






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

UpperCamelCase        =				TypeVar("T")




def 				A		(					lowercase__  :  int  )		-> int:
	return (position - 1) // 2




def 				A		(					lowercase__  :  int  )		-> int:
	return (2 * position) + 1




def 				A		(					lowercase__  :  int  )		-> int:
	return (2 * position) + 2

class        lowerCAmelCase_				(      Generic[T]	):







				"""simple docstring"""




				def __init__( self     :Any     ):
					UpperCamelCase__ :list[tuple[T, int]]						=      []
					UpperCamelCase__ :dict[T, int]						=      {}
					UpperCamelCase__ :int						=      0







				def __len__( self     :Dict     ):
					return self.elements







				def __repr__( self     :Tuple     ):
					return str(self.heap     )







				def 				__a   ( self     :List[str]     ):
					# Check if the priority queue is empty
					return self.elements == 0







				def 				__a   ( self     :List[str]  ,     lowerCamelCase__     :T  ,     lowerCamelCase__     :int     ):
					# Add an element with given priority to the queue
					self.heap.append((elem, weight)     )
					UpperCamelCase__ :Dict						=      self.elements
					self.elements += 1
					self._bubble_up(lowerCamelCase__     )







				def 				__a   ( self     :Tuple     ):
					# Remove and return the element with lowest weight (highest priority)
					if self.elements > 1:
						self._swap_nodes(0  ,     self.elements - 1     )
					UpperCamelCase__				,		UpperCamelCase__ :Tuple						=      self.heap.pop()
					del self.position_map[elem]
					self.elements -= 1
					if self.elements > 0:
						UpperCamelCase__				,		UpperCamelCase__ :int						=      self.heap[0]
						self._bubble_down(lowerCamelCase__     )
					return elem







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :T  ,     lowerCamelCase__     :int     ):
					# Update the weight of the given key
					UpperCamelCase__ :Optional[Any]						=      self.position_map[elem]
					UpperCamelCase__ :Dict						=      (elem, weight)
					if position > 0:
						UpperCamelCase__ :Optional[Any]						=      get_parent_position(lowerCamelCase__     )
						UpperCamelCase__				,		UpperCamelCase__ :Optional[Any]						=      self.heap[parent_position]
						if parent_weight > weight:
							self._bubble_up(lowerCamelCase__     )
						else:
							self._bubble_down(lowerCamelCase__     )
					else:
						self._bubble_down(lowerCamelCase__     )







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







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







				def 				__a   ( self     :List[Any]  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :int     ):
					# Swap the nodes at the given positions
					UpperCamelCase__ :Optional[int]						=      self.heap[nodea_pos][0]
					UpperCamelCase__ :Optional[int]						=      self.heap[nodea_pos][0]
					UpperCamelCase__				,		UpperCamelCase__ :Optional[int]						=      (
					    self.heap[nodea_pos],
					    self.heap[nodea_pos],
					)
					UpperCamelCase__ :Dict						=      nodea_pos
					UpperCamelCase__ :Any						=      nodea_pos

class        lowerCAmelCase_				(      Generic[T]	):







				"""simple docstring"""




				def __init__( self     :str     ):
					UpperCamelCase__ :dict[T, dict[T, int]]						=      {}
					UpperCamelCase__ :int						=      0







				def __repr__( self     :int     ):
					return str(self.connections     )







				def __len__( self     :Dict     ):
					return self.nodes







				def 				__a   ( self     :List[Any]  ,     lowerCamelCase__     :T     ):
					# Add a node in the graph if it is not in the graph
					if node not in self.connections:
						UpperCamelCase__ :Optional[int]						=      {}
						self.nodes += 1







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :T  ,     lowerCamelCase__     :T  ,     lowerCamelCase__     :int     ):
					# Add an edge between 2 nodes in the graph
					self.add_node(lowerCamelCase__     )
					self.add_node(lowerCamelCase__     )
					UpperCamelCase__ :Tuple						=      weight
					UpperCamelCase__ :int						=      weight




def 				A		(					lowercase__  :  GraphUndirectedWeighted[T] ,		)		-> tuple[dict[T, int], dict[T, T | None]]:
	UpperCamelCase__ :dict[T, int]						=      {node: maxsize for node in graph.connections}
	UpperCamelCase__ :dict[T, T | None]						=      {node: None for node in graph.connections}

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

	if priority_queue.is_empty():
		return dist, parent

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

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

"""simple docstring"""




# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_A     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			List[Any]  						=			FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_A     :			int  						=			FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
_A     :			Dict  						=			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_A     :			List[Any]  						=			tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_A     :			List[Any]  						=			"""tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de


