






'''simple docstring'''







from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any

@dataclass
class _lowercase			:
							a          = 42
							a          = None
							a          = None





def 				SCREAMING_SNAKE_CASE_ ()   -> Any:
							lowerCamelCase__      :			Optional[Any] 						= Node(1   )
							lowerCamelCase__      :			List[str] 						= Node(2   )
							lowerCamelCase__      :			Union[str, Any] 						= Node(3   )
							lowerCamelCase__      :			Tuple 						= Node(4   )
							lowerCamelCase__      :			int 						= Node(5   )
							return tree





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[str]:
							return [root.data, *preorder(root.left   ), *preorder(root.right   )] if root else []





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Any:
							return postorder(root.left   ) + postorder(root.right   ) + [root.data] if root else []





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Any:
							return [*inorder(root.left   ), root.data, *inorder(root.right   )] if root else []





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[int]:
							return (max(height(root.left   )       ,    height(root.right   )   ) + 1) if root else 0





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Union[str, Any]:
							lowerCamelCase__      :			list[Any] 						= []

							if root is None:
														return output

							lowerCamelCase__      :			Tuple 						= deque([root]   )

							while process_queue:
														lowerCamelCase__      :			Optional[Any] 						= process_queue.popleft()
														output.append(node.data   )

														if node.left:
																					process_queue.append(node.left   )
														if node.right:
																					process_queue.append(node.right   )
							return output





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> List[Any]:
							lowerCamelCase__      :			list[Any] 						= []

							def populate_output(UpperCamelCase       ,    UpperCamelCase   ) -> None:
														if not root:
																					return
														if level == 1:
																					output.append(root.data   )
														elif level > 1:
																					populate_output(root.left       ,    level - 1   )
																					populate_output(root.right       ,    level - 1   )

							populate_output(_lowerCamelCase       ,    _lowerCamelCase   )
							return output





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Optional[int]:
							lowerCamelCase__      :			list[Any] 						= []

							def populate_output(UpperCamelCase       ,    UpperCamelCase   ) -> None:
														if root is None:
																					return
														if level == 1:
																					output.append(root.data   )
														elif level > 1:
																					populate_output(root.right       ,    level - 1   )
																					populate_output(root.left       ,    level - 1   )

							populate_output(_lowerCamelCase       ,    _lowerCamelCase   )
							return output





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[Any]:
							if root is None:
														return []

							lowerCamelCase__      :			list[Sequence[Node | None]] 						= []

							lowerCamelCase__      :			List[str] 						= 0
							lowerCamelCase__      :			Optional[Any] 						= height(_lowerCamelCase   )

							for h in range(1       ,    height_tree + 1   ):
														if not flag:
																					output.append(get_nodes_from_left_to_right(_lowerCamelCase       ,    _lowerCamelCase   )   )
																					lowerCamelCase__      :			Any 						= 1
														else:
																					output.append(get_nodes_from_right_to_left(_lowerCamelCase       ,    _lowerCamelCase   )   )
																					lowerCamelCase__      :			Optional[Any] 						= 0

							return output





def 				SCREAMING_SNAKE_CASE_ ()   -> Tuple:  # Main function for testing.
							lowerCamelCase__      :			Union[str, Any] 						= make_tree()
							print(f'''In-order Traversal: {inorder(_lowerCamelCase   )}'''   )
							print(f'''Pre-order Traversal: {preorder(_lowerCamelCase   )}'''   )
							print(f'''Post-order Traversal: {postorder(_lowerCamelCase   )}'''       ,    """\n"""   )

							print(f'''Height of Tree: {height(_lowerCamelCase   )}'''       ,    """\n"""   )

							print("""Complete Level Order Traversal: """   )
							print(level_order(_lowerCamelCase   )       ,    """\n"""   )

							print("""Level-wise order Traversal: """   )

							for level in range(1       ,    height(_lowerCamelCase   ) + 1   ):
														print(f'''Level {level}:'''       ,    get_nodes_from_left_to_right(_lowerCamelCase       ,    level=_lowerCamelCase   )   )

							print("""\nZigZag order Traversal: """   )
							print(zigzag(_lowerCamelCase   )   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()









'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:    Any					  =logging.get_logger(__name__)

_A			:    Dict					  ={
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}

class _lowercase			(       _lowercase	):
							a          = """trocr"""
							a          = ["""past_key_values"""]
							a          = {
							    """num_attention_heads""": """decoder_attention_heads""",
							    """hidden_size""": """d_model""",
							    """num_hidden_layers""": """decoder_layers""",
							}




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       int=50_265    ,							UpperCamelCase__:       int=1_024    ,							UpperCamelCase__:       Optional[Any]=12    ,							UpperCamelCase__:       Dict=16    ,							UpperCamelCase__:       int=4_096    ,							UpperCamelCase__:       Tuple="gelu"    ,							UpperCamelCase__:       int=512    ,							UpperCamelCase__:       Dict=0.1    ,							UpperCamelCase__:       Tuple=0.0    ,							UpperCamelCase__:       str=0.0    ,							UpperCamelCase__:       Any=2    ,							UpperCamelCase__:       Dict=0.02    ,							UpperCamelCase__:       Optional[Any]=0.0    ,							UpperCamelCase__:       str=True    ,							UpperCamelCase__:       Tuple=False    ,							UpperCamelCase__:       Union[str, Any]=True    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Dict=1    ,							UpperCamelCase__:       List[str]=0    ,							UpperCamelCase__:       Union[str, Any]=2    ,							**UpperCamelCase__:       str    ,							):
														lowerCamelCase__      :			Any 						= vocab_size
														lowerCamelCase__      :			Tuple 						= d_model
														lowerCamelCase__      :			Any 						= decoder_layers
														lowerCamelCase__      :			Dict 						= decoder_attention_heads
														lowerCamelCase__      :			str 						= decoder_ffn_dim
														lowerCamelCase__      :			Tuple 						= activation_function
														lowerCamelCase__      :			Dict 						= max_position_embeddings
														lowerCamelCase__      :			int 						= dropout
														lowerCamelCase__      :			int 						= attention_dropout
														lowerCamelCase__      :			List[Any] 						= activation_dropout
														lowerCamelCase__      :			Union[str, Any] 						= init_std
														lowerCamelCase__      :			Optional[int] 						= decoder_layerdrop
														lowerCamelCase__      :			Dict 						= use_cache
														lowerCamelCase__      :			Any 						= scale_embedding
														lowerCamelCase__      :			Optional[int] 						= use_learned_position_embeddings
														lowerCamelCase__      :			List[str] 						= layernorm_embedding

														super().__init__(
														    pad_token_id=UpperCamelCase__    ,							bos_token_id=UpperCamelCase__    ,							eos_token_id=UpperCamelCase__    ,							decoder_start_token_id=UpperCamelCase__    ,							**UpperCamelCase__    ,							)

