

def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			generate_pascal_triangle(A    )
	for row_idx in range(A    ):
		# Print left spaces
		for _ in range(num_rows - row_idx - 1    ):
			print(end=" "    )
		# Print row values
		for col_idx in range(row_idx + 1    ):
			if col_idx != row_idx:
				print(triangle[row_idx][col_idx]       ,     end=" "    )
			else:
				print(triangle[row_idx][col_idx]       ,     end=""    )
		print()



def       __lowerCAmelCase      (						A    ):

	if not isinstance(A       ,     A    ):
		raise TypeError("The input value of 'num_rows' should be 'int'"    )

	if num_rows == 0:
		return []
	elif num_rows < 0:
		raise ValueError(
		    "The input value of 'num_rows' should be greater than or equal to 0"    )

	UpperCAmelCase_       				=			[]
	for current_row_idx in range(A    ):
		UpperCAmelCase_       				=			populate_current_row(A       ,     A    )
		triangle.append(A    )
	return triangle



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			[-1] * (current_row_idx + 1)
	# first and last elements of current row are equal to 1
	UpperCAmelCase_				,   UpperCAmelCase_       				=			1, 1
	for current_col_idx in range(1       ,     A    ):
		calculate_current_element(
		    A       ,     A       ,     A       ,     A    )
	return current_row



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A       ,     ):
	UpperCAmelCase_       				=			triangle[current_row_idx - 1][current_col_idx - 1]
	UpperCAmelCase_       				=			triangle[current_row_idx - 1][current_col_idx]
	UpperCAmelCase_       				=			above_to_left_elt + above_to_right_elt



def       __lowerCAmelCase      (						A    ):

	if not isinstance(A       ,     A    ):
		raise TypeError("The input value of 'num_rows' should be 'int'"    )

	if num_rows == 0:
		return []
	elif num_rows < 0:
		raise ValueError(
		    "The input value of 'num_rows' should be greater than or equal to 0"    )

	UpperCAmelCase_       				=			[[1]]

	for row_index in range(1       ,     A    ):
		UpperCAmelCase_       				=			[0] + result[-1] + [0]
		UpperCAmelCase_       				=			row_index + 1
		# Calculate the number of distinct elements in a row
		UpperCAmelCase_       				=			sum(divmod(A       ,     2    )    )
		UpperCAmelCase_       				=			[
		    temp_row[i - 1] + temp_row[i] for i in range(1       ,     distinct_elements + 1    )
		]
		UpperCAmelCase_       				=			row_first_half[: (row_index + 1) // 2]
		row_second_half.reverse()
		UpperCAmelCase_       				=			row_first_half + row_second_half
		result.append(A    )

	return result



def       __lowerCAmelCase      (						):
	from collections.abc import Callable
	from timeit import timeit

	def benchmark_a_function(A       ,     A    ) -> None:
		UpperCAmelCase_       				=			F"{func.__name__}({value})"
		UpperCAmelCase_       				=			timeit(F"__main__.{call}"       ,     setup="import __main__"    )
		# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
		print(F"{call:38} -- {timing:.4f} seconds"    )

	for value in range(15    ):  # (1, 7, 14):
		for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
			benchmark_a_function(A       ,     A    )
		print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()


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


_a:    int 							=			logging.getLogger(__name__)



def       __lowerCAmelCase      (						A       ,     A    ):
	return (preds == labels).mean()

@dataclass
class 						__UpperCamelCase    :
	SCREAMING_SNAKE_CASE__    				=     field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     ,     )


@dataclass
class 						__UpperCamelCase    :
	SCREAMING_SNAKE_CASE__    				=     field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()						)}						)
	SCREAMING_SNAKE_CASE__    				=     field(metadata={'help': 'Should contain the data files for the task.'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=1_28     ,     metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }     ,     )
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     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.

	UpperCAmelCase_       				=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			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"       ,     A    )

	# Set seed
	set_seed(training_args.seed    )

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

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

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

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

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

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

		UpperCAmelCase_       				=			trainer.evaluate()

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

				results.update(A    )

	return results



def       __lowerCAmelCase      (						A    ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
				main()