



"""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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: int     =   copy.deepcopy(__UpperCAmelCase      )
		_lowercase				: List[Any]     =   sorted(__UpperCAmelCase      )
		_lowercase				: str     =   [bisect.bisect_right(__UpperCAmelCase   ,				__UpperCAmelCase      ) for y in x]
		return quantized


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase=0      ):
		_lowercase				: List[Any]     =   np.arange(start=3   ,				stop=__UpperCAmelCase   ,				step=4      ).tolist() if k > 0 else [10]
		_lowercase				: Optional[Any]     =   _quantize(__UpperCAmelCase   ,				__UpperCAmelCase      )
		# count number of elements per group
		_lowercase				: Optional[Any]     =   np.unique(__UpperCAmelCase   ,				return_counts=__UpperCAmelCase      )[1]
		_lowercase				: Optional[Any]     =   [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   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""






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






	def __iter__(     self   ):
			_lowercase				: Dict     =   defaultdict(UpperCAmelCase_   )
			_lowercase				: int     =   defaultdict(UpperCAmelCase_   )

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






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






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A	:       Dict        =						{'configuration_cvt': ['CVT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CvtConfig']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       Any        =						[
	    'CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'CvtForImageClassification',
	    'CvtModel',
	    'CvtPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       List[str]        =						[
	    'TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFCvtForImageClassification',
	    'TFCvtModel',
	    'TFCvtPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_cvt import (
		    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CvtForImageClassification,
		    CvtModel,
		    CvtPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_cvt import (
		    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCvtForImageClassification,
		    TFCvtModel,
		    TFCvtPreTrainedModel,
		)


else:
	import sys

	__A	:       str        =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





