
'''simple docstring'''





import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			**SCREAMING_SNAKE_CASE__       )				->					Union[str, Any]:
       return RunBeamCommand(
           args.dataset      ,			args.name      ,			args.cache_dir      ,			args.beam_pipeline_options      ,			args.data_dir      ,			args.all_configs      ,			args.save_info or args.save_infos      ,			args.ignore_verifications      ,			args.force_redownload      ,			**__lowerCAmelCase      ,			)




class __snake_case	(   A__  ):
    @staticmethod
    def    _SCREAMING_SNAKE_CASE (						snake_case   ):



           '''simple docstring'''







           lowercase							:  str 			=   parser.add_parser("""run_beam"""	,help="""Run a Beam dataset processing pipeline"""   )
           run_beam_parser.add_argument("""dataset"""	,type=snake_case	,help="""Name of the dataset to download"""   )
           run_beam_parser.add_argument("""--name"""	,type=snake_case	,default=snake_case	,help="""Dataset config name"""   )
           run_beam_parser.add_argument(
               """--cache_dir"""	,type=snake_case	,default=snake_case	,help="""Cache directory where the datasets are stored"""	,)
           run_beam_parser.add_argument(
               """--beam_pipeline_options"""	,type=snake_case	,default=""""""	,help="""Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"""	,)
           run_beam_parser.add_argument(
               """--data_dir"""	,type=snake_case	,default=snake_case	,help="""Can be used to specify a manual directory to get the files from"""	,)
           run_beam_parser.add_argument("""--all_configs"""	,action="""store_true"""	,help="""Test all dataset configurations"""   )
           run_beam_parser.add_argument("""--save_info"""	,action="""store_true"""	,help="""Save the dataset infos file"""   )
           run_beam_parser.add_argument(
               """--ignore_verifications"""	,action="""store_true"""	,help="""Run the test without checksums and splits checks"""   )
           run_beam_parser.add_argument("""--force_redownload"""	,action="""store_true"""	,help="""Force dataset redownload"""   )
           # aliases
           run_beam_parser.add_argument("""--save_infos"""	,action="""store_true"""	,help="""alias for save_info"""   )
           run_beam_parser.set_defaults(func=snake_case   )
    def __init__(						self	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,**snake_case	,):



           '''simple docstring'''







           lowercase							:  int 			=   dataset
           lowercase							:  Dict 			=   name
           lowercase							:  Optional[Any] 			=   cache_dir
           lowercase							:  int 			=   beam_pipeline_options
           lowercase							:  Any 			=   data_dir
           lowercase							:  Union[str, Any] 			=   all_configs
           lowercase							:  List[Any] 			=   save_infos
           lowercase							:  Optional[Any] 			=   ignore_verifications
           lowercase							:  List[str] 			=   force_redownload
           lowercase							:  Optional[Any] 			=   config_kwargs
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           import apache_beam as beam

           if self._name is not None and self._all_configs:
                  print("""Both parameters `name` and `all_configs` can't be used at once."""   )
                  exit(1   )
           lowercase							:  Optional[int] 			=   self._dataset, self._name
           lowercase							:  Dict 			=   dataset_module_factory(snake_case   )
           lowercase							:  List[str] 			=   import_main_class(dataset_module.module_path   )
           lowercase							:  List[DatasetBuilder] 			=   []
           if self._beam_pipeline_options:
                  lowercase							:  Optional[int] 			=   beam.options.pipeline_options.PipelineOptions(
                      flags=[f"--{opt.strip()}" for opt in self._beam_pipeline_options.split(""","""   ) if opt]   )
           else:
                  lowercase							:  str 			=   None
           if self._all_configs and len(builder_cls.BUILDER_CONFIGS   ) > 0:
                  for builder_config in builder_cls.BUILDER_CONFIGS:
                         builders.append(
                             builder_cls(
                                 config_name=builder_config.name	,data_dir=self._data_dir	,hash=dataset_module.hash	,beam_options=snake_case	,cache_dir=self._cache_dir	,base_path=dataset_module.builder_kwargs.get("""base_path"""   )	,)   )
           else:
                  builders.append(
                      builder_cls(
                          config_name=snake_case	,data_dir=self._data_dir	,beam_options=snake_case	,cache_dir=self._cache_dir	,base_path=dataset_module.builder_kwargs.get("""base_path"""   )	,**self._config_kwargs	,)   )

           for builder in builders:
                  builder.download_and_prepare(
                      download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                      if not self._force_redownload
                      else DownloadMode.FORCE_REDOWNLOAD	,download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH   )	,verification_mode=VerificationMode.NO_CHECKS
                      if self._ignore_verifications
                      else VerificationMode.ALL_CHECKS	,try_from_hf_gcs=snake_case	,)
                  if self._save_infos:
                         builder._save_infos()

           print("""Apache beam run successful."""   )

           # If save_infos=True, the dataset infos file is created next to the loaded module file.
           # Let's move it to the original directory of the dataset script, to allow the user to
           # upload them on S3 at the same time afterwards.
           if self._save_infos:
                  lowercase							:  str 			=   os.path.join(builder_cls.get_imported_module_dir()	,config.DATASETDICT_INFOS_FILENAME   )

                  lowercase							:  int 			=   Path(snake_case   ).name + ".py"

                  lowercase							:  int 			=   os.path.join(snake_case	,snake_case   )
                  if os.path.isfile(snake_case   ):
                         lowercase							:  List[str] 			=   os.path.dirname(snake_case   )
                  elif os.path.isfile(snake_case   ):
                         lowercase							:  Optional[int] 			=   path
                  else:  # in case of a remote dataset
                         print(f"Dataset Infos file saved at {dataset_infos_path}"   )
                         exit(1   )

                  # Move datasetinfo back to the user
                  lowercase							:  int 			=   os.path.join(snake_case	,config.DATASETDICT_INFOS_FILENAME   )
                  copyfile(snake_case	,snake_case   )
                  print(f"Dataset Infos file saved at {user_dataset_infos_path}"   )


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


lowercase						:      Any							=							logging.get_logger(__name__)

class __snake_case	:
    def __init__(						self	,snake_case	,snake_case=21	,snake_case=[]	,snake_case=None   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   model
           lowercase							:  List[Any] 			=   trace_batch_nums
           lowercase							:  Union[str, Any] 			=   abort_after_batch_num

           # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
           lowercase							:  List[Any] 			=   collections.deque([]	,snake_case   )
           lowercase							:  List[Any] 			=   []
           lowercase							:  Tuple 			=   0
           lowercase							:  Optional[Any] 			=   0
           lowercase							:  int 			=   False
           lowercase							:  List[str] 			=   """                 """

           self.analyse_model()

           self.register_forward_hook()
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case=None   ):



           '''simple docstring'''







           if frame is not None:
                  self.expand_frame(snake_case   )
           self.frames.append("""\n""".join(self.frame   )   )
           lowercase							:  List[str] 			=   []  # start a new frame
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           self.frame.append(snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           print("""\n""".join(self.frames   )   )
           lowercase							:  Any 			=   []
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   []
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           print(f"\nDetected inf/nan during batch_number={self.batch_number}"   )
           print(f"Last {len(self.frames   )} forward frames:"   )
           print(f"{'abs min':8} {'abs max':8} metadata"   )
           print("""\n""".join(self.frames   )   )
           print("""\n\n"""   )
           lowercase							:  Dict 			=   []
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  int 			=   {m: name for name, m in self.model.named_modules()}
           # self.longest_module_name = max(len(v) for v in self.module_names.values())
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case   ):



           '''simple docstring'''







           if torch.is_tensor(snake_case   ):
                  self.expand_frame(get_abs_min_max(snake_case	,snake_case   )   )
                  if detect_overflow(snake_case	,snake_case   ):
                         lowercase							:  str 			=   True
           elif var is None:
                  self.expand_frame(f"{'None':>17} {ctx}"   )
           else:
                  self.expand_frame(f"{'not a tensor':>17} {ctx}"   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.expand_frame(f"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"   )
           self.expand_frame(f"{'abs min':8} {'abs max':8} metadata"   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







           self.expand_frame(f"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"   )

           # params
           for name, p in module.named_parameters(recurse=snake_case   ):
                  self.analyse_variable(snake_case	,snake_case   )

           # inputs
           if isinstance(snake_case	,snake_case   ):
                  for i, x in enumerate(snake_case   ):
                         self.analyse_variable(snake_case	,f"input[{i}]"   )
           else:
                  self.analyse_variable(snake_case	,"""input"""   )

           # outputs
           if isinstance(snake_case	,snake_case   ):
                  for i, x in enumerate(snake_case   ):
                         # possibly a tuple of tuples
                         if isinstance(snake_case	,snake_case   ):
                                for j, y in enumerate(snake_case   ):
                                       self.analyse_variable(snake_case	,f"output[{i}][{j}]"   )
                         else:
                                self.analyse_variable(snake_case	,f"output[{i}]"   )
           else:
                  self.analyse_variable(snake_case	,"""output"""   )

           self.save_frame()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.model.apply(self._register_forward_hook   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           module.register_forward_hook(self.forward_hook   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   False

           lowercase							:  str 			=   True if self.batch_number in self.trace_batch_nums else False
           if trace_mode:
                  self.reset_saved_frames()

           if self.total_calls == 0:
                  self.batch_start_frame()
           self.total_calls += 1

           # count batch numbers - the very first forward hook of the batch will be called when the
           # batch completes - i.e. it gets called very last - we know this batch has finished
           if module == self.model:
                  self.batch_number += 1
                  lowercase							:  Tuple 			=   True

           self.create_frame(snake_case	,snake_case	,snake_case   )

           # if last_frame_of_batch:
           #     self.batch_end_frame()

           if trace_mode:
                  self.trace_frames()

           if last_frame_of_batch:
                  self.batch_start_frame()

           if self.detected_overflow and not trace_mode:
                  self.dump_saved_frames()

                  # now we can abort, as it's pointless to continue running
                  raise ValueError(
                      """DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. """
                      """Please scroll up above this traceback to see the activation values prior to this event."""   )

           # abort after certain batch if requested to do so
           if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
                  raise ValueError(
                      f"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
                      f" `abort_after_batch_num={self.abort_after_batch_num}` arg"   )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					List[str]:
       lowercase							:  str 			=   var.abs()
       return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[int]:
       lowercase							:  Any 			=   False
       if torch.isnan(SCREAMING_SNAKE_CASE__       ).any().item():
              lowercase							:  Tuple 			=   True
              print(f"{ctx} has nans"       )
       if torch.isinf(SCREAMING_SNAKE_CASE__       ).any().item():
              lowercase							:  Union[str, Any] 			=   True
              print(f"{ctx} has infs"       )

       # if needed to monitor large elements can enable the following
       if 0:  # and detected:
              lowercase							:  Optional[int] 			=   var[torch.ge(var.abs()      ,			100       )]
              if naaa.numel() > 0:
                     print(f"{ctx}:  n100={naaa.numel()}"       )
              lowercase							:  Union[str, Any] 			=   var[torch.ge(var.abs()      ,			1_000       )]
              if naaaa.numel() > 0:
                     print(f"{ctx}: n1000={naaaa.numel()}"       )
              lowercase							:  List[str] 			=   var[torch.ge(var.abs()      ,			10_000       )]
              if naaaaa.numel() > 0:
                     print(f"{ctx}: n10000={naaaaa.numel()}"       )

       if 0:
              print(f"min={var.min():9.2e} max={var.max():9.2e}"       )

       if 0:
              print(f"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"       )

       return detected




class __snake_case	(   lowerCAmelCase  ):
    _a	:		Dict=       "underflow_overflow"
    _a	:		Union[str, Any]=       "tpu_metrics_debug"
