


"""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       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      """naver-clova-ix/donut-base-finetuned-docvqa"""
							__lowercase:						List[str]							 =      (
							    """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."""
							)
							__lowercase:						List[str]							 =      """document_qa"""
							__lowercase:						Optional[Any]							 =      AutoProcessor
							__lowercase:						str							 =      VisionEncoderDecoderModel

							__lowercase:						int							 =      ["""image""", """text"""]
							__lowercase:						List[Any]							 =      ["""text"""]



							def __init__(			self							: Tuple						,    *UpperCAmelCase_							: Any						,    **UpperCAmelCase_							: Union[str, Any]    )   ->List[Any]:



														"""simple docstring"""



														if not is_vision_available():
																					raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""    )

														super().__init__(*UpperCAmelCase_						,    **UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Tuple						,    UpperCAmelCase_							: "Image"						,    UpperCAmelCase_							: str    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						"""<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
														snake_case_          =						task_prompt.replace("""{user_input}"""						,    UpperCAmelCase_    )
														snake_case_          =						self.pre_processor.tokenizer(
														    UpperCAmelCase_						,    add_special_tokens=UpperCAmelCase_						,    return_tensors="""pt"""    ).input_ids
														snake_case_          =						self.pre_processor(UpperCAmelCase_						,    return_tensors="""pt"""    ).pixel_values

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



							def 						lowerCAmelCase    (			self							: Tuple						,    UpperCAmelCase_							: List[str]    )   ->int:



														"""simple docstring"""



														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=UpperCAmelCase_						,    pad_token_id=self.pre_processor.tokenizer.pad_token_id						,    eos_token_id=self.pre_processor.tokenizer.eos_token_id						,    use_cache=UpperCAmelCase_						,    num_beams=1						,    bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]						,    return_dict_in_generate=UpperCAmelCase_						,    ).sequences



							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: int    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						self.pre_processor.batch_decode(UpperCAmelCase_    )[0]
														snake_case_          =						sequence.replace(self.pre_processor.tokenizer.eos_token						,    """"""    )
														snake_case_          =						sequence.replace(self.pre_processor.tokenizer.pad_token						,    """"""    )
														snake_case_          =						re.sub(R"""<.*?>"""						,    """"""						,    UpperCAmelCase_						,    count=1    ).strip()  # remove first task start token
														snake_case_          =						self.pre_processor.tokenajson(UpperCAmelCase_    )

														return sequence["answer"]







"""simple docstring"""
from collections import namedtuple

import requests
from lxml import html  # type: ignore

__SCREAMING_SNAKE_CASE							:      List[str]						=      namedtuple('covid_data', 'cases deaths recovered')







def   _a			( _SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus/"			)      ->			covid_data:
							snake_case_          =						"""//div[@class = \"maincounter-number\"]/span/text()"""
							return covid_data(*html.fromstring(requests.get(_SCREAMING_SNAKE_CASE			).content			).xpath(_SCREAMING_SNAKE_CASE			)			)


__SCREAMING_SNAKE_CASE							:      List[str]						=      'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'





print(fmt.format(*covid_stats()))



