



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()










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__  :  Union[str, Any]								=					logging.get_logger(__name__)

A__  :  Tuple								=					{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''xlm-roberta-xl'''




							def __init__(       self     :       int				,				A_     :       List[str]=2_5_0_8_8_0				,				A_     :       List[str]=2_5_6_0				,				A_     :       Optional[int]=3_6				,				A_     :       List[Any]=3_2				,				A_     :       Optional[int]=1_0_2_4_0				,				A_     :       Dict="gelu"				,				A_     :       int=0.1				,				A_     :       Optional[Any]=0.1				,				A_     :       int=5_1_4				,				A_     :       Any=1				,				A_     :       Optional[Any]=0.02				,				A_     :       str=1e-05				,				A_     :       Dict=1				,				A_     :       Any=0				,				A_     :       Tuple=2				,				A_     :       str="absolute"				,				A_     :       str=True				,				A_     :       List[str]=None				,				**A_     :       Dict				,				):
									super().__init__(pad_token_id=A_				,				bos_token_id=A_				,				eos_token_id=A_				,				**A_)
									lowerCAmelCase_			:     Tuple									= vocab_size
									lowerCAmelCase_			:     List[str]									= hidden_size
									lowerCAmelCase_			:     int									= num_hidden_layers
									lowerCAmelCase_			:     int									= num_attention_heads
									lowerCAmelCase_			:     Dict									= hidden_act
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Tuple									= hidden_dropout_prob
									lowerCAmelCase_			:     Optional[Any]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Union[str, Any]									= max_position_embeddings
									lowerCAmelCase_			:     Dict									= type_vocab_size
									lowerCAmelCase_			:     str									= initializer_range
									lowerCAmelCase_			:     str									= layer_norm_eps
									lowerCAmelCase_			:     Optional[Any]									= position_embedding_type
									lowerCAmelCase_			:     Optional[Any]									= use_cache
									lowerCAmelCase_			:     List[str]									= classifier_dropout





class 				__snake_case						(     UpperCamelCase_						):




							@property
							def    UpperCAmelCase__			(       self     :       List[str]):
									if self.task == "multiple-choice":
											lowerCAmelCase_			:     Union[str, Any]									= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
											lowerCAmelCase_			:     List[Any]									= {0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ])





