


'''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,
)


_lowerCAmelCase  :		Optional[int]		     =	getLogger(__name__)

def 			__UpperCamelCase    (    _A    :				Union[str, Any]					,   _A    :				str					,   _A    :				str					,   _A    :				int = 8					,   _A    :				int = 10_24					,   _A    :				List[str]="val"					,   _A    :				Optional[int]=None					,   _A    :				Optional[int]=False					,   _A    :				Optional[int]="summarization"					,   _A    :				Any=None					,   _A    :				Optional[Any]=1					,   _A    :				Dict = None					,   _A    :				List[str]=""					,   **_A    :				Dict					,   )    ->      Dict:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= str(_A				)
	assert local_rank is not None
	torch.distributed.init_process_group(backend='nccl'					,   rank=_A				)

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

	lowerCAmelCase					:    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:
		lowerCAmelCase					:    List[str]      							= tokenizer.model_max_length
	if prefix is None:
		lowerCAmelCase					:    Optional[int]      							= prefix or getattr(model.config					,   'prefix'					,   ''				) or ''
	lowerCAmelCase					:    Any      							= SeqaSeqDataset(
	    _A					,   _A					,   _A					,   max_target_length=10_24					,   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.
	lowerCAmelCase					:    Tuple      							= ds.make_sortish_sampler(_A					,   distributed=_A					,   add_extra_examples=_A					,   shuffle=_A				)
	lowerCAmelCase					:    Optional[int]      							= DataLoader(_A					,   sampler=_A					,   batch_size=_A					,   collate_fn=ds.collate_fn				)
	lowerCAmelCase					:    Union[str, Any]      							= []
	for batch in tqdm(_A				):
		lowerCAmelCase					:    Optional[Any]      							= 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					,   )
		lowerCAmelCase					:    Dict      							= tokenizer.batch_decode(_A					,   skip_special_tokens=_A					,   clean_up_tokenization_spaces=_A				)
		lowerCAmelCase					:    Union[str, Any]      							= batch['ids']
		if num_return_sequences > 1:
			lowerCAmelCase					:    int      							= 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 			__UpperCamelCase    (    )    ->      int:




	"""simple docstring"""





	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=_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=6_00					,   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'				)
	lowerCAmelCase					:    Dict      							= time.time()
	lowerCAmelCase							,   lowerCAmelCase					:    Any      							= parser.parse_known_args()
	lowerCAmelCase					:    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}"				)
	lowerCAmelCase					:    List[Any]      							= Path(args.save_dir + '_tmp'				)
	Path(_A				).mkdir(exist_ok=_A				)  # this handles locking.
	lowerCAmelCase					:    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					:    Dict      							= {}
	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=_A				)
	lowerCAmelCase							,   lowerCAmelCase					:    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:
		lowerCAmelCase					:    int      							= Path(args.save_dir				)
		save_dir.mkdir(exist_ok=_A				)
		lowerCAmelCase					:    Dict      							= gather_results_from_each_node(_A					,   _A					,   args.sync_timeout				)
		lowerCAmelCase					:    str      							= combine_partial_results(_A				)
		if args.num_return_sequences > 1:
			lowerCAmelCase					:    List[str]      							= save_dir.joinpath('pseudolabel_results.json'				)
			print(F"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"				)
			save_json(_A					,   _A				)
			return
		lowerCAmelCase					:    List[str]      							= Path(args.data_dir				).joinpath(args.type_path + '.target'				)
		with open(_A				) as f:
			lowerCAmelCase					:    Optional[Any]      							= [x.rstrip() for x in f.readlines()][: len(_A				)]

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




	"""simple docstring"""





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

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				Union[str, Any]					,   _A    :				List[Any]				)    ->      List[Dict[str, List]]:




	"""simple docstring"""





	lowerCAmelCase					:    List[str]      							= time.time()
	logger.info('waiting for all nodes to finish'				)
	lowerCAmelCase					:    Optional[int]      							= None
	while (time.time() - start_wait) < timeout:
		lowerCAmelCase					:    Optional[int]      							= list(save_dir.glob('rank_*.json'				)				)
		if len(_A				) < num_replicas:
			continue
		try:
			# make sure all json files are fully saved
			lowerCAmelCase					:    Union[str, 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 collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase  :		Tuple		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		List[str]		     =	{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					int			      = """xmod"""



	def __init__(					self ,					snake_case__=3_0522 ,					snake_case__=768 ,					snake_case__=12 ,					snake_case__=12 ,					snake_case__=3072 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=1e-1_2 ,					snake_case__=1 ,					snake_case__=0 ,					snake_case__=2 ,					snake_case__="absolute" ,					snake_case__=True ,					snake_case__=None ,					snake_case__=False ,					snake_case__=2 ,					snake_case__=False ,					snake_case__=True ,					snake_case__=True ,					snake_case__=("en_XX",) ,					snake_case__=None ,					**snake_case__ ,					):
		super().__init__(pad_token_id=snake_case__ ,					bos_token_id=snake_case__ ,					eos_token_id=snake_case__ ,					**snake_case__    )

		lowerCAmelCase					:    Dict      							= vocab_size
		lowerCAmelCase					:    Optional[Any]      							= hidden_size
		lowerCAmelCase					:    int      							= num_hidden_layers
		lowerCAmelCase					:    List[Any]      							= num_attention_heads
		lowerCAmelCase					:    List[Any]      							= hidden_act
		lowerCAmelCase					:    Optional[int]      							= intermediate_size
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    Optional[Any]      							= attention_probs_dropout_prob
		lowerCAmelCase					:    str      							= max_position_embeddings
		lowerCAmelCase					:    int      							= type_vocab_size
		lowerCAmelCase					:    List[Any]      							= initializer_range
		lowerCAmelCase					:    Any      							= layer_norm_eps
		lowerCAmelCase					:    Dict      							= position_embedding_type
		lowerCAmelCase					:    Optional[Any]      							= use_cache
		lowerCAmelCase					:    Union[str, Any]      							= classifier_dropout
		lowerCAmelCase					:    int      							= pre_norm
		lowerCAmelCase					:    Optional[Any]      							= adapter_reduction_factor
		lowerCAmelCase					:    Any      							= adapter_layer_norm
		lowerCAmelCase					:    Dict      							= adapter_reuse_layer_norm
		lowerCAmelCase					:    Any      							= ln_before_adapter
		lowerCAmelCase					:    Optional[Any]      							= list(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= default_language



class  lowerCAmelCase				(				a							):



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