


'''simple docstring'''





import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
						from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image







class  snake_case_     :
					def __init__(   self     :Optional[int]    ,__snake_case     :Optional[int]    ,__snake_case     :List[str]=3    ,__snake_case     :List[str]=32    ,__snake_case     :Optional[Any]=3    ,__snake_case     :List[Any]=10    ,__snake_case     :Union[str, Any]=[8, 16, 32, 64]    ,__snake_case     :List[str]=[1, 1, 2, 1]    ,__snake_case     :List[str]=True    ,__snake_case     :Optional[int]=True    ,__snake_case     :Any="relu"    ,__snake_case     :Union[str, Any]=3    ,__snake_case     :int=None    ,__snake_case     :str=["stage2", "stage3", "stage4"]    ,__snake_case     :List[Any]=[2, 3, 4]    ,__snake_case     :Union[str, Any]=1    ,)      ->					List[Any]:
												a__    =  parent
												a__    =  batch_size
												a__    =  image_size
												a__    =  num_channels
												a__    =  embeddings_size
												a__    =  hidden_sizes
												a__    =  depths
												a__    =  is_training
												a__    =  use_labels
												a__    =  hidden_act
												a__    =  num_labels
												a__    =  scope
												a__    =  len(__snake_case   )
												a__    =  out_features
												a__    =  out_indices
												a__    =  num_groups
					def 				lowerCamelCase__(   self     :Dict   )      ->					Optional[int]:
												a__    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

												a__    =  None
												if self.use_labels:
																			a__    =  ids_tensor([self.batch_size]    ,self.num_labels   )

												a__    =  self.get_config()

												return config, pixel_values, labels
					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					Optional[int]:
												return BitConfig(
												    num_channels=self.num_channels    ,embeddings_size=self.embeddings_size    ,hidden_sizes=self.hidden_sizes    ,depths=self.depths    ,hidden_act=self.hidden_act    ,num_labels=self.num_labels    ,out_features=self.out_features    ,out_indices=self.out_indices    ,num_groups=self.num_groups    ,)
					def 				lowerCamelCase__(   self     :str    ,__snake_case     :Any    ,__snake_case     :Dict    ,__snake_case     :Optional[int]   )      ->					List[Any]:
												a__    =  BitModel(config=__snake_case   )
												model.to(__snake_case   )
												model.eval()
												a__    =  model(__snake_case   )
												self.parent.assertEqual(
												    result.last_hidden_state.shape    ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,)
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :List[str]    ,__snake_case     :Optional[int]    ,__snake_case     :List[str]   )      ->					List[str]:
												a__    =  self.num_labels
												a__    =  BitForImageClassification(__snake_case   )
												model.to(__snake_case   )
												model.eval()
												a__    =  model(__snake_case    ,labels=__snake_case   )
												self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)   )
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :List[Any]    ,__snake_case     :List[Any]    ,__snake_case     :List[str]   )      ->					Union[str, Any]:
												a__    =  BitBackbone(config=__snake_case   )
												model.to(__snake_case   )
												model.eval()
												a__    =  model(__snake_case   )

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps   )    ,len(config.out_features   )   )
												self.parent.assertListEqual(list(result.feature_maps[0].shape   )    ,[self.batch_size, self.hidden_sizes[1], 4, 4]   )

												# verify channels
												self.parent.assertEqual(len(model.channels   )    ,len(config.out_features   )   )
												self.parent.assertListEqual(model.channels    ,config.hidden_sizes[1:]   )

												# verify backbone works with out_features=None
												a__    =  None
												a__    =  BitBackbone(config=__snake_case   )
												model.to(__snake_case   )
												model.eval()
												a__    =  model(__snake_case   )

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps   )    ,1   )
												self.parent.assertListEqual(list(result.feature_maps[0].shape   )    ,[self.batch_size, self.hidden_sizes[-1], 1, 1]   )

												# verify channels
												self.parent.assertEqual(len(model.channels   )    ,1   )
												self.parent.assertListEqual(model.channels    ,[config.hidden_sizes[-1]]   )



					def 				lowerCamelCase__(   self     :Tuple   )      ->					List[str]:
												a__    =  self.prepare_config_and_inputs()
												a__   ,      a__   ,      a__    =  config_and_inputs
												a__    =  {'pixel_values': pixel_values}
												return config, inputs_dict







@require_torch
class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					Union[str, Any]					   =    (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
					UpperCAmelCase__      :					Dict					   =    (
					    {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCAmelCase__      :					Dict					   =    False
					UpperCAmelCase__      :					Union[str, Any]					   =    False
					UpperCAmelCase__      :					Optional[Any]					   =    False
					UpperCAmelCase__      :					Union[str, Any]					   =    False
					UpperCAmelCase__      :					Any					   =    False
					def 				lowerCamelCase__(   self     :Any   )      ->					Optional[int]:
												a__    =  BitModelTester(self   )
												a__    =  ConfigTester(self    ,config_class=__snake_case    ,has_text_modality=__snake_case   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					List[str]:
												self.create_and_test_config_common_properties()
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()
					def 				lowerCamelCase__(   self     :int   )      ->					List[str]:
												return
					@unittest.skip(reason='Bit does not output attentions'   )
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Optional[Any]:
												pass
					@unittest.skip(reason='Bit does not use inputs_embeds'   )
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					Optional[int]:
												pass
					@unittest.skip(reason='Bit does not support input and output embeddings'   )
					def 				lowerCamelCase__(   self     :List[str]   )      ->					Dict:
												pass
					def 				lowerCamelCase__(   self     :List[str]   )      ->					Optional[Any]:
												a__   ,      a__    =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__    =  model_class(__snake_case   )
																			a__    =  inspect.signature(model.forward   )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			a__    =  [*signature.parameters.keys()]

																			a__    =  ['pixel_values']
																			self.assertListEqual(arg_names[:1]    ,__snake_case   )
					def 				lowerCamelCase__(   self     :List[str]   )      ->					Union[str, Any]:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__snake_case   )
					def 				lowerCamelCase__(   self     :str   )      ->					str:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*__snake_case   )
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Union[str, Any]:
												a__   ,      a__    =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__    =  model_class(config=__snake_case   )
																			for name, module in model.named_modules():
																										if isinstance(__snake_case    ,(nn.BatchNormad, nn.GroupNorm)   ):
																																	self.assertTrue(
																																	    torch.all(module.weight == 1   )    ,msg=F'Parameter {name} of model {model_class} seems not properly initialized'    ,)
																																	self.assertTrue(
																																	    torch.all(module.bias == 0   )    ,msg=F'Parameter {name} of model {model_class} seems not properly initialized'    ,)
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					List[Any]:







												def check_hidden_states_output(__snake_case     :int    ,__snake_case     :Optional[Any]    ,__snake_case     :Dict   ):
																			a__    =  model_class(__snake_case   )
																			model.to(__snake_case   )
																			model.eval()

																			with torch.no_grad():
																										a__    =  model(**self._prepare_for_class(__snake_case    ,__snake_case   )   )

																			a__    =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																			a__    =  self.model_tester.num_stages
																			self.assertEqual(len(__snake_case   )    ,expected_num_stages + 1   )

																			# Bit's feature maps are of shape (batch_size, num_channels, height, width)
																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]   )    ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,)

												a__   ,      a__    =  self.model_tester.prepare_config_and_inputs_for_common()
												a__    =  ['preactivation', 'bottleneck']
												for model_class in self.all_model_classes:
																			for layer_type in layers_type:
																										a__    =  layer_type
																										a__    =  True
																										check_hidden_states_output(__snake_case    ,__snake_case    ,__snake_case   )

																										# check that output_hidden_states also work using config
																										del inputs_dict["output_hidden_states"]
																										a__    =  True

																										check_hidden_states_output(__snake_case    ,__snake_case    ,__snake_case   )
					@unittest.skip(reason='Bit does not use feedforward chunking'   )
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					int:
												pass
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Dict:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__snake_case   )



					@slow
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Optional[Any]:







												for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			a__    =  BitModel.from_pretrained(__snake_case   )
																			self.assertIsNotNone(__snake_case   )






def 							__lowercase     (				):
							a__    =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
							return image







@require_torch
@require_vision
class  snake_case_     (unittest.TestCase   ):
					@cached_property
					def 				lowerCamelCase__(   self     :Any   )      ->					Dict:
												return (
												    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ) if is_vision_available() else None
												)



					@slow
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Dict:
												a__    =  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(__snake_case   )

												a__    =  self.default_image_processor
												a__    =  prepare_img()
												a__    =  image_processor(images=__snake_case    ,return_tensors='pt'   ).to(__snake_case   )

												# forward pass
												with torch.no_grad():
																			a__    =  model(**__snake_case   )

												# verify the logits
												a__    =  torch.Size((1, 10_00)   )
												self.assertEqual(outputs.logits.shape    ,__snake_case   )

												a__    =  torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]   ).to(__snake_case   )

												self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,__snake_case    ,atol=1E-4   )   )







@require_torch
class  snake_case_     (lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					List[str]					   =    (BitBackbone,) if is_torch_available() else ()
					UpperCAmelCase__      :					Any					   =    BitConfig

					UpperCAmelCase__      :					Optional[int]					   =    False
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					Union[str, Any]:
												a__    =  BitModelTester(self   )








def 							__lowercase     (				__lowerCAmelCase							:       list      ,					__lowerCAmelCase							:       list      ,					__lowerCAmelCase							:       int     ):
							if len(__lowerCAmelCase     ) != len(__lowerCAmelCase     ):
														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(__lowerCAmelCase      ,					__lowerCAmelCase     )]

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

							# declaring useful variables
							a__    =  len(__lowerCAmelCase     )
							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(__lowerCAmelCase     )
														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.'''
						)

						snake_case							: Tuple      =   [int(x) for x in input('''Input profits separated by spaces: ''').split()]
						snake_case							: Optional[int]      =   [int(x) for x in input('''Input weights separated by spaces: ''').split()]
						snake_case							: List[str]      =   int(input('''Max weight allowed: '''))

						# Function Call
						calc_profit(profit, weight, max_weight)




