


"""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,
)


SCREAMING_SNAKE_CASE :    List[str]									=				getLogger(__name__)






def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      str     ,   _snake_case						:      str     ,   _snake_case						:      int = 8     ,   _snake_case						:      int = 1_024     ,   _snake_case						:      Any="val"     ,   _snake_case						:      Tuple=None     ,   _snake_case						:      Any=False     ,   _snake_case						:      str="summarization"     ,   _snake_case						:      Dict=None     ,   _snake_case						:      Optional[Any]=1     ,   _snake_case						:      Dict = None     ,   _snake_case						:      List[Any]=""     ,   **_snake_case						:      int     ,   )     ->Dict:
	"""simple docstring"""





	__snake_case	:						int						=				str(_snake_case     )
	assert local_rank is not None
	torch.distributed.init_process_group(backend='''nccl'''     ,   rank=_snake_case     )

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

	__snake_case	:						Dict						=				AutoTokenizer.from_pretrained(_snake_case     )
	logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.

	if max_source_length is None:
		__snake_case	:						List[str]						=				tokenizer.model_max_length
	if prefix is None:
		__snake_case	:						List[str]						=				prefix or getattr(model.config     ,   '''prefix'''     ,   ''''''     ) or ''''''
	__snake_case	:						List[str]						=				SeqaSeqDataset(
	    _snake_case     ,   _snake_case     ,   _snake_case     ,   max_target_length=1_024     ,   type_path=_snake_case     ,   n_obs=_snake_case     ,   prefix=_snake_case     ,   **_snake_case     ,   )
	# 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.
	__snake_case	:						Union[str, Any]						=				ds.make_sortish_sampler(_snake_case     ,   distributed=_snake_case     ,   add_extra_examples=_snake_case     ,   shuffle=_snake_case     )
	__snake_case	:						List[Any]						=				DataLoader(_snake_case     ,   sampler=_snake_case     ,   batch_size=_snake_case     ,   collate_fn=ds.collate_fn     )
	__snake_case	:						Union[str, Any]						=				[]
	for batch in tqdm(_snake_case     ):
		__snake_case	:						Tuple						=				model.generate(
		    input_ids=batch['''input_ids'''].to(model.device     )     ,   attention_mask=batch['''attention_mask'''].to(model.device     )     ,   num_return_sequences=_snake_case     ,   num_beams=_snake_case     ,   **_snake_case     ,   )
		__snake_case	:						List[Any]						=				tokenizer.batch_decode(_snake_case     ,   skip_special_tokens=_snake_case     ,   clean_up_tokenization_spaces=_snake_case     )
		__snake_case	:						List[str]						=				batch['''ids''']
		if num_return_sequences > 1:
			__snake_case	:						Dict						=				chunks(_snake_case     ,   _snake_case     )  # batch size chunks, each of size num_return_seq
		for i, pred in enumerate(_snake_case     ):
			results.append({'''pred''': pred, '''id''': ids[i].item()}     )
	save_json(_snake_case     ,   _snake_case     )
	return results, sampler.num_replicas






def 		lowercase							(     )     ->int:
	"""simple docstring"""





	__snake_case	:						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=_snake_case     ,   help='''like cnn_dm/test.source'''     )
	parser.add_argument(
	    '''--model_name'''     ,   type=_snake_case     ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''     ,   default='''sshleifer/distilbart-xsum-12-3'''     ,   )
	parser.add_argument('''--save_dir'''     ,   type=_snake_case     ,   help='''where to save'''     ,   default='''tmp_gen'''     )
	parser.add_argument('''--max_source_length'''     ,   type=_snake_case     ,   default=_snake_case     )
	parser.add_argument(
	    '''--type_path'''     ,   type=_snake_case     ,   default='''test'''     ,   help='''which subset to evaluate typically train/val/test'''     )
	parser.add_argument('''--task'''     ,   type=_snake_case     ,   default='''summarization'''     ,   help='''used for task_specific_params + metrics'''     )
	parser.add_argument('''--bs'''     ,   type=_snake_case     ,   default=8     ,   required=_snake_case     ,   help='''batch size'''     )
	parser.add_argument(
	    '''--local_rank'''     ,   type=_snake_case     ,   default=-1     ,   required=_snake_case     ,   help='''should be passed by distributed.launch'''     )

	parser.add_argument(
	    '''--n_obs'''     ,   type=_snake_case     ,   default=_snake_case     ,   required=_snake_case     ,   help='''How many observations. Defaults to all.'''     )
	parser.add_argument(
	    '''--num_return_sequences'''     ,   type=_snake_case     ,   default=1     ,   required=_snake_case     ,   help='''How many sequences to return'''     )
	parser.add_argument(
	    '''--sync_timeout'''     ,   type=_snake_case     ,   default=600     ,   required=_snake_case     ,   help='''How long should master process wait for other processes to finish.'''     ,   )
	parser.add_argument('''--src_lang'''     ,   type=_snake_case     ,   default=_snake_case     ,   required=_snake_case     )
	parser.add_argument('''--tgt_lang'''     ,   type=_snake_case     ,   default=_snake_case     ,   required=_snake_case     )
	parser.add_argument(
	    '''--prefix'''     ,   type=_snake_case     ,   required=_snake_case     ,   default=_snake_case     ,   help='''will be added to the begininng of src examples'''     )
	parser.add_argument('''--fp16'''     ,   action='''store_true'''     )
	parser.add_argument('''--debug'''     ,   action='''store_true'''     )
	__snake_case	:						str						=				time.time()
	__snake_case	,							__snake_case	:						Any						=				parser.parse_known_args()
	__snake_case	:						List[Any]						=				parse_numeric_n_bool_cl_kwargs(_snake_case     )
	if generate_kwargs and args.local_rank <= 0:
		print(f"""parsed the following generate kwargs: {generate_kwargs}"""     )
	__snake_case	:						List[Any]						=				Path(args.save_dir + '''_tmp'''     )
	Path(_snake_case     ).mkdir(exist_ok=_snake_case     )  # this handles locking.
	__snake_case	:						Optional[int]						=				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.
	__snake_case	:						Dict						=				{}
	if args.src_lang is not None:
		__snake_case	:						Dict						=				args.src_lang
	if args.tgt_lang is not None:
		__snake_case	:						Dict						=				args.tgt_lang

	Path(args.save_dir     ).mkdir(exist_ok=_snake_case     )
	__snake_case	,							__snake_case	:						List[Any]						=				eval_data_dir(
	    args.data_dir     ,   _snake_case     ,   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=_snake_case     ,   **_snake_case     ,   )

	if args.local_rank <= 0:
		__snake_case	:						int						=				Path(args.save_dir     )
		save_dir.mkdir(exist_ok=_snake_case     )
		__snake_case	:						Optional[Any]						=				gather_results_from_each_node(_snake_case     ,   _snake_case     ,   args.sync_timeout     )
		__snake_case	:						str						=				combine_partial_results(_snake_case     )
		if args.num_return_sequences > 1:
			__snake_case	:						List[Any]						=				save_dir.joinpath('''pseudolabel_results.json'''     )
			print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""     )
			save_json(_snake_case     ,   _snake_case     )
			return
		__snake_case	:						Tuple						=				Path(args.data_dir     ).joinpath(args.type_path + '''.target'''     )
		with open(_snake_case     ) as f:
			__snake_case	:						Optional[Any]						=				[x.rstrip() for x in f.readlines()][: len(_snake_case     )]

		# Calculate metrics, save metrics,  and save _generations.txt
		__snake_case	:						List[str]						=				'''translation''' in args.task
		__snake_case	:						List[Any]						=				calculate_bleu if calc_bleu else calculate_rouge
		__snake_case	:						Dict						=				'''bleu''' if calc_bleu else '''rouge'''
		__snake_case	:						Dict						=				score_fn(_snake_case     ,   _snake_case     )
		__snake_case	:						int						=				len(_snake_case     )
		__snake_case	:						Dict						=				time.time() - start_time
		__snake_case	:						Optional[Any]						=				round(runtime / metrics['''n_obs''']     ,   4     )
		__snake_case	:						List[Any]						=				num_replicas
		# TODO(@stas00): add whatever metadata to metrics
		__snake_case	:						int						=				save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""     )
		save_json(_snake_case     ,   _snake_case     ,   indent=_snake_case     )
		print(_snake_case     )
		write_txt_file(_snake_case     ,   save_dir.joinpath(f"""{args.type_path}_generations.txt"""     )     )
		if args.debug:
			write_txt_file(_snake_case     ,   save_dir.joinpath(f"""{args.type_path}.target"""     )     )
		else:
			shutil.rmtree(_snake_case     )






def 		lowercase							(     _snake_case						:      Union[str, Any]     )     ->List:
	"""simple docstring"""





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






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      List[str]     ,   _snake_case						:      List[Any]     )     ->List[Dict[str, List]]:
	"""simple docstring"""





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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "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 __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'xmod'



 def __init__(self ,   A=30_522 ,   A=768 ,   A=12 ,   A=12 ,   A=3_072 ,   A="gelu" ,   A=0.1 ,   A=0.1 ,   A=512 ,   A=2 ,   A=0.02 ,   A=1E-12 ,   A=1 ,   A=0 ,   A=2 ,   A="absolute" ,   A=True ,   A=None ,   A=False ,   A=2 ,   A=False ,   A=True ,   A=True ,   A=("en_XX",) ,   A=None ,   **A ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(pad_token_id=A ,   bos_token_id=A ,   eos_token_id=A ,   **A       )

      _a      =  vocab_size
      _a      =  hidden_size
      _a      =  num_hidden_layers
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  intermediate_size
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  max_position_embeddings
      _a      =  type_vocab_size
      _a      =  initializer_range
      _a      =  layer_norm_eps
      _a      =  position_embedding_type
      _a      =  use_cache
      _a      =  classifier_dropout
      _a      =  pre_norm
      _a      =  adapter_reduction_factor
      _a      =  adapter_layer_norm
      _a      =  adapter_reuse_layer_norm
      _a      =  ln_before_adapter
      _a      =  list(A       )
      _a      =  default_language






class __A			(  A						):



 '''simple docstring'''







 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      if self.task == "multiple-choice":
           _a      =  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]       )



