
import warnings

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







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Optional[Any]										=	["""image_processor""", """tokenizer"""]
	_UpperCamelCase					:					Dict										=	"""AutoImageProcessor"""
	_UpperCamelCase					:					Union[str, Any]										=	"""AutoTokenizer"""





	def __init__(	self		,   snake_case=None		,   snake_case=None		,   **snake_case	):
		lowercase					  =   None
		if "feature_extractor" in kwargs:
			warnings.warn(
			    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
			    ' instead.'		,   snake_case		,   )
			lowercase					  =   kwargs.pop('feature_extractor'	)

		lowercase					  =   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__(snake_case		,   snake_case	)
		lowercase					  =   self.image_processor





	def __call__(	self		,   snake_case=None		,   snake_case=None		,   snake_case=None		,   **snake_case	):

		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:
			lowercase					  =   self.tokenizer(snake_case		,   return_tensors=snake_case		,   **snake_case	)

		if images is not None:
			lowercase					  =   self.image_processor(snake_case		,   return_tensors=snake_case		,   **snake_case	)

		if text is not None and images is not None:
			lowercase					  =   image_features.pixel_values
			return encoding
		elif text is not None:
			return encoding
		else:
			return BatchEncoding(data=dict(**snake_case	)		,   tensor_type=snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   *snake_case		,   **snake_case	):
		return self.tokenizer.batch_decode(*snake_case		,   **snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   *snake_case		,   **snake_case	):
		return self.tokenizer.decode(*snake_case		,   **snake_case	)





	@property
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.tokenizer.model_input_names
		lowercase					  =   self.image_processor.model_input_names
		return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)





	@property
	def      SCREAMING_SNAKE_CASE__						(	self	):
		warnings.warn(
		    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,   snake_case		,   )
		return self.image_processor_class





	@property
	def      SCREAMING_SNAKE_CASE__						(	self	):
		warnings.warn(
		    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,   snake_case		,   )
		return self.image_processor



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

