



# 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   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Union[str, Any]      							=      """naver-clova-ix/donut-base-finetuned-docvqa"""
						_snake_case			:     List[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."""
						)
						_snake_case			:     Tuple      							=      """document_qa"""
						_snake_case			:     Tuple      							=      AutoProcessor
						_snake_case			:     int      							=      VisionEncoderDecoderModel

						_snake_case			:     Dict      							=      ["""image""", """text"""]
						_snake_case			:     Any      							=      ["""text"""]



						def __init__(	self	:							Tuple							,      *lowerCamelCase	:							int							,      **lowerCamelCase	:							Union[str, Any]	):






							'''simple docstring'''



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

							super().__init__(*lowerCamelCase							,      **lowerCamelCase	)



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							"Image"							,      lowerCamelCase	:							str	):






							'''simple docstring'''



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

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



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							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=lowerCamelCase							,      pad_token_id=self.pre_processor.tokenizer.pad_token_id							,      eos_token_id=self.pre_processor.tokenizer.eos_token_id							,      use_cache=lowerCamelCase							,      num_beams=1							,      bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]							,      return_dict_in_generate=lowerCamelCase							,      ).sequences



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							List[Any]	):






							'''simple docstring'''



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

							return sequence["answer"]








def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if len(_SCREAMING_SNAKE_CASE   ) != len(_SCREAMING_SNAKE_CASE   ):
		raise ValueError("The length of profit and weight must be same."   )
	if max_weight <= 0:
		raise ValueError("max_weight must greater than zero."   )
	if any(p < 0 for p in profit   ):
		raise ValueError("Profit can not be negative."   )
	if any(w < 0 for w in weight   ):
		raise ValueError("Weight can not be negative."   )

	# List created to store profit gained for the 1kg in case of each weight
	# respectively.  Calculate and append profit/weight for each element.
	__lowercase		       =			[p / w for p, w in zip(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )]

	# Creating a copy of the list and sorting profit/weight in ascending order
	__lowercase		       =			sorted(_SCREAMING_SNAKE_CASE   )

	# declaring useful variables
	__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			0
	__lowercase		       =			0
	__lowercase		       =			0

	# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
	while limit <= max_weight and i < length:
		# flag value for encountered greatest element in sorted_profit_by_weight
		__lowercase		       =			sorted_profit_by_weight[length - i - 1]
		__lowercase		       =			profit_by_weight.index(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			-1

		# check if the weight encountered is less than the total weight
		# encountered before.
		if max_weight - limit >= weight[index]:
			limit += weight[index]
			# Adding profit gained for the given weight 1 ===
			# weight[index]/weight[index]
			gain += 1 * profit[index]
		else:
			# Since the weight encountered is greater than limit, therefore take the
			# required number of remaining kgs and calculate profit for it.
			# weight remaining / weight[index]
			gain += (max_weight - limit) / weight[index] * profit[index]
			break
		i += 1
	return gain


if __name__ == "__main__":
	print(
	    """Input profits, weights, and then max_weight (all positive ints) separated by """
	    """spaces."""
	)

	snake_case__					:     str    		=   [int(x) for x in input("""Input profits separated by spaces: """).split()]
	snake_case__					:     str    		=   [int(x) for x in input("""Input weights separated by spaces: """).split()]
	snake_case__					:     Optional[Any]    		=   int(input("""Max weight allowed: """))

	# Function Call
	calc_profit(profit, weight, max_weight)



