




"""simple docstring"""

from typing import TYPE_CHECKING

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


_lowerCamelCase					:     Optional[int]				       =    {
    'configuration_flava': [
        'FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'FlavaConfig',
        'FlavaImageCodebookConfig',
        'FlavaImageConfig',
        'FlavaMultimodalConfig',
        'FlavaTextConfig',
    ],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase					:     Dict				       =    ['FlavaFeatureExtractor']
		_lowerCamelCase					:     List[Any]				       =    ['FlavaImageProcessor']
		_lowerCamelCase					:     Any				       =    ['FlavaProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase					:     Any				       =    [
		    'FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'FlavaForPreTraining',
		    'FlavaImageCodebook',
		    'FlavaImageModel',
		    'FlavaModel',
		    'FlavaMultimodalModel',
		    'FlavaPreTrainedModel',
		    'FlavaTextModel',
		]

if TYPE_CHECKING:
		from .configuration_flava import (
		    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    FlavaConfig,
		    FlavaImageCodebookConfig,
		    FlavaImageConfig,
		    FlavaMultimodalConfig,
		    FlavaTextConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_flava import FlavaFeatureExtractor
				from .image_processing_flava import FlavaImageProcessor
				from .processing_flava import FlavaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flava import (
				    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlavaForPreTraining,
				    FlavaImageCodebook,
				    FlavaImageModel,
				    FlavaModel,
				    FlavaMultimodalModel,
				    FlavaPreTrainedModel,
				    FlavaTextModel,
				)

else:
		import sys

		_lowerCamelCase					:     Dict				       =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











"""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 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						copy.deepcopy(_UpperCAmelCase		)
	A_					:			int						=						sorted(_UpperCAmelCase		)
	A_					:			Tuple						=						[bisect.bisect_right(_UpperCAmelCase					, _UpperCAmelCase		) for y in x]
	return quantized

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase=0		):




	"""simple docstring"""






	A_					:			List[Any]						=						np.arange(start=3					, stop=_UpperCAmelCase					, step=4		).tolist() if k > 0 else [10]
	A_					:			List[Any]						=						_quantize(_UpperCAmelCase					, _UpperCAmelCase		)
	# count number of elements per group
	A_					:			Tuple						=						np.unique(_UpperCAmelCase					, return_counts=_UpperCAmelCase		)[1]
	A_					:			Optional[int]						=						[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     lowercase (      __UpperCAmelCase):







				def __init__(							self				:   Union[str, Any] ,       _lowerCamelCase				:   List[Any] ,       _lowerCamelCase				:   Dict ,       _lowerCamelCase				:   Dict    ):



					"""simple docstring"""

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







				def __iter__(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			List[str]						=						defaultdict(_lowerCamelCase    )
					A_					:			Dict						=						defaultdict(_lowerCamelCase    )

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



					"""simple docstring"""

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





