



"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__lowerCAmelCase							:       int          =       logging.get_logger(__name__)

__lowerCAmelCase							:       Optional[Any]          =       {
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							'''perceiver'''





		def __init__(       self				,     _lowercase=2_5_6				,     _lowercase=1_2_8_0				,     _lowercase=7_6_8				,     _lowercase=1				,     _lowercase=2_6				,     _lowercase=8				,     _lowercase=8				,     _lowercase=None				,     _lowercase=None				,     _lowercase="kv"				,     _lowercase=1				,     _lowercase=1				,     _lowercase="gelu"				,     _lowercase=0.1				,     _lowercase=0.02				,     _lowercase=1E-12				,     _lowercase=True				,     _lowercase=2_6_2				,     _lowercase=2_0_4_8				,     _lowercase=5_6				,     _lowercase=[3_6_8, 4_9_6]				,     _lowercase=1_6				,     _lowercase=1_9_2_0				,     _lowercase=1_6				,     _lowercase=[1, 1_6, 2_2_4, 2_2_4]				,     **_lowercase				,     )			->    Any:







								'''simple docstring'''

								super().__init__(**_lowercase )

								snake_case_ :			int            =      num_latents
								snake_case_ :			Optional[Any]            =      d_latents
								snake_case_ :			Tuple            =      d_model
								snake_case_ :			List[str]            =      num_blocks
								snake_case_ :			str            =      num_self_attends_per_block
								snake_case_ :			List[Any]            =      num_self_attention_heads
								snake_case_ :			Any            =      num_cross_attention_heads
								snake_case_ :			str            =      qk_channels
								snake_case_ :			Optional[int]            =      v_channels
								snake_case_ :			Tuple            =      cross_attention_shape_for_attention
								snake_case_ :			List[str]            =      self_attention_widening_factor
								snake_case_ :			Optional[Any]            =      cross_attention_widening_factor
								snake_case_ :			List[str]            =      hidden_act
								snake_case_ :			Tuple            =      attention_probs_dropout_prob
								snake_case_ :			int            =      initializer_range
								snake_case_ :			Optional[int]            =      layer_norm_eps
								snake_case_ :			Dict            =      use_query_residual
								# masked language modeling attributes
								snake_case_ :			Any            =      vocab_size
								snake_case_ :			Optional[int]            =      max_position_embeddings
								# image classification attributes
								snake_case_ :			List[Any]            =      image_size
								# flow attributes
								snake_case_ :			Optional[Any]            =      train_size
								# multimodal autoencoding attributes
								snake_case_ :			Dict            =      num_frames
								snake_case_ :			Optional[int]            =      audio_samples_per_frame
								snake_case_ :			List[Any]            =      samples_per_patch
								snake_case_ :			List[Any]            =      output_shape




class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		@property
		def  UpperCAmelCase__				(       self )			->    Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

								if self.task == "multiple-choice":
														snake_case_ :			str            =      {0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
														snake_case_ :			Tuple            =      {0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""inputs""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								    ] )





		@property
		def  UpperCAmelCase__				(       self )			->    float:







								'''simple docstring'''

								return 1E-4





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = -1				,     _lowercase = -1				,     _lowercase = -1				,     _lowercase = False				,     _lowercase = None				,     _lowercase = 3				,     _lowercase = 4_0				,     _lowercase = 4_0				,     )			->    Mapping[str, Any]:







								'''simple docstring'''

								if isinstance(_lowercase				,     _lowercase ):
														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														snake_case_ :			str            =      compute_effective_axis_dimension(
														    _lowercase				,     fixed_dimension=OnnxConfig.default_fixed_batch				,     num_token_to_add=0 )
														# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
														snake_case_ :			List[str]            =      preprocessor.num_special_tokens_to_add(_lowercase )
														snake_case_ :			Optional[Any]            =      compute_effective_axis_dimension(
														    _lowercase				,     fixed_dimension=OnnxConfig.default_fixed_sequence				,     num_token_to_add=_lowercase )
														# Generate dummy inputs according to compute batch and sequence
														snake_case_ :			List[str]            =      [""" """.join(["""a"""] ) * seq_length] * batch_size
														snake_case_ :			str            =      dict(preprocessor(_lowercase				,     return_tensors=_lowercase ) )
														snake_case_ :			Optional[int]            =      inputs.pop("""input_ids""" )
														return inputs
								elif isinstance(_lowercase				,     _lowercase ) and preprocessor.model_input_names[0] == "pixel_values":
														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														snake_case_ :			List[Any]            =      compute_effective_axis_dimension(_lowercase				,     fixed_dimension=OnnxConfig.default_fixed_batch )
														snake_case_ :			Dict            =      self._generate_dummy_images(_lowercase				,     _lowercase				,     _lowercase				,     _lowercase )
														snake_case_ :			List[Any]            =      dict(preprocessor(images=_lowercase				,     return_tensors=_lowercase ) )
														snake_case_ :			Any            =      inputs.pop("""pixel_values""" )
														return inputs
								else:
														raise ValueError(
														    """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.""" )



from collections.abc import Iterable
from typing import Any





class     lowerCamelCase      :







	def __init__(     self      :Optional[int]   ,     lowercase      :int | None = None			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= value
				SCREAMING_SNAKE_CASE     						= None  # Added in order to delete a node easier
				SCREAMING_SNAKE_CASE     						= None
				SCREAMING_SNAKE_CASE     						= None







	def __repr__(     self      :Tuple			)		->      str:







				"""simple docstring"""






				from pprint import pformat

				if self.left is None and self.right is None:
							return str(self.value			)
				return pformat({f"""{self.value}""": (self.left, self.right)}   ,     indent=1			)





class     lowerCamelCase      :







	def __init__(     self      :Union[str, Any]   ,     lowercase      :Node | None = None			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= root







	def __str__(     self      :int			)		->      str:







				"""simple docstring"""






				return str(self.root			)







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :Node   ,     lowercase      :Node | None			)		->      None:







				"""simple docstring"""






				if new_children is not None:  # reset its kids
							SCREAMING_SNAKE_CASE     						= node.parent
				if node.parent is not None:  # reset its parent
							if self.is_right(lowercase			):  # If it is the right children
										SCREAMING_SNAKE_CASE     						= new_children
							else:
										SCREAMING_SNAKE_CASE     						= new_children
				else:
							SCREAMING_SNAKE_CASE     						= new_children







	def  snake_case__     (     self      :List[str]   ,     lowercase      :Node			)		->      bool:







				"""simple docstring"""






				if node.parent and node.parent.right:
							return node == node.parent.right
				return False







	def  snake_case__     (     self      :Tuple			)		->      bool:







				"""simple docstring"""






				return self.root is None







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :List[Any]			)		->      None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= Node(lowercase			)  # create a new Node
				if self.empty():  # if Tree is empty
							SCREAMING_SNAKE_CASE     						= new_node  # set its root
				else:  # Tree is not empty
							SCREAMING_SNAKE_CASE     						= self.root  # from root
							if parent_node is None:
										return
							while True:  # While we don't get to a leaf
										if value < parent_node.value:  # We go left
													if parent_node.left is None:
																SCREAMING_SNAKE_CASE     						= new_node  # We insert the new node in a leaf
																break
													else:
																SCREAMING_SNAKE_CASE     						= parent_node.left
										else:
													if parent_node.right is None:
																SCREAMING_SNAKE_CASE     						= new_node
																break
													else:
																SCREAMING_SNAKE_CASE     						= parent_node.right
							SCREAMING_SNAKE_CASE     						= parent_node







	def  snake_case__     (     self      :Union[str, Any]   ,     *lowercase      :Optional[int]			)		->      None:







				"""simple docstring"""






				for value in values:
							self.__insert(lowercase			)







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :Any			)		->      Node | None:







				"""simple docstring"""






				if self.empty():
							raise IndexError('''Warning: Tree is empty! please use another.'''			)
				else:
							SCREAMING_SNAKE_CASE     						= self.root
							# use lazy evaluation here to avoid NoneType Attribute error
							while node is not None and node.value is not value:
										SCREAMING_SNAKE_CASE     						= node.left if value < node.value else node.right
							return node







	def  snake_case__     (     self      :str   ,     lowercase      :Node | None = None			)		->      Node | None:







				"""simple docstring"""






				if node is None:
							if self.root is None:
										return None
							SCREAMING_SNAKE_CASE     						= self.root

				if not self.empty():
							while node.right is not None:
										SCREAMING_SNAKE_CASE     						= node.right
				return node







	def  snake_case__     (     self      :int   ,     lowercase      :Node | None = None			)		->      Node | None:







				"""simple docstring"""






				if node is None:
							SCREAMING_SNAKE_CASE     						= self.root
				if self.root is None:
							return None
				if not self.empty():
							SCREAMING_SNAKE_CASE     						= self.root
							while node.left is not None:
										SCREAMING_SNAKE_CASE     						= node.left
				return node







	def  snake_case__     (     self      :Optional[int]   ,     lowercase      :int			)		->      None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.search(lowercase			)  # Look for the node with that label
				if node is not None:
							if node.left is None and node.right is None:  # If it has no children
										self.__reassign_nodes(lowercase   ,     lowercase			)
							elif node.left is None:  # Has only right children
										self.__reassign_nodes(lowercase   ,     node.right			)
							elif node.right is None:  # Has only left children
										self.__reassign_nodes(lowercase   ,     node.left			)
							else:
										SCREAMING_SNAKE_CASE     						= self.get_max(
										    node.left			)  # Gets the max value of the left branch
										self.remove(tmp_node.value			)  # type: ignore
										SCREAMING_SNAKE_CASE     						= (
										    tmp_node.value  # type: ignore
										)  # Assigns the value to the node to delete and keep tree structure







	def  snake_case__     (     self      :Dict   ,     lowercase      :Node | None			)		->      Iterable:







				"""simple docstring"""






				if node is not None:
							yield node  # Preorder Traversal
							yield from self.preorder_traverse(node.left			)
							yield from self.preorder_traverse(node.right			)







	def  snake_case__     (     self      :Tuple   ,     lowercase      :List[str]=None			)		->      Any:







				"""simple docstring"""






				if traversal_function is None:
							return self.preorder_traverse(self.root			)
				else:
							return traversal_function(self.root			)







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :list   ,     lowercase      :Node | None			)		->      None:







				"""simple docstring"""






				if node:
							self.inorder(lowercase   ,     node.left			)
							arr.append(node.value			)
							self.inorder(lowercase   ,     node.right			)







	def  snake_case__     (     self      :Tuple   ,     lowercase      :int   ,     lowercase      :Node			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= []
				self.inorder(lowercase   ,     lowercase			)  # append all values to list using inorder traversal
				return arr[k - 1]



def    a      ( a	)     ->list[Node]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= []
			if curr_node is not None:
						SCREAMING_SNAKE_CASE     						= postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
			return node_list


def    a      ( )     ->None:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= (8, 3, 6, 1, 10, 14, 13, 4, 7)
			SCREAMING_SNAKE_CASE     						= BinarySearchTree()
			for i in testlist:
						t.insert(a	)

			# Prints all the elements of the list in order traversal
			print(a	)

			if t.search(6	) is not None:
						print('''The value 6 exists'''	)
			else:
						print('''The value 6 doesn\'t exist'''	)

			if t.search(-1	) is not None:
						print('''The value -1 exists'''	)
			else:
						print('''The value -1 doesn\'t exist'''	)

			if not t.empty():
						print('''Max Value: '''		,						t.get_max().value	)  # type: ignore
						print('''Min Value: '''		,						t.get_min().value	)  # type: ignore

			for i in testlist:
						t.remove(a	)
						print(a	)


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)