



'''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,
)


lowercase      			=	getLogger(__name__)



def        __A   (							_SCREAMING_SNAKE_CASE     :  List[str]					,   _SCREAMING_SNAKE_CASE     :  str					,   _SCREAMING_SNAKE_CASE     :  int					,   _SCREAMING_SNAKE_CASE     :  int = 8					,   _SCREAMING_SNAKE_CASE     :  int = 1_0_2_4					,   _SCREAMING_SNAKE_CASE     :  str="val"					,   _SCREAMING_SNAKE_CASE     :  str=None					,   _SCREAMING_SNAKE_CASE     :  str=False					,   _SCREAMING_SNAKE_CASE     :  List[Any]="summarization"					,   _SCREAMING_SNAKE_CASE     :  Any=None					,   _SCREAMING_SNAKE_CASE     :  Optional[int]=1					,   _SCREAMING_SNAKE_CASE     :  str = None					,   _SCREAMING_SNAKE_CASE     :  List[Any]=""					,   **_SCREAMING_SNAKE_CASE     :  List[Any]					,   ):







					"""simple docstring"""






					__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							str(lowerCamelCase_	)
					assert local_rank is not None
					torch.distributed.init_process_group(backend="nccl"					,   rank=lowerCamelCase_	)

					__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							Path(lowerCamelCase_	)
					__SCREAMING_SNAKE_CASE				:    List[str]						    =							save_dir.joinpath(f'rank_{local_rank}_output.json'	)
					torch.cuda.set_device(lowerCamelCase_	)
					__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_	).cuda()
					if fpaa:
										__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							model.half()
					# determine if we need to increase num_beams
					use_task_specific_params(lowerCamelCase_					,   lowerCamelCase_	)  # 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				:    Union[str, Any]						    =							num_return_sequences

					__SCREAMING_SNAKE_CASE				:    Dict						    =							AutoTokenizer.from_pretrained(lowerCamelCase_	)
					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[int]						    =							tokenizer.model_max_length
					if prefix is None:
										__SCREAMING_SNAKE_CASE				:    Tuple						    =							prefix or getattr(model.config					,   "prefix"					,   ""	) or ""
					__SCREAMING_SNAKE_CASE				:    List[str]						    =							SeqaSeqDataset(
					    lowerCamelCase_					,   lowerCamelCase_					,   lowerCamelCase_					,   max_target_length=1_0_2_4					,   type_path=lowerCamelCase_					,   n_obs=lowerCamelCase_					,   prefix=lowerCamelCase_					,   **lowerCamelCase_					,   )
					# 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				:    int						    =							ds.make_sortish_sampler(lowerCamelCase_					,   distributed=lowerCamelCase_					,   add_extra_examples=lowerCamelCase_					,   shuffle=lowerCamelCase_	)
					__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							DataLoader(lowerCamelCase_					,   sampler=lowerCamelCase_					,   batch_size=lowerCamelCase_					,   collate_fn=ds.collate_fn	)
					__SCREAMING_SNAKE_CASE				:    int						    =							[]
					for batch in tqdm(lowerCamelCase_	):
										__SCREAMING_SNAKE_CASE				:    List[str]						    =							model.generate(
										    input_ids=batch["input_ids"].to(model.device	)					,   attention_mask=batch["attention_mask"].to(model.device	)					,   num_return_sequences=lowerCamelCase_					,   num_beams=lowerCamelCase_					,   **lowerCamelCase_					,   )
										__SCREAMING_SNAKE_CASE				:    List[str]						    =							tokenizer.batch_decode(lowerCamelCase_					,   skip_special_tokens=lowerCamelCase_					,   clean_up_tokenization_spaces=lowerCamelCase_	)
										__SCREAMING_SNAKE_CASE				:    str						    =							batch["ids"]
										if num_return_sequences > 1:
															__SCREAMING_SNAKE_CASE				:    List[Any]						    =							chunks(lowerCamelCase_					,   lowerCamelCase_	)  # batch size chunks, each of size num_return_seq
										for i, pred in enumerate(lowerCamelCase_	):
															results.append({"pred": pred, "id": ids[i].item()}	)
					save_json(lowerCamelCase_					,   lowerCamelCase_	)
					return results, sampler.num_replicas



def        __A   (							):







					"""simple docstring"""






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

					parser.add_argument(
					    "--n_obs"					,   type=lowerCamelCase_					,   default=lowerCamelCase_					,   required=lowerCamelCase_					,   help="How many observations. Defaults to all."	)
					parser.add_argument(
					    "--num_return_sequences"					,   type=lowerCamelCase_					,   default=1					,   required=lowerCamelCase_					,   help="How many sequences to return"	)
					parser.add_argument(
					    "--sync_timeout"					,   type=lowerCamelCase_					,   default=6_0_0					,   required=lowerCamelCase_					,   help="How long should master process wait for other processes to finish."					,   )
					parser.add_argument("--src_lang"					,   type=lowerCamelCase_					,   default=lowerCamelCase_					,   required=lowerCamelCase_	)
					parser.add_argument("--tgt_lang"					,   type=lowerCamelCase_					,   default=lowerCamelCase_					,   required=lowerCamelCase_	)
					parser.add_argument(
					    "--prefix"					,   type=lowerCamelCase_					,   required=lowerCamelCase_					,   default=lowerCamelCase_					,   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				:    List[Any]						    =							time.time()
					__SCREAMING_SNAKE_CASE				:    Tuple						    =							parser.parse_known_args()
					__SCREAMING_SNAKE_CASE				:    List[Any]						    =							parse_numeric_n_bool_cl_kwargs(lowerCamelCase_	)
					if generate_kwargs and args.local_rank <= 0:
										print(f'parsed the following generate kwargs: {generate_kwargs}'	)
					__SCREAMING_SNAKE_CASE				:    List[Any]						    =							Path(args.save_dir + "_tmp"	)
					Path(lowerCamelCase_	).mkdir(exist_ok=lowerCamelCase_	)  # this handles locking.
					__SCREAMING_SNAKE_CASE				:    Tuple						    =							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				:    Optional[Any]						    =							{}
					if args.src_lang is not None:
										__SCREAMING_SNAKE_CASE				:    Dict						    =							args.src_lang
					if args.tgt_lang is not None:
										__SCREAMING_SNAKE_CASE				:    int						    =							args.tgt_lang

					Path(args.save_dir	).mkdir(exist_ok=lowerCamelCase_	)
					__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							eval_data_dir(
					    args.data_dir					,   lowerCamelCase_					,   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=lowerCamelCase_					,   **lowerCamelCase_					,   )

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

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



def        __A   (							_SCREAMING_SNAKE_CASE     :  int	):







					"""simple docstring"""






					__SCREAMING_SNAKE_CASE				:    Any						    =							[]
					for partial_result in partial_results:
										records.extend(lowerCamelCase_	)
					__SCREAMING_SNAKE_CASE				:    Any						    =							sorted(lowerCamelCase_					,   key=lambda _SCREAMING_SNAKE_CASE	: x["id"]	)
					__SCREAMING_SNAKE_CASE				:    str						    =							[x["pred"] for x in records]
					return preds



def        __A   (							_SCREAMING_SNAKE_CASE     :  Tuple					,   _SCREAMING_SNAKE_CASE     :  Tuple					,   _SCREAMING_SNAKE_CASE     :  Tuple	):







					"""simple docstring"""






					__SCREAMING_SNAKE_CASE				:    int						    =							time.time()
					logger.info("waiting for all nodes to finish"	)
					__SCREAMING_SNAKE_CASE				:    Any						    =							None
					while (time.time() - start_wait) < timeout:
										__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							list(save_dir.glob("rank_*.json"	)	)
										if len(lowerCamelCase_	) < num_replicas:
															continue
										try:
															# make sure all json files are fully saved
															__SCREAMING_SNAKE_CASE				:    Any						    =							lmap(lowerCamelCase_					,   lowerCamelCase_	)
															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()




'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	img.shape[0], img.shape[1]

 # converting each pixel's color to its negative
 for i in range(lowerCamelCase_			):
  for j in range(lowerCamelCase_			):
   lowerCAmelCase__     :   Optional[int]     		=	[2_5_5, 2_5_5, 2_5_5] - img[i][j]

 return img


if __name__ == "__main__":
     # read original image
     snake_case									=    imread("""image_data/lena.jpg""", 1)

     # convert to its negative
     snake_case									=    convert_to_negative(img)

     # show result image
     imshow("""negative of original image""", img)
     waitKey(0)
     destroyAllWindows()





