



'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
					import torch


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


snake_case_ :	Any         =  logging.get_logger(__name__)
snake_case_ :	Tuple         =  {'vocab_file': 'spiece.model'}

snake_case_ :	Tuple         =  {
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

snake_case_ :	Union[str, Any]         =  {
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}






class 							lowercase__	(							lowercase   ):
	lowercase__  				=			VOCAB_FILES_NAMES
	lowercase__  				=			PRETRAINED_VOCAB_FILES_MAP
	lowercase__  				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__  				=			["""input_ids""", """attention_mask"""]





	def __init__( self					:				List[Any] ,lowerCamelCase__					:				str ,lowerCamelCase__					:				Tuple=False ,lowerCamelCase__					:				List[Any]=False ,lowerCamelCase__					:				Any=False ,lowerCamelCase__					:				Union[str, Any]=None ,lowerCamelCase__					:				Optional[int]=None ,lowerCamelCase__					:				Optional[Any]=None ,lowerCamelCase__					:				Tuple=None ,lowerCamelCase__					:				Optional[Dict[str, Any]] = None ,**lowerCamelCase__					:				List[Any] ,):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   {} if sp_model_kwargs is None else sp_model_kwargs

							_UpperCamelCase					:		int 				=   kwargs.get('name_or_path' )
							if name_or_path is None:
													logger.warning(
													    'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
													    ' you are testing the model, this can safely be ignored' )
													_UpperCamelCase					:		str 				=   'None'

							# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
							_UpperCamelCase					:		Tuple 				=   '<|endoftext|>' if eos_token is None else eos_token
							_UpperCamelCase					:		Any 				=   '<unk>' if unk_token is None else unk_token
							if "gpt-sw3-7b" in name_or_path:
													_UpperCamelCase					:		Union[str, Any] 				=   unk_token if pad_token is None else pad_token
													_UpperCamelCase					:		Tuple 				=   eos_token if bos_token is None else bos_token
							else:
													_UpperCamelCase					:		str 				=   '<pad>' if pad_token is None else pad_token
													_UpperCamelCase					:		Dict 				=   '<s>' if bos_token is None else bos_token

							super().__init__(
							    do_lower_case=lowerCamelCase__ ,remove_space=lowerCamelCase__ ,keep_accents=lowerCamelCase__ ,bos_token=lowerCamelCase__ ,eos_token=lowerCamelCase__ ,unk_token=lowerCamelCase__ ,pad_token=lowerCamelCase__ ,sp_model_kwargs=self.sp_model_kwargs ,**lowerCamelCase__ ,)

							_UpperCamelCase					:		int 				=   do_lower_case
							_UpperCamelCase					:		Tuple 				=   remove_space
							_UpperCamelCase					:		int 				=   keep_accents
							_UpperCamelCase					:		Union[str, Any] 				=   vocab_file

							_UpperCamelCase					:		List[str] 				=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(lowerCamelCase__ )

							# Used for whitespace normalization in input texts
							# fmt : off
							_UpperCamelCase					:		List[Any] 				=   {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
							# fmt : on

							# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
							_UpperCamelCase					:		Optional[int] 				=   re.compile(
							    F'[{"".join(map(lowerCamelCase__ ,list(range(0 ,9 ) ) + list(range(11 ,32 ) ) + list(range(127 ,160 ) ) + [160, 173, 8203] ) )}]' )





	def __getstate__( self					:				Any ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   self.__dict__.copy()
							_UpperCamelCase					:		List[str] 				=   None
							return state





	def __setstate__( self					:				Dict ,lowerCamelCase__					:				Union[str, Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   d

							# for backward compatibility
							if not hasattr(self ,'sp_model_kwargs' ):
													_UpperCamelCase					:		List[str] 				=   {}

							_UpperCamelCase					:		Tuple 				=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(self.vocab_file )





	@property
	# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							return len(self.sp_model )





	def 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   self.non_printing_characters_re.sub('' ,lowerCamelCase__ )

							# Normalize whitespaces
							_UpperCamelCase					:		List[Any] 				=   ''.join([char if char not in self.whitespaces else ' ' for char in text] )

							# NFC Unicode normalization
							_UpperCamelCase					:		Union[str, Any] 				=   unicodedata.normalize('NFC' ,lowerCamelCase__ )
							return text





	def 	UpperCamelCase_  ( self					:				List[str] ,lowerCamelCase__					:				str ,**lowerCamelCase__					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   self.preprocess_text(lowerCamelCase__ )
							return self.sp_model.encode(lowerCamelCase__ ,out_type=lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Dict ,lowerCamelCase__					:				str ):







							'''simple docstring'''
							return self.sp_model.PieceToId(lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ,lowerCamelCase__					:				int ):







							'''simple docstring'''
							return self.sp_model.IdToPiece(lowerCamelCase__ )





	@staticmethod
	def 	UpperCamelCase_  ( lowerCamelCase__					:				str ):







							'''simple docstring'''
							return out_string





	def 	UpperCamelCase_  ( self					:				str ,lowerCamelCase__					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   []
							_UpperCamelCase					:		Optional[int] 				=   ''
							_UpperCamelCase					:		Optional[int] 				=   False
							for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																			# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
																			if not prev_is_special:
																									out_string += " "

																			out_string += self.sp_model.decode(lowerCamelCase__ ) + token
																			_UpperCamelCase					:		Optional[Any] 				=   True
																			_UpperCamelCase					:		Union[str, Any] 				=   []
													else:
																			current_sub_tokens.append(lowerCamelCase__ )
																			_UpperCamelCase					:		str 				=   False
							out_string += self.sp_model.decode(lowerCamelCase__ )

							return out_string





	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   {self.convert_ids_to_tokens(lowerCamelCase__ ): i for i in range(self.vocab_size )}
							vocab.update(self.added_tokens_encoder )
							return vocab





	def 	UpperCamelCase_  ( self					:				str ,lowerCamelCase__					:				str ,lowerCamelCase__					:				Optional[str] = None ):







							'''simple docstring'''
							if not os.path.isdir(lowerCamelCase__ ):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
													return
							_UpperCamelCase					:		Dict 				=   os.path.join(
							    lowerCamelCase__ ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

							if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase__ ) and os.path.isfile(self.vocab_file ):
													copyfile(self.vocab_file ,lowerCamelCase__ )
							elif not os.path.isfile(self.vocab_file ):
													with open(lowerCamelCase__ ,'wb' ) as fi:
																			_UpperCamelCase					:		str 				=   self.sp_model.serialized_model_proto()
																			fi.write(lowerCamelCase__ )

							return (out_vocab_file,)





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ,lowerCamelCase__					:				Union[str, List[str]] ,lowerCamelCase__					:				Union[str, bool] = False ):







							'''simple docstring'''

							if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
													_UpperCamelCase					:		List[str] 				=   self.preprocess_text(lowerCamelCase__ )
													_UpperCamelCase					:		Any 				=   self.sp_model.encode(lowerCamelCase__ )
							else:
													_UpperCamelCase					:		str 				=   [self.preprocess_text(lowerCamelCase__ ) for t in text]
													_UpperCamelCase					:		Dict 				=   self.sp_model.encode(lowerCamelCase__ )

							if return_tensors is True or return_tensors == "pt":
													_UpperCamelCase					:		Dict 				=   torch.tensor(lowerCamelCase__ )

							return token_ids





	def 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				Union[int, List[int]] ):







							'''simple docstring'''
							return self.sp_model.decode(lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Dict ,lowerCamelCase__					:				"Conversation" ):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   [F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
							_UpperCamelCase					:		List[Any] 				=   (
							    F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(lowerCamelCase__ ) + F'{self.bos_token}Bot:'
							)
							return self.encode(text=lowerCamelCase__ )








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 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_ = 8,snake_case_ = 1024,snake_case_="val",snake_case_=None,snake_case_=False,snake_case_="summarization",snake_case_=None,snake_case_=1,snake_case_ = None,snake_case_="",**snake_case_,):
      _A							:     Dict			=						str(snake_case_     )
      assert local_rank is not None
      torch.distributed.init_process_group(backend="""nccl""",rank=snake_case_     )

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

      _A							:     Optional[Any]			=						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:
            _A							:     Optional[int]			=						tokenizer.model_max_length
      if prefix is None:
            _A							:     Tuple			=						prefix or getattr(model.config,"""prefix""",""""""     ) or """"""
      _A							:     Optional[int]			=						SeqaSeqDataset(
          snake_case_,snake_case_,snake_case_,max_target_length=1024,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.
      _A							:     Optional[int]			=						ds.make_sortish_sampler(snake_case_,distributed=snake_case_,add_extra_examples=snake_case_,shuffle=snake_case_     )
      _A							:     Dict			=						DataLoader(snake_case_,sampler=snake_case_,batch_size=snake_case_,collate_fn=ds.collate_fn     )
      _A							:     Optional[Any]			=						[]
      for batch in tqdm(snake_case_     ):
            _A							:     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_,)
            _A							:     Any			=						tokenizer.batch_decode(snake_case_,skip_special_tokens=snake_case_,clean_up_tokenization_spaces=snake_case_     )
            _A							:     Dict			=						batch["""ids"""]
            if num_return_sequences > 1:
                  _A							:     Any			=						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 				lowerCAmelCase_	(     ):
      _A							:     Tuple			=						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"""     )
      _A							:     Union[str, Any]			=						time.time()
      _A			,   _A							:     List[str]			=						parser.parse_known_args()
      _A							:     List[str]			=						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}'''     )
      _A							:     Dict			=						Path(args.save_dir + """_tmp"""     )
      Path(snake_case_     ).mkdir(exist_ok=snake_case_     )  # this handles locking.
      _A							:     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.
      _A							:     Any			=						{}
      if args.src_lang is not None:
            _A							:     int			=						args.src_lang
      if args.tgt_lang is not None:
            _A							:     Dict			=						args.tgt_lang

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

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







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

