"""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      _UpperCAmelCase						( __snake_case						):






    """simple docstring"""
    __snake_case    =       """naver-clova-ix/donut-base-finetuned-docvqa"""
    __snake_case    =       (
        """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."""
    )
    __snake_case    =       """document_qa"""
    __snake_case    =       AutoProcessor
    __snake_case    =       VisionEncoderDecoderModel

    __snake_case    =       ["""image""", """text"""]
    __snake_case    =       ["""text"""]







    def __init__(			self    , *_lowercase    , **_lowercase					)	->	List[str]:
        if not is_vision_available():
            raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.'''					)

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







    def 				a__					(			self    , _lowercase    , _lowercase					)	->	Dict:
        _lowerCamelCase	: Any       =       '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
        _lowerCamelCase	: int       =       task_prompt.replace('''{user_input}'''    , a__					)
        _lowerCamelCase	: Optional[Any]       =       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    , _lowercase					)	->	Optional[Any]:
        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    , _lowercase					)	->	Union[str, Any]:
        _lowerCamelCase	: Any       =       self.pre_processor.batch_decode(a__					)[0]
        _lowerCamelCase	: List[Any]       =       sequence.replace(self.pre_processor.tokenizer.eos_token    , ''''''					)
        _lowerCamelCase	: int       =       sequence.replace(self.pre_processor.tokenizer.pad_token    , ''''''					)
        _lowerCamelCase	: Union[str, Any]       =       re.sub(R'''<.*?>'''    , ''''''    , a__    , count=1					).strip()  # remove first task start token
        _lowerCamelCase	: int       =       self.pre_processor.tokenajson(a__					)

        return sequence["answer"]






from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:   Any						=						logging.get_logger(__name__)

_lowercase							:   Union[str, Any]						=						{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			'efficientformer'

				def __init__(    self					, a__ = [3, 2, 6, 4]					, a__ = [48, 96, 224, 448]					, a__ = [True, True, True, True]					, a__ = 448					, a__ = 32					, a__ = 4					, a__ = 7					, a__ = 5					, a__ = 8					, a__ = 4					, a__ = 0.0					, a__ = 16					, a__ = 3					, a__ = 3					, a__ = 3					, a__ = 2					, a__ = 1					, a__ = 0.0					, a__ = 1					, a__ = True					, a__ = True					, a__ = 1e-5					, a__ = "gelu"					, a__ = 0.02					, a__ = 1e-12					, a__ = 224					, a__ = 1e-05					, **a__					, )						->					None:
					super().__init__(**a__ )

					A             =					hidden_act
					A             =					hidden_dropout_prob
					A             =					hidden_sizes
					A             =					num_hidden_layers
					A             =					num_attention_heads
					A             =					initializer_range
					A             =					layer_norm_eps
					A             =					patch_size
					A             =					num_channels
					A             =					depths
					A             =					mlp_expansion_ratio
					A             =					downsamples
					A             =					dim
					A             =					key_dim
					A             =					attention_ratio
					A             =					resolution
					A             =					pool_size
					A             =					downsample_patch_size
					A             =					downsample_stride
					A             =					downsample_pad
					A             =					drop_path_rate
					A             =					num_metaad_blocks
					A             =					distillation
					A             =					use_layer_scale
					A             =					layer_scale_init_value
					A             =					image_size
					A             =					batch_norm_eps



