





'''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 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int]		, **_lowerCamelCase      :      Optional[int] ):
    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		, **a_		, )






class      UpperCAmelCase			( lowerCAmelCase_       ):
 @staticmethod
 def   UpperCAmelCase_							(       lowercase_   :Any							)->							List[Any]:
     A__       =    parser.add_parser("run_beam"		, help="Run a Beam dataset processing pipeline"							)
     run_beam_parser.add_argument("dataset"		, type=lowerCamelCase_		, help="Name of the dataset to download"							)
     run_beam_parser.add_argument("--name"		, type=lowerCamelCase_		, default=lowerCamelCase_		, help="Dataset config name"							)
     run_beam_parser.add_argument(
         "--cache_dir"		, type=lowerCamelCase_		, default=lowerCamelCase_		, help="Cache directory where the datasets are stored"		, )
     run_beam_parser.add_argument(
         "--beam_pipeline_options"		, type=lowerCamelCase_		, 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=lowerCamelCase_		, default=lowerCamelCase_		, 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=lowerCamelCase_							)
 def __init__(       self   :List[Any]		, lowercase_   :Dict		, lowercase_   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :List[Any]		, lowercase_   :Union[str, Any]		, lowercase_   :Dict		, lowercase_   :str		, lowercase_   :Any		, lowercase_   :Optional[Any]		, **lowercase_   :str		, )->							Optional[Any]:
     A__       =    dataset
     A__       =    name
     A__       =    cache_dir
     A__       =    beam_pipeline_options
     A__       =    data_dir
     A__       =    all_configs
     A__       =    save_infos
     A__       =    ignore_verifications
     A__       =    force_redownload
     A__       =    config_kwargs



 def   UpperCAmelCase_							(       self   :Dict							)->							List[str]:
     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							)
     A__,			A__       =    self._dataset, self._name
     A__       =    dataset_module_factory(lowerCamelCase_							)
     A__       =    import_main_class(dataset_module.module_path							)
     A__       =    []
     if self._beam_pipeline_options:
         A__       =    beam.options.pipeline_options.PipelineOptions(
             flags=[F"--{opt.strip()}" for opt in self._beam_pipeline_options.split(","							) if opt]							)
     else:
         A__       =    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=lowerCamelCase_		, cache_dir=self._cache_dir		, base_path=dataset_module.builder_kwargs.get("base_path"							)		, )							)
     else:
         builders.append(
             builder_cls(
                 config_name=lowerCamelCase_		, data_dir=self._data_dir		, beam_options=lowerCamelCase_		, 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=lowerCamelCase_		, )
         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:
         A__       =    os.path.join(builder_cls.get_imported_module_dir()		, config.DATASETDICT_INFOS_FILENAME							)

         A__       =    Path(lowerCamelCase_							).name + ".py"

         A__       =    os.path.join(lowerCamelCase_		, lowerCamelCase_							)
         if os.path.isfile(lowerCamelCase_							):
             A__       =    os.path.dirname(lowerCamelCase_							)
         elif os.path.isfile(lowerCamelCase_							):
             A__       =    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
         A__       =    os.path.join(lowerCamelCase_		, config.DATASETDICT_INFOS_FILENAME							)
         copyfile(lowerCamelCase_		, lowerCamelCase_							)
         print(F"Dataset Infos file saved at {user_dataset_infos_path}"							)








'''simple docstring'''

import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      Optional[Any] ):
    A__       =    copy.deepcopy(_lowerCamelCase )
    A__       =    sorted(_lowerCamelCase )
    A__       =    [bisect.bisect_right(_lowerCamelCase		, _lowerCamelCase ) for y in x]
    return quantized





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      Tuple=0 ):
    A__       =    np.arange(start=3		, stop=_lowerCamelCase		, step=4 ).tolist() if k > 0 else [10]
    A__       =    _quantize(_lowerCamelCase		, _lowerCamelCase )
    # count number of elements per group
    A__       =    np.unique(_lowerCamelCase		, return_counts=_lowerCamelCase )[1]
    A__       =    [0] + bins + [np.inf]
    logger.info("Using {} as bins for aspect lengths quantization".format(_lowerCamelCase ) )
    logger.info("Count of instances per bin: {}".format(_lowerCamelCase ) )
    return groups






class      UpperCAmelCase			( UpperCamelCase__       ):
 def __init__(       self   :Optional[Any]		, lowercase_   :List[Any]		, lowercase_   :Optional[int]		, lowercase_   :str							)->							Dict:
     if not isinstance(lowercase_		, lowercase_							):
         raise ValueError(
             "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(lowercase_							)							)
     A__       =    sampler
     A__       =    group_ids
     A__       =    batch_size
 def __iter__(       self   :int							)->							Tuple:
     A__       =    defaultdict(lowercase_							)
     A__       =    defaultdict(lowercase_							)

     A__       =    0
     for idx in self.sampler:
         A__       =    self.group_ids[idx]
         buffer_per_group[group_id].append(lowercase_							)
         samples_per_group[group_id].append(lowercase_							)
         if len(buffer_per_group[group_id]							) == self.batch_size:
             yield buffer_per_group[group_id]  # TODO
             num_batches += 1
             del buffer_per_group[group_id]
         assert len(buffer_per_group[group_id]							) < self.batch_size

     # now we have run out of elements that satisfy
     # the group criteria, let's return the remaining
     # elements so that the size of the sampler is
     # deterministic
     A__       =    len(self							)
     A__       =    expected_num_batches - num_batches
     if num_remaining > 0:
         # for the remaining batches, group the batches by similar lengths
         A__       =    []
         for group_id, idxs in sorted(buffer_per_group.items()		, key=lambda lowercase_							: x[0]							):
             batch_idx.extend(lowercase_							)
             if len(lowercase_							) >= self.batch_size:
                 yield batch_idx[: self.batch_size]
                 A__       =    batch_idx[self.batch_size :]
                 num_remaining -= 1
         if len(lowercase_							) > 0:
             yield batch_idx
             num_remaining -= 1
     assert num_remaining == 0



 def __len__(       self   :int							)->							Tuple:
     return (len(self.sampler							) + self.batch_size - 1) // self.batch_size



