



'''simple docstring'''

def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							)      ->						Dict:







	"""simple docstring"""
	if not head:
		return True
	# split the list to two parts
	lowerCAmelCase,       lowerCAmelCase   =     head.next, head
	while fast and fast.next:
		lowerCAmelCase   =     fast.next.next
		lowerCAmelCase   =     slow.next
	lowerCAmelCase   =     slow.next
	lowerCAmelCase   =     None  # Don't forget here! But forget still works!
	# reverse the second part
	lowerCAmelCase   =     None
	while second:
		lowerCAmelCase   =     second.next
		lowerCAmelCase   =     node
		lowerCAmelCase   =     second
		lowerCAmelCase   =     nxt
	# compare two parts
	# second part has the same or one less node
	while node:
		if node.val != head.val:
			return False
		lowerCAmelCase   =     node.next
		lowerCAmelCase   =     head.next
	return True






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							)      ->						Optional[Any]:







	"""simple docstring"""
	if not head or not head.next:
		return True

	# 1. Get the midpoint (slow)
	lowerCAmelCase   =     lowerCAmelCase   =     lowerCAmelCase   =     head
	while fast and fast.next:
		lowerCAmelCase,       lowerCAmelCase   =     fast.next.next, slow.next

	# 2. Push the second half into the stack
	lowerCAmelCase   =     [slow.val]
	while slow.next:
		lowerCAmelCase   =     slow.next
		stack.append(slow.val							)

	# 3. Comparison
	while stack:
		if stack.pop() != cur.val:
			return False
		lowerCAmelCase   =     cur.next

	return True






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							)      ->						Dict:







	"""simple docstring"""
	if not head or not head.next:
		return True
	lowerCAmelCase   =     {}
	lowerCAmelCase   =     0
	while head:
		if head.val in d:
			d[head.val].append(_SCREAMING_SNAKE_CASE							)
		else:
			lowerCAmelCase   =     [pos]
		lowerCAmelCase   =     head.next
		pos += 1
	lowerCAmelCase   =     pos - 1
	lowerCAmelCase   =     0
	for v in d.values():
		if len(_SCREAMING_SNAKE_CASE							) % 2 != 0:
			middle += 1
		else:
			lowerCAmelCase   =     0
			for i in range(0							,		len(_SCREAMING_SNAKE_CASE							)							):
				if v[i] + v[len(_SCREAMING_SNAKE_CASE							) - 1 - step] != checksum:
					return False
				step += 1
		if middle > 1:
			return False
	return True




'''simple docstring'''

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		["image_processor", "tokenizer"]
						UpperCAmelCase							:   Tuple									=		"BlipImageProcessor"
						UpperCAmelCase							:   Union[str, Any]									=		("BertTokenizer", "BertTokenizerFast")


						def __init__( self     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     False
							super().__init__(A_     ,	A_				)
							lowerCAmelCase   =     self.image_processor


						def __call__( self     ,	A_ = None     ,	A_ = None     ,	A_ = True     ,	A_ = False     ,	A_ = None     ,	A_ = None     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = True     ,	A_ = None     ,	**A_     ,	)     ->     BatchEncoding:
							if images is None and text is None:
								raise ValueError("""You have to specify either images or text."""				)

							# Get only text
							if images is None:
								lowerCAmelCase   =     self.tokenizer
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
								return text_encoding

							# add pixel_values
							lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_				)

							if text is not None:
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
							else:
								lowerCAmelCase   =     None

							if text_encoding is not None:
								encoding_image_processor.update(A_				)

							return encoding_image_processor


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Union[str, Any]:
							return self.tokenizer.batch_decode(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Tuple:
							return self.tokenizer.decode(*A_     ,	**A_				)




						@property
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.tokenizer.model_input_names
							lowerCAmelCase   =     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)