
'''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,
)


__snake_case							   =  getLogger(__name__)


def     a   (    __a				,      __a				,      __a				,      __a = 8				,      __a = 1024				,      __a="val"				,      __a=None				,      __a=False				,      __a="summarization"				,      __a=None				,      __a=1				,      __a = None				,      __a=""				,      **__a				,      )						->			Dict:



					'''simple docstring'''






					UpperCamelCase__     :Any							      =				str(__a     )
					assert local_rank is not None
					torch.distributed.init_process_group(backend='''nccl'''				,      rank=__a     )

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

					UpperCamelCase__     :Optional[Any]							      =				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:
										UpperCamelCase__     :Optional[int]							      =				tokenizer.model_max_length
					if prefix is None:
										UpperCamelCase__     :List[Any]							      =				prefix or getattr(model.config				,      '''prefix'''				,      ''''''     ) or ''''''
					UpperCamelCase__     :str							      =				SeqaSeqDataset(
					    __a				,      __a				,      __a				,      max_target_length=1024				,      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.
					UpperCamelCase__     :int							      =				ds.make_sortish_sampler(__a				,      distributed=__a				,      add_extra_examples=__a				,      shuffle=__a     )
					UpperCamelCase__     :Optional[int]							      =				DataLoader(__a				,      sampler=__a				,      batch_size=__a				,      collate_fn=ds.collate_fn     )
					UpperCamelCase__     :Optional[Any]							      =				[]
					for batch in tqdm(__a     ):
										UpperCamelCase__     :str							      =				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				,      )
										UpperCamelCase__     :Tuple							      =				tokenizer.batch_decode(__a				,      skip_special_tokens=__a				,      clean_up_tokenization_spaces=__a     )
										UpperCamelCase__     :Union[str, Any]							      =				batch['''ids''']
										if num_return_sequences > 1:
															UpperCamelCase__     :List[str]							      =				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     a   (    )						->			List[Any]:



					'''simple docstring'''






					UpperCamelCase__     :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=600				,      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'''     )
					UpperCamelCase__     :List[str]							      =				time.time()
					UpperCamelCase__     :int							      =				parser.parse_known_args()
					UpperCamelCase__     :int							      =				parse_numeric_n_bool_cl_kwargs(__a     )
					if generate_kwargs and args.local_rank <= 0:
										print(f'''parsed the following generate kwargs: {generate_kwargs}'''     )
					UpperCamelCase__     :str							      =				Path(args.save_dir + '''_tmp'''     )
					Path(__a     ).mkdir(exist_ok=__a     )  # this handles locking.
					UpperCamelCase__     :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.
					UpperCamelCase__     :Union[str, Any]							      =				{}
					if args.src_lang is not None:
										UpperCamelCase__     :List[Any]							      =				args.src_lang
					if args.tgt_lang is not None:
										UpperCamelCase__     :List[str]							      =				args.tgt_lang

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

										# Calculate metrics, save metrics,  and save _generations.txt
										UpperCamelCase__     :Optional[int]							      =				'''translation''' in args.task
										UpperCamelCase__     :Optional[int]							      =				calculate_bleu if calc_bleu else calculate_rouge
										UpperCamelCase__     :Union[str, Any]							      =				'''bleu''' if calc_bleu else '''rouge'''
										UpperCamelCase__     :Dict							      =				score_fn(__a				,      __a     )
										UpperCamelCase__     :Tuple							      =				len(__a     )
										UpperCamelCase__     :str							      =				time.time() - start_time
										UpperCamelCase__     :str							      =				round(runtime / metrics['''n_obs''']				,      4     )
										UpperCamelCase__     :int							      =				num_replicas
										# TODO(@stas00): add whatever metadata to metrics
										UpperCamelCase__     :Tuple							      =				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     a   (    __a     )						->			List:



					'''simple docstring'''






					UpperCamelCase__     :Any							      =				[]
					for partial_result in partial_results:
										records.extend(__a     )
					UpperCamelCase__     :Tuple							      =				sorted(__a				,      key=lambda __a     : x["id"]     )
					UpperCamelCase__     :Union[str, Any]							      =				[x['''pred'''] for x in records]
					return preds


def     a   (    __a				,      __a				,      __a     )						->			List[Dict[str, List]]:



					'''simple docstring'''






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



'''simple docstring'''
from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__snake_case							   =  logging.get_logger(__name__)
class      lowercase			:

		"""simple docstring"""

		_a									=       42
		_a									=       None




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							if not self.is_available():
												raise RuntimeError(
												    F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''							)




		@classmethod
		def 					lowerCAmelCase__       (	cls							):

							'''simple docstring'''


							return F'''`pip install {cls.pip_package or cls.name}`'''



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'optuna'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_optuna_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_optuna(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_optuna(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'ray'
		_a									=       '\'ray[tune]\''




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_ray_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_ray(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_ray(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'sigopt'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_sigopt_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_sigopt(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_sigopt(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'wandb'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_wandb_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_wandb(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_wandb(UpperCamelCase_							)



__snake_case							   =  {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}


def     a   (    )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
					if len(__a     ) > 0:
										UpperCamelCase__     :Tuple							      =				available_backends[0].name
										if len(__a     ) > 1:
															logger.info(
															    f'''{len(__a     )} hyperparameter search backends available. Using {name} as the default.'''     )
										return name
					raise RuntimeError(
					    '''No hyperparameter search backend available.\n'''
					    + '''\n'''.join(
					        f''' - To install {backend.name} run {backend.pip_install()}'''
					        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()     )     )