんに",
  							    "にちは",
  							    "ばんは",
  							    "世界,㔺界",
  							    "、",
  							    "。",
  							    "<BR>",
  							    "<SP>",
  							    "<TAB>",
  							    "<URL>",
  							    "<EMAIL>",
  							    "<TEL>",
  							    "<DATE>",
  							    "<PRICE>",
  							    "<BLOCK>",
  							    "<KIGOU>",
  							    "<U2000U2BFF>",
  							    "<|emoji1|>",
  							    "<unk>",
  							    "<|startoftext|>",
  							    "<|endoftext|>",
  							]
  							_snake_case			 = {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
  							_snake_case			 = {"unk_token": "<unk>"}
  
  							_snake_case			 = os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES["vocab_file"]				)
  							_snake_case			 = os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES["emoji_file"]				)
  							with open(self.vocab_file,						"w",						encoding="utf-8"				) as vocab_writer:
  									vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)
  							with open(self.emoji_file,						"w"				) as emoji_writer:
  									emoji_writer.write(json.dumps(__UpperCamelCase				)				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						**__UpperCamelCase				):
  							kwargs.update(self.special_tokens_map				)
  							return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname,						**__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = "こんにちは、世界。 \nこんばんは、㔺界。😀"
  							_snake_case			 = "こんにちは、世界。 \nこんばんは、世界。😀"
  							return input_text, output_text
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case ,					_snake_case			 = self.get_input_output_texts(__UpperCamelCase				)
  							_snake_case			 = tokenizer.encode(__UpperCamelCase,						add_special_tokens=__UpperCamelCase				)
  							_snake_case			 = tokenizer.decode(__UpperCamelCase,						clean_up_tokenization_spaces=__UpperCamelCase				)
  							return text, ids
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							pass  # TODO add if relevant
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							pass  # TODO add if relevant
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							pass  # TODO add if relevant
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.get_tokenizer()
  
  							# Testing tokenization
  							_snake_case			 = "こんにちは、世界。　こんばんは、㔺界。"
  							_snake_case			 = ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
  							_snake_case			 = tokenizer.tokenize(__UpperCamelCase				)
  							self.assertListEqual(__UpperCamelCase,						__UpperCamelCase				)
  
  							# Testing conversion to ids without special tokens
  							_snake_case			 = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
  							_snake_case			 = tokenizer.convert_tokens_to_ids(__UpperCamelCase				)
  							self.assertListEqual(__UpperCamelCase,						__UpperCamelCase				)
  
  							# Testing conversion to ids with special tokens
  							_snake_case			 = tokens + [tokenizer.unk_token]
  							_snake_case			 = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
  							_snake_case			 = tokenizer.convert_tokens_to_ids(__UpperCamelCase				)
  							self.assertListEqual(__UpperCamelCase,						__UpperCamelCase				)
  
  
  
  
  
  
  					@slow
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b"				)
  
  							_snake_case			 = tokenizer.encode("ありがとう。",						add_special_tokens=__UpperCamelCase				)
  							_snake_case			 = tokenizer.encode("どういたしまして。",						add_special_tokens=__UpperCamelCase				)
  
  							_snake_case			 = tokenizer.build_inputs_with_special_tokens(__UpperCamelCase				)
  							_snake_case			 = tokenizer.build_inputs_with_special_tokens(__UpperCamelCase,						__UpperCamelCase				)
  
  							assert encoded_sentence == ids_a
  							assert encoded_pair == ids_a + ids_a
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							# Intentionally convert some words to accommodate character fluctuations unique to Japanese
  							pass
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							# tokenizer has no padding token
  							pass
  
  
  
  
  
  