





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,
)


_UpperCAmelCase        =				getLogger(__name__)




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

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

       UpperCamelCase_          =  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:
              UpperCamelCase_          =  tokenizer.model_max_length
       if prefix is None:
              UpperCamelCase_          =  prefix or getattr(model.config					,		"prefix"					,		""  ) or ""
       UpperCamelCase_          =  SeqaSeqDataset(
           UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		max_target_length=1024					,		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.
       UpperCamelCase_          =  ds.make_sortish_sampler(UpperCamelCase_					,		distributed=UpperCamelCase_					,		add_extra_examples=UpperCamelCase_					,		shuffle=UpperCamelCase_  )
       UpperCamelCase_          =  DataLoader(UpperCamelCase_					,		sampler=UpperCamelCase_					,		batch_size=UpperCamelCase_					,		collate_fn=ds.collate_fn  )
       UpperCamelCase_          =  []
       for batch in tqdm(UpperCamelCase_  ):
              UpperCamelCase_          =  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_					,		)
              UpperCamelCase_          =  tokenizer.batch_decode(UpperCamelCase_					,		skip_special_tokens=UpperCamelCase_					,		clean_up_tokenization_spaces=UpperCamelCase_  )
              UpperCamelCase_          =  batch["ids"]
              if num_return_sequences > 1:
                     UpperCamelCase_          =  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  lowerCAmelCase_ (							)     ->		Tuple:
       UpperCamelCase_          =  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"  )
       UpperCamelCase_          =  time.time()
       UpperCamelCase_       , UpperCamelCase_          =  parser.parse_known_args()
       UpperCamelCase_          =  parse_numeric_n_bool_cl_kwargs(UpperCamelCase_  )
       if generate_kwargs and args.local_rank <= 0:
              print(F'''parsed the following generate kwargs: {generate_kwargs}'''  )
       UpperCamelCase_          =  Path(args.save_dir + "_tmp"  )
       Path(UpperCamelCase_  ).mkdir(exist_ok=UpperCamelCase_  )  # this handles locking.
       UpperCamelCase_          =  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.
       UpperCamelCase_          =  {}
       if args.src_lang is not None:
              UpperCamelCase_          =  args.src_lang
       if args.tgt_lang is not None:
              UpperCamelCase_          =  args.tgt_lang

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

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




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








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


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					List[str]         =   '''openai-gpt'''
       _UpperCamelCase				:					List[str]         =   {
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }

       def __init__(		self: int					,   _SCREAMING_SNAKE_CASE: int=40478					,   _SCREAMING_SNAKE_CASE: Optional[Any]=512					,   _SCREAMING_SNAKE_CASE: List[Any]=768					,   _SCREAMING_SNAKE_CASE: str=12					,   _SCREAMING_SNAKE_CASE: Any=12					,   _SCREAMING_SNAKE_CASE: Optional[int]="gelu"					,   _SCREAMING_SNAKE_CASE: int=0.1					,   _SCREAMING_SNAKE_CASE: Optional[int]=0.1					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=0.1					,   _SCREAMING_SNAKE_CASE: Any=1e-5					,   _SCREAMING_SNAKE_CASE: Tuple=0.02					,   _SCREAMING_SNAKE_CASE: str="cls_index"					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=True					,   _SCREAMING_SNAKE_CASE: List[Any]=None					,   _SCREAMING_SNAKE_CASE: List[Any]=True					,   _SCREAMING_SNAKE_CASE: List[Any]=0.1					,   **_SCREAMING_SNAKE_CASE: List[Any]					,   )       -> int:




              """simple docstring"""


              UpperCamelCase_          =  vocab_size
              UpperCamelCase_          =  n_positions
              UpperCamelCase_          =  n_embd
              UpperCamelCase_          =  n_layer
              UpperCamelCase_          =  n_head
              UpperCamelCase_          =  afn
              UpperCamelCase_          =  resid_pdrop
              UpperCamelCase_          =  embd_pdrop
              UpperCamelCase_          =  attn_pdrop
              UpperCamelCase_          =  layer_norm_epsilon
              UpperCamelCase_          =  initializer_range
              UpperCamelCase_          =  summary_type
              UpperCamelCase_          =  summary_use_proj
              UpperCamelCase_          =  summary_activation
              UpperCamelCase_          =  summary_first_dropout
              UpperCamelCase_          =  summary_proj_to_labels
              super().__init__(**_SCREAMING_SNAKE_CASE		)

