
"""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


_lowercase				 =					logging.getLogger(__name__)






def    _snake_case					(		snake_case__ :  List[str]						,   snake_case__ :  Dict  ):
	return (preds == labels).mean()







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


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







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


						_lowerCamelCase:     str   =     field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()     )}     )
						_lowerCamelCase:     str   =     field(metadata={'''help''': '''Should contain the data files for the task.'''}     )
						_lowerCamelCase:     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.'''
						        )
						    }		,       )
						_lowerCamelCase:     bool   =     field(
						    default=SCREAMING_SNAKE_CASE_		,       metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )






def    _snake_case					(		):
	A     = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
	A ,   A ,   A     = 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'						,   snake_case__  )

	# Set seed
	set_seed(training_args.seed  )

	try:
		A     = processors[data_args.task_name]()
		A     = processor.get_labels()
		A     = len(snake_case__  )
	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.

	A     = AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,   num_labels=snake_case__						,   finetuning_task=data_args.task_name						,   cache_dir=model_args.cache_dir						,   )
	A     = 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						,   )
	A     = AutoModelForMultipleChoice.from_pretrained(
	    model_args.model_name_or_path						,   from_tf=bool('.ckpt' in model_args.model_name_or_path  )						,   config=snake_case__						,   cache_dir=model_args.cache_dir						,   )

	# Get datasets
	A     = (
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir						,   tokenizer=snake_case__						,   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
	)
	A     = (
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir						,   tokenizer=snake_case__						,   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(snake_case__ :  EvalPrediction  ) -> Dict:
		A     = np.argmax(p.predictions						,   axis=1  )
		return {"acc": simple_accuracy(snake_case__						,   p.label_ids  )}

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

	# Initialize our Trainer
	A     = Trainer(
	    model=snake_case__						,   args=snake_case__						,   train_dataset=snake_case__						,   eval_dataset=snake_case__						,   compute_metrics=snake_case__						,   data_collator=snake_case__						,   )

	# 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
	A     = {}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'  )

		A     = trainer.evaluate()

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

				results.update(snake_case__  )

	return results






def    _snake_case					(		snake_case__ :  List[str]  ):
	main()


if __name__ == "__main__":
		main()

"""simple docstring"""

from __future__ import annotations

from collections.abc import Callable

SCREAMING_SNAKE_CASE_            =    list[list[float | int]]




def      lowercase__    (       lowerCAmelCase					:       Matrix			,					lowerCAmelCase					:       Matrix			)			->   Matrix:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       [[0 for _ in range(size + 1			)] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for row in range(lowerCAmelCase			):
        for col in range(lowerCAmelCase			):
            UpperCAmelCase          =       matrix[row][col]

        UpperCAmelCase          =       vector[row][0]

    UpperCAmelCase          =       0
    UpperCAmelCase          =       0
    while row < size and col < size:
        # pivoting
        UpperCAmelCase          =       max((abs(augmented[rowa][col]			), rowa) for rowa in range(lowerCAmelCase			,					lowerCAmelCase			)			)[
            1
        ]
        if augmented[pivot_row][col] == 0:
            col += 1
            continue
        else:
            UpperCAmelCase      ,				UpperCAmelCase          =       augmented[pivot_row], augmented[row]

        for rowa in range(row + 1			,					lowerCAmelCase			):
            UpperCAmelCase          =       augmented[rowa][col] / augmented[row][col]
            UpperCAmelCase          =       0
            for cola in range(col + 1			,					size + 1			):
                augmented[rowa][cola] -= augmented[row][cola] * ratio

        row += 1
        col += 1

    # back substitution
    for col in range(1			,					lowerCAmelCase			):
        for row in range(lowerCAmelCase			):
            UpperCAmelCase          =       augmented[row][col] / augmented[col][col]
            for cola in range(lowerCAmelCase			,					size + 1			):
                augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
    return [
        [round(augmented[row][size] / augmented[row][row]			,					10			)] for row in range(lowerCAmelCase			)
    ]




def      lowercase__    (       lowerCAmelCase					:       list[int]			)			->   Callable[[int], int]:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       [[0 for _ in range(lowerCAmelCase			)] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       [[0] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for x_val, y_val in enumerate(lowerCAmelCase			):
        for col in range(lowerCAmelCase			):
            UpperCAmelCase          =       (x_val + 1) ** (size - col - 1)
        UpperCAmelCase          =       y_val

    UpperCAmelCase          =       solve(lowerCAmelCase			,					lowerCAmelCase			)

    def interpolated_func(lowerCAmelCase					:       int			) -> int:
        return sum(
            round(coeffs[x_val][0]			) * (var ** (size - x_val - 1))
            for x_val in range(lowerCAmelCase			)			)

    return interpolated_func




def      lowercase__    (       lowerCAmelCase					:       int			)			->   int:






    """simple docstring"""




    return (
        1
        - variable
        + variable**2
        - variable**3
        + variable**4
        - variable**5
        + variable**6
        - variable**7
        + variable**8
        - variable**9
        + variable**10
    )




def      lowercase__    (       lowerCAmelCase					:       Callable[[int], int] = question_function			,					lowerCAmelCase					:       int = 10			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       [func(lowerCAmelCase			) for x_val in range(1			,					order + 1			)]

    UpperCAmelCase          =       [
        interpolate(data_points[:max_coeff]			) for max_coeff in range(1			,					order + 1			)
    ]

    UpperCAmelCase          =       0
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for poly in polynomials:
        UpperCAmelCase          =       1
        while func(lowerCAmelCase			) == poly(lowerCAmelCase			):
            x_val += 1

        ret += poly(lowerCAmelCase			)

    return ret


if __name__ == "__main__":
   print(F'{solution() = }')



