




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,
)


SCREAMING_SNAKE_CASE__			: List[str]    =  getLogger(__name__)

def 	A ( _SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE = 8					,_SCREAMING_SNAKE_CASE = 1024					,_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					,)      ->					Optional[int]:
       lowerCamelCase  : str     					=   str(a__       )
       assert local_rank is not None
       torch.distributed.init_process_group(backend="nccl"					,rank=a__       )

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

       lowerCamelCase  : List[Any]     					=   AutoTokenizer.from_pretrained(a__       )
       logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''       )  # if this is wrong, check config.model_type.

       if max_source_length is None:
              lowerCamelCase  : List[Any]     					=   tokenizer.model_max_length
       if prefix is None:
              lowerCamelCase  : Dict     					=   prefix or getattr(model.config					,"prefix"					,""       ) or ""
       lowerCamelCase  : List[str]     					=   SeqaSeqDataset(
           a__					,a__					,a__					,max_target_length=1024					,type_path=a__					,n_obs=a__					,prefix=a__					,**a__					,)
       # 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.
       lowerCamelCase  : Dict     					=   ds.make_sortish_sampler(a__					,distributed=a__					,add_extra_examples=a__					,shuffle=a__       )
       lowerCamelCase  : int     					=   DataLoader(a__					,sampler=a__					,batch_size=a__					,collate_fn=ds.collate_fn       )
       lowerCamelCase  : Optional[Any]     					=   []
       for batch in tqdm(a__       ):
              lowerCamelCase  : List[Any]     					=   model.generate(
                  input_ids=batch["input_ids"].to(model.device       )					,attention_mask=batch["attention_mask"].to(model.device       )					,num_return_sequences=a__					,num_beams=a__					,**a__					,)
              lowerCamelCase  : List[str]     					=   tokenizer.batch_decode(a__					,skip_special_tokens=a__					,clean_up_tokenization_spaces=a__       )
              lowerCamelCase  : List[str]     					=   batch["ids"]
              if num_return_sequences > 1:
                     lowerCamelCase  : List[str]     					=   chunks(a__					,a__       )  # batch size chunks, each of size num_return_seq
              for i, pred in enumerate(a__       ):
                     results.append({"pred": pred, "id": ids[i].item()}       )
       save_json(a__					,a__       )
       return results, sampler.num_replicas

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

       parser.add_argument(
           "--n_obs"					,type=a__					,default=a__					,required=a__					,help="How many observations. Defaults to all."       )
       parser.add_argument(
           "--num_return_sequences"					,type=a__					,default=1					,required=a__					,help="How many sequences to return"       )
       parser.add_argument(
           "--sync_timeout"					,type=a__					,default=600					,required=a__					,help="How long should master process wait for other processes to finish."					,)
       parser.add_argument("--src_lang"					,type=a__					,default=a__					,required=a__       )
       parser.add_argument("--tgt_lang"					,type=a__					,default=a__					,required=a__       )
       parser.add_argument(
           "--prefix"					,type=a__					,required=a__					,default=a__					,help="will be added to the begininng of src examples"       )
       parser.add_argument("--fp16"					,action="store_true"       )
       parser.add_argument("--debug"					,action="store_true"       )
       lowerCamelCase  : Optional[int]     					=   time.time()
       lowerCamelCase					, lowerCamelCase  : Union[str, Any]     					=   parser.parse_known_args()
       lowerCamelCase  : Tuple     					=   parse_numeric_n_bool_cl_kwargs(a__       )
       if generate_kwargs and args.local_rank <= 0:
              print(f'''parsed the following generate kwargs: {generate_kwargs}'''       )
       lowerCamelCase  : Union[str, Any]     					=   Path(args.save_dir + "_tmp"       )
       Path(a__       ).mkdir(exist_ok=a__       )  # this handles locking.
       lowerCamelCase  : Optional[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.
       lowerCamelCase  : int     					=   {}
       if args.src_lang is not None:
              lowerCamelCase  : Union[str, Any]     					=   args.src_lang
       if args.tgt_lang is not None:
              lowerCamelCase  : Optional[int]     					=   args.tgt_lang

       Path(args.save_dir       ).mkdir(exist_ok=a__       )
       lowerCamelCase					, lowerCamelCase  : Optional[int]     					=   eval_data_dir(
           args.data_dir					,a__					,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=a__					,**a__					,)

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

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

def 	A ( _SCREAMING_SNAKE_CASE       )      ->					Tuple:
       lowerCamelCase  : List[Any]     					=   []
       for partial_result in partial_results:
              records.extend(a__       )
       lowerCamelCase  : Optional[Any]     					=   sorted(a__					,key=lambda _SCREAMING_SNAKE_CASE       : x["id"]       )
       lowerCamelCase  : str     					=   [x["pred"] for x in records]
       return preds

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

import pytest

from transformers.dynamic_module_utils import get_imports


UpperCamelCase__           =			"\nimport os\n"

UpperCamelCase__           =			"\ndef foo():\n    import os\n    return False\n"

UpperCamelCase__           =			"\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n"

UpperCamelCase__           =			[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]





@pytest.mark.parametrize("""case""" ,      a__							)
def      _UpperCamelCase  (a__ :Any ,      a__ :Optional[Any]							):






							"""simple docstring"""





							UpperCamelCase__					  =      os.path.join(a__ ,      """test_file.py"""							)
							with open(a__ ,      """w"""							) as _tmp_file:
														_tmp_file.write(a__							)

							UpperCamelCase__					  =      get_imports(a__							)
							assert parsed_imports == ["os"]




