


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    unittest.TestCase			):
      snake_case__				:     Optional[int]        =						RobertaTokenizer
      snake_case__				:     Tuple        =						RobertaTokenizerFast
      snake_case__				:     Dict        =						True
      snake_case__				:     Union[str, Any]        =						{'''cls_token''': '''<s>'''}
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			List[Any]:
             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             a_					:		Dict							=  [
                 'l',
                 'o',
                 'w',
                 'e',
                 'r',
                 's',
                 't',
                 'i',
                 'd',
                 'n',
                 '\u0120',
                 '\u0120l',
                 '\u0120n',
                 '\u0120lo',
                 '\u0120low',
                 'er',
                 '\u0120lowest',
                 '\u0120newer',
                 '\u0120wider',
                 '<unk>',
             ]
             a_					:		Any							=  dict(zip(SCREAMING_SNAKE_CASE__     ,							range(len(SCREAMING_SNAKE_CASE__     )     )     )     )
             a_					:		Dict							=  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
             a_					:		Optional[int]							=  {'unk_token': '<unk>'}

             a_					:		Optional[int]							=  os.path.join(self.tmpdirname     ,							VOCAB_FILES_NAMES['vocab_file']     )
             a_					:		int							=  os.path.join(self.tmpdirname     ,							VOCAB_FILES_NAMES['merges_file']     )
             with open(self.vocab_file     ,							'w'     ,							encoding='utf-8'     ) as fp:
                    fp.write(json.dumps(SCREAMING_SNAKE_CASE__     ) + '\n'     )
             with open(self.merges_file     ,							'w'     ,							encoding='utf-8'     ) as fp:
                    fp.write('\n'.join(SCREAMING_SNAKE_CASE__     )     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     )		->			Optional[Any]:
             kwargs.update(self.special_tokens_map     )
             return self.tokenizer_class.from_pretrained(self.tmpdirname     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							**SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Optional[int]:
             kwargs.update(self.special_tokens_map     )
             return RobertaTokenizerFast.from_pretrained(self.tmpdirname     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Any:
             a_					:		str							=  'lower newer'
             a_					:		Optional[int]							=  'lower newer'
             return input_text, output_text
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Tuple:
             a_					:		Tuple							=  self.tokenizer_class(self.vocab_file     ,							self.merges_file     ,							**self.special_tokens_map     )
             a_					:		List[Any]							=  'lower newer'
             a_					:		List[Any]							=  ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
             a_					:		Union[str, Any]							=  tokenizer.tokenize(SCREAMING_SNAKE_CASE__     )  # , add_prefix_space=True)
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             a_					:		Dict							=  tokens + [tokenizer.unk_token]
             a_					:		int							=  [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Dict:
             a_					:		Tuple							=  self.get_tokenizer()

             self.assertListEqual(tokenizer.encode('Hello world!'     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )     ,							[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]     )
             self.assertListEqual(
                 tokenizer.encode('Hello world! cécé herlolip 418'     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )     ,							[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]     ,							)
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[int]:
             a_					:		str							=  self.tokenizer_class.from_pretrained('roberta-base'     )

             a_					:		Optional[int]							=  tokenizer.encode('sequence builders'     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  tokenizer.encode('multi-sequence build'     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )

             a_					:		Any							=  tokenizer.encode(
                 'sequence builders'     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     ,							add_prefix_space=SCREAMING_SNAKE_CASE__     )
             a_					:		List[str]							=  tokenizer.encode(
                 'sequence builders'     ,							'multi-sequence build'     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     ,							add_prefix_space=SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__     )
             a_					:		int							=  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             assert encoded_sentence == encoded_text_from_decode
             assert encoded_pair == encoded_pair_from_decode
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			str:
             a_					:		List[Any]							=  self.get_tokenizer()

             a_					:		List[Any]							=  'Encode this sequence.'
             a_					:		Tuple							=  tokenizer.byte_encoder[' '.encode('utf-8'     )[0]]

             # Testing encoder arguments
             a_					:		str							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     ,							add_prefix_space=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
             self.assertNotEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[Any]							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     ,							add_prefix_space=SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
             self.assertEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             tokenizer.add_special_tokens({'bos_token': '<s>'}     )
             a_					:		str							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  tokenizer.convert_ids_to_tokens(encoded[1]     )[0]
             self.assertNotEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             # Testing spaces after special tokens
             a_					:		Dict							=  '<mask>'
             tokenizer.add_special_tokens(
                 {'mask_token': AddedToken(SCREAMING_SNAKE_CASE__     ,							lstrip=SCREAMING_SNAKE_CASE__     ,							rstrip=SCREAMING_SNAKE_CASE__     )}     )  # mask token has a left space
             a_					:		Tuple							=  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  'Encode <mask> sequence'
             a_					:		Tuple							=  'Encode <mask>sequence'

             a_					:		Dict							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  encoded.index(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[Any]							=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
             self.assertEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  encoded.index(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
             self.assertNotEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			int:
             pass
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
                           a_					:		str							=  self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
                           a_					:		Optional[Any]							=  self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
                           a_					:		Any							=  'A, <mask> AllenNLP sentence.'
                           a_					:		Dict							=  tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     ,							return_token_type_ids=SCREAMING_SNAKE_CASE__     )
                           a_					:		Optional[int]							=  tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     ,							return_token_type_ids=SCREAMING_SNAKE_CASE__     )

                           # token_type_ids should put 0 everywhere
                           self.assertEqual(sum(tokens_r['token_type_ids']     )     ,							sum(tokens_p['token_type_ids']     )     )

                           # attention_mask should put 1 everywhere, so sum over length should be 1
                           self.assertEqual(
                               sum(tokens_r['attention_mask']     ) / len(tokens_r['attention_mask']     )     ,							sum(tokens_p['attention_mask']     ) / len(tokens_p['attention_mask']     )     ,							)

                           a_					:		Any							=  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']     )
                           a_					:		int							=  tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']     )

                           # Rust correctly handles the space before the mask while python doesnt
                           self.assertSequenceEqual(tokens_p['input_ids']     ,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]     )
                           self.assertSequenceEqual(tokens_r['input_ids']     ,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]     )

                           self.assertSequenceEqual(
                               SCREAMING_SNAKE_CASE__     ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']     )
                           self.assertSequenceEqual(
                               SCREAMING_SNAKE_CASE__     ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			str:
             for trim_offsets, add_prefix_space in itertools.product([True, False]     ,							repeat=2     ):
                    a_					:		int							=  self.rust_tokenizer_class.from_pretrained(
                        self.tmpdirname     ,							use_fast=SCREAMING_SNAKE_CASE__     ,							add_prefix_space=SCREAMING_SNAKE_CASE__     ,							trim_offsets=SCREAMING_SNAKE_CASE__     )

                    a_					:		Union[str, Any]							=  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()     )
                    a_					:		Dict							=  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()     )

                    self.assertEqual(pre_tokenizer_state['add_prefix_space']     ,							SCREAMING_SNAKE_CASE__     )

                    self.assertEqual(post_processor_state['add_prefix_space']     ,							SCREAMING_SNAKE_CASE__     )
                    self.assertEqual(post_processor_state['trim_offsets']     ,							SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
             # `trim_offsets`
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
                           a_					:		int							=  'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                           a_					:		List[Any]							=  F"""{text_of_1_token} {text_of_1_token}"""

                           a_					:		Optional[Any]							=  self.rust_tokenizer_class.from_pretrained(
                               SCREAMING_SNAKE_CASE__     ,							use_fast=SCREAMING_SNAKE_CASE__     ,							add_prefix_space=SCREAMING_SNAKE_CASE__     ,							trim_offsets=SCREAMING_SNAKE_CASE__     )
                           a_					:		Union[str, Any]							=  tokenizer_r(SCREAMING_SNAKE_CASE__     ,							return_offsets_mapping=SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
                           self.assertEqual(encoding.offset_mapping[0]     ,							(0, len(SCREAMING_SNAKE_CASE__     ))     )
                           self.assertEqual(
                               encoding.offset_mapping[1]     ,							(len(SCREAMING_SNAKE_CASE__     ) + 1, len(SCREAMING_SNAKE_CASE__     ) + 1 + len(SCREAMING_SNAKE_CASE__     ))     ,							)

                           a_					:		int							=  self.rust_tokenizer_class.from_pretrained(
                               SCREAMING_SNAKE_CASE__     ,							use_fast=SCREAMING_SNAKE_CASE__     ,							add_prefix_space=SCREAMING_SNAKE_CASE__     ,							trim_offsets=SCREAMING_SNAKE_CASE__     )
                           a_					:		Dict							=  tokenizer_r(SCREAMING_SNAKE_CASE__     ,							return_offsets_mapping=SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
                           self.assertEqual(encoding.offset_mapping[0]     ,							(0, len(SCREAMING_SNAKE_CASE__     ))     )
                           self.assertEqual(
                               encoding.offset_mapping[1]     ,							(len(SCREAMING_SNAKE_CASE__     ) + 1, len(SCREAMING_SNAKE_CASE__     ) + 1 + len(SCREAMING_SNAKE_CASE__     ))     ,							)

                           a_					:		Dict							=  self.rust_tokenizer_class.from_pretrained(
                               SCREAMING_SNAKE_CASE__     ,							use_fast=SCREAMING_SNAKE_CASE__     ,							add_prefix_space=SCREAMING_SNAKE_CASE__     ,							trim_offsets=SCREAMING_SNAKE_CASE__     )
                           a_					:		Union[str, Any]							=  tokenizer_r(SCREAMING_SNAKE_CASE__     ,							return_offsets_mapping=SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
                           self.assertEqual(encoding.offset_mapping[0]     ,							(0, len(SCREAMING_SNAKE_CASE__     ))     )
                           self.assertEqual(
                               encoding.offset_mapping[1]     ,							(len(SCREAMING_SNAKE_CASE__     ), len(SCREAMING_SNAKE_CASE__     ) + 1 + len(SCREAMING_SNAKE_CASE__     ))     ,							)

                           a_					:		Any							=  self.rust_tokenizer_class.from_pretrained(
                               SCREAMING_SNAKE_CASE__     ,							use_fast=SCREAMING_SNAKE_CASE__     ,							add_prefix_space=SCREAMING_SNAKE_CASE__     ,							trim_offsets=SCREAMING_SNAKE_CASE__     )
                           a_					:		Any							=  tokenizer_r(SCREAMING_SNAKE_CASE__     ,							return_offsets_mapping=SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
                           self.assertEqual(encoding.offset_mapping[0]     ,							(0, len(SCREAMING_SNAKE_CASE__     ))     )
                           self.assertEqual(
                               encoding.offset_mapping[1]     ,							(len(SCREAMING_SNAKE_CASE__     ), len(SCREAMING_SNAKE_CASE__     ) + 1 + len(SCREAMING_SNAKE_CASE__     ))     ,							)

                           a_					:		Tuple							=  F""" {text}"""

                           # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                           #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                           # )
                           # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                           # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                           # self.assertEqual(
                           #     encoding.offset_mapping[1],
                           #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                           # )

                           a_					:		List[Any]							=  self.rust_tokenizer_class.from_pretrained(
                               SCREAMING_SNAKE_CASE__     ,							use_fast=SCREAMING_SNAKE_CASE__     ,							add_prefix_space=SCREAMING_SNAKE_CASE__     ,							trim_offsets=SCREAMING_SNAKE_CASE__     )
                           a_					:		Optional[Any]							=  tokenizer_r(SCREAMING_SNAKE_CASE__     ,							return_offsets_mapping=SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
                           self.assertEqual(encoding.offset_mapping[0]     ,							(1, 1 + len(SCREAMING_SNAKE_CASE__     ))     )
                           self.assertEqual(
                               encoding.offset_mapping[1]     ,							(1 + len(SCREAMING_SNAKE_CASE__     ) + 1, 1 + len(SCREAMING_SNAKE_CASE__     ) + 1 + len(SCREAMING_SNAKE_CASE__     ))     ,							)

                           a_					:		Optional[int]							=  self.rust_tokenizer_class.from_pretrained(
                               SCREAMING_SNAKE_CASE__     ,							use_fast=SCREAMING_SNAKE_CASE__     ,							add_prefix_space=SCREAMING_SNAKE_CASE__     ,							trim_offsets=SCREAMING_SNAKE_CASE__     )
                           a_					:		Any							=  tokenizer_r(SCREAMING_SNAKE_CASE__     ,							return_offsets_mapping=SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
                           self.assertEqual(encoding.offset_mapping[0]     ,							(0, 1 + len(SCREAMING_SNAKE_CASE__     ))     )
                           self.assertEqual(
                               encoding.offset_mapping[1]     ,							(1 + len(SCREAMING_SNAKE_CASE__     ), 1 + len(SCREAMING_SNAKE_CASE__     ) + 1 + len(SCREAMING_SNAKE_CASE__     ))     ,							)

                           a_					:		Dict							=  self.rust_tokenizer_class.from_pretrained(
                               SCREAMING_SNAKE_CASE__     ,							use_fast=SCREAMING_SNAKE_CASE__     ,							add_prefix_space=SCREAMING_SNAKE_CASE__     ,							trim_offsets=SCREAMING_SNAKE_CASE__     )
                           a_					:		List[Any]							=  tokenizer_r(SCREAMING_SNAKE_CASE__     ,							return_offsets_mapping=SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
                           self.assertEqual(encoding.offset_mapping[0]     ,							(0, 1 + len(SCREAMING_SNAKE_CASE__     ))     )
                           self.assertEqual(
                               encoding.offset_mapping[1]     ,							(1 + len(SCREAMING_SNAKE_CASE__     ), 1 + len(SCREAMING_SNAKE_CASE__     ) + 1 + len(SCREAMING_SNAKE_CASE__     ))     ,							)



"""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.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
     from PIL import Image
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[Any]							=		"naver-clova-ix/donut-base-finetuned-docvqa"
     _UpperCamelCase							:			Dict							=		(
         "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
         "should be the document containing the information, as well as a `question` that is the question about the "
         "document. It returns a text that contains the answer to the question."
     )
     _UpperCamelCase							:			Optional[int]							=		"document_qa"
     _UpperCamelCase							:			Any							=		AutoProcessor
     _UpperCamelCase							:			Union[str, Any]							=		VisionEncoderDecoderModel

     _UpperCamelCase							:			Union[str, Any]							=		["image", "text"]
     _UpperCamelCase							:			List[str]							=		["text"]






     def __init__(		self				,     *a__				,     **a__  ):
       if not is_vision_available():
         raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""  )

       super().__init__(*a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
       _lowerCAmelCase						:       Dict  			=     task_prompt.replace("""{user_input}"""				,     a__  )
       _lowerCAmelCase						:       str  			=     self.pre_processor.tokenizer(
           a__				,     add_special_tokens=a__				,     return_tensors="""pt"""  ).input_ids
       _lowerCAmelCase						:       Dict  			=     self.pre_processor(a__				,     return_tensors="""pt"""  ).pixel_values

       return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}






     def     __A  (		self				,     a__  ):
       return self.model.generate(
           inputs["""pixel_values"""].to(self.device  )				,     decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device  )				,     max_length=self.model.decoder.config.max_position_embeddings				,     early_stopping=a__				,     pad_token_id=self.pre_processor.tokenizer.pad_token_id				,     eos_token_id=self.pre_processor.tokenizer.eos_token_id				,     use_cache=a__				,     num_beams=1				,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]				,     return_dict_in_generate=a__				,     ).sequences






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Tuple  			=     self.pre_processor.batch_decode(a__  )[0]
       _lowerCAmelCase						:       int  			=     sequence.replace(self.pre_processor.tokenizer.eos_token				,     """"""  )
       _lowerCAmelCase						:       List[str]  			=     sequence.replace(self.pre_processor.tokenizer.pad_token				,     """"""  )
       _lowerCAmelCase						:       List[str]  			=     re.sub(r"""<.*?>"""				,     """"""				,     a__				,     count=1  ).strip()  # remove first task start token
       _lowerCAmelCase						:       List[str]  			=     self.pre_processor.tokenajson(a__  )

       return sequence["answer"]




