import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__lowerCamelCase      :			List[str]							       =		getLogger(__name__)



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	int = 8			,					__UpperCamelCase   :	int = 10_24			,					__UpperCamelCase   :	Tuple="val"			,					__UpperCamelCase   :	int=None			,					__UpperCamelCase   :	Tuple=False			,					__UpperCamelCase   :	Optional[int]="summarization"			,					__UpperCamelCase   :	int=None			,					__UpperCamelCase   :	str=1			,					__UpperCamelCase   :	Dict = None			,					__UpperCamelCase   :	Union[str, Any]=""			,					**__UpperCamelCase   :	Tuple			,					)							-> Any:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__      							=				str(__UpperCamelCase     )
		assert local_rank is not None
		torch.distributed.init_process_group(backend="""nccl"""			,					rank=__UpperCamelCase     )

		SCREAMING_SNAKE_CASE__      							=				Path(__UpperCamelCase     )
		SCREAMING_SNAKE_CASE__      							=				save_dir.joinpath(f"""rank_{local_rank}_output.json"""     )
		torch.cuda.set_device(__UpperCamelCase     )
		SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase     ).cuda()
		if fpaa:
				SCREAMING_SNAKE_CASE__      							=				model.half()
		# determine if we need to increase num_beams
		use_task_specific_params(__UpperCamelCase			,					__UpperCamelCase     )  # update config with task specific params
		SCREAMING_SNAKE_CASE__      							=				generate_kwargs.pop("""num_beams"""			,					model.config.num_beams     )  # AttributeError risk?
		if num_return_sequences > num_beams:
				SCREAMING_SNAKE_CASE__      							=				num_return_sequences

		SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained(__UpperCamelCase     )
		logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.

		if max_source_length is None:
				SCREAMING_SNAKE_CASE__      							=				tokenizer.model_max_length
		if prefix is None:
				SCREAMING_SNAKE_CASE__      							=				prefix or getattr(model.config			,					"""prefix"""			,					""""""     ) or ''
		SCREAMING_SNAKE_CASE__      							=				SeqaSeqDataset(
		    __UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					max_target_length=10_24			,					type_path=__UpperCamelCase			,					n_obs=__UpperCamelCase			,					prefix=__UpperCamelCase			,					**__UpperCamelCase			,					)
		# I set shuffle=True for a more accurate progress bar.
		# If all the longest samples are first, the prog bar estimate is too high at the beginning.
		SCREAMING_SNAKE_CASE__      							=				ds.make_sortish_sampler(__UpperCamelCase			,					distributed=__UpperCamelCase			,					add_extra_examples=__UpperCamelCase			,					shuffle=__UpperCamelCase     )
		SCREAMING_SNAKE_CASE__      							=				DataLoader(__UpperCamelCase			,					sampler=__UpperCamelCase			,					batch_size=__UpperCamelCase			,					collate_fn=ds.collate_fn     )
		SCREAMING_SNAKE_CASE__      							=				[]
		for batch in tqdm(__UpperCamelCase     ):
				SCREAMING_SNAKE_CASE__      							=				model.generate(
				    input_ids=batch["""input_ids"""].to(model.device     )			,					attention_mask=batch["""attention_mask"""].to(model.device     )			,					num_return_sequences=__UpperCamelCase			,					num_beams=__UpperCamelCase			,					**__UpperCamelCase			,					)
				SCREAMING_SNAKE_CASE__      							=				tokenizer.batch_decode(__UpperCamelCase			,					skip_special_tokens=__UpperCamelCase			,					clean_up_tokenization_spaces=__UpperCamelCase     )
				SCREAMING_SNAKE_CASE__      							=				batch['ids']
				if num_return_sequences > 1:
						SCREAMING_SNAKE_CASE__      							=				chunks(__UpperCamelCase			,					__UpperCamelCase     )  # batch size chunks, each of size num_return_seq
				for i, pred in enumerate(__UpperCamelCase     ):
						results.append({"""pred""": pred, """id""": ids[i].item()}     )
		save_json(__UpperCamelCase			,					__UpperCamelCase     )
		return results, sampler.num_replicas



def      __SCREAMING_SNAKE_CASE  ( )							-> int:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__      							=				argparse.ArgumentParser(
		    epilog="""Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"""     )
		parser.add_argument("""--data_dir"""			,					type=__UpperCamelCase			,					help="""like cnn_dm/test.source"""     )
		parser.add_argument(
		    """--model_name"""			,					type=__UpperCamelCase			,					help="""like facebook/bart-large-cnn,t5-base, etc."""			,					default="""sshleifer/distilbart-xsum-12-3"""			,					)
		parser.add_argument("""--save_dir"""			,					type=__UpperCamelCase			,					help="""where to save"""			,					default="""tmp_gen"""     )
		parser.add_argument("""--max_source_length"""			,					type=__UpperCamelCase			,					default=__UpperCamelCase     )
		parser.add_argument(
		    """--type_path"""			,					type=__UpperCamelCase			,					default="""test"""			,					help="""which subset to evaluate typically train/val/test"""     )
		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(
		    """--local_rank"""			,					type=__UpperCamelCase			,					default=-1			,					required=__UpperCamelCase			,					help="""should be passed by distributed.launch"""     )

		parser.add_argument(
		    """--n_obs"""			,					type=__UpperCamelCase			,					default=__UpperCamelCase			,					required=__UpperCamelCase			,					help="""How many observations. Defaults to all."""     )
		parser.add_argument(
		    """--num_return_sequences"""			,					type=__UpperCamelCase			,					default=1			,					required=__UpperCamelCase			,					help="""How many sequences to return"""     )
		parser.add_argument(
		    """--sync_timeout"""			,					type=__UpperCamelCase			,					default=6_00			,					required=__UpperCamelCase			,					help="""How long should master process wait for other processes to finish."""			,					)
		parser.add_argument("""--src_lang"""			,					type=__UpperCamelCase			,					default=__UpperCamelCase			,					required=__UpperCamelCase     )
		parser.add_argument("""--tgt_lang"""			,					type=__UpperCamelCase			,					default=__UpperCamelCase			,					required=__UpperCamelCase     )
		parser.add_argument(
		    """--prefix"""			,					type=__UpperCamelCase			,					required=__UpperCamelCase			,					default=__UpperCamelCase			,					help="""will be added to the begininng of src examples"""     )
		parser.add_argument("""--fp16"""			,					action="""store_true"""     )
		parser.add_argument("""--debug"""			,					action="""store_true"""     )
		SCREAMING_SNAKE_CASE__      							=				time.time()
		SCREAMING_SNAKE_CASE__      							=				parser.parse_known_args()
		SCREAMING_SNAKE_CASE__      							=				parse_numeric_n_bool_cl_kwargs(__UpperCamelCase     )
		if generate_kwargs and args.local_rank <= 0:
				print(f"""parsed the following generate kwargs: {generate_kwargs}"""     )
		SCREAMING_SNAKE_CASE__      							=				Path(args.save_dir + """_tmp"""     )
		Path(__UpperCamelCase     ).mkdir(exist_ok=__UpperCamelCase     )  # this handles locking.
		SCREAMING_SNAKE_CASE__      							=				list(json_save_dir.glob("""rank_*.json"""     )     )
		if intermediate_files:
				raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""     )
				# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
		SCREAMING_SNAKE_CASE__      							=				{}
		if args.src_lang is not None:
				SCREAMING_SNAKE_CASE__      							=				args.src_lang
		if args.tgt_lang is not None:
				SCREAMING_SNAKE_CASE__      							=				args.tgt_lang

		Path(args.save_dir     ).mkdir(exist_ok=__UpperCamelCase     )
		SCREAMING_SNAKE_CASE__      							=				eval_data_dir(
		    args.data_dir			,					__UpperCamelCase			,					args.model_name			,					type_path=args.type_path			,					bs=args.bs			,					fpaa=args.fpaa			,					task=args.task			,					local_rank=args.local_rank			,					n_obs=args.n_obs			,					max_source_length=args.max_source_length			,					num_return_sequences=args.num_return_sequences			,					prefix=args.prefix			,					dataset_kwargs=__UpperCamelCase			,					**__UpperCamelCase			,					)

		if args.local_rank <= 0:
				SCREAMING_SNAKE_CASE__      							=				Path(args.save_dir     )
				save_dir.mkdir(exist_ok=__UpperCamelCase     )
				SCREAMING_SNAKE_CASE__      							=				gather_results_from_each_node(__UpperCamelCase			,					__UpperCamelCase			,					args.sync_timeout     )
				SCREAMING_SNAKE_CASE__      							=				combine_partial_results(__UpperCamelCase     )
				if args.num_return_sequences > 1:
						SCREAMING_SNAKE_CASE__      							=				save_dir.joinpath("""pseudolabel_results.json"""     )
						print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""     )
						save_json(__UpperCamelCase			,					__UpperCamelCase     )
						return
				SCREAMING_SNAKE_CASE__      							=				Path(args.data_dir     ).joinpath(args.type_path + """.target"""     )
				with open(__UpperCamelCase     ) as f:
						SCREAMING_SNAKE_CASE__      							=				[x.rstrip() for x in f.readlines()][: len(__UpperCamelCase     )]

				# Calculate metrics, save metrics,  and save _generations.txt
				SCREAMING_SNAKE_CASE__      							=				'translation' in args.task
				SCREAMING_SNAKE_CASE__      							=				calculate_bleu if calc_bleu else calculate_rouge
				SCREAMING_SNAKE_CASE__      							=				'bleu' if calc_bleu else 'rouge'
				SCREAMING_SNAKE_CASE__      							=				score_fn(__UpperCamelCase			,					__UpperCamelCase     )
				SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
				SCREAMING_SNAKE_CASE__      							=				time.time() - start_time
				SCREAMING_SNAKE_CASE__      							=				round(runtime / metrics["""n_obs"""]			,					4     )
				SCREAMING_SNAKE_CASE__      							=				num_replicas
				# TODO(@stas00): add whatever metadata to metrics
				SCREAMING_SNAKE_CASE__      							=				save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""     )
				save_json(__UpperCamelCase			,					__UpperCamelCase			,					indent=__UpperCamelCase     )
				print(__UpperCamelCase     )
				write_txt_file(__UpperCamelCase			,					save_dir.joinpath(f"""{args.type_path}_generations.txt"""     )     )
				if args.debug:
						write_txt_file(__UpperCamelCase			,					save_dir.joinpath(f"""{args.type_path}.target"""     )     )
				else:
						shutil.rmtree(__UpperCamelCase     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]     )							-> Union[str, Any]:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__      							=				[]
		for partial_result in partial_results:
				records.extend(__UpperCamelCase     )
		SCREAMING_SNAKE_CASE__      							=				sorted(__UpperCamelCase			,					key=lambda __UpperCamelCase     : x["id"]     )
		SCREAMING_SNAKE_CASE__      							=				[x['pred'] for x in records]
		return preds



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	List[str]     )							-> List[str]:







		"""simple docstring"""





		SCREAMING_SNAKE_CASE__      							=				time.time()
		logger.info("""waiting for all nodes to finish"""     )
		SCREAMING_SNAKE_CASE__      							=				None
		while (time.time() - start_wait) < timeout:
				SCREAMING_SNAKE_CASE__      							=				list(save_dir.glob("""rank_*.json"""     )     )
				if len(__UpperCamelCase     ) < num_replicas:
						continue
				try:
						# make sure all json files are fully saved
						SCREAMING_SNAKE_CASE__      							=				lmap(__UpperCamelCase			,					__UpperCamelCase     )
						return json_data
				except JSONDecodeError:
						continue
		else:
				raise TimeoutError("""Rank 0 gave up on waiting for other processes"""     )
		# Unreachable


if __name__ == "__main__":
			# Usage for MT:
			run_generate()

def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  for i in range(length - 1     ):
    SCREAMING_SNAKE_CASE__      							=				i
    for k in range(i + 1			,					__UpperCamelCase     ):
      if collection[k] < collection[least]:
        SCREAMING_SNAKE_CASE__      							=				k
    if least != i:
      SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				(collection[i], collection[least])
  return collection


if __name__ == "__main__":
   __lowerCamelCase      :			Any							       =		input('''Enter numbers separated by a comma:\n''').strip()
   __lowerCamelCase      :			Dict							       =		[int(item) for item in user_input.split(''',''')]
   print(selection_sort(unsorted))
