

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							:  int            =						getLogger(__name__)







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

						SCREAMING_SNAKE_CASE_:			int  		=	Path(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	save_dir.joinpath(f"rank_{local_rank}_output.json"   )
						torch.cuda.set_device(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase   ).cuda()
						if fpaa:
												SCREAMING_SNAKE_CASE_:			int  		=	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_:			Any  		=	generate_kwargs.pop("num_beams"    ,     model.config.num_beams   )  # AttributeError risk?
						if num_return_sequences > num_beams:
												SCREAMING_SNAKE_CASE_:			int  		=	num_return_sequences

						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	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_:			Optional[Any]  		=	tokenizer.model_max_length
						if prefix is None:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	prefix or getattr(model.config    ,     "prefix"    ,     ""   ) or ""
						SCREAMING_SNAKE_CASE_:			int  		=	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_:			Tuple  		=	ds.make_sortish_sampler(_UpperCAmelCase    ,     distributed=_UpperCAmelCase    ,     add_extra_examples=_UpperCAmelCase    ,     shuffle=_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	DataLoader(_UpperCAmelCase    ,     sampler=_UpperCAmelCase    ,     batch_size=_UpperCAmelCase    ,     collate_fn=ds.collate_fn   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[]
						for batch in tqdm(_UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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_:			str  		=	tokenizer.batch_decode(_UpperCAmelCase    ,     skip_special_tokens=_UpperCAmelCase    ,     clean_up_tokenization_spaces=_UpperCAmelCase   )
												SCREAMING_SNAKE_CASE_:			Any  		=	batch["ids"]
												if num_return_sequences > 1:
																		SCREAMING_SNAKE_CASE_:			List[Any]  		=	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       A_				(   ):
						SCREAMING_SNAKE_CASE_:			Optional[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=_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_:			Any  		=	time.time()
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	parser.parse_known_args()
						SCREAMING_SNAKE_CASE_:			Tuple  		=	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_:			Tuple  		=	Path(args.save_dir + "_tmp"   )
						Path(_UpperCAmelCase   ).mkdir(exist_ok=_UpperCAmelCase   )  # this handles locking.
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	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_:			Dict  		=	{}
						if args.src_lang is not None:
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	args.src_lang
						if args.tgt_lang is not None:
												SCREAMING_SNAKE_CASE_:			int  		=	args.tgt_lang

						Path(args.save_dir   ).mkdir(exist_ok=_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	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_:			Optional[Any]  		=	Path(args.save_dir   )
												save_dir.mkdir(exist_ok=_UpperCAmelCase   )
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	gather_results_from_each_node(_UpperCAmelCase    ,     _UpperCAmelCase    ,     args.sync_timeout   )
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	combine_partial_results(_UpperCAmelCase   )
												if args.num_return_sequences > 1:
																		SCREAMING_SNAKE_CASE_:			str  		=	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_:			int  		=	Path(args.data_dir   ).joinpath(args.type_path + ".target"   )
												with open(_UpperCAmelCase   ) as f:
																		SCREAMING_SNAKE_CASE_:			List[str]  		=	[x.rstrip() for x in f.readlines()][: len(_UpperCAmelCase   )]

												# Calculate metrics, save metrics,  and save _generations.txt
												SCREAMING_SNAKE_CASE_:			Dict  		=	"translation" in args.task
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	calculate_bleu if calc_bleu else calculate_rouge
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"bleu" if calc_bleu else "rouge"
												SCREAMING_SNAKE_CASE_:			Dict  		=	score_fn(_UpperCAmelCase    ,     _UpperCAmelCase   )
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	len(_UpperCAmelCase   )
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	time.time() - start_time
												SCREAMING_SNAKE_CASE_:			int  		=	round(runtime / metrics["n_obs"]    ,     4   )
												SCREAMING_SNAKE_CASE_:			Any  		=	num_replicas
												# TODO(@stas00): add whatever metadata to metrics
												SCREAMING_SNAKE_CASE_:			Dict  		=	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       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			int  		=	[]
						for partial_result in partial_results:
												records.extend(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	sorted(_UpperCAmelCase    ,     key=lambda _UpperCAmelCase   : x["id"]   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	[x["pred"] for x in records]
						return preds







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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase							:  Optional[int]            =						{"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any            =						["""YolosFeatureExtractor"""]
						lowerCAmelCase							:  Tuple            =						["""YolosImageProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any            =						[
						    """YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """YolosForObjectDetection""",
						    """YolosModel""",
						    """YolosPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_yolos import YolosFeatureExtractor
												from .image_processing_yolos import YolosImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_yolos import (
												    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    YolosForObjectDetection,
												    YolosModel,
												    YolosPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase							:  int            =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






