
"""simple docstring"""





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


_a														=		logging.get_logger(__name__)

_a														=		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_a														=		{
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

_a														=		{
    """gpt-neox-20b""": 2048,
}






class    _UpperCAmelCase(   __lowerCAmelCase     ):
		lowercase__      =				VOCAB_FILES_NAMES
		lowercase__      =				PRETRAINED_VOCAB_FILES_MAP
		lowercase__      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase__      =				['''input_ids''', '''attention_mask''']



		def __init__( self    ,		__a=None    ,		__a=None    ,		__a=None    ,		__a="<|endoftext|>"    ,		__a="<|endoftext|>"    ,		__a="<|endoftext|>"    ,		__a=False    ,		**__a    ,		)	->	Dict:






							'''simple docstring'''
							super().__init__(
							    lowerCAmelCase_    ,		lowerCAmelCase_    ,		tokenizer_file=lowerCAmelCase_    ,		unk_token=lowerCAmelCase_    ,		bos_token=lowerCAmelCase_    ,		eos_token=lowerCAmelCase_    ,		add_prefix_space=lowerCAmelCase_    ,		**lowerCAmelCase_    ,		)

							_UpperCamelCase									=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
							if pre_tok_state.get('''add_prefix_space'''    ,		lowerCAmelCase_) != add_prefix_space:
												_UpperCamelCase									=  getattr(lowerCAmelCase_    ,		pre_tok_state.pop('''type'''))
												_UpperCamelCase									=  add_prefix_space
												_UpperCamelCase									=  pre_tok_class(**lowerCAmelCase_)

							_UpperCamelCase									=  add_prefix_space



		def 						UpperCAmelCase							( self    ,		__a    ,		__a = None)	->	Tuple[str]:






							'''simple docstring'''
							_UpperCamelCase									=  self._tokenizer.model.save(lowerCAmelCase_    ,		name=lowerCAmelCase_)
							return tuple(lowerCAmelCase_)




		def 						UpperCAmelCase							( self    ,		__a)	->	List[int]:






							'''simple docstring'''
							_UpperCamelCase									=  []
							for is_user, text in conversation.iter_texts():
												input_ids.extend(self.encode(lowerCAmelCase_    ,		add_special_tokens=lowerCAmelCase_) + [self.eos_token_id])

							if len(lowerCAmelCase_) > self.model_max_length:
												_UpperCamelCase									=  input_ids[-self.model_max_length :]
							return input_ids








"""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 typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
				from PIL import Image






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'dandelin/vilt-b32-finetuned-vqa'
		lowercase__      =				(
		    'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
		    'image containing the information, as well as a `question` which should be the question in English. It '
		    'returns a text that is the answer to the question.'
		)
		lowercase__      =				'image_qa'
		lowercase__      =				AutoProcessor
		lowercase__      =				AutoModelForVisualQuestionAnswering

		lowercase__      =				['image', 'text']
		lowercase__      =				['text']



		def __init__( self    ,		*__a    ,		**__a)	->	int:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(__a    ,		__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												return self.model(**__a).logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.argmax(-1).item()
							return self.model.config.idalabel[idx]






