






'''simple docstring'''




import warnings
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       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  ['''image_processor''', '''tokenizer''']
	_lowerCamelCase              =  '''FlavaImageProcessor'''
	_lowerCamelCase              =  ('''BertTokenizer''', '''BertTokenizerFast''')

	def __init__(      self   :						List[Any]  ,					UpperCAmelCase_   :						int=None  ,					UpperCAmelCase_   :						Dict=None  ,					**UpperCAmelCase_   :						Union[str, Any]):
			UpperCamelCase__				:					int					=					None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
					    ' instead.'  ,					UpperCAmelCase_  ,					)
					UpperCamelCase__				:					Any					=					kwargs.pop('feature_extractor')

			UpperCamelCase__				:					Dict					=					image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError('You need to specify an `image_processor`.')
			if tokenizer is None:
					raise ValueError('You need to specify a `tokenizer`.')

			super().__init__(UpperCAmelCase_  ,					UpperCAmelCase_)
			UpperCamelCase__				:					Dict					=					self.image_processor

	def __call__(      self   :						Dict  ,					UpperCAmelCase_   :						Optional[ImageInput] = None  ,					UpperCAmelCase_   :						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None  ,					UpperCAmelCase_   :						bool = True  ,					UpperCAmelCase_   :						Union[bool, str, PaddingStrategy] = False  ,					UpperCAmelCase_   :						Union[bool, str, TruncationStrategy] = False  ,					UpperCAmelCase_   :						Optional[int] = None  ,					UpperCAmelCase_   :						int = 0  ,					UpperCAmelCase_   :						Optional[int] = None  ,					UpperCAmelCase_   :						Optional[bool] = None  ,					UpperCAmelCase_   :						Optional[bool] = None  ,					UpperCAmelCase_   :						Optional[bool] = None  ,					UpperCAmelCase_   :						Optional[bool] = None  ,					UpperCAmelCase_   :						bool = False  ,					UpperCAmelCase_   :						bool = False  ,					UpperCAmelCase_   :						bool = False  ,					UpperCAmelCase_   :						bool = False  ,					UpperCAmelCase_   :						bool = True  ,					UpperCAmelCase_   :						Optional[Union[str, TensorType]] = None  ,					**UpperCAmelCase_   :						Tuple  ,					):

			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__				:					Dict					=					self.tokenizer(
					    text=UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_  ,					padding=UpperCAmelCase_  ,					truncation=UpperCAmelCase_  ,					max_length=UpperCAmelCase_  ,					stride=UpperCAmelCase_  ,					pad_to_multiple_of=UpperCAmelCase_  ,					return_token_type_ids=UpperCAmelCase_  ,					return_attention_mask=UpperCAmelCase_  ,					return_overflowing_tokens=UpperCAmelCase_  ,					return_special_tokens_mask=UpperCAmelCase_  ,					return_offsets_mapping=UpperCAmelCase_  ,					return_length=UpperCAmelCase_  ,					verbose=UpperCAmelCase_  ,					return_tensors=UpperCAmelCase_  ,					**UpperCAmelCase_  ,					)
			if images is not None:
					UpperCamelCase__				:					Optional[int]					=					self.image_processor(
					    UpperCAmelCase_  ,					return_image_mask=UpperCAmelCase_  ,					return_codebook_pixels=UpperCAmelCase_  ,					return_tensors=UpperCAmelCase_  ,					**UpperCAmelCase_  ,					)

			if text is not None and images is not None:
					encoding.update(UpperCAmelCase_)
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**UpperCAmelCase_)  ,					tensor_type=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict  ,					*UpperCAmelCase_   :						List[str]  ,					**UpperCAmelCase_   :						Tuple):
			return self.tokenizer.batch_decode(*UpperCAmelCase_  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict  ,					*UpperCAmelCase_   :						Dict  ,					**UpperCAmelCase_   :						int):
			return self.tokenizer.decode(*UpperCAmelCase_  ,					**UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Any					=					self.tokenizer.model_input_names
			UpperCamelCase__				:					List[str]					=					self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,					UpperCAmelCase_  ,					)
			return self.image_processor_class





	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			warnings.warn(
			    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,					UpperCAmelCase_  ,					)
			return self.image_processor












'''simple docstring'''




class       __lowercase					:

	def __init__(      self   :						List[str]  ,					UpperCAmelCase_   :						str = ""  ,					UpperCAmelCase_   :						bool = False):
			# Mapping from the first character of the prefix of the node
			UpperCamelCase__				:					dict[str, RadixNode]					=					{}

			# A node will be a leaf if the tree contains its word
			UpperCamelCase__				:					List[Any]					=					is_leaf

			UpperCamelCase__				:					Optional[Any]					=					prefix

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Optional[int]					=					0
			for q, w in zip(self.prefix  ,					UpperCAmelCase_):
					if q != w:
							break

					x += 1

			return self.prefix[:x], self.prefix[x:], word[x:]

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						list[str]):
			for word in words:
					self.insert(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						str):
			# Case 1: If the word is the prefix of the node
			# Solution: We set the current node as leaf
			if self.prefix == word:
					UpperCamelCase__				:					Optional[Any]					=					True

			# Case 2: The node has no edges that have a prefix to the word
			# Solution: We create an edge from the current node to a new one
			# containing the word
			elif word[0] not in self.nodes:
					UpperCamelCase__				:					Optional[Any]					=					RadixNode(prefix=UpperCAmelCase_  ,					is_leaf=UpperCAmelCase_)

			else:
					UpperCamelCase__				:					int					=					self.nodes[word[0]]
					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					List[Any]					=					incoming_node.match(
					    UpperCAmelCase_)

					# Case 3: The node prefix is equal to the matching
					# Solution: We insert remaining word on the next node
					if remaining_prefix == "":
							self.nodes[matching_string[0]].insert(UpperCAmelCase_)

					# Case 4: The word is greater equal to the matching
					# Solution: Create a node in between both nodes, change
					# prefixes and add the new node for the remaining word
					else:
							UpperCamelCase__				:					Tuple					=					remaining_prefix

							UpperCamelCase__				:					str					=					self.nodes[matching_string[0]]
							UpperCamelCase__				:					Optional[Any]					=					RadixNode(UpperCAmelCase_  ,					UpperCAmelCase_)
							UpperCamelCase__				:					str					=					aux_node

							if remaining_word == "":
									UpperCamelCase__				:					int					=					True
							else:
									self.nodes[matching_string[0]].insert(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Optional[Any]					=					self.nodes.get(word[0]  ,					UpperCAmelCase_)
			if not incoming_node:
					return False
			else:
					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					int					=					incoming_node.match(
					    UpperCAmelCase_)
					# If there is remaining prefix, the word can't be on the tree
					if remaining_prefix != "":
							return False
					# This applies when the word and the prefix are equal
					elif remaining_word == "":
							return incoming_node.is_leaf
					# We have word remaining so we check the next node
					else:
							return incoming_node.find(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Optional[int]					=					self.nodes.get(word[0]  ,					UpperCAmelCase_)
			if not incoming_node:
					return False
			else:
					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					incoming_node.match(
					    UpperCAmelCase_)
					# If there is remaining prefix, the word can't be on the tree
					if remaining_prefix != "":
							return False
					# We have word remaining so we check the next node
					elif remaining_word != "":
							return incoming_node.delete(UpperCAmelCase_)
					else:
							# If it is not a leaf, we don't have to delete
							if not incoming_node.is_leaf:
									return False
							else:
									# We delete the nodes if no edges go from it
									if len(incoming_node.nodes) == 0:
											del self.nodes[word[0]]
											# We merge the current node with its only child
											if len(self.nodes) == 1 and not self.is_leaf:
													UpperCamelCase__				:					List[str]					=					list(self.nodes.values())[0]
													UpperCamelCase__				:					Tuple					=					merging_node.is_leaf
													self.prefix += merging_node.prefix
													UpperCamelCase__				:					Tuple					=					merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
									elif len(incoming_node.nodes) > 1:
											UpperCamelCase__				:					str					=					False
									# If there is 1 edge, we merge it with its child
									else:
											UpperCamelCase__				:					List[Any]					=					list(incoming_node.nodes.values())[0]
											UpperCamelCase__				:					Optional[Any]					=					merging_node.is_leaf
											incoming_node.prefix += merging_node.prefix
											UpperCamelCase__				:					Union[str, Any]					=					merging_node.nodes

									return True





	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						int = 0):
			if self.prefix != "":
					print('-' * height  ,					self.prefix  ,					'  (leaf)' if self.is_leaf else '')

			for value in self.nodes.values():
					value.print_tree(height + 1)







def       __UpperCAmelCase						(  )							->     bool:
		UpperCamelCase__				:					Union[str, Any]					=					'banana bananas bandana band apple all beast'.split()
		UpperCamelCase__				:					List[Any]					=					RadixNode()
		root.insert_many(lowerCamelCase_)

		assert all(root.find(lowerCamelCase_) for word in words)
		assert not root.find('bandanas')
		assert not root.find('apps')
		root.delete('all')
		assert not root.find('all')
		root.delete('banana')
		assert not root.find('banana')
		assert root.find('bananas')

		return True







def       __UpperCAmelCase						(  )							->     None:
		assert test_trie()







def       __UpperCAmelCase						(  )							->     None:
		UpperCamelCase__				:					List[Any]					=					RadixNode()
		UpperCamelCase__				:					List[str]					=					'banana bananas bandanas bandana band apple all beast'.split()
		root.insert_many(lowerCamelCase_)

		print('Words:'     ,      lowerCamelCase_)
		print('Tree:')
		root.print_tree()


if __name__ == "__main__":
		main()




