
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,
)


lowercase  :					str						    =							getLogger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = 8					,	SCREAMING_SNAKE_CASE__ = 1_024					,	SCREAMING_SNAKE_CASE__="val"					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=False					,	SCREAMING_SNAKE_CASE__="summarization"					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=1					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__=""					,	**SCREAMING_SNAKE_CASE__					,	) ->    List[str]:
       lowercase							:     Any       	=      str(_UpperCAmelCase				)
       assert local_rank is not None
       torch.distributed.init_process_group(backend="""nccl"""					,	rank=_UpperCAmelCase				)

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

       lowercase							:     Tuple       	=      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:
              lowercase							:     Optional[Any]       	=      tokenizer.model_max_length
       if prefix is None:
              lowercase							:     str       	=      prefix or getattr(model.config					,	"""prefix"""					,	""""""				) or ''''''
       lowercase							:     Union[str, Any]       	=      SeqaSeqDataset(
           _UpperCAmelCase					,	_UpperCAmelCase					,	_UpperCAmelCase					,	max_target_length=1_024					,	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.
       lowercase							:     str       	=      ds.make_sortish_sampler(_UpperCAmelCase					,	distributed=_UpperCAmelCase					,	add_extra_examples=_UpperCAmelCase					,	shuffle=_UpperCAmelCase				)
       lowercase							:     List[Any]       	=      DataLoader(_UpperCAmelCase					,	sampler=_UpperCAmelCase					,	batch_size=_UpperCAmelCase					,	collate_fn=ds.collate_fn				)
       lowercase							:     List[Any]       	=      []
       for batch in tqdm(_UpperCAmelCase				):
              lowercase							:     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					,	)
              lowercase							:     Optional[int]       	=      tokenizer.batch_decode(_UpperCAmelCase					,	skip_special_tokens=_UpperCAmelCase					,	clean_up_tokenization_spaces=_UpperCAmelCase				)
              lowercase							:     List[str]       	=      batch['''ids''']
              if num_return_sequences > 1:
                     lowercase							:     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 						_snake_case(  ) ->    List[Any]:
       lowercase							:     Union[str, 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=_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"""				)
       lowercase							:     Any       	=      time.time()
       lowercase							:     Any       	=      parser.parse_known_args()
       lowercase							:     List[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}"				)
       lowercase							:     Union[str, Any]       	=      Path(args.save_dir + """_tmp"""				)
       Path(_UpperCAmelCase				).mkdir(exist_ok=_UpperCAmelCase				)  # this handles locking.
       lowercase							:     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.
       lowercase							:     List[Any]       	=      {}
       if args.src_lang is not None:
              lowercase							:     List[str]       	=      args.src_lang
       if args.tgt_lang is not None:
              lowercase							:     List[Any]       	=      args.tgt_lang

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

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







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





import warnings
from typing import Dict, List, Optional, Tuple

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


__A   			=logging.get_logger(__name__)

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       ["""input_ids""", """attention_mask"""]




			def __init__(   self							: List[str]       ,       a_							: List[Any]="</s>"       ,       a_							: str="<unk>"       ,       a_							: Tuple="<pad>"       ,       a_							: List[Any]=1_25       ,       a_							: Any=None       ,       **a_							: Tuple       ,       ):
					'''simple docstring'''





					if extra_ids > 0 and additional_special_tokens is None:
							__UpperCAmelCase    :     Optional[Any]													=	[F'<extra_id_{i}>' for i in range(a_ )]
					elif extra_ids > 0 and additional_special_tokens is not None:
							# Check that we have the right number of extra_id special tokens
							__UpperCAmelCase    :     Optional[Any]													=	len(set(filter(lambda a_ : bool('''extra_id''' in str(a_ ) )       ,       a_ ) ) )
							if extra_tokens != extra_ids:
									raise ValueError(
									    F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
									    ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
									    ''' extra_ids tokens''' )

					__UpperCAmelCase    :     str													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else pad_token
					__UpperCAmelCase    :     str													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else eos_token
					__UpperCAmelCase    :     int													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else unk_token

					super().__init__(
					    eos_token=a_       ,       unk_token=a_       ,       pad_token=a_       ,       extra_ids=a_       ,       additional_special_tokens=a_       ,       **a_       ,       )

					__UpperCAmelCase    :     Dict													=	extra_ids

					__UpperCAmelCase    :     Any													=	2**8  # utf is 8 bits

					# define special tokens dict
					__UpperCAmelCase    :     Dict[int, str]													=	{
					    self.pad_token: 0,
					    self.eos_token: 1,
					    self.unk_token: 2,
					}
					__UpperCAmelCase    :     Optional[int]													=	len(self.special_tokens_encoder )
					__UpperCAmelCase    :     Union[str, Any]													=	len(a_ )
					for i, token in enumerate(a_ ):
							__UpperCAmelCase    :     Union[str, Any]													=	self.vocab_size + i - n
					__UpperCAmelCase    :     Dict[str, int]													=	{v: k for k, v in self.special_tokens_encoder.items()}




			@property
			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					return self._utf_vocab_size + self._num_special_tokens + self._extra_ids




			def  snake_case__     (   self							: Any       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None       ,       a_							: bool = False ):
					'''simple docstring'''





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

					# normal case: some special tokens
					if token_ids_a is None:
							return ([0] * len(a_ )) + [1]
					return ([0] * len(a_ )) + [1] + ([0] * len(a_ )) + [1]




			def  snake_case__     (   self							: Any       ,       a_							: List[int] ):
					'''simple docstring'''





					if len(a_ ) > 0 and token_ids[-1] == self.eos_token_id:
							warnings.warn(
							    F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
							    ''' eos tokens being added.''' )
							return token_ids
					else:
							return token_ids + [self.eos_token_id]




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	[self.eos_token_id]

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




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self._add_eos_if_not_present(a_ )
					if token_ids_a is None:
							return token_ids_a
					else:
							__UpperCAmelCase    :     List[str]													=	self._add_eos_if_not_present(a_ )
							return token_ids_a + token_ids_a




			def  snake_case__     (   self							: List[Any]       ,       a_							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	[chr(a_ ) for i in text.encode('''utf-8''' )]
					return tokens




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: int ):
					'''simple docstring'''





					if token in self.special_tokens_encoder:
							__UpperCAmelCase    :     Union[str, Any]													=	self.special_tokens_encoder[token]
					elif token in self.added_tokens_encoder:
							__UpperCAmelCase    :     Optional[Any]													=	self.added_tokens_encoder[token]
					elif len(a_ ) != 1:
							__UpperCAmelCase    :     int													=	self.unk_token_id
					else:
							__UpperCAmelCase    :     List[str]													=	ord(a_ ) + self._num_special_tokens
					return token_id




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: Optional[int] ):
					'''simple docstring'''





					if index in self.special_tokens_decoder:
							__UpperCAmelCase    :     Dict													=	self.special_tokens_decoder[index]
					else:
							__UpperCAmelCase    :     List[Any]													=	chr(index - self._num_special_tokens )
					return token




			def  snake_case__     (   self							: int       ,       a_							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	b''''''
					for token in tokens:
							if token in self.special_tokens_decoder:
									__UpperCAmelCase    :     Optional[int]													=	self.special_tokens_decoder[token].encode('''utf-8''' )
							elif token in self.added_tokens_decoder:
									__UpperCAmelCase    :     List[str]													=	self.special_tokens_decoder[token].encode('''utf-8''' )
							elif token in self.special_tokens_encoder:
									__UpperCAmelCase    :     Tuple													=	token.encode('''utf-8''' )
							elif token in self.added_tokens_encoder:
									__UpperCAmelCase    :     int													=	token.encode('''utf-8''' )
							else:
									__UpperCAmelCase    :     Any													=	bytes([ord(a_ )] )
							bstring += tok_string
					__UpperCAmelCase    :     Dict													=	bstring.decode('''utf-8'''       ,       errors='''ignore''' )
					return string




			def  snake_case__     (   self							: Tuple       ,       a_							: str       ,       a_							: Optional[str] = None ):
					'''simple docstring'''





					return ()





