
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__       =logging.get_logger(__name__)

lowercase__       ={
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							int	       =      "canine"
   def __init__(self :					Any					,				snake_case_ :					Tuple=7_6_8					,				snake_case_ :					Any=1_2					,				snake_case_ :					Union[str, Any]=1_2					,				snake_case_ :					Optional[Any]=3_0_7_2					,				snake_case_ :					Optional[Any]="gelu"					,				snake_case_ :					Optional[int]=0.1					,				snake_case_ :					List[Any]=0.1					,				snake_case_ :					Tuple=1_6_3_8_4					,				snake_case_ :					str=1_6					,				snake_case_ :					Dict=0.02					,				snake_case_ :					int=1E-12					,				snake_case_ :					Any=0					,				snake_case_ :					int=0Xe000					,				snake_case_ :					Any=0Xe001					,				snake_case_ :					List[Any]=4					,				snake_case_ :					Tuple=4					,				snake_case_ :					Union[str, Any]=8					,				snake_case_ :					Dict=1_6_3_8_4					,				snake_case_ :					Optional[int]=1_2_8					,				**snake_case_ :					Union[str, Any]					,				):
    super().__init__(pad_token_id=snake_case_					,				bos_token_id=snake_case_					,				eos_token_id=snake_case_					,				**snake_case_   )

    __a :  Union[str, Any]						=  max_position_embeddings
    __a :  Tuple						=  hidden_size
    __a :  Union[str, Any]						=  num_hidden_layers
    __a :  Optional[Any]						=  num_attention_heads
    __a :  List[Any]						=  intermediate_size
    __a :  Union[str, Any]						=  hidden_act
    __a :  List[str]						=  hidden_dropout_prob
    __a :  Optional[int]						=  attention_probs_dropout_prob
    __a :  Any						=  initializer_range
    __a :  Dict						=  type_vocab_size
    __a :  Optional[Any]						=  layer_norm_eps

    # Character config:
    __a :  int						=  downsampling_rate
    __a :  Union[str, Any]						=  upsampling_kernel_size
    __a :  int						=  num_hash_functions
    __a :  int						=  num_hash_buckets
    __a :  Union[str, Any]						=  local_transformer_stride


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__       =getLogger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				int = 8  ,   lowerCAmelCase__							:				int = 1_0_2_4  ,   lowerCAmelCase__							:				Union[str, Any]="val"  ,   lowerCAmelCase__							:				int=None  ,   lowerCAmelCase__							:				Any=False  ,   lowerCAmelCase__							:				Union[str, Any]="summarization"  ,   lowerCAmelCase__							:				Tuple=None  ,   lowerCAmelCase__							:				Optional[int]=1  ,   lowerCAmelCase__							:				Dict = None  ,   lowerCAmelCase__							:				int=""  ,   **lowerCAmelCase__							:				int  ,   ):
 __a :  List[Any]						=  str(lowerCAmelCase__  )
 assert local_rank is not None
 torch.distributed.init_process_group(backend='''nccl'''  ,   rank=lowerCAmelCase__  )

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

 __a :  Optional[Any]						=  AutoTokenizer.from_pretrained(lowerCAmelCase__  )
 logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"  )  # if this is wrong, check config.model_type.

 if max_source_length is None:
  __a :  Dict						=  tokenizer.model_max_length
 if prefix is None:
  __a :  Dict						=  prefix or getattr(model.config  ,   '''prefix'''  ,   ''''''  ) or ''''''
 __a :  List[Any]						=  SeqaSeqDataset(
     lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   max_target_length=1_0_2_4  ,   type_path=lowerCAmelCase__  ,   n_obs=lowerCAmelCase__  ,   prefix=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
 # 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 :  Tuple						=  ds.make_sortish_sampler(lowerCAmelCase__  ,   distributed=lowerCAmelCase__  ,   add_extra_examples=lowerCAmelCase__  ,   shuffle=lowerCAmelCase__  )
 __a :  List[Any]						=  DataLoader(lowerCAmelCase__  ,   sampler=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  ,   collate_fn=ds.collate_fn  )
 __a :  List[Any]						=  []
 for batch in tqdm(lowerCAmelCase__  ):
  __a :  Any						=  model.generate(
      input_ids=batch['''input_ids'''].to(model.device  )  ,   attention_mask=batch['''attention_mask'''].to(model.device  )  ,   num_return_sequences=lowerCAmelCase__  ,   num_beams=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
  __a :  List[Any]						=  tokenizer.batch_decode(lowerCAmelCase__  ,   skip_special_tokens=lowerCAmelCase__  ,   clean_up_tokenization_spaces=lowerCAmelCase__  )
  __a :  int						=  batch['''ids''']
  if num_return_sequences > 1:
   __a :  List[str]						=  chunks(lowerCAmelCase__  ,   lowerCAmelCase__  )  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(lowerCAmelCase__  ):
   results.append({'''pred''': pred, '''id''': ids[i].item()}  )
 save_json(lowerCAmelCase__  ,   lowerCAmelCase__  )
 return results, sampler.num_replicas




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

 parser.add_argument(
     '''--n_obs'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   help='''How many observations. Defaults to all.'''  )
 parser.add_argument(
     '''--num_return_sequences'''  ,   type=lowerCAmelCase__  ,   default=1  ,   required=lowerCAmelCase__  ,   help='''How many sequences to return'''  )
 parser.add_argument(
     '''--sync_timeout'''  ,   type=lowerCAmelCase__  ,   default=6_0_0  ,   required=lowerCAmelCase__  ,   help='''How long should master process wait for other processes to finish.'''  ,   )
 parser.add_argument('''--src_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument('''--tgt_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument(
     '''--prefix'''  ,   type=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   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 :  int						=  time.time()
 __a				,       __a :  Tuple						=  parser.parse_known_args()
 __a :  Optional[int]						=  parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__  )
 if generate_kwargs and args.local_rank <= 0:
  print(f"parsed the following generate kwargs: {generate_kwargs}"  )
 __a :  Union[str, Any]						=  Path(args.save_dir + '''_tmp'''  )
 Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )  # this handles locking.
 __a :  Dict						=  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 :  Optional[Any]						=  {}
 if args.src_lang is not None:
  __a :  int						=  args.src_lang
 if args.tgt_lang is not None:
  __a :  Optional[Any]						=  args.tgt_lang

 Path(args.save_dir  ).mkdir(exist_ok=lowerCAmelCase__  )
 __a				,       __a :  Tuple						=  eval_data_dir(
     args.data_dir  ,   lowerCAmelCase__  ,   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=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )

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

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




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ):
 __a :  Optional[int]						=  []
 for partial_result in partial_results:
  records.extend(lowerCAmelCase__  )
 __a :  Tuple						=  sorted(lowerCAmelCase__  ,   key=lambda lowerCAmelCase__  : x["id"]  )
 __a :  Tuple						=  [x['''pred'''] for x in records]
 return preds




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