


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			:  Optional[int]     =getLogger(__name__)



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ = 8 ,   lowerCAmelCase_ = 1_024 ,   lowerCAmelCase_="val" ,   lowerCAmelCase_=None ,   lowerCAmelCase_=False ,   lowerCAmelCase_="summarization" ,   lowerCAmelCase_=None ,   lowerCAmelCase_=1 ,   lowerCAmelCase_ = None ,   lowerCAmelCase_="" ,   **lowerCAmelCase_ ,   )-> Dict:
				lowerCAmelCase_ :      List[str]    				=				str(lowerCAmelCase_ )
				assert local_rank is not None
				torch.distributed.init_process_group(backend='''nccl''' ,   rank=lowerCAmelCase_ )

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

				lowerCAmelCase_ :      Union[str, Any]    				=				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:
								lowerCAmelCase_ :      List[str]    				=				tokenizer.model_max_length
				if prefix is None:
								lowerCAmelCase_ :      List[str]    				=				prefix or getattr(model.config ,   '''prefix''' ,   '''''' ) or ''''''
				lowerCAmelCase_ :      Any    				=				SeqaSeqDataset(
				    lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   max_target_length=1_024 ,   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.
				lowerCAmelCase_ :      int    				=				ds.make_sortish_sampler(lowerCAmelCase_ ,   distributed=lowerCAmelCase_ ,   add_extra_examples=lowerCAmelCase_ ,   shuffle=lowerCAmelCase_ )
				lowerCAmelCase_ :      Union[str, Any]    				=				DataLoader(lowerCAmelCase_ ,   sampler=lowerCAmelCase_ ,   batch_size=lowerCAmelCase_ ,   collate_fn=ds.collate_fn )
				lowerCAmelCase_ :      Union[str, Any]    				=				[]
				for batch in tqdm(lowerCAmelCase_ ):
								lowerCAmelCase_ :      Optional[int]    				=				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_ ,   )
								lowerCAmelCase_ :      Union[str, Any]    				=				tokenizer.batch_decode(lowerCAmelCase_ ,   skip_special_tokens=lowerCAmelCase_ ,   clean_up_tokenization_spaces=lowerCAmelCase_ )
								lowerCAmelCase_ :      Dict    				=				batch['''ids''']
								if num_return_sequences > 1:
												lowerCAmelCase_ :      int    				=				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 			lowerCAmelCase (		)-> Optional[int]:
				lowerCAmelCase_ :      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=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=600 ,   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''' )
				lowerCAmelCase_ :      List[str]    				=				time.time()
				lowerCAmelCase_				,  lowerCAmelCase_ :      Optional[Any]    				=				parser.parse_known_args()
				lowerCAmelCase_ :      Tuple    				=				parse_numeric_n_bool_cl_kwargs(lowerCAmelCase_ )
				if generate_kwargs and args.local_rank <= 0:
								print(f"""parsed the following generate kwargs: {generate_kwargs}""" )
				lowerCAmelCase_ :      Dict    				=				Path(args.save_dir + '''_tmp''' )
				Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )  # this handles locking.
				lowerCAmelCase_ :      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.
				lowerCAmelCase_ :      List[str]    				=				{}
				if args.src_lang is not None:
								lowerCAmelCase_ :      List[Any]    				=				args.src_lang
				if args.tgt_lang is not None:
								lowerCAmelCase_ :      Optional[int]    				=				args.tgt_lang

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

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



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



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase			:  str     =logging.get_logger(__name__)







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							"""encoder-decoder"""
						SCREAMING_SNAKE_CASE__      :  str					=							True




						def __init__(				self   ,						**__lowercase							)					->    Union[str, Any]:
										super().__init__(**__lowercase							)
										assert (
										    "encoder" in kwargs and "decoder" in kwargs
										), "Config has to be initialized with encoder and decoder config"
										lowerCAmelCase_ :      str    				=				kwargs.pop('''encoder'''							)
										lowerCAmelCase_ :      int    				=				encoder_config.pop('''model_type'''							)
										lowerCAmelCase_ :      Optional[Any]    				=				kwargs.pop('''decoder'''							)
										lowerCAmelCase_ :      Optional[Any]    				=				decoder_config.pop('''model_type'''							)

										from ..auto.configuration_auto import AutoConfig

										lowerCAmelCase_ :      Union[str, Any]    				=				AutoConfig.for_model(__lowercase   ,						**__lowercase							)
										lowerCAmelCase_ :      List[str]    				=				AutoConfig.for_model(__lowercase   ,						**__lowercase							)
										lowerCAmelCase_ :      Any    				=				True




						@classmethod
						def 						lowercase_ (				cls   ,						__lowercase   ,						__lowercase   ,						**__lowercase							)					->    PretrainedConfig:
										logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''							)
										lowerCAmelCase_ :      int    				=				True
										lowerCAmelCase_ :      List[Any]    				=				True

										return cls(encoder=encoder_config.to_dict()   ,						decoder=decoder_config.to_dict()   ,						**__lowercase							)




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Optional[Any]    				=				copy.deepcopy(self.__dict__							)
										lowerCAmelCase_ :      List[str]    				=				self.encoder.to_dict()
										lowerCAmelCase_ :      Dict    				=				self.decoder.to_dict()
										lowerCAmelCase_ :      Optional[Any]    				=				self.__class__.model_type
										return output