
"""simple docstring"""





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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__magic_name__       =			logging.getLogger(__name__)



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		return (preds == labels).mean()
@dataclass
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		__lowercase							:  str						   =		field(
		    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
		__lowercase							:  Optional[str]						   =		field(
		    default=__a     ,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
		__lowercase							:  Optional[str]						   =		field(
		    default=__a     ,				metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
		__lowercase							:  Optional[str]						   =		field(
		    default=__a     ,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,				)



@dataclass
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		__lowercase							:  str						   =		field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys() )} )
		__lowercase							:  str						   =		field(metadata={'''help''': '''Should contain the data files for the task.'''} )
		__lowercase							:  int						   =		field(
		    default=128     ,				metadata={
		        '''help''': (
		            '''The maximum total input sequence length after tokenization. Sequences longer '''
		            '''than this will be truncated, sequences shorter will be padded.'''
		        )
		    }     ,				)
		__lowercase							:  bool						   =		field(
		    default=__a     ,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )



def 						_lowerCAmelCase				(				):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		__SCREAMING_SNAKE_CASE            = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir				)
		    and os.listdir(training_args.output_dir				)
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
				    """ --overwrite_output_dir to overcome."""				)

		# Setup logging
		logging.basicConfig(
		    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""  ,				datefmt="""%m/%d/%Y %H:%M:%S"""  ,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,				)
		logger.warning(
		    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""  ,				training_args.local_rank  ,				training_args.device  ,				training_args.n_gpu  ,				bool(training_args.local_rank != -1				)  ,				training_args.fpaa  ,				)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank				):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info("""Training/evaluation parameters %s"""  ,				UpperCamelCase_				)

		# Set seed
		set_seed(training_args.seed				)

		try:
				__SCREAMING_SNAKE_CASE            = processors[data_args.task_name]()
				__SCREAMING_SNAKE_CASE            = processor.get_labels()
				__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)
		except KeyError:
				raise ValueError("""Task not found: %s""" % (data_args.task_name)				)

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		__SCREAMING_SNAKE_CASE            = AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,				num_labels=UpperCamelCase_  ,				finetuning_task=data_args.task_name  ,				cache_dir=model_args.cache_dir  ,				)
		__SCREAMING_SNAKE_CASE            = AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,				cache_dir=model_args.cache_dir  ,				)
		__SCREAMING_SNAKE_CASE            = AutoModelForMultipleChoice.from_pretrained(
		    model_args.model_name_or_path  ,				from_tf=bool(""".ckpt""" in model_args.model_name_or_path				)  ,				config=UpperCamelCase_  ,				cache_dir=model_args.cache_dir  ,				)

		# Get datasets
		__SCREAMING_SNAKE_CASE            = (
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir  ,				tokenizer=UpperCamelCase_  ,				task=data_args.task_name  ,				max_seq_length=data_args.max_seq_length  ,				overwrite_cache=data_args.overwrite_cache  ,				mode=Split.train  ,				)
		    if training_args.do_train
		    else None
		)
		__SCREAMING_SNAKE_CASE            = (
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir  ,				tokenizer=UpperCamelCase_  ,				task=data_args.task_name  ,				max_seq_length=data_args.max_seq_length  ,				overwrite_cache=data_args.overwrite_cache  ,				mode=Split.dev  ,				)
		    if training_args.do_eval
		    else None
		)

		def compute_metrics(UpperCamelCase_				) -> Dict:
				__SCREAMING_SNAKE_CASE            = np.argmax(p.predictions  ,				axis=1				)
				return {"acc": simple_accuracy(UpperCamelCase_  ,				p.label_ids				)}

		# Data collator
		__SCREAMING_SNAKE_CASE            = DataCollatorWithPadding(UpperCamelCase_  ,				pad_to_multiple_of=8				) if training_args.fpaa else None

		# Initialize our Trainer
		__SCREAMING_SNAKE_CASE            = Trainer(
		    model=UpperCamelCase_  ,				args=UpperCamelCase_  ,				train_dataset=UpperCamelCase_  ,				eval_dataset=UpperCamelCase_  ,				compute_metrics=UpperCamelCase_  ,				data_collator=UpperCamelCase_  ,				)

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
		__SCREAMING_SNAKE_CASE            = {}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""				)

				__SCREAMING_SNAKE_CASE            = trainer.evaluate()

				__SCREAMING_SNAKE_CASE            = os.path.join(training_args.output_dir  ,				"""eval_results.txt"""				)
				if trainer.is_world_master():
						with open(UpperCamelCase_  ,				"""w"""				) as writer:
								logger.info("""***** Eval results *****"""				)
								for key, value in result.items():
										logger.info("""  %s = %s"""  ,				UpperCamelCase_  ,				UpperCamelCase_				)
										writer.write("""%s = %s\n""" % (key, value)				)

								results.update(UpperCamelCase_				)

		return results



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
							main()







def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''simple docstring'''






    global f  # a global dp table for knapsack
    if f[i][j] < 0:
        if j < wt[i - 1]:
            A__          =   mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            A__          =   max(
                mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )    ,			mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			j - wt[i - 1]      ) + val[i - 1]    ,			)
        A__          =   val
    return f[i][j]
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Tuple:





    '''simple docstring'''






    A__          =   [[0] * (w + 1) for _ in range(n + 1      )]

    for i in range(1    ,			n + 1      ):
        for w_ in range(1    ,			w + 1      ):
            if wt[i - 1] <= w_:
                A__          =   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]    ,			dp[i - 1][w_]      )
            else:
                A__          =   dp[i - 1][w_]

    return dp[n][w_], dp
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list      )							->      Union[str, Any]:





    '''simple docstring'''






    if not (isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      ) and isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      )):
        raise ValueError(
            'Both the weights and values vectors must be either lists or tuples'      )

    A__          =   len(SCREAMING_SNAKE_CASE__      )
    if num_items != len(SCREAMING_SNAKE_CASE__      ):
        A__          =   (
            'The number of weights must be the same as the number of values.\n'
            f'But got {num_items} weights and {len(SCREAMING_SNAKE_CASE__      )} values'
        )
        raise ValueError(SCREAMING_SNAKE_CASE__      )
    for i in range(SCREAMING_SNAKE_CASE__      ):
        if not isinstance(wt[i]    ,			SCREAMING_SNAKE_CASE__      ):
            A__          =   (
                'All weights must be integers but got weight of '
                f'type {type(wt[i]      )} at index {i}'
            )
            raise TypeError(SCREAMING_SNAKE_CASE__      )

    A__      ,						A__          =   knapsack(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   set()
    _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    return optimal_val, example_optional_set
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	set      )							->      Optional[int]:





    '''simple docstring'''






    if i > 0 and j > 0:
        if dp[i - 1][j] == dp[i][j]:
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            optimal_set.add(SCREAMING_SNAKE_CASE__      )
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			j - wt[i - 1]    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     lowercase_       		=  [3, 2, 4, 4]
     lowercase_       		=  [4, 3, 2, 3]
     lowercase_       		=  4
     lowercase_       		=  6
     lowercase_       		=  [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     lowercase_		,			lowercase_       		=  knapsack(w, wt, val, n)
     print(optimal_solution)
     print(mf_knapsack(n, wt, val, w))  # switched the n and w

     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4
     lowercase_		,			lowercase_       		=  knapsack_with_example_solution(w, wt, val)
     assert optimal_solution == 8
     assert optimal_subset == {3, 4}
     print("optimal_value = ", optimal_solution)
     print("An optimal subset corresponding to the optimal value", optimal_subset)

