
import bisect
import copy
from collections import defaultdict

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

from utils import logger






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   copy.deepcopy(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   sorted(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   [bisect.bisect_right(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) for y in x]
	return quantized






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




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case		,   snake_case	):
		if not isinstance(snake_case		,   snake_case	):
			raise ValueError(
			    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(snake_case	)	)
		lowercase					  =   sampler
		lowercase					  =   group_ids
		lowercase					  =   batch_size





	def __iter__(	self	):
		lowercase					  =   defaultdict(snake_case	)
		lowercase					  =   defaultdict(snake_case	)

		lowercase					  =   0
		for idx in self.sampler:
			lowercase					  =   self.group_ids[idx]
			buffer_per_group[group_id].append(snake_case	)
			samples_per_group[group_id].append(snake_case	)
			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 snake_case	: x[0]	):
				batch_idx.extend(snake_case	)
				if len(snake_case	) >= self.batch_size:
					yield batch_idx[: self.batch_size]
					lowercase					  =   batch_idx[self.batch_size :]
					num_remaining -= 1
			if len(snake_case	) > 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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase     				=			{
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			[
	    '''DPRContextEncoderTokenizerFast''',
	    '''DPRQuestionEncoderTokenizerFast''',
	    '''DPRReaderTokenizerFast''',
	]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			[
	    '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DPRContextEncoder''',
	    '''DPRPretrainedContextEncoder''',
	    '''DPRPreTrainedModel''',
	    '''DPRPretrainedQuestionEncoder''',
	    '''DPRPretrainedReader''',
	    '''DPRQuestionEncoder''',
	    '''DPRReader''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			[
	    '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFDPRContextEncoder''',
	    '''TFDPRPretrainedContextEncoder''',
	    '''TFDPRPretrainedQuestionEncoder''',
	    '''TFDPRPretrainedReader''',
	    '''TFDPRQuestionEncoder''',
	    '''TFDPRReader''',
	]


if TYPE_CHECKING:
	from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
	from .tokenization_dpr import (
	    DPRContextEncoderTokenizer,
	    DPRQuestionEncoderTokenizer,
	    DPRReaderOutput,
	    DPRReaderTokenizer,
	)

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_dpr_fast import (
		    DPRContextEncoderTokenizerFast,
		    DPRQuestionEncoderTokenizerFast,
		    DPRReaderTokenizerFast,
		)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dpr import (
		    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPRContextEncoder,
		    DPRPretrainedContextEncoder,
		    DPRPreTrainedModel,
		    DPRPretrainedQuestionEncoder,
		    DPRPretrainedReader,
		    DPRQuestionEncoder,
		    DPRReader,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_dpr import (
		    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDPRContextEncoder,
		    TFDPRPretrainedContextEncoder,
		    TFDPRPretrainedQuestionEncoder,
		    TFDPRPretrainedReader,
		    TFDPRQuestionEncoder,
		    TFDPRReader,
		)

else:
	import sys

	UpperCAmelCase     				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

