

"""simple docstring"""





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,
)


__UpperCamelCase  :      str							      =		getLogger(__name__)

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ = 8						,     A_ = 10_24						,     A_="val"						,     A_=None						,     A_=False						,     A_="summarization"						,     A_=None						,     A_=1						,     A_ = None						,     A_=""						,     **A_						,     ):
	lowerCAmelCase__	:						Any  				=	str(A_ )
	assert local_rank is not None
	torch.distributed.init_process_group(backend='''nccl'''						,     rank=A_ )

	lowerCAmelCase__	:						Union[str, Any]  				=	Path(A_ )
	lowerCAmelCase__	:						Any  				=	save_dir.joinpath(f'rank_{local_rank}_output.json' )
	torch.cuda.set_device(A_ )
	lowerCAmelCase__	:						Tuple  				=	AutoModelForSeqaSeqLM.from_pretrained(A_ ).cuda()
	if fpaa:
		lowerCAmelCase__	:						int  				=	model.half()
	# determine if we need to increase num_beams
	use_task_specific_params(A_						,     A_ )  # update config with task specific params
	lowerCAmelCase__	:						Any  				=	generate_kwargs.pop('''num_beams'''						,     model.config.num_beams )  # AttributeError risk?
	if num_return_sequences > num_beams:
		lowerCAmelCase__	:						Union[str, Any]  				=	num_return_sequences

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

	if max_source_length is None:
		lowerCAmelCase__	:						Optional[int]  				=	tokenizer.model_max_length
	if prefix is None:
		lowerCAmelCase__	:						str  				=	prefix or getattr(model.config						,     '''prefix'''						,     '''''' ) or ''''''
	lowerCAmelCase__	:						List[Any]  				=	SeqaSeqDataset(
	    A_						,     A_						,     A_						,     max_target_length=10_24						,     type_path=A_						,     n_obs=A_						,     prefix=A_						,     **A_						,     )
	# 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.
	lowerCAmelCase__	:						List[Any]  				=	ds.make_sortish_sampler(A_						,     distributed=A_						,     add_extra_examples=A_						,     shuffle=A_ )
	lowerCAmelCase__	:						List[str]  				=	DataLoader(A_						,     sampler=A_						,     batch_size=A_						,     collate_fn=ds.collate_fn )
	lowerCAmelCase__	:						List[str]  				=	[]
	for batch in tqdm(A_ ):
		lowerCAmelCase__	:						Tuple  				=	model.generate(
		    input_ids=batch['''input_ids'''].to(model.device )						,     attention_mask=batch['''attention_mask'''].to(model.device )						,     num_return_sequences=A_						,     num_beams=A_						,     **A_						,     )
		lowerCAmelCase__	:						Tuple  				=	tokenizer.batch_decode(A_						,     skip_special_tokens=A_						,     clean_up_tokenization_spaces=A_ )
		lowerCAmelCase__	:						List[Any]  				=	batch['''ids''']
		if num_return_sequences > 1:
			lowerCAmelCase__	:						List[Any]  				=	chunks(A_						,     A_ )  # batch size chunks, each of size num_return_seq
		for i, pred in enumerate(A_ ):
			results.append({'''pred''': pred, '''id''': ids[i].item()} )
	save_json(A_						,     A_ )
	return results, sampler.num_replicas

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[Any]  				=	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=A_						,     help='''like cnn_dm/test.source''' )
	parser.add_argument(
	    '''--model_name'''						,     type=A_						,     help='''like facebook/bart-large-cnn,t5-base, etc.'''						,     default='''sshleifer/distilbart-xsum-12-3'''						,     )
	parser.add_argument('''--save_dir'''						,     type=A_						,     help='''where to save'''						,     default='''tmp_gen''' )
	parser.add_argument('''--max_source_length'''						,     type=A_						,     default=A_ )
	parser.add_argument(
	    '''--type_path'''						,     type=A_						,     default='''test'''						,     help='''which subset to evaluate typically train/val/test''' )
	parser.add_argument('''--task'''						,     type=A_						,     default='''summarization'''						,     help='''used for task_specific_params + metrics''' )
	parser.add_argument('''--bs'''						,     type=A_						,     default=8						,     required=A_						,     help='''batch size''' )
	parser.add_argument(
	    '''--local_rank'''						,     type=A_						,     default=-1						,     required=A_						,     help='''should be passed by distributed.launch''' )

	parser.add_argument(
	    '''--n_obs'''						,     type=A_						,     default=A_						,     required=A_						,     help='''How many observations. Defaults to all.''' )
	parser.add_argument(
	    '''--num_return_sequences'''						,     type=A_						,     default=1						,     required=A_						,     help='''How many sequences to return''' )
	parser.add_argument(
	    '''--sync_timeout'''						,     type=A_						,     default=6_00						,     required=A_						,     help='''How long should master process wait for other processes to finish.'''						,     )
	parser.add_argument('''--src_lang'''						,     type=A_						,     default=A_						,     required=A_ )
	parser.add_argument('''--tgt_lang'''						,     type=A_						,     default=A_						,     required=A_ )
	parser.add_argument(
	    '''--prefix'''						,     type=A_						,     required=A_						,     default=A_						,     help='''will be added to the begininng of src examples''' )
	parser.add_argument('''--fp16'''						,     action='''store_true''' )
	parser.add_argument('''--debug'''						,     action='''store_true''' )
	lowerCAmelCase__	:						List[Any]  				=	time.time()
	lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	parser.parse_known_args()
	lowerCAmelCase__	:						List[Any]  				=	parse_numeric_n_bool_cl_kwargs(A_ )
	if generate_kwargs and args.local_rank <= 0:
		print(f'parsed the following generate kwargs: {generate_kwargs}' )
	lowerCAmelCase__	:						Union[str, Any]  				=	Path(args.save_dir + '''_tmp''' )
	Path(A_ ).mkdir(exist_ok=A_ )  # this handles locking.
	lowerCAmelCase__	:						List[str]  				=	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.
	lowerCAmelCase__	:						Any  				=	{}
	if args.src_lang is not None:
		lowerCAmelCase__	:						List[Any]  				=	args.src_lang
	if args.tgt_lang is not None:
		lowerCAmelCase__	:						Tuple  				=	args.tgt_lang

	Path(args.save_dir ).mkdir(exist_ok=A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	eval_data_dir(
	    args.data_dir						,     A_						,     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=A_						,     **A_						,     )

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

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

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Dict  				=	[]
	for partial_result in partial_results:
		records.extend(A_ )
	lowerCAmelCase__	:						str  				=	sorted(A_						,     key=lambda A_ : x["id"] )
	lowerCAmelCase__	:						str  				=	[x['''pred'''] for x in records]
	return preds

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	# WAIT FOR lots of .json files
	lowerCAmelCase__	:						Union[str, Any]  				=	time.time()
	logger.info('''waiting for all nodes to finish''' )
	lowerCAmelCase__	:						Union[str, Any]  				=	None
	while (time.time() - start_wait) < timeout:
		lowerCAmelCase__	:						Union[str, Any]  				=	list(save_dir.glob('''rank_*.json''' ) )
		if len(A_ ) < num_replicas:
			continue
		try:
			# make sure all json files are fully saved
			lowerCAmelCase__	:						int  				=	lmap(A_						,     A_ )
			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()






# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase			:     int           =   subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__lowerCAmelCase			:     Any           =   (
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode('utf-8').split()
)

__lowerCAmelCase			:     str           =   '|'.join(sys.argv[1:])
__lowerCAmelCase			:     Tuple           =   re.compile(RF'''^({joined_dirs}).*?\.py$''')

__lowerCAmelCase			:     Union[str, Any]           =   [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')

