



import math

def        a						(      _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					int = 0 ,   _UpperCAmelCase							:					int = 0						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	end or len(_UpperCAmelCase						)
		for i in range(_UpperCAmelCase ,   _UpperCAmelCase						):
				__UpperCAmelCase    :     Tuple													=	i
				__UpperCAmelCase    :     Tuple													=	array[i]
				while temp_index != start and temp_index_value < array[temp_index - 1]:
						__UpperCAmelCase    :     List[Any]													=	array[temp_index - 1]
						temp_index -= 1
				__UpperCAmelCase    :     List[Any]													=	temp_index_value
		return array

def        a						(      _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					int						):  # Max Heap







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	index
		__UpperCAmelCase    :     Dict													=	2 * index + 1  # Left Node
		__UpperCAmelCase    :     Tuple													=	2 * index + 2  # Right Node

		if left_index < heap_size and array[largest] < array[left_index]:
				__UpperCAmelCase    :     List[Any]													=	left_index

		if right_index < heap_size and array[largest] < array[right_index]:
				__UpperCAmelCase    :     str													=	right_index

		if largest != index:
				__UpperCAmelCase							,					__UpperCAmelCase    :     List[str]													=	array[largest], array[index]
				heapify(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

def        a						(      _UpperCAmelCase							:					list						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	len(_UpperCAmelCase						)

		for i in range(n // 2 ,   -1 ,   -1						):
				heapify(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

		for i in range(n - 1 ,   0 ,   -1						):
				__UpperCAmelCase							,					__UpperCAmelCase    :     str													=	array[0], array[i]
				heapify(_UpperCAmelCase ,   0 ,   _UpperCAmelCase						)

		return array

def        a						(      _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		if (array[first_index] > array[middle_index]) != (
		    array[first_index] > array[last_index]
		):
				return array[first_index]
		elif (array[middle_index] > array[first_index]) != (
		    array[middle_index] > array[last_index]
		):
				return array[middle_index]
		else:
				return array[last_index]

def        a						(      _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	low
		__UpperCAmelCase    :     Optional[int]													=	high
		while True:
				while array[i] < pivot:
						i += 1
				j -= 1
				while pivot < array[j]:
						j -= 1
				if i >= j:
						return i
				__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	array[j], array[i]
				i += 1

def        a						(      _UpperCAmelCase							:					list						):







		'''simple docstring'''





		if len(_UpperCAmelCase						) == 0:
				return array
		__UpperCAmelCase    :     int													=	2 * math.ceil(math.loga(len(_UpperCAmelCase						)						)						)
		__UpperCAmelCase    :     List[Any]													=	16
		return intro_sort(_UpperCAmelCase ,   0 ,   len(_UpperCAmelCase						) ,   _UpperCAmelCase ,   _UpperCAmelCase						)

def        a						(      _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		while end - start > size_threshold:
				if max_depth == 0:
						return heap_sort(_UpperCAmelCase						)
				max_depth -= 1
				__UpperCAmelCase    :     Optional[Any]													=	median_of_a(_UpperCAmelCase ,   _UpperCAmelCase ,   start + ((end - start) // 2) + 1 ,   end - 1						)
				__UpperCAmelCase    :     List[str]													=	partition(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
				intro_sort(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	p
		return insertion_sort(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__A   			=input("Enter numbers separated by a comma : ").strip()
				__A   			=[float(item) for item in user_input.split(",")]
				print(sort(unsorted))










import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__A   			=getLogger(__name__)


__A   			="cuda" if torch.cuda.is_available() else "cpu"

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int = 8 ,   _UpperCAmelCase							:					str = DEFAULT_DEVICE ,   _UpperCAmelCase							:					Optional[Any]=False ,   _UpperCAmelCase							:					Optional[int]="summarization" ,   _UpperCAmelCase							:					Dict=None ,   **_UpperCAmelCase							:					List[str] ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	Path(_UpperCAmelCase						).open('''w''' ,   encoding='''utf-8'''						)
		__UpperCAmelCase    :     Optional[int]													=	str(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase						).to(_UpperCAmelCase						)
		if fpaa:
				__UpperCAmelCase    :     Dict													=	model.half()

		__UpperCAmelCase    :     Tuple													=	AutoTokenizer.from_pretrained(_UpperCAmelCase						)
		logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'						)  # if this is wrong, check config.model_type.

		__UpperCAmelCase    :     Dict													=	time.time()
		# update config with task specific params
		use_task_specific_params(_UpperCAmelCase ,   _UpperCAmelCase						)
		if prefix is None:
				__UpperCAmelCase    :     List[Any]													=	prefix or getattr(model.config ,   '''prefix''' ,   ''''''						) or ''''''
		for examples_chunk in tqdm(list(chunks(_UpperCAmelCase ,   _UpperCAmelCase						)						)						):
				__UpperCAmelCase    :     Any													=	[prefix + text for text in examples_chunk]
				__UpperCAmelCase    :     Union[str, Any]													=	tokenizer(_UpperCAmelCase ,   return_tensors='''pt''' ,   truncation=_UpperCAmelCase ,   padding='''longest'''						).to(_UpperCAmelCase						)
				__UpperCAmelCase    :     Dict													=	model.generate(
				    input_ids=batch.input_ids ,   attention_mask=batch.attention_mask ,   **_UpperCAmelCase ,   )
				__UpperCAmelCase    :     List[Any]													=	tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase ,   clean_up_tokenization_spaces=_UpperCAmelCase						)
				for hypothesis in dec:
						fout.write(hypothesis + '''\n'''						)
						fout.flush()
		fout.close()
		__UpperCAmelCase    :     Dict													=	int(time.time() - start_time						)  # seconds
		__UpperCAmelCase    :     Union[str, Any]													=	len(_UpperCAmelCase						)
		return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,   4						)}

def        a						(      ):







		'''simple docstring'''





		return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)

def        a						(      _UpperCAmelCase							:					Any=True						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	argparse.ArgumentParser()
		parser.add_argument('''model_name''' ,   type=_UpperCAmelCase ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''						)
		parser.add_argument('''input_path''' ,   type=_UpperCAmelCase ,   help='''like cnn_dm/test.source'''						)
		parser.add_argument('''save_path''' ,   type=_UpperCAmelCase ,   help='''where to save summaries'''						)
		parser.add_argument('''--reference_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''like cnn_dm/test.target'''						)
		parser.add_argument('''--score_path''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default='''metrics.json''' ,   help='''where to save metrics'''						)
		parser.add_argument('''--device''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''cuda, cuda:1, cpu etc.'''						)
		parser.add_argument(
		    '''--prefix''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''will be added to the begininng of src examples'''						)
		parser.add_argument('''--task''' ,   type=_UpperCAmelCase ,   default='''summarization''' ,   help='''used for task_specific_params + metrics'''						)
		parser.add_argument('''--bs''' ,   type=_UpperCAmelCase ,   default=8 ,   required=_UpperCAmelCase ,   help='''batch size'''						)
		parser.add_argument(
		    '''--n_obs''' ,   type=_UpperCAmelCase ,   default=-1 ,   required=_UpperCAmelCase ,   help='''How many observations. Defaults to all.'''						)
		parser.add_argument('''--fp16''' ,   action='''store_true'''						)
		parser.add_argument('''--dump-args''' ,   action='''store_true''' ,   help='''print the custom hparams with the results'''						)
		parser.add_argument(
		    '''--info''' ,   nargs='''?''' ,   type=_UpperCAmelCase ,   const=datetime_now() ,   help=(
		        '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
		        ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
		    ) ,   )
		# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
		__UpperCAmelCase							,					__UpperCAmelCase    :     Dict													=	parser.parse_known_args()
		__UpperCAmelCase    :     Optional[Any]													=	parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase						)
		if parsed_args and verbose:
				print(f'parsed the following generate kwargs: {parsed_args}'						)
		__UpperCAmelCase    :     Optional[int]													=	[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
		if args.n_obs > 0:
				__UpperCAmelCase    :     Optional[int]													=	examples[: args.n_obs]
		Path(args.save_path						).parent.mkdir(exist_ok=_UpperCAmelCase						)

		if args.reference_path is None and Path(args.score_path						).exists():
				warnings.warn(f'score_path {args.score_path} will be overwritten unless you type ctrl-c.'						)

		if args.device == "cpu" and args.fpaa:
				# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
				raise ValueError('''Can\'t mix --fp16 and --device cpu'''						)

		__UpperCAmelCase    :     int													=	generate_summaries_or_translations(
		    _UpperCAmelCase ,   args.save_path ,   args.model_name ,   batch_size=args.bs ,   device=args.device ,   fpaa=args.fpaa ,   task=args.task ,   prefix=args.prefix ,   **_UpperCAmelCase ,   )

		if args.reference_path is None:
				return {}

		# Compute scores
		__UpperCAmelCase    :     str													=	calculate_bleu if '''translation''' in args.task else calculate_rouge
		__UpperCAmelCase    :     Tuple													=	[x.rstrip() for x in open(args.save_path						).readlines()]
		__UpperCAmelCase    :     int													=	[x.rstrip() for x in open(args.reference_path						).readlines()][: len(_UpperCAmelCase						)]
		__UpperCAmelCase    :     dict													=	score_fn(_UpperCAmelCase ,   _UpperCAmelCase						)
		scores.update(_UpperCAmelCase						)

		if args.dump_args:
				scores.update(_UpperCAmelCase						)
		if args.info:
				__UpperCAmelCase    :     Dict													=	args.info

		if verbose:
				print(_UpperCAmelCase						)

		if args.score_path is not None:
				json.dump(_UpperCAmelCase ,   open(args.score_path ,   '''w'''						)						)

		return scores


if __name__ == "__main__":
				# Usage for MT:
				# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
				run_generate(verbose=True)





