


import bisect
import copy
from collections import defaultdict

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

from utils import logger

def 		snake_case_   (				lowercase__     :    Optional[int]       ,     lowercase__     :    Any     ):






				'''simple docstring'''






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

def 		snake_case_   (				lowercase__     :    str       ,     lowercase__     :    Dict=0     ):






				'''simple docstring'''






				_lowerCAmelCase  						=np.arange(start=3       ,     stop=lowercase__       ,     step=4     ).tolist() if k > 0 else [10]
				_lowerCAmelCase  						=_quantize(lowercase__       ,     lowercase__     )
				# count number of elements per group
				_lowerCAmelCase  						=np.unique(lowercase__       ,     return_counts=lowercase__     )[1]
				_lowerCAmelCase  						=[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        __lowerCamelCase      (				lowerCamelCase_ ):


				"""simple docstring"""







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






				def __iter__(				self     :    Union[str, Any]					):
								_lowerCAmelCase  						=defaultdict(lowerCamelCase_					)
								_lowerCAmelCase  						=defaultdict(lowerCamelCase_					)

								_lowerCAmelCase  						=0
								for idx in self.sampler:
												_lowerCAmelCase  						=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
								_lowerCAmelCase  						=len(self					)
								_lowerCAmelCase  						=expected_num_batches - num_batches
								if num_remaining > 0:
												# for the remaining batches, group the batches by similar lengths
												_lowerCAmelCase  						=[]
												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]
																				_lowerCAmelCase  						=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     :    Dict					):
								return (len(self.sampler					) + self.batch_size - 1) // self.batch_size





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE	:							Optional[int]           =  {'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SEWDForCTC''',
		    '''SEWDForSequenceClassification''',
		    '''SEWDModel''',
		    '''SEWDPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew_d import (
				    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWDForCTC,
				    SEWDForSequenceClassification,
				    SEWDModel,
				    SEWDPreTrainedModel,
				)


else:
		import sys

		__SCREAMING_SNAKE_CASE	:							Tuple           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

