
'''simple docstring'''



from ..utils import DummyObject, requires_backends





class 			A							(       metaclass=__UpperCAmelCase				):
			lowerCamelCase							:							Optional[Any]       		=  ["""torch""", """librosa"""]



			def __init__(					self       ,     *lowerCamelCase__       ,     **lowerCamelCase__       )			->		Dict:




									'''simple docstring'''



									requires_backends(self       ,     ["""torch""", """librosa"""]       )



			@classmethod
			def A__ (					cls       ,     *lowerCamelCase__       ,     **lowerCamelCase__       )			->		Dict:




									'''simple docstring'''



									requires_backends(cls       ,     ["""torch""", """librosa"""]       )



			@classmethod
			def A__ (					cls       ,     *lowerCamelCase__       ,     **lowerCamelCase__       )			->		Dict:




									'''simple docstring'''



									requires_backends(cls       ,     ["""torch""", """librosa"""]       )





class 			A							(       metaclass=__UpperCAmelCase				):
			lowerCamelCase							:							List[Any]       		=  ["""torch""", """librosa"""]



			def __init__(					self       ,     *lowerCamelCase__       ,     **lowerCamelCase__       )			->		List[str]:




									'''simple docstring'''



									requires_backends(self       ,     ["""torch""", """librosa"""]       )



			@classmethod
			def A__ (					cls       ,     *lowerCamelCase__       ,     **lowerCamelCase__       )			->		Tuple:




									'''simple docstring'''



									requires_backends(cls       ,     ["""torch""", """librosa"""]       )



			@classmethod
			def A__ (					cls       ,     *lowerCamelCase__       ,     **lowerCamelCase__       )			->		Tuple:




									'''simple docstring'''



									requires_backends(cls       ,     ["""torch""", """librosa"""]       )








'''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		(							lowercase__		,		lowercase__	):
						lowercase__							 =       copy.deepcopy(lowercase__	)
						lowercase__							 =       sorted(lowercase__	)
						lowercase__							 =       [bisect.bisect_right(lowercase__		,		lowercase__	) for y in x]
						return quantized





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







class 			A							(       __UpperCAmelCase				):



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:




									'''simple docstring'''



									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
															raise ValueError(
															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__       )       )
									lowercase__							 =       sampler
									lowercase__							 =       group_ids
									lowercase__							 =       batch_size



			def __iter__(					self       )			->		List[str]:




									'''simple docstring'''



									lowercase__							 =       defaultdict(lowerCamelCase__       )
									lowercase__							 =       defaultdict(lowerCamelCase__       )

									lowercase__							 =       0
									for idx in self.sampler:
															lowercase__							 =       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
									lowercase__							 =       len(self       )
									lowercase__							 =       expected_num_batches - num_batches
									if num_remaining > 0:
															# for the remaining batches, group the batches by similar lengths
															lowercase__							 =       []
															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]
																											lowercase__							 =       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       )			->		Any:




									'''simple docstring'''



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






