"""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 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->List[Any]:
  a__:	Optional[Any]     				=      copy.deepcopy(_SCREAMING_SNAKE_CASE  )
  a__:	List[str]     				=      sorted(_SCREAMING_SNAKE_CASE  )
  a__:	List[Any]     				=      [bisect.bisect_right(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ) for y in x]
  return quantized







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




class   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase       ,     lowercase       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        if not isinstance(lowercase       ,     lowercase):
          raise ValueError(
              'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowercase))
        a__:	List[str]     				=      sampler
        a__:	List[str]     				=      group_ids
        a__:	Tuple     				=      batch_size



      def __iter__(		self)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      defaultdict(lowercase)
        a__:	Dict     				=      defaultdict(lowercase)

        a__:	Optional[int]     				=      0
        for idx in self.sampler:
          a__:	Optional[int]     				=      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__:	List[str]     				=      len(self)
        a__:	List[str]     				=      expected_num_batches - num_batches
        if num_remaining > 0:
          # for the remaining batches, group the batches by similar lengths
          a__:	Dict     				=      []
          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__:	List[Any]     				=      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:





        '''simple docstring'''





        return (len(self.sampler) + self.batch_size - 1) // self.batch_size








import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


lowercase_      :							Optional[int]    							=					set()
lowercase_      :							List[Any]    							=					get_logger(__name__)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_ = None		):

						'''simple docstring'''


						def decorator(snake_case_		):
												global _emitted_deprecation_warnings

												if inspect.isclass(snake_case_		):
																		_UpperCAmelCase						=	deprecated_class_or_function.__init__
																		_UpperCAmelCase						=	deprecated_class_or_function.__name__
												else:
																		_UpperCAmelCase						=	deprecated_class_or_function
																		_UpperCAmelCase						=	deprecated_function.__name__
																		# Support deprecating __init__ class method: class name instead
																		_UpperCAmelCase						=	name if name != "__init__" else deprecated_function.__qualname__.split("."		)[-2]

												_UpperCAmelCase						=	(
												    f"""{name} is deprecated and will be removed in the next major version of datasets.""" + f""" {help_message}"""
												    if help_message
												    else ""
												)

												@wraps(snake_case_		)
												def wrapper(*snake_case_      ,       **snake_case_		):
																		_UpperCAmelCase						=	hash(snake_case_		)
																		if func_hash not in _emitted_deprecation_warnings:
																								warnings.warn(snake_case_      ,       category=snake_case_      ,       stacklevel=2		)
																								_emitted_deprecation_warnings.add(snake_case_		)
																		return deprecated_function(*snake_case_      ,       **snake_case_		)

												_UpperCAmelCase						=	"deprecated"

												if inspect.isclass(snake_case_		):
																		_UpperCAmelCase						=	wrapper
																		return deprecated_class_or_function
												else:
																		return wrapper

						return decorator






class        __lowerCAmelCase					(			enum.EnumMeta					):







			def __getattribute__(			cls       :     str       ,       snake_case__       :     Optional[Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	super().__getattribute__(snake_case__ )
									if isinstance(snake_case__       ,       enum.Enum ) and obj._on_access:
															obj._on_access()
									return obj







			def __getitem__(			cls       :     str       ,       snake_case__       :     Union[str, Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	super().__getitem__(snake_case__ )
									if member._on_access:
															member._on_access()
									return member







			def __call__(			cls       :     Dict       ,       snake_case__       :     Any       ,       snake_case__       :     List[str]=None       ,       *, snake_case__       :     Optional[int]=None       ,       snake_case__       :     List[Any]=None       ,       snake_case__       :     Tuple=None       ,       snake_case__       :     List[Any]=1 ):




									"""simple docstring"""


									_UpperCAmelCase						=	super().__call__(snake_case__       ,       snake_case__       ,       module=snake_case__       ,       qualname=snake_case__       ,       type=snake_case__       ,       start=snake_case__ )
									if isinstance(snake_case__       ,       enum.Enum ) and obj._on_access:
															obj._on_access()
									return obj






class        __lowerCAmelCase					(			enum.Enum   ,						metaclass=UpperCAmelCase__					):







			def __new__(			cls       :     Any       ,       snake_case__       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase						=	object.__new__(cls )
									_UpperCAmelCase						=	value
									_UpperCAmelCase						=	member.deprecate
									return member







			@property
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									return ""







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									_UpperCAmelCase						=	F""" {self.help_message}""" if self.help_message else ""
									warnings.warn(
									    F"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
									    + help_message       ,       snake_case__       ,       stacklevel=3       ,       )




