
'''simple docstring'''



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCamelCase	 =       logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-classification/requirements.txt""")

lowerCamelCase	 =       list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCamelCase	 =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							with open(lowerCamelCase_  ,   'rb'					) as f:
														__lowercase       	=Image.open(lowerCamelCase_					)
														return im.convert('RGB'					)





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    field(
				    default=lowerCAmelCase__      ,       metadata={
				        """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
				    }      ,       )
				lowerCAmelCase__					    =    field(
				    default=lowerCAmelCase__      ,       metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}		)
				lowerCAmelCase__					    =    field(default=lowerCAmelCase__      ,       metadata={"""help""": """A folder containing the training data."""}		)
				lowerCAmelCase__					    =    field(default=lowerCAmelCase__      ,       metadata={"""help""": """A folder containing the validation data."""}		)
				lowerCAmelCase__					    =    field(
				    default=0.15      ,       metadata={"""help""": """Percent to split off of train for validation."""}		)
				lowerCAmelCase__					    =    field(
				    default=lowerCAmelCase__      ,       metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of training examples to this """
				            """value if set."""
				        )
				    }      ,       )
				lowerCAmelCase__					    =    field(
				    default=lowerCAmelCase__      ,       metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
				            """value if set."""
				        )
				    }      ,       )

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
																		raise ValueError(
																		    'You must specify either a dataset name from the hub or a train and/or validation directory.')





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    field(
				    default="""google/vit-base-patch16-224-in21k"""      ,       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}      ,       )
				lowerCAmelCase__					    =    field(
				    default=lowerCAmelCase__      ,       metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCAmelCase__		)}      ,       )
				lowerCAmelCase__					    =    field(
				    default=lowerCAmelCase__      ,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}		)
				lowerCAmelCase__					    =    field(
				    default=lowerCAmelCase__      ,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}		)
				lowerCAmelCase__					    =    field(
				    default="""main"""      ,       metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}      ,       )
				lowerCAmelCase__					    =    field(default=lowerCAmelCase__      ,       metadata={"""help""": """Name or path of preprocessor config."""}		)
				lowerCAmelCase__					    =    field(
				    default=lowerCAmelCase__      ,       metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    }      ,       )
				lowerCAmelCase__					    =    field(
				    default=lowerCAmelCase__      ,       metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}      ,       )



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=torch.stack([example['pixel_values'] for example in examples]					)
							__lowercase       	=torch.tensor([example['labels'] for example in examples]					)
							return {"pixel_values": pixel_values, "labels": labels}



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
							if len(sys.argv					) == 2 and sys.argv[1].endswith('.json'					):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														__lowercase	,				__lowercase	,				__lowercase       	=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
							else:
														__lowercase	,				__lowercase	,				__lowercase       	=parser.parse_args_into_dataclasses()

							# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
							# information sent is the one passed as arguments along with your Python/PyTorch versions.
							send_example_telemetry('run_image_classification'  ,   lowerCamelCase_  ,   lowerCamelCase_					)

							# Setup logging
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'  ,   datefmt='%m/%d/%Y %H:%M:%S'  ,   handlers=[logging.StreamHandler(sys.stdout					)]  ,   )

							if training_args.should_log:
														# The default of training_args.log_level is passive, so we set log level at info here to have that default.
														transformers.utils.logging.set_verbosity_info()

							__lowercase       	=training_args.get_process_log_level()
							logger.setLevel(lowerCamelCase_					)
							transformers.utils.logging.set_verbosity(lowerCamelCase_					)
							transformers.utils.logging.enable_default_handler()
							transformers.utils.logging.enable_explicit_format()

							# Log on each process the small summary:
							logger.warning(
							    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
							    + f"""distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}"""					)
							logger.info(f"""Training/evaluation parameters {training_args}"""					)

							# Detecting last checkpoint.
							__lowercase       	=None
							if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
														__lowercase       	=get_last_checkpoint(training_args.output_dir					)
														if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
																					raise ValueError(
																					    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
																					    'Use --overwrite_output_dir to overcome.'					)
														elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
																					logger.info(
																					    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
																					    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'					)

    # Set seed before initializing model.
							set_seed(training_args.seed					)

							# Initialize our dataset and prepare it for the 'image-classification' task.
							if data_args.dataset_name is not None:
														__lowercase       	=load_dataset(
														    data_args.dataset_name  ,   data_args.dataset_config_name  ,   cache_dir=model_args.cache_dir  ,   task='image-classification'  ,   use_auth_token=True if model_args.use_auth_token else None  ,   )
							else:
														__lowercase       	={}
														if data_args.train_dir is not None:
																					__lowercase       	=os.path.join(data_args.train_dir  ,   '**'					)
														if data_args.validation_dir is not None:
																					__lowercase       	=os.path.join(data_args.validation_dir  ,   '**'					)
														__lowercase       	=load_dataset(
														    'imagefolder'  ,   data_files=lowerCamelCase_  ,   cache_dir=model_args.cache_dir  ,   task='image-classification'  ,   )

							# If we don't have a validation split, split off a percentage of train as validation.
							__lowercase       	=None if 'validation' in dataset.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split  ,   lowerCamelCase_					) and data_args.train_val_split > 0.0:
														__lowercase       	=dataset['train'].train_test_split(data_args.train_val_split					)
														__lowercase       	=split['train']
														__lowercase       	=split['test']

							# Prepare label mappings.
							# We'll include these in the model's config to get human readable labels in the Inference API.
							__lowercase       	=dataset['train'].features['labels'].names
							__lowercase	,				__lowercase       	={}, {}
							for i, label in enumerate(lowerCamelCase_					):
														__lowercase       	=str(lowerCamelCase_					)
														__lowercase       	=label

							# Load the accuracy metric from the datasets package
							__lowercase       	=evaluate.load('accuracy'					)

							# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
							# predictions and label_ids field) and has to return a dictionary string to float.
							def compute_metrics(_lowerCAmelCase					):
														return metric.compute(predictions=np.argmax(p.predictions  ,   axis=1					)  ,   references=p.label_ids					)

							__lowercase       	=AutoConfig.from_pretrained(
							    model_args.config_name or model_args.model_name_or_path  ,   num_labels=len(lowerCamelCase_					)  ,   labelaid=lowerCamelCase_  ,   idalabel=lowerCamelCase_  ,   finetuning_task='image-classification'  ,   cache_dir=model_args.cache_dir  ,   revision=model_args.model_revision  ,   use_auth_token=True if model_args.use_auth_token else None  ,   )
							__lowercase       	=AutoModelForImageClassification.from_pretrained(
							    model_args.model_name_or_path  ,   from_tf=bool('.ckpt' in model_args.model_name_or_path					)  ,   config=lowerCamelCase_  ,   cache_dir=model_args.cache_dir  ,   revision=model_args.model_revision  ,   use_auth_token=True if model_args.use_auth_token else None  ,   ignore_mismatched_sizes=model_args.ignore_mismatched_sizes  ,   )
							__lowercase       	=AutoImageProcessor.from_pretrained(
							    model_args.image_processor_name or model_args.model_name_or_path  ,   cache_dir=model_args.cache_dir  ,   revision=model_args.model_revision  ,   use_auth_token=True if model_args.use_auth_token else None  ,   )

							# Define torchvision transforms to be applied to each image.
							if "shortest_edge" in image_processor.size:
														__lowercase       	=image_processor.size['shortest_edge']
							else:
														__lowercase       	=(image_processor.size['height'], image_processor.size['width'])
							__lowercase       	=Normalize(mean=image_processor.image_mean  ,   std=image_processor.image_std					)
							__lowercase       	=Compose(
							    [
							        RandomResizedCrop(lowerCamelCase_					),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ]					)
							__lowercase       	=Compose(
							    [
							        Resize(lowerCamelCase_					),
							        CenterCrop(lowerCamelCase_					),
							        ToTensor(),
							        normalize,
							    ]					)

							def train_transforms(_lowerCAmelCase					):
														__lowercase       	=[
														    _train_transforms(pil_img.convert('RGB'					)					) for pil_img in example_batch['image']
														]
														return example_batch

							def val_transforms(_lowerCAmelCase					):
														__lowercase       	=[_val_transforms(pil_img.convert('RGB'					)					) for pil_img in example_batch['image']]
														return example_batch

							if training_args.do_train:
														if "train" not in dataset:
																					raise ValueError('--do_train requires a train dataset'					)
														if data_args.max_train_samples is not None:
																					__lowercase       	=(
																					    dataset['train'].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
																					)
														# Set the training transforms
														dataset["train"].set_transform(lowerCamelCase_					)

							if training_args.do_eval:
														if "validation" not in dataset:
																					raise ValueError('--do_eval requires a validation dataset'					)
														if data_args.max_eval_samples is not None:
																					__lowercase       	=(
																					    dataset['validation'].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
																					)
														# Set the validation transforms
														dataset["validation"].set_transform(lowerCamelCase_					)

							# Initalize our trainer
							__lowercase       	=Trainer(
							    model=lowerCamelCase_  ,   args=lowerCamelCase_  ,   train_dataset=dataset['train'] if training_args.do_train else None  ,   eval_dataset=dataset['validation'] if training_args.do_eval else None  ,   compute_metrics=lowerCamelCase_  ,   tokenizer=lowerCamelCase_  ,   data_collator=lowerCamelCase_  ,   )

							# Training
							if training_args.do_train:
														__lowercase       	=None
														if training_args.resume_from_checkpoint is not None:
																					__lowercase       	=training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					__lowercase       	=last_checkpoint
														__lowercase       	=trainer.train(resume_from_checkpoint=lowerCamelCase_					)
														trainer.save_model()
														trainer.log_metrics('train'  ,   train_result.metrics					)
														trainer.save_metrics('train'  ,   train_result.metrics					)
														trainer.save_state()

							# Evaluation
							if training_args.do_eval:
														__lowercase       	=trainer.evaluate()
														trainer.log_metrics('eval'  ,   lowerCamelCase_					)
														trainer.save_metrics('eval'  ,   lowerCamelCase_					)

							# Write model card and (optionally) push to hub
							__lowercase       	={
							    'finetuned_from': model_args.model_name_or_path,
							    'tasks': 'image-classification',
							    'dataset': data_args.dataset_name,
							    'tags': ['image-classification', 'vision'],
							}
							if training_args.push_to_hub:
														trainer.push_to_hub(**lowerCamelCase_					)
							else:
														trainer.create_model_card(**lowerCamelCase_					)


if __name__ == "__main__":
		main()








'''simple docstring'''



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							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.
							__lowercase       	=[p / w for p, w in zip(_lowerCAmelCase  ,   _lowerCAmelCase					)]

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

							# declaring useful variables
							__lowercase       	=len(_lowerCAmelCase					)
							__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(_lowerCAmelCase					)
														__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."""
		)

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

		# Function Call
		calc_profit(profit, weight, max_weight)



