


'''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__      ,       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__      ,       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'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
		import transformers


def     a   (    __a     )						->			Optional[int]:



					'''simple docstring'''






					random.seed(__a     )
					np.random.seed(__a     )
					torch.manual_seed(__a     )
					torch.cuda.manual_seed_all(__a     )
					# ^^ safe to call this function even if cuda is not available







class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_ = 0.9999					,     UpperCamelCase_ = 0.0					,     UpperCamelCase_ = 0					,     UpperCamelCase_ = False					,     UpperCamelCase_ = 1.0					,     UpperCamelCase_ = 2 / 3					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							if isinstance(UpperCamelCase_					,     torch.nn.Module							):
												UpperCamelCase__     :Optional[Any]							      =				(
												    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
												    '''Please pass the parameters of the module instead.'''
												)
												deprecate(
												    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''					,     '''1.0.0'''					,     UpperCamelCase_					,     standard_warn=UpperCamelCase_					,     )
												UpperCamelCase__     :Optional[int]							      =				parameters.parameters()

												# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
												UpperCamelCase__     :str							      =				True

							if kwargs.get('''max_value'''					,     UpperCamelCase_							) is not None:
												UpperCamelCase__     :List[str]							      =				'''The `max_value` argument is deprecated. Please use `decay` instead.'''
												deprecate('''max_value'''					,     '''1.0.0'''					,     UpperCamelCase_					,     standard_warn=UpperCamelCase_							)
												UpperCamelCase__     :int							      =				kwargs['''max_value''']

							if kwargs.get('''min_value'''					,     UpperCamelCase_							) is not None:
												UpperCamelCase__     :Union[str, Any]							      =				'''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
												deprecate('''min_value'''					,     '''1.0.0'''					,     UpperCamelCase_					,     standard_warn=UpperCamelCase_							)
												UpperCamelCase__     :Any							      =				kwargs['''min_value''']

							UpperCamelCase__     :Optional[int]							      =				list(UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				[p.clone().detach() for p in parameters]

							if kwargs.get('''device'''					,     UpperCamelCase_							) is not None:
												UpperCamelCase__     :str							      =				'''The `device` argument is deprecated. Please use `to` instead.'''
												deprecate('''device'''					,     '''1.0.0'''					,     UpperCamelCase_					,     standard_warn=UpperCamelCase_							)
												self.to(device=kwargs['''device''']							)

							UpperCamelCase__     :Union[str, Any]							      =				None

							UpperCamelCase__     :List[Any]							      =				decay
							UpperCamelCase__     :List[str]							      =				min_decay
							UpperCamelCase__     :Optional[int]							      =				update_after_step
							UpperCamelCase__     :int							      =				use_ema_warmup
							UpperCamelCase__     :Any							      =				inv_gamma
							UpperCamelCase__     :Union[str, Any]							      =				power
							UpperCamelCase__     :Union[str, Any]							      =				0
							UpperCamelCase__     :Dict							      =				None  # set in `step()`

							UpperCamelCase__     :List[Any]							      =				model_cls
							UpperCamelCase__     :Optional[Any]							      =				model_config




		@classmethod
		def 					lowerCAmelCase__       (	cls					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				model_cls.load_config(UpperCamelCase_					,     return_unused_kwargs=UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				model_cls.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :str							      =				cls(model.parameters()					,     model_cls=UpperCamelCase_					,     model_config=model.config							)

							ema_model.load_state_dict(UpperCamelCase_							)
							return ema_model




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.model_cls is None:
												raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''							)

							if self.model_config is None:
												raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''							)

							UpperCamelCase__     :Optional[Any]							      =				self.model_cls.from_config(self.model_config							)
							UpperCamelCase__     :Optional[int]							      =				self.state_dict()
							state_dict.pop('''shadow_params'''					,     UpperCamelCase_							)

							model.register_to_config(**UpperCamelCase_							)
							self.copy_to(model.parameters()							)
							model.save_pretrained(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				max(0					,     optimization_step - self.update_after_step - 1							)

							if step <= 0:
												return 0.0

							if self.use_ema_warmup:
												UpperCamelCase__     :Dict							      =				1 - (1 + step / self.inv_gamma) ** -self.power
							else:
												UpperCamelCase__     :int							      =				(1 + step) / (10 + step)

							UpperCamelCase__     :Any							      =				min(UpperCamelCase_					,     self.decay							)
							# make sure decay is not smaller than min_decay
							UpperCamelCase__     :Dict							      =				max(UpperCamelCase_					,     self.min_decay							)
							return cur_decay_value




		@torch.no_grad()
		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if isinstance(UpperCamelCase_					,     torch.nn.Module							):
												UpperCamelCase__     :Optional[Any]							      =				(
												    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
												    '''Please pass the parameters of the module instead.'''
												)
												deprecate(
												    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''					,     '''1.0.0'''					,     UpperCamelCase_					,     standard_warn=UpperCamelCase_					,     )
												UpperCamelCase__     :str							      =				parameters.parameters()

							UpperCamelCase__     :Dict							      =				list(UpperCamelCase_							)

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							UpperCamelCase__     :Any							      =				self.get_decay(self.optimization_step							)
							UpperCamelCase__     :Tuple							      =				decay
							UpperCamelCase__     :List[Any]							      =				1 - decay

							UpperCamelCase__     :Optional[Any]							      =				contextlib.nullcontext
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
												import deepspeed

							for s_param, param in zip(self.shadow_params					,     UpperCamelCase_							):
												if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																	UpperCamelCase__     :Dict							      =				deepspeed.zero.GatheredParameters(UpperCamelCase_					,     modifier_rank=UpperCamelCase_							)

												with context_manager():
																	if param.requires_grad:
																						s_param.sub_(one_minus_decay * (s_param - param)							)
																	else:
																						s_param.copy_(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				list(UpperCamelCase_							)
							for s_param, param in zip(self.shadow_params					,     UpperCamelCase_							):
												param.data.copy_(s_param.to(param.device							).data							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=None					,     UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				[
							    p.to(device=UpperCamelCase_					,     dtype=UpperCamelCase_							) if p.is_floating_point() else p.to(device=UpperCamelCase_							)
							    for p in self.shadow_params
							]




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return {
							    "decay": self.decay,
							    "min_decay": self.min_decay,
							    "optimization_step": self.optimization_step,
							    "update_after_step": self.update_after_step,
							    "use_ema_warmup": self.use_ema_warmup,
							    "inv_gamma": self.inv_gamma,
							    "power": self.power,
							    "shadow_params": self.shadow_params,
							}




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				[param.detach().cpu().clone() for param in parameters]




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.temp_stored_params is None:
												raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''							)
							for c_param, param in zip(self.temp_stored_params					,     UpperCamelCase_							):
												param.data.copy_(c_param.data							)

							# Better memory-wise.
							UpperCamelCase__     :Optional[Any]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				copy.deepcopy(UpperCamelCase_							)

							UpperCamelCase__     :Dict							      =				state_dict.get('''decay'''					,     self.decay							)
							if self.decay < 0.0 or self.decay > 1.0:
												raise ValueError('''Decay must be between 0 and 1'''							)

							UpperCamelCase__     :Union[str, Any]							      =				state_dict.get('''min_decay'''					,     self.min_decay							)
							if not isinstance(self.min_decay					,     UpperCamelCase_							):
												raise ValueError('''Invalid min_decay'''							)

							UpperCamelCase__     :Union[str, Any]							      =				state_dict.get('''optimization_step'''					,     self.optimization_step							)
							if not isinstance(self.optimization_step					,     UpperCamelCase_							):
												raise ValueError('''Invalid optimization_step'''							)

							UpperCamelCase__     :List[Any]							      =				state_dict.get('''update_after_step'''					,     self.update_after_step							)
							if not isinstance(self.update_after_step					,     UpperCamelCase_							):
												raise ValueError('''Invalid update_after_step'''							)

							UpperCamelCase__     :List[str]							      =				state_dict.get('''use_ema_warmup'''					,     self.use_ema_warmup							)
							if not isinstance(self.use_ema_warmup					,     UpperCamelCase_							):
												raise ValueError('''Invalid use_ema_warmup'''							)

							UpperCamelCase__     :Optional[int]							      =				state_dict.get('''inv_gamma'''					,     self.inv_gamma							)
							if not isinstance(self.inv_gamma					,     (float, int)							):
												raise ValueError('''Invalid inv_gamma'''							)

							UpperCamelCase__     :str							      =				state_dict.get('''power'''					,     self.power							)
							if not isinstance(self.power					,     (float, int)							):
												raise ValueError('''Invalid power'''							)

							UpperCamelCase__     :Tuple							      =				state_dict.get('''shadow_params'''					,     UpperCamelCase_							)
							if shadow_params is not None:
												UpperCamelCase__     :Dict							      =				shadow_params
												if not isinstance(self.shadow_params					,     UpperCamelCase_							):
																	raise ValueError('''shadow_params must be a list'''							)
												if not all(isinstance(UpperCamelCase_					,     torch.Tensor							) for p in self.shadow_params							):
																	raise ValueError('''shadow_params must all be Tensors'''							)