



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,
)


A__  :  List[Any]								=					getLogger(__name__)


def 			UpperCamelCase(  __UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		str     ,__UpperCamelCase							:		str     ,__UpperCamelCase							:		int = 8     ,__UpperCamelCase							:		int = 1024     ,__UpperCamelCase							:		str="val"     ,__UpperCamelCase							:		List[str]=None     ,__UpperCamelCase							:		Optional[Any]=False     ,__UpperCamelCase							:		List[str]="summarization"     ,__UpperCamelCase							:		Union[str, Any]=None     ,__UpperCamelCase							:		Optional[int]=1     ,__UpperCamelCase							:		Dict = None     ,__UpperCamelCase							:		str=""     ,**__UpperCamelCase							:		Optional[int]     ,):
		lowerCAmelCase_			:     Optional[Any]									= str(__UpperCamelCase )
		assert local_rank is not None
		torch.distributed.init_process_group(backend='''nccl'''     ,rank=__UpperCamelCase )

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

		lowerCAmelCase_			:     List[str]									= 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:
				lowerCAmelCase_			:     str									= tokenizer.model_max_length
		if prefix is None:
				lowerCAmelCase_			:     str									= prefix or getattr(model.config     ,'''prefix'''     ,'''''' ) or ''''''
		lowerCAmelCase_			:     Union[str, Any]									= SeqaSeqDataset(
		    __UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,max_target_length=1024     ,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.
		lowerCAmelCase_			:     Optional[int]									= ds.make_sortish_sampler(__UpperCamelCase     ,distributed=__UpperCamelCase     ,add_extra_examples=__UpperCamelCase     ,shuffle=__UpperCamelCase )
		lowerCAmelCase_			:     List[Any]									= DataLoader(__UpperCamelCase     ,sampler=__UpperCamelCase     ,batch_size=__UpperCamelCase     ,collate_fn=ds.collate_fn )
		lowerCAmelCase_			:     List[str]									= []
		for batch in tqdm(__UpperCamelCase ):
				lowerCAmelCase_			:     List[str]									= 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     ,)
				lowerCAmelCase_			:     Tuple									= tokenizer.batch_decode(__UpperCamelCase     ,skip_special_tokens=__UpperCamelCase     ,clean_up_tokenization_spaces=__UpperCamelCase )
				lowerCAmelCase_			:     List[str]									= batch['''ids''']
				if num_return_sequences > 1:
						lowerCAmelCase_			:     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 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Optional[int]									= 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=600     ,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''' )
		lowerCAmelCase_			:     Tuple									= time.time()
		lowerCAmelCase_					,  lowerCAmelCase_			:     str									= parser.parse_known_args()
		lowerCAmelCase_			:     Dict									= parse_numeric_n_bool_cl_kwargs(__UpperCamelCase )
		if generate_kwargs and args.local_rank <= 0:
				print(f"""parsed the following generate kwargs: {generate_kwargs}""" )
		lowerCAmelCase_			:     Tuple									= Path(args.save_dir + '''_tmp''' )
		Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )  # this handles locking.
		lowerCAmelCase_			:     Optional[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_			:     Optional[Any]									= {}
		if args.src_lang is not None:
				lowerCAmelCase_			:     Dict									= args.src_lang
		if args.tgt_lang is not None:
				lowerCAmelCase_			:     Optional[int]									= args.tgt_lang

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

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


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







"""simple docstring"""




import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


UpperCAmelCase__      :    Optional[int]    							= {
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

UpperCAmelCase__      :    List[Any]    							= logging.WARNING

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   os.getenv("""DATASETS_VERBOSITY"""   ,_snake_case						)
							if env_level_str:
														if env_level_str in log_levels:
																					return log_levels[env_level_str]
														else:
																					logging.getLogger().warning(
																					    f'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
																					    f'''has to be one of: { ', '.join(log_levels.keys()						) }'''						)
							return _default_log_level

def lowercase_  (   ):
							return __name__.split("""."""						)[0]

def lowercase_  (   ):
							return logging.getLogger(_get_library_name()						)

def lowercase_  (   ):
							# Apply our default configuration to the library root logger.
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   _get_library_root_logger()
							library_root_logger.setLevel(_get_default_logging_level()						)

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   _get_library_root_logger()
							library_root_logger.setLevel(logging.NOTSET						)

def lowercase_  (   _snake_case = None						):
							if name is None:
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   _get_library_name()
							return logging.getLogger(_snake_case						)

def lowercase_  (   ):
							return _get_library_root_logger().getEffectiveLevel()

def lowercase_  (   _snake_case						):
							_get_library_root_logger().setLevel(_snake_case						)

def lowercase_  (   ):
							return set_verbosity(_snake_case						)

def lowercase_  (   ):
							return set_verbosity(_snake_case						)

def lowercase_  (   ):
							return set_verbosity(_snake_case						)

def lowercase_  (   ):
							return set_verbosity(_snake_case						)

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Tuple           =   False

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       str           =   True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		int:  # pylint: disable=unused-argument



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   args[0] if args else None



				def __iter__(self    )					->		int:



											"""simple docstring"""




											return iter(self._iterator    )



				def __getattr__(self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											def empty_fn(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    ):  # pylint: disable=unused-argument
																		return

											return empty_fn



				def __enter__(self    )					->		Dict:



											"""simple docstring"""




											return self



				def __exit__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											return






UpperCAmelCase__      :    str    							= True



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __call__(self   ,       *SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=False   ,       **SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											if _tqdm_active and not disable:
																		return tqdm_lib.tqdm(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											else:
																		return EmptyTqdm(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   None
											if _tqdm_active:
																		return tqdm_lib.tqdm.set_lock(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											if _tqdm_active:
																		return tqdm_lib.tqdm.get_lock()






UpperCAmelCase__      :    Tuple    							= _tqdm_cls()

def lowercase_  (   ):
							global _tqdm_active
							return bool(_tqdm_active						)

def lowercase_  (   ):
							global _tqdm_active
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   True

def lowercase_  (   ):
							global _tqdm_active
							SCREAMING_SNAKE_CASE__						:       str           =   False




