
'''simple docstring'''
from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

lowercase__											=logging.get_logger(__name__)







@add_end_docstrings(UpperCamelCase__					)
class   a_ (      UpperCamelCase__					):




			def __init__(       self					,   *UpperCAmelCase					,   **UpperCAmelCase	):
				super().__init__(*UpperCAmelCase					,   **UpperCAmelCase	)
				self.check_model_type(UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=None					,   UpperCAmelCase=None					,   UpperCAmelCase=None					,   **UpperCAmelCase	):
				a_				,  a_      				=						{}, {}
				if padding is not None:
					a_      				=						padding
				if truncation is not None:
					a_      				=						truncation
				if top_k is not None:
					a_      				=						top_k
				return preprocess_params, {}, postprocess_params




			def __call__(       self					,   UpperCAmelCase					,   UpperCAmelCase = None					,   **UpperCAmelCase	):
				if isinstance(UpperCAmelCase					,   (Image.Image, str)	) and isinstance(UpperCAmelCase					,   UpperCAmelCase	):
					a_      				=						{"""image""": image, """question""": question}
				else:
					a_      				=						image
				a_      				=						super().__call__(UpperCAmelCase					,   **UpperCAmelCase	)
				return results




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase=False					,   UpperCAmelCase=False	):
				a_      				=						load_image(inputs["""image"""]	)
				a_      				=						self.tokenizer(
				    inputs["""question"""]					,   return_tensors=self.framework					,   padding=UpperCAmelCase					,   truncation=UpperCAmelCase	)
				a_      				=						self.image_processor(images=UpperCAmelCase					,   return_tensors=self.framework	)
				model_inputs.update(UpperCAmelCase	)
				return model_inputs




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						self.model(**UpperCAmelCase	)
				return model_outputs






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase=5	):
				if top_k > self.model.config.num_labels:
					a_      				=						self.model.config.num_labels

				if self.framework == "pt":
					a_      				=						model_outputs.logits.sigmoid()[0]
					a_				,  a_      				=						probs.topk(UpperCAmelCase	)
				else:
					raise ValueError(f'''Unsupported framework: {self.framework}'''	)

				a_      				=						scores.tolist()
				a_      				=						ids.tolist()
				return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(UpperCAmelCase					,   UpperCAmelCase	)]


'''simple docstring'''
def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	if len(A__						) != len(A__						):
		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.
	a_      				=						[p / w for p, w in zip(A__  ,      A__						)]

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

	# declaring useful variables
	a_      				=						len(A__						)
	a_      				=						0
	a_      				=						0
	a_      				=						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
		a_      				=						sorted_profit_by_weight[length - i - 1]
		a_      				=						profit_by_weight.index(A__						)
		a_      				=						-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.'
		)

		lowercase__											=[int(x) for x in input('Input profits separated by spaces: ').split()]
		lowercase__											=[int(x) for x in input('Input weights separated by spaces: ').split()]
		lowercase__											=int(input('Max weight allowed: '))

		# Function Call
		calc_profit(profit, weight, max_weight)
