



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase		       =      logging.get_logger(__name__)

lowerCamelCase		       =      {"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCamelCase		       =      {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

lowerCamelCase		       =      {
    """moussaKam/mbarthez""": 10_24,
    """moussaKam/barthez""": 10_24,
    """moussaKam/barthez-orangesum-title""": 10_24,
}

lowerCamelCase		       =      """▁"""


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:str =       VOCAB_FILES_NAMES
							A				:str =       PRETRAINED_VOCAB_FILES_MAP
							A				:Dict =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A				:List[Any] =       ["input_ids", "attention_mask"]



							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase="<s>" ,					__UpperCAmelCase="</s>" ,					__UpperCAmelCase="</s>" ,					__UpperCAmelCase="<s>" ,					__UpperCAmelCase="<unk>" ,					__UpperCAmelCase="<pad>" ,					__UpperCAmelCase="<mask>" ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= AddedToken(__UpperCAmelCase ,					lstrip=__UpperCAmelCase ,					rstrip=__UpperCAmelCase				) if isinstance(__UpperCAmelCase ,					__UpperCAmelCase				) else mask_token

											a__	:							Optional[int]      						= {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=__UpperCAmelCase ,					eos_token=__UpperCAmelCase ,					unk_token=__UpperCAmelCase ,					sep_token=__UpperCAmelCase ,					cls_token=__UpperCAmelCase ,					pad_token=__UpperCAmelCase ,					mask_token=__UpperCAmelCase ,					sp_model_kwargs=self.sp_model_kwargs ,					**__UpperCAmelCase ,					)

											a__	:							Optional[Any]      						= vocab_file
											a__	:							Tuple      						= spm.SentencePieceProcessor(**self.sp_model_kwargs				)
											self.sp_model.Load(str(__UpperCAmelCase				)				)

											a__	:							List[Any]      						= {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

											a__	:							List[Any]      						= len(self.sp_model				) - 1
											a__	:							Dict      						= {v: k for k, v in self.fairseq_tokens_to_ids.items()}



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None				):







											"""simple docstring"""



											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											a__	:							Tuple      						= [self.cls_token_id]
											a__	:							Optional[int]      						= [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = False				):







											"""simple docstring"""



											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__UpperCAmelCase ,					token_ids_a=__UpperCAmelCase ,					already_has_special_tokens=__UpperCAmelCase				)

											if token_ids_a is None:
															return [1] + ([0] * len(__UpperCAmelCase				)) + [1]
											return [1] + ([0] * len(__UpperCAmelCase				)) + [1, 1] + ([0] * len(__UpperCAmelCase				)) + [1]



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None				):







											"""simple docstring"""



											a__	:							List[Any]      						= [self.sep_token_id]
											a__	:							Optional[Any]      						= [self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep				) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return len(self.sp_model				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= {self.convert_ids_to_tokens(__UpperCAmelCase				): i for i in range(self.vocab_size				)}
											vocab.update(self.added_tokens_encoder				)
											return vocab



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											return self.sp_model.encode(__UpperCAmelCase ,					out_type=__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
											a__	:							Union[str, Any]      						= self.sp_model.PieceToId(__UpperCAmelCase				)

											return spm_id if spm_id else self.unk_token_id



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= []
											a__	:							Dict      						= ""
											a__	:							Union[str, Any]      						= False
											for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																			if not prev_is_special:
																							out_string += " "
																			out_string += self.sp_model.decode(__UpperCAmelCase				) + token
																			a__	:							List[Any]      						= True
																			a__	:							Optional[int]      						= []
															else:
																			current_sub_tokens.append(__UpperCAmelCase				)
																			a__	:							List[str]      						= False
											out_string += self.sp_model.decode(__UpperCAmelCase				)
											return out_string.strip()



							def __getstate__(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.__dict__.copy()
											a__	:							Optional[Any]      						= None
											return state



							def __setstate__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Tuple      						= d

											# for backward compatibility
											if not hasattr(self ,					"sp_model_kwargs"				):
															a__	:							List[Any]      						= {}

											a__	:							Optional[Any]      						= spm.SentencePieceProcessor(**self.sp_model_kwargs				)
											self.sp_model.Load(self.vocab_file				)




							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None				):







											"""simple docstring"""



											if not os.path.isdir(__UpperCAmelCase				):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'				)
															return
											a__	:							List[str]      						= os.path.join(
											    __UpperCAmelCase ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

											if os.path.abspath(self.vocab_file				) != os.path.abspath(__UpperCAmelCase				) and os.path.isfile(self.vocab_file				):
															copyfile(self.vocab_file ,					__UpperCAmelCase				)
											elif not os.path.isfile(self.vocab_file				):
															with open(__UpperCAmelCase ,					"wb"				) as fi:
																			a__	:							Tuple      						= self.sp_model.serialized_model_proto()
																			fi.write(__UpperCAmelCase				)

											return (out_vocab_file,)







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		       =      getLogger(__name__)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = 8	,				__UpperCamelCase = 10_24	,				__UpperCamelCase="val"	,				__UpperCamelCase=None	,				__UpperCamelCase=False	,				__UpperCamelCase="summarization"	,				__UpperCamelCase=None	,				__UpperCamelCase=1	,				__UpperCamelCase = None	,				__UpperCamelCase=""	,				**__UpperCamelCase	,				)	->						Dict:
				a__	:							Any      						= str(__UpperCamelCase )
				assert local_rank is not None
				torch.distributed.init_process_group(backend="nccl"	,				rank=__UpperCamelCase )

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

				a__	:							Any      						= 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:
								a__	:							Optional[Any]      						= tokenizer.model_max_length
				if prefix is None:
								a__	:							str      						= prefix or getattr(model.config	,				"prefix"	,				"" ) or ""
				a__	:							str      						= SeqaSeqDataset(
				    __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				max_target_length=10_24	,				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.
				a__	:							Any      						= ds.make_sortish_sampler(__UpperCamelCase	,				distributed=__UpperCamelCase	,				add_extra_examples=__UpperCamelCase	,				shuffle=__UpperCamelCase )
				a__	:							Optional[int]      						= DataLoader(__UpperCamelCase	,				sampler=__UpperCamelCase	,				batch_size=__UpperCamelCase	,				collate_fn=ds.collate_fn )
				a__	:							Optional[int]      						= []
				for batch in tqdm(__UpperCamelCase ):
								a__	:							Optional[Any]      						= 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	,				)
								a__	:							Any      						= tokenizer.batch_decode(__UpperCamelCase	,				skip_special_tokens=__UpperCamelCase	,				clean_up_tokenization_spaces=__UpperCamelCase )
								a__	:							Dict      						= batch["ids"]
								if num_return_sequences > 1:
												a__	:							int      						= 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     SCREAMING_SNAKE_CASE(      )	->						List[str]:
				a__	:							List[str]      						= 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=6_00	,				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" )
				a__	:							str      						= time.time()
				a__						,   a__	:							Dict      						= parser.parse_known_args()
				a__	:							Optional[Any]      						= parse_numeric_n_bool_cl_kwargs(__UpperCamelCase )
				if generate_kwargs and args.local_rank <= 0:
								print(F'parsed the following generate kwargs: {generate_kwargs}' )
				a__	:							List[str]      						= Path(args.save_dir + "_tmp" )
				Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )  # this handles locking.
				a__	:							List[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.
				a__	:							List[Any]      						= {}
				if args.src_lang is not None:
								a__	:							str      						= args.src_lang
				if args.tgt_lang is not None:
								a__	:							int      						= args.tgt_lang

				Path(args.save_dir ).mkdir(exist_ok=__UpperCamelCase )
				a__						,   a__	:							Tuple      						= 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:
								a__	:							str      						= Path(args.save_dir )
								save_dir.mkdir(exist_ok=__UpperCamelCase )
								a__	:							Optional[int]      						= gather_results_from_each_node(__UpperCamelCase	,				__UpperCamelCase	,				args.sync_timeout )
								a__	:							Tuple      						= combine_partial_results(__UpperCamelCase )
								if args.num_return_sequences > 1:
												a__	:							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
								a__	:							Any      						= Path(args.data_dir ).joinpath(args.type_path + ".target" )
								with open(__UpperCamelCase ) as f:
												a__	:							Optional[int]      						= [x.rstrip() for x in f.readlines()][: len(__UpperCamelCase )]

								# Calculate metrics, save metrics,  and save _generations.txt
								a__	:							Any      						= "translation" in args.task
								a__	:							List[Any]      						= calculate_bleu if calc_bleu else calculate_rouge
								a__	:							List[Any]      						= "bleu" if calc_bleu else "rouge"
								a__	:							Dict      						= score_fn(__UpperCamelCase	,				__UpperCamelCase )
								a__	:							int      						= len(__UpperCamelCase )
								a__	:							Union[str, Any]      						= time.time() - start_time
								a__	:							List[Any]      						= round(runtime / metrics["n_obs"]	,				4 )
								a__	:							List[str]      						= num_replicas
								# TODO(@stas00): add whatever metadata to metrics
								a__	:							int      						= 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     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List:
				a__	:							Optional[Any]      						= []
				for partial_result in partial_results:
								records.extend(__UpperCamelCase )
				a__	:							Tuple      						= sorted(__UpperCamelCase	,				key=lambda __UpperCamelCase : x["id"] )
				a__	:							Tuple      						= [x["pred"] for x in records]
				return preds






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[Dict[str, List]]:
				# WAIT FOR lots of .json files
				a__	:							Optional[Any]      						= time.time()
				logger.info("waiting for all nodes to finish" )
				a__	:							int      						= None
				while (time.time() - start_wait) < timeout:
								a__	:							List[str]      						= list(save_dir.glob("rank_*.json" ) )
								if len(__UpperCamelCase ) < num_replicas:
												continue
								try:
												# make sure all json files are fully saved
												a__	:							int      						= 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()


