

'''simple docstring'''







from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
      import torch

if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm


__lowerCamelCase										=			logging.get_logger(__name__)


@dataclass
class     A__					(					lowerCamelCase_	):
    lowercase		    =						[
        "no_inference",
        "no_cuda",
        "no_tpu",
        "no_speed",
        "no_memory",
        "no_env_print",
        "no_multi_process",
    ]



    def __init__(    self						, **UpperCamelCase__				)  ->					Any:





           '''simple docstring'''
           for deprecated_arg in self.deprecated_args:
                  if deprecated_arg in kwargs:
                         A_		    =					deprecated_arg[3:]
                         setattr(self						, __a						, not kwargs.pop(__a				)				)
                         logger.warning(
                             f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
                             f''' {positive_arg}={kwargs[positive_arg]}'''				)

           A_		    =					kwargs.pop("""torchscript"""						, self.torchscript				)
           A_		    =					kwargs.pop("""torch_xla_tpu_print_metrics"""						, self.torch_xla_tpu_print_metrics				)
           A_		    =					kwargs.pop("""fp16_opt_level"""						, self.fpaa_opt_level				)
           super().__init__(**__a				)

    lowercase		    =						field(default=lowerCamelCase_  ,  metadata={"help": "Trace the models using torchscript"}	)
    lowercase		    =						field(default=lowerCamelCase_  ,  metadata={"help": "Print Xla/PyTorch tpu metrics"}	)
    lowercase		    =						field(
        default="O1"  ,  metadata={
            "help": (
                "For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. "
                "See details at https://nvidia.github.io/apex/amp.html"
            )
        }  ,  )



    @cached_property
    def     snake_case_	(    self				)  ->					Optional[Any]:





           '''simple docstring'''
           requires_backends(self						, ["""torch"""]				)
           logger.info("""PyTorch: setting up devices"""				)
           if not self.cuda:
                  A_		    =					torch.device("""cpu"""				)
                  A_		    =					0
           elif is_torch_tpu_available():
                  A_		    =					xm.xla_device()
                  A_		    =					0
           else:
                  A_		    =					torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""				)
                  A_		    =					torch.cuda.device_count()
           return device, n_gpu



    @property
    def     snake_case_	(    self				)  ->					Any:





           '''simple docstring'''
           return is_torch_tpu_available() and self.tpu



    @property
    def     snake_case_	(    self				)  ->					int:





           '''simple docstring'''
           requires_backends(self						, ["""torch"""]				)
           # TODO(PVP): currently only single GPU is supported
           return torch.cuda.current_device()



    @property
    def     snake_case_	(    self				)  ->					List[Any]:





           '''simple docstring'''
           requires_backends(self						, ["""torch"""]				)
           return self._setup_devices[0]



    @property
    def     snake_case_	(    self				)  ->					Optional[Any]:





           '''simple docstring'''
           requires_backends(self						, ["""torch"""]				)
           return self._setup_devices[1]







    @property
    def     snake_case_	(    self				)  ->					List[str]:





           '''simple docstring'''
           return self.n_gpu > 0







'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      int    ,							lowercase						:      int=1024    ,							lowercase						:      int=1024    ,							lowercase						:      Tuple=False    ,							**lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="train"    ,							**lowercase						)
				_a								= tok.pad_token_id

				def get_lens(lowercase						:      Optional[int]						):
								_a								= tqdm(
								    DataLoader(lowercase    ,							batch_size=512    ,							num_workers=8    ,							shuffle=lowercase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
								_a								= []
								for batch in dl:
												_a								= batch["input_ids"].ne(lowercase						).sum(1						).tolist()
												_a								= batch["labels"].ne(lowercase						).sum(1						).tolist()
												if consider_target:
																for src, tgt in zip(lowercase    ,							lowercase						):
																				max_lens.append(max(lowercase    ,							lowercase						)						)
												else:
																max_lens.extend(lowercase						)
								return max_lens

				_a								= get_lens(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="val"    ,							**lowercase						)
				_a								= get_lens(lowercase						)
				pickle_save(lowercase    ,							train_ds.len_file						)
				pickle_save(lowercase    ,							val_ds.len_file						)


if __name__ == "__main__":
					fire.Fire(save_len_file)
