


import bisect
import copy
from collections import defaultdict

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

from utils import logger

def  lowerCAmelCase__				(    a__:	List[Any]    ,						a__:	Optional[int] )						->	str:


							'''simple docstring'''





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

def  lowerCAmelCase__				(    a__:	int    ,						a__:	List[str]=0 )						->	Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=    np.arange(start=3    ,						stop=a__    ,						step=4 ).tolist() if k > 0 else [1_0]
							_UpperCAmelCase					=    _quantize(a__    ,						a__ )
							# count number of elements per group
							_UpperCAmelCase					=    np.unique(a__    ,						return_counts=a__ )[1]
							_UpperCAmelCase					=    [0] + bins + [np.inf]
							logger.info('Using {} as bins for aspect lengths quantization'.format(a__ ) )
							logger.info('Count of instances per bin: {}'.format(a__ ) )
							return groups







class 	__a   (       UpperCAmelCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





									if not isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																raise ValueError(
																    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    sampler
									_UpperCAmelCase					=    group_ids
									_UpperCAmelCase					=    batch_size
		def __iter__(    self		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    defaultdict(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    defaultdict(_SCREAMING_SNAKE_CASE		)

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






		def __len__(    self		)				-> Optional[Any]:




									"""simple docstring"""





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




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 	__a   (       UpperCAmelCase    ):
		_a			:						int    			=			['image_processor', 'tokenizer']
		_a			:						Union[str, Any]    			=			'AutoImageProcessor'
		_a			:						Optional[int]    			=			'AutoTokenizer'
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    None
									if "feature_extractor" in kwargs:
																warnings.warn(
																    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																    ' instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
																_UpperCAmelCase					=    kwargs.pop('feature_extractor'		)

									_UpperCAmelCase					=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
																raise ValueError('You have to specify an image_processor.'		)
									if tokenizer is None:
																raise ValueError('You have to specify a tokenizer.'		)

									super().__init__(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    self.image_processor
		def __call__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Dict:




									"""simple docstring"""





									if text is None and images is None:
																raise ValueError('You have to specify either text or images. Both cannot be none.'		)

									if text is not None:
																_UpperCAmelCase					=    self.tokenizer(_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

									if images is not None:
																_UpperCAmelCase					=    self.image_processor(_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

									if text is not None and images is not None:
																_UpperCAmelCase					=    image_features.pixel_values
																return encoding
									elif text is not None:
																return encoding
									else:
																return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE		)    ,					tensor_type=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> int:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> Tuple:




									"""simple docstring"""





									return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.tokenizer.model_input_names
									_UpperCAmelCase					=    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self.image_processor_class






		@property
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self.image_processor
