
"""simple docstring"""




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


A__      :	List[str]          =    logging.get_logger(__name__)

A__      :	Tuple          =    {
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							'umt5'
     _A								=							['past_key_values']



     def __init__(      self ,			__UpperCamelCase=25_01_12 ,			__UpperCamelCase=5_12 ,			__UpperCamelCase=64 ,			__UpperCamelCase=10_24 ,			__UpperCamelCase=8 ,			__UpperCamelCase=None ,			__UpperCamelCase=6 ,			__UpperCamelCase=32 ,			__UpperCamelCase=1_28 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=1E-6 ,			__UpperCamelCase=1.0 ,			__UpperCamelCase="gated-gelu" ,			__UpperCamelCase=True ,			__UpperCamelCase=True ,			__UpperCamelCase="T5Tokenizer" ,			__UpperCamelCase=True ,			__UpperCamelCase=0 ,			__UpperCamelCase=1 ,			__UpperCamelCase=0 ,			**__UpperCamelCase ,			)->					str:
          super().__init__(
              is_encoder_decoder=__UpperCamelCase ,			tokenizer_class=__UpperCamelCase ,			tie_word_embeddings=__UpperCamelCase ,			pad_token_id=__UpperCamelCase ,			eos_token_id=__UpperCamelCase ,			decoder_start_token_id=__UpperCamelCase ,			**__UpperCamelCase ,			)
          UpperCAmelCase__							: Any 							=						vocab_size
          UpperCAmelCase__							: str 							=						d_model
          UpperCAmelCase__							: Dict 							=						d_kv
          UpperCAmelCase__							: List[str] 							=						d_ff
          UpperCAmelCase__							: Tuple 							=						num_layers
          UpperCAmelCase__							: Tuple 							=						(
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          UpperCAmelCase__							: Optional[int] 							=						num_heads
          UpperCAmelCase__							: Dict 							=						relative_attention_num_buckets
          UpperCAmelCase__							: Any 							=						relative_attention_max_distance
          UpperCAmelCase__							: int 							=						dropout_rate
          UpperCAmelCase__							: Optional[Any] 							=						layer_norm_epsilon
          UpperCAmelCase__							: Tuple 							=						initializer_factor
          UpperCAmelCase__							: Optional[Any] 							=						feed_forward_proj
          UpperCAmelCase__							: Optional[int] 							=						use_cache

          UpperCAmelCase__							: str 							=						self.feed_forward_proj.split("-"  )
          UpperCAmelCase__							: List[str] 							=						act_info[-1]
          UpperCAmelCase__							: Optional[Any] 							=						act_info[0] == "gated"

          if len(__UpperCamelCase  ) > 1 and act_info[0] != "gated" or len(__UpperCamelCase  ) > 2:
               raise ValueError(
                   F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                   "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                   "'gated-gelu' or 'relu'"  )

          if feed_forward_proj == "gated-gelu":
               UpperCAmelCase__							: Union[str, Any] 							=						"gelu_new"



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          return self.d_model



     @property
     def  lowerCAmelCase__       (      self  )->					Dict:
          return self.num_heads



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return self.num_layers







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def  lowerCAmelCase__       (      self  )->					Mapping[str, Mapping[int, str]]:
          UpperCAmelCase__							: Any 							=						{
              "input_ids": {0: "batch", 1: "encoder_sequence"},
              "attention_mask": {0: "batch", 1: "encoder_sequence"},
          }
          if self.use_past:
               UpperCAmelCase__							: Optional[int] 							=						"past_encoder_sequence + sequence"
               UpperCAmelCase__							: List[str] 							=						{0: "batch"}
               UpperCAmelCase__							: Dict 							=						{0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
               UpperCAmelCase__							: Optional[Any] 							=						{0: "batch", 1: "decoder_sequence"}
               UpperCAmelCase__							: Dict 							=						{0: "batch", 1: "decoder_sequence"}

          if self.use_past:
               self.fill_with_past_key_values_(__UpperCamelCase ,			direction="inputs"  )

          return common_inputs



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def  lowerCAmelCase__       (      self  )->					int:
          return 13



     @property
     def  lowerCAmelCase__       (      self  )->					float:
          return 5E-4





"""simple docstring"""




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,
)


A__      :	int          =    getLogger(__name__)



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       int = 8       ,    lowerCAmelCase	:       int = 1024       ,    lowerCAmelCase	:       List[Any]="val"       ,    lowerCAmelCase	:       str=None       ,    lowerCAmelCase	:       int=False       ,    lowerCAmelCase	:       Dict="summarization"       ,    lowerCAmelCase	:       int=None       ,    lowerCAmelCase	:       List[str]=1       ,    lowerCAmelCase	:       Dict = None       ,    lowerCAmelCase	:       List[str]=""       ,    **lowerCAmelCase	:       int       ,    ):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						str(lowerCAmelCase		)
     assert local_rank is not None
     torch.distributed.init_process_group(backend="nccl"       ,    rank=lowerCAmelCase		)

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

     UpperCAmelCase__							: List[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:
          UpperCAmelCase__							: int 							=						tokenizer.model_max_length
     if prefix is None:
          UpperCAmelCase__							: Union[str, Any] 							=						prefix or getattr(model.config       ,    "prefix"       ,    ""		) or ""
     UpperCAmelCase__							: str 							=						SeqaSeqDataset(
         lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    max_target_length=1024       ,    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.
     UpperCAmelCase__							: Union[str, Any] 							=						ds.make_sortish_sampler(lowerCAmelCase       ,    distributed=lowerCAmelCase       ,    add_extra_examples=lowerCAmelCase       ,    shuffle=lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						DataLoader(lowerCAmelCase       ,    sampler=lowerCAmelCase       ,    batch_size=lowerCAmelCase       ,    collate_fn=ds.collate_fn		)
     UpperCAmelCase__							: str 							=						[]
     for batch in tqdm(lowerCAmelCase		):
          UpperCAmelCase__							: Dict 							=						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       ,    )
          UpperCAmelCase__							: int 							=						tokenizer.batch_decode(lowerCAmelCase       ,    skip_special_tokens=lowerCAmelCase       ,    clean_up_tokenization_spaces=lowerCAmelCase		)
          UpperCAmelCase__							: int 							=						batch["ids"]
          if num_return_sequences > 1:
               UpperCAmelCase__							: 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 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: 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=600       ,    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"		)
     UpperCAmelCase__							: Optional[int] 							=						time.time()
     UpperCAmelCase__							,	UpperCAmelCase__							: List[Any] 							=						parser.parse_known_args()
     UpperCAmelCase__							: 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}"		)
     UpperCAmelCase__							: Dict 							=						Path(args.save_dir + "_tmp"		)
     Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)  # this handles locking.
     UpperCAmelCase__							: List[str] 							=						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__							: List[str] 							=						{}
     if args.src_lang is not None:
          UpperCAmelCase__							: str 							=						args.src_lang
     if args.tgt_lang is not None:
          UpperCAmelCase__							: List[str] 							=						args.tgt_lang

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

          # Calculate metrics, save metrics,  and save _generations.txt
          UpperCAmelCase__							: List[Any] 							=						"translation" in args.task
          UpperCAmelCase__							: Optional[Any] 							=						calculate_bleu if calc_bleu else calculate_rouge
          UpperCAmelCase__							: Optional[Any] 							=						"bleu" if calc_bleu else "rouge"
          UpperCAmelCase__							: Dict 							=						score_fn(lowerCAmelCase       ,    lowerCAmelCase		)
          UpperCAmelCase__							: List[Any] 							=						len(lowerCAmelCase		)
          UpperCAmelCase__							: Union[str, Any] 							=						time.time() - start_time
          UpperCAmelCase__							: Optional[int] 							=						round(runtime / metrics["n_obs"]       ,    4		)
          UpperCAmelCase__							: Tuple 							=						num_replicas
          # TODO(@stas00): add whatever metadata to metrics
          UpperCAmelCase__							: Any 							=						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 							a__ (				lowerCAmelCase	:       Tuple		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						[]
     for partial_result in partial_results:
          records.extend(lowerCAmelCase		)
     UpperCAmelCase__							: Dict 							=						sorted(lowerCAmelCase       ,    key=lambda lowerCAmelCase		: x["id"]		)
     UpperCAmelCase__							: List[str] 							=						[x["pred"] for x in records]
     return preds



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     # WAIT FOR lots of .json files
     UpperCAmelCase__							: int 							=						time.time()
     logger.info("waiting for all nodes to finish"		)
     UpperCAmelCase__							: Dict 							=						None
     while (time.time() - start_wait) < timeout:
          UpperCAmelCase__							: str 							=						list(save_dir.glob("rank_*.json"		)		)
          if len(lowerCAmelCase		) < num_replicas:
               continue
          try:
               # make sure all json files are fully saved
               UpperCAmelCase__							: Union[str, Any] 							=						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()



