

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a__												=						TypeVar('''T''')



def   __UpperCAmelCase					(      __a :     int      )     ->	int:

					"""simple docstring"""

					return (position - 1) // 2



def   __UpperCAmelCase					(      __a :     int      )     ->	int:

					"""simple docstring"""

					return (2 * position) + 1



def   __UpperCAmelCase					(      __a :     int      )     ->	int:

					"""simple docstring"""

					return (2 * position) + 2

class 							UpperCAmelCase_						(     Generic[T]  ):
							"""simple docstring"""







							def __init__(     self			)       ->       None:
												_a      :       list[tuple[T, int]]   =						[]
												_a      :       dict[T, int]   =						{}
												_a      :       int   =						0


							def __len__(     self			)       ->       int:
												return self.elements


							def __repr__(     self			)       ->       str:
												return str(self.heap			)


							def 							__lowercase							(     self			)       ->       bool:
												# Check if the priority queue is empty
												return self.elements == 0


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       None:
												# Add an element with given priority to the queue
												self.heap.append((elem, weight)			)
												_a      :       Union[str, Any]   =						self.elements
												self.elements += 1
												self._bubble_up(_a			)


							def 							__lowercase							(     self			)       ->       T:
												# Remove and return the element with lowest weight (highest priority)
												if self.elements > 1:
																	self._swap_nodes(0			,				self.elements - 1			)
												_a ,   _a      :       str   =						self.heap.pop()
												del self.position_map[elem]
												self.elements -= 1
												if self.elements > 0:
																	_a ,   _a      :       List[Any]   =						self.heap[0]
																	self._bubble_down(_a			)
												return elem


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       None:
												# Update the weight of the given key
												_a      :       Optional[int]   =						self.position_map[elem]
												_a      :       Any   =						(elem, weight)
												if position > 0:
																	_a      :       Any   =						get_parent_position(_a			)
																	_a ,   _a      :       Optional[int]   =						self.heap[parent_position]
																	if parent_weight > weight:
																						self._bubble_up(_a			)
																	else:
																						self._bubble_down(_a			)
												else:
																	self._bubble_down(_a			)


							def 							__lowercase							(     self			,				_a			)       ->       None:
												# Place a node at the proper position (upward movement) [to be used internally
												# only]
												_a      :       Union[str, Any]   =						self.position_map[elem]
												if curr_pos == 0:
																	return None
												_a      :       str   =						get_parent_position(_a			)
												_a ,   _a      :       List[str]   =						self.heap[curr_pos]
												_a ,   _a      :       List[str]   =						self.heap[parent_position]
												if parent_weight > weight:
																	self._swap_nodes(_a			,				_a			)
																	return self._bubble_up(_a			)
												return None


							def 							__lowercase							(     self			,				_a			)       ->       None:
												# Place a node at the proper position (downward movement) [to be used
												# internally only]
												_a      :       Union[str, Any]   =						self.position_map[elem]
												_a ,   _a      :       List[str]   =						self.heap[curr_pos]
												_a      :       int   =						get_child_left_position(_a			)
												_a      :       Optional[int]   =						get_child_right_position(_a			)
												if child_left_position < self.elements and child_right_position < self.elements:
																	_a ,   _a      :       Union[str, Any]   =						self.heap[child_left_position]
																	_a ,   _a      :       Tuple   =						self.heap[child_right_position]
																	if child_right_weight < child_left_weight and child_right_weight < weight:
																						self._swap_nodes(_a			,				_a			)
																						return self._bubble_down(_a			)
												if child_left_position < self.elements:
																	_a ,   _a      :       List[str]   =						self.heap[child_left_position]
																	if child_left_weight < weight:
																						self._swap_nodes(_a			,				_a			)
																						return self._bubble_down(_a			)
												else:
																	return None
												if child_right_position < self.elements:
																	_a ,   _a      :       Optional[Any]   =						self.heap[child_right_position]
																	if child_right_weight < weight:
																						self._swap_nodes(_a			,				_a			)
																						return self._bubble_down(_a			)
												return None




							def 							__lowercase							(     self			,				_a			,				_a			)       ->       None:
												# Swap the nodes at the given positions
												_a      :       str   =						self.heap[nodea_pos][0]
												_a      :       str   =						self.heap[nodea_pos][0]
												_a ,   _a      :       Optional[Any]   =						(
												    self.heap[nodea_pos],
												    self.heap[nodea_pos],
												)
												_a      :       List[str]   =						nodea_pos
												_a      :       List[str]   =						nodea_pos



class 							UpperCAmelCase_						(     Generic[T]  ):
							"""simple docstring"""







							def __init__(     self			)       ->       None:
												_a      :       dict[T, dict[T, int]]   =						{}
												_a      :       int   =						0


							def __repr__(     self			)       ->       str:
												return str(self.connections			)


							def __len__(     self			)       ->       int:
												return self.nodes


							def 							__lowercase							(     self			,				_a			)       ->       None:
												# Add a node in the graph if it is not in the graph
												if node not in self.connections:
																	_a      :       Optional[Any]   =						{}
																	self.nodes += 1




							def 							__lowercase							(     self			,				_a			,				_a			,				_a			)       ->       None:
												# Add an edge between 2 nodes in the graph
												self.add_node(_a			)
												self.add_node(_a			)
												_a      :       int   =						weight
												_a      :       int   =						weight



def   __UpperCAmelCase					(      __a :     GraphUndirectedWeighted[T]						,)     ->	tuple[dict[T, int], dict[T, T | None]]:

					"""simple docstring"""

					_a      :       dict[T, int]   =						{node: maxsize for node in graph.connections}
					_a      :       dict[T, T | None]   =						{node: None for node in graph.connections}

					_a      :       MinPriorityQueue[T]   =						MinPriorityQueue()
					for node, weight in dist.items():
										priority_queue.push(__a						,__a      )

					if priority_queue.is_empty():
										return dist, parent

					# initialization
					_a      :       List[str]   =						priority_queue.extract_min()
					_a      :       int   =						0
					for neighbour in graph.connections[node]:
										if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
															_a      :       Tuple   =						dist[node] + graph.connections[node][neighbour]
															priority_queue.update_key(__a						,dist[neighbour]      )
															_a      :       List[Any]   =						node

    # running prim's algorithm
					while not priority_queue.is_empty():
										_a      :       List[str]   =						priority_queue.extract_min()
										for neighbour in graph.connections[node]:
															if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																				_a      :       Dict   =						dist[node] + graph.connections[node][neighbour]
																				priority_queue.update_key(__a						,dist[neighbour]      )
																				_a      :       Optional[Any]   =						node
					return dist, parent




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


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "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     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "trocr"
							UpperCAmelCase				   =     ["past_key_values"]
							UpperCAmelCase				   =     {
							    "num_attention_heads": "decoder_attention_heads",
							    "hidden_size": "d_model",
							    "num_hidden_layers": "decoder_layers",
							}







							def __init__(   self      :     List[str] ,			_A      :     Optional[Any]=5_0265 ,			_A      :     Optional[Any]=1024 ,			_A      :     Optional[Any]=12 ,			_A      :     Any=16 ,			_A      :     Any=4096 ,			_A      :     Optional[Any]="gelu" ,			_A      :     Union[str, Any]=512 ,			_A      :     Dict=0.1 ,			_A      :     List[str]=0.0 ,			_A      :     Optional[Any]=0.0 ,			_A      :     Union[str, Any]=2 ,			_A      :     Any=0.02 ,			_A      :     List[str]=0.0 ,			_A      :     List[str]=True ,			_A      :     str=False ,			_A      :     List[str]=True ,			_A      :     Optional[Any]=True ,			_A      :     Optional[int]=1 ,			_A      :     int=0 ,			_A      :     Any=2 ,			**_A      :     Optional[int] ,			):
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     d_model
												_UpperCamelCase			     =     decoder_layers
												_UpperCamelCase			     =     decoder_attention_heads
												_UpperCamelCase			     =     decoder_ffn_dim
												_UpperCamelCase			     =     activation_function
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     dropout
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     activation_dropout
												_UpperCamelCase			     =     init_std
												_UpperCamelCase			     =     decoder_layerdrop
												_UpperCamelCase			     =     use_cache
												_UpperCamelCase			     =     scale_embedding
												_UpperCamelCase			     =     use_learned_position_embeddings
												_UpperCamelCase			     =     layernorm_embedding

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