






from __future__ import annotations

from functools import lru_cache
from math import ceil

lowerCamelCase       :       Tuple        =				1_00

lowerCamelCase       :       Optional[Any]        =				set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCamelCase       :       int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
	if prime not in primes:
		continue
	primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100					)
def 					snake_case_						(   lowerCAmelCase_				:  int					):
							if number_to_partition < 0:
														return set()
							elif number_to_partition == 0:
														return {1}

							__lowercase					:							set[int]			=   set()
							__lowercase					:							int
							__lowercase					:							int

							for prime in primes:
														if prime > number_to_partition:
																					continue
														for sub in partition(number_to_partition - prime					):
																					ret.add(sub * prime					)

							return ret
def 					snake_case_						(   lowerCAmelCase_				:  int = 5000					):
							for number_to_partition in range(1				,				lowerCAmelCase_					):
														if len(partition(lowerCAmelCase_					)					) > number_unique_partitions:
																					return number_to_partition
							return None


if __name__ == "__main__":
	print(f'''{solution() = }''')







# 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 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[str]     =					'''naver-clova-ix/donut-base-finetuned-docvqa'''
					_A  :	Any     =					(
					    '''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.'''
					)
					_A  :	Tuple     =					'''document_qa'''
					_A  :	Dict     =					AutoProcessor
					_A  :	Tuple     =					VisionEncoderDecoderModel

					_A  :	Optional[int]     =					['''image''', '''text''']
					_A  :	Optional[int]     =					['''text''']



					def __init__(     self :		Any						,   *__a :		List[str]						,   **__a :		Any							)	->     Optional[Any]:


												"""simple docstring"""

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

												super().__init__(*__a						,   **__a							)



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		"Image"						,   __a :		str							)	->     List[str]:


												"""simple docstring"""

												__lowercase					:							int			=   """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
												__lowercase					:							str			=   task_prompt.replace("""{user_input}"""						,   __a							)
												__lowercase					:							Union[str, Any]			=   self.pre_processor.tokenizer(
												    __a						,   add_special_tokens=__a						,   return_tensors="""pt"""							).input_ids
												__lowercase					:							int			=   self.pre_processor(__a						,   return_tensors="""pt"""							).pixel_values

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



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		int							)	->     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=__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 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		Union[str, Any]							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   self.pre_processor.batch_decode(__a							)[0]
												__lowercase					:							int			=   sequence.replace(self.pre_processor.tokenizer.eos_token						,   """"""							)
												__lowercase					:							Union[str, Any]			=   sequence.replace(self.pre_processor.tokenizer.pad_token						,   """"""							)
												__lowercase					:							Optional[Any]			=   re.sub(r"""<.*?>"""						,   """"""						,   __a						,   count=1							).strip()  # remove first task start token
												__lowercase					:							Dict			=   self.pre_processor.tokenajson(__a							)

												return sequence["answer"]