


import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


__lowerCamelCase   	=  get_logger(__name__)


if TYPE_CHECKING:
       from .builder import DatasetBuilder



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase = None			):
     a_     :  int				      =     importlib.import_module(__UpperCamelCase			)

     # TODO(QL): always update the module to add subsequent new authentication
     if hasattr(__UpperCamelCase	,	"""_patched_for_streaming"""			) and module._patched_for_streaming:
          return

     def wrap_auth(__UpperCamelCase			):
          @wraps(__UpperCamelCase			)
          def wrapper(*__UpperCamelCase	,	**__UpperCamelCase			):
               return function(*__UpperCamelCase	,	use_auth_token=__UpperCamelCase	,	**__UpperCamelCase			)

          a_     :  str				      =     """wrap_auth"""
          return wrapper

     # open files in a streaming fashion
     patch_submodule(__UpperCamelCase	,	"""open"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.listdir"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.walk"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""glob.glob"""	,	wrap_auth(__UpperCamelCase			)			).start()
     # allow to navigate in remote zip files
     patch_submodule(__UpperCamelCase	,	"""os.path.join"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.dirname"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.basename"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.relpath"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.split"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.splitext"""	,	__UpperCamelCase			).start()
     # allow checks on paths
     patch_submodule(__UpperCamelCase	,	"""os.path.exists"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.isdir"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.isfile"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.getsize"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""pathlib.Path"""	,	__UpperCamelCase			).start()
     # file readers
     patch_submodule(__UpperCamelCase	,	"""gzip.open"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""numpy.load"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""pandas.read_csv"""	,	wrap_auth(__UpperCamelCase			)	,	attrs=["""__version__"""]			).start()
     patch_submodule(__UpperCamelCase	,	"""pandas.read_excel"""	,	wrap_auth(__UpperCamelCase			)	,	attrs=["""__version__"""]			).start()
     patch_submodule(__UpperCamelCase	,	"""scipy.io.loadmat"""	,	wrap_auth(__UpperCamelCase			)	,	attrs=["""__version__"""]			).start()
     patch_submodule(__UpperCamelCase	,	"""xml.etree.ElementTree.parse"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""xml.dom.minidom.parse"""	,	wrap_auth(__UpperCamelCase			)			).start()
     a_     :  Optional[int]				      =     True



def 			_a      (    __UpperCamelCase			):
     extend_module_for_streaming(builder.__module__	,	use_auth_token=builder.use_auth_token			)
     # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
     if not builder.__module__.startswith("""datasets."""			):  # check that it's not a packaged builder like csv
          for imports in get_imports(inspect.getfile(builder.__class__			)			):
               if imports[0] == "internal":
                    a_     :  int				      =     imports[1]
                    a_     :  int				      =     """.""".join(builder.__module__.split("""."""			)[:-1] + [internal_import_name]			)
                    extend_module_for_streaming(__UpperCamelCase	,	use_auth_token=builder.use_auth_token			)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
     from .builder import DatasetBuilder

     a_     :  Optional[int]				      =     [
         cls.__module__
         for cls in type(__UpperCamelCase			).__mro__[1:]  # make sure it's not the same module we've already patched
         if issubclass(cls	,	__UpperCamelCase			) and cls.__module__ != DatasetBuilder.__module__
     ]  # check it's not a standard builder from datasets.builder
     for module in parent_builder_modules:
          extend_module_for_streaming(__UpperCamelCase	,	use_auth_token=builder.use_auth_token			)







import bisect
import copy
from collections import defaultdict

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

from utils import logger



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[Any]				      =     copy.deepcopy(__UpperCamelCase			)
     a_     :  Dict				      =     sorted(__UpperCamelCase			)
     a_     :  Dict				      =     [bisect.bisect_right(__UpperCamelCase	,	__UpperCamelCase			) for y in x]
     return quantized



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase=0			):
     a_     :  str				      =     np.arange(start=3	,	stop=__UpperCamelCase	,	step=4			).tolist() if k > 0 else [1_0]
     a_     :  Any				      =     _quantize(__UpperCamelCase	,	__UpperCamelCase			)
     # count number of elements per group
     a_     :  Optional[int]				      =     np.unique(__UpperCamelCase	,	return_counts=__UpperCamelCase			)[1]
     a_     :  List[str]				      =     [0] + bins + [np.inf]
     logger.info("""Using {} as bins for aspect lengths quantization""".format(__UpperCamelCase			)			)
     logger.info("""Count of instances per bin: {}""".format(__UpperCamelCase			)			)
     return groups


class 	a__						(    lowerCAmelCase_     ):

       def __init__( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  str     ):
            if not isinstance(lowerCamelCase_   ,				lowerCamelCase_     ):
                 raise ValueError(
                     """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase_     )     )
            a_     :  Optional[Any]				      =     sampler
            a_     :  List[Any]				      =     group_ids
            a_     :  Tuple				      =     batch_size

       def __iter__( self   :  Dict     ):
            a_     :  Tuple				      =     defaultdict(lowerCamelCase_     )
            a_     :  Any				      =     defaultdict(lowerCamelCase_     )

            a_     :  Any				      =     0
            for idx in self.sampler:
                 a_     :  str				      =     self.group_ids[idx]
                 buffer_per_group[group_id].append(lowerCamelCase_     )
                 samples_per_group[group_id].append(lowerCamelCase_     )
                 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_     :  Optional[Any]				      =     len(self     )
            a_     :  Any				      =     expected_num_batches - num_batches
            if num_remaining > 0:
                 # for the remaining batches, group the batches by similar lengths
                 a_     :  str				      =     []
                 for group_id, idxs in sorted(buffer_per_group.items()   ,				key=lambda lowerCamelCase_     : x[0]     ):
                      batch_idx.extend(lowerCamelCase_     )
                      if len(lowerCamelCase_     ) >= self.batch_size:
                           yield batch_idx[: self.batch_size]
                           a_     :  Union[str, Any]				      =     batch_idx[self.batch_size :]
                           num_remaining -= 1
                 if len(lowerCamelCase_     ) > 0:
                      yield batch_idx
                      num_remaining -= 1
            assert num_remaining == 0

       def __len__( self   :  List[Any]     ):
            return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size



