


import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402







class  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					int:
												a__    =  0
					def 				lowerCamelCase__(   self     :List[str]   )      ->					Optional[int]:
												a__    =  AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'   )
												self.assertIsInstance(__snake_case    ,__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					int:
												with tempfile.TemporaryDirectory() as tmpdirname:
																			a__    =  Path(__snake_case   ) / 'preprocessor_config.json'
																			a__    =  Path(__snake_case   ) / 'config.json'
																			json.dump(
																			    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}    ,open(__snake_case    ,'w'   )    ,)
																			json.dump({'model_type': 'clip'}    ,open(__snake_case    ,'w'   )   )

																			a__    =  AutoImageProcessor.from_pretrained(__snake_case   )
																			self.assertIsInstance(__snake_case    ,__snake_case   )
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Dict:
												# Ensure we can load the image processor from the feature extractor config
												with tempfile.TemporaryDirectory() as tmpdirname:
																			a__    =  Path(__snake_case   ) / 'preprocessor_config.json'
																			a__    =  Path(__snake_case   ) / 'config.json'
																			json.dump(
																			    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}    ,open(__snake_case    ,'w'   )    ,)
																			json.dump({'model_type': 'clip'}    ,open(__snake_case    ,'w'   )   )

																			a__    =  AutoImageProcessor.from_pretrained(__snake_case   )
																			self.assertIsInstance(__snake_case    ,__snake_case   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					str:
												with tempfile.TemporaryDirectory() as tmpdirname:
																			a__    =  CLIPConfig()

																			# Create a dummy config file with image_proceesor_type
																			a__    =  Path(__snake_case   ) / 'preprocessor_config.json'
																			a__    =  Path(__snake_case   ) / 'config.json'
																			json.dump(
																			    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}    ,open(__snake_case    ,'w'   )    ,)
																			json.dump({'model_type': 'clip'}    ,open(__snake_case    ,'w'   )   )

																			# remove image_processor_type to make sure config.json alone is enough to load image processor locally
																			a__    =  AutoImageProcessor.from_pretrained(__snake_case   ).to_dict()

																			config_dict.pop('image_processor_type'   )
																			a__    =  CLIPImageProcessor(**__snake_case   )

																			# save in new folder
																			model_config.save_pretrained(__snake_case   )
																			config.save_pretrained(__snake_case   )

																			a__    =  AutoImageProcessor.from_pretrained(__snake_case   )

																			# make sure private variable is not incorrectly saved
																			a__    =  json.loads(config.to_json_string()   )
																			self.assertTrue('_processor_class' not in dict_as_saved   )

												self.assertIsInstance(__snake_case    ,__snake_case   )
					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					str:
												with tempfile.TemporaryDirectory() as tmpdirname:
																			a__    =  Path(__snake_case   ) / 'preprocessor_config.json'
																			json.dump(
																			    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}    ,open(__snake_case    ,'w'   )    ,)

																			a__    =  AutoImageProcessor.from_pretrained(__snake_case   )
																			self.assertIsInstance(__snake_case    ,__snake_case   )
					def 				lowerCamelCase__(   self     :Any   )      ->					Any:
												with self.assertRaisesRegex(
												    __snake_case    ,'clip-base is not a local folder and is not a valid model identifier'   ):
																			a__    =  AutoImageProcessor.from_pretrained('clip-base'   )
					def 				lowerCamelCase__(   self     :List[str]   )      ->					str:
												with self.assertRaisesRegex(
												    __snake_case    ,R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'   ):
																			a__    =  AutoImageProcessor.from_pretrained(__snake_case    ,revision='aaaaaa'   )
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					List[Any]:
												with self.assertRaisesRegex(
												    __snake_case    ,'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'    ,):
																			a__    =  AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'   )
					def 				lowerCamelCase__(   self     :Any   )      ->					Dict:
												# If remote code is not set, we will time out when asking whether to load the model.
												with self.assertRaises(__snake_case   ):
																			a__    =  AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'   )
												# If remote code is disabled, we can't load this config.
												with self.assertRaises(__snake_case   ):
																			a__    =  AutoImageProcessor.from_pretrained(
																			    'hf-internal-testing/test_dynamic_image_processor'    ,trust_remote_code=__snake_case   )

												a__    =  AutoImageProcessor.from_pretrained(
												    'hf-internal-testing/test_dynamic_image_processor'    ,trust_remote_code=__snake_case   )
												self.assertEqual(image_processor.__class__.__name__    ,'NewImageProcessor'   )

												# Test image processor can be reloaded.
												with tempfile.TemporaryDirectory() as tmp_dir:
																			image_processor.save_pretrained(__snake_case   )
																			a__    =  AutoImageProcessor.from_pretrained(__snake_case    ,trust_remote_code=__snake_case   )
												self.assertEqual(reloaded_image_processor.__class__.__name__    ,'NewImageProcessor'   )
					def 				lowerCamelCase__(   self     :Dict   )      ->					int:
												try:
																			AutoConfig.register('custom'    ,__snake_case   )
																			AutoImageProcessor.register(__snake_case    ,__snake_case   )
																			# Trying to register something existing in the Transformers library will raise an error
																			with self.assertRaises(__snake_case   ):
																										AutoImageProcessor.register(__snake_case    ,__snake_case   )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																										a__    =  Path(__snake_case   ) / 'preprocessor_config.json'
																										a__    =  Path(__snake_case   ) / 'config.json'
																										json.dump(
																										    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}    ,open(__snake_case    ,'w'   )    ,)
																										json.dump({'model_type': 'clip'}    ,open(__snake_case    ,'w'   )   )

																										a__    =  CustomImageProcessor.from_pretrained(__snake_case   )

																			# Now that the config is registered, it can be used as any other config with the auto-API
																			with tempfile.TemporaryDirectory() as tmp_dir:
																										image_processor.save_pretrained(__snake_case   )
																										a__    =  AutoImageProcessor.from_pretrained(__snake_case   )
																										self.assertIsInstance(__snake_case    ,__snake_case   )

												finally:
																			if "custom" in CONFIG_MAPPING._extra_content:
																										del CONFIG_MAPPING._extra_content["custom"]
																			if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																										del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]



					def 				lowerCamelCase__(   self     :int   )      ->					int:







												class  snake_case_     (lowerCamelCase_   ):
																	UpperCAmelCase__      :					List[str]					   =    True

												try:
																			AutoConfig.register('custom'    ,__snake_case   )
																			AutoImageProcessor.register(__snake_case    ,__snake_case   )
																			# If remote code is not set, the default is to use local
																			a__    =  AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'   )
																			self.assertEqual(image_processor.__class__.__name__    ,'NewImageProcessor'   )
																			self.assertTrue(image_processor.is_local   )

																			# If remote code is disabled, we load the local one.
																			a__    =  AutoImageProcessor.from_pretrained(
																			    'hf-internal-testing/test_dynamic_image_processor'    ,trust_remote_code=__snake_case   )
																			self.assertEqual(image_processor.__class__.__name__    ,'NewImageProcessor'   )
																			self.assertTrue(image_processor.is_local   )

																			# If remote is enabled, we load from the Hub
																			a__    =  AutoImageProcessor.from_pretrained(
																			    'hf-internal-testing/test_dynamic_image_processor'    ,trust_remote_code=__snake_case   )
																			self.assertEqual(image_processor.__class__.__name__    ,'NewImageProcessor'   )
																			self.assertTrue(not hasattr(__snake_case    ,'is_local'   )   )

												finally:
																			if "custom" in CONFIG_MAPPING._extra_content:
																										del CONFIG_MAPPING._extra_content["custom"]
																			if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																										del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]








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)




