


'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       Optional[int]       ):







					if len(_lowercase       ) != len(_lowercase       ):
										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      :	Optional[int]    =  [p / w for p, w in zip(_lowercase						,				_lowercase       )]

					# Creating a copy of the list and sorting profit/weight in ascending order
					__a      :	List[str]    =  sorted(_lowercase       )

					# declaring useful variables
					__a      :	Tuple    =  len(_lowercase       )
					__a      :	Any    =  0
					__a      :	Optional[Any]    =  0
					__a      :	Union[str, Any]    =  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      :	str    =  sorted_profit_by_weight[length - i - 1]
										__a      :	List[str]    =  profit_by_weight.index(_lowercase       )
										__a      :	Dict    =  -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     :						List[Any]	=     [int(x) for x in input('Input profits separated by spaces: ').split()]
	__lowercase     :						Tuple	=     [int(x) for x in input('Input weights separated by spaces: ').split()]
	__lowercase     :						Union[str, Any]	=     int(input('Max weight allowed: '))

	# Function Call
	calc_profit(profit, weight, max_weight)







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase     :						Tuple	=     {
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						str	=     ['DistilBertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						Any	=     [
	    'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'DistilBertForMaskedLM',
	    'DistilBertForMultipleChoice',
	    'DistilBertForQuestionAnswering',
	    'DistilBertForSequenceClassification',
	    'DistilBertForTokenClassification',
	    'DistilBertModel',
	    'DistilBertPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						List[str]	=     [
	    'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFDistilBertForMaskedLM',
	    'TFDistilBertForMultipleChoice',
	    'TFDistilBertForQuestionAnswering',
	    'TFDistilBertForSequenceClassification',
	    'TFDistilBertForTokenClassification',
	    'TFDistilBertMainLayer',
	    'TFDistilBertModel',
	    'TFDistilBertPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						List[str]	=     [
	    'FlaxDistilBertForMaskedLM',
	    'FlaxDistilBertForMultipleChoice',
	    'FlaxDistilBertForQuestionAnswering',
	    'FlaxDistilBertForSequenceClassification',
	    'FlaxDistilBertForTokenClassification',
	    'FlaxDistilBertModel',
	    'FlaxDistilBertPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_distilbert import (
	    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DistilBertConfig,
	    DistilBertOnnxConfig,
	)
	from .tokenization_distilbert import DistilBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_distilbert_fast import DistilBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_distilbert import (
		    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DistilBertForMaskedLM,
		    DistilBertForMultipleChoice,
		    DistilBertForQuestionAnswering,
		    DistilBertForSequenceClassification,
		    DistilBertForTokenClassification,
		    DistilBertModel,
		    DistilBertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_distilbert import (
		    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDistilBertForMaskedLM,
		    TFDistilBertForMultipleChoice,
		    TFDistilBertForQuestionAnswering,
		    TFDistilBertForSequenceClassification,
		    TFDistilBertForTokenClassification,
		    TFDistilBertMainLayer,
		    TFDistilBertModel,
		    TFDistilBertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_distilbert import (
		    FlaxDistilBertForMaskedLM,
		    FlaxDistilBertForMultipleChoice,
		    FlaxDistilBertForQuestionAnswering,
		    FlaxDistilBertForSequenceClassification,
		    FlaxDistilBertForTokenClassification,
		    FlaxDistilBertModel,
		    FlaxDistilBertPreTrainedModel,
		)

else:
	import sys

	__lowercase     :						int	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



