





from dataclasses import dataclass

UpperCAmelCase__  :	Tuple     ='''0.1'''
UpperCAmelCase__  :	Dict     ='''Lucia Harcekova'''

@dataclass
class 					__A   :
				__A            = 42
				__A            = 42
				__A            = 42


				def __repr__(						self							):
								return f"""({self.offset}, {self.length}, {self.indicator})"""




class 					__A   :


				def __init__(						self      ,       UpperCAmelCase_ = 13      ,       UpperCAmelCase_ = 6							):
								lowerCamelCase		       =window_size
								lowerCamelCase		       =lookahead_buffer_size
								lowerCamelCase		       =self.window_size - self.lookahead_buffer_size


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =[]
								lowerCamelCase		       =""""""

								# while there are still characters in text to compress
								while text:
												# find the next encoding phrase
												# - triplet with offset, length, indicator (the next encoding character)
												lowerCamelCase		       =self._find_encoding_token(UpperCAmelCase_      ,       UpperCAmelCase_							)

												# update the search buffer:
												# - add new characters from text into it
												# - check if size exceed the max search buffer size, if so, drop the
												#   oldest elements
												search_buffer += text[: token.length + 1]
												if len(UpperCAmelCase_							) > self.search_buffer_size:
																lowerCamelCase		       =search_buffer[-self.search_buffer_size :]

												# update the text
												lowerCamelCase		       =text[token.length + 1 :]

												# append the token to output
												output.append(UpperCAmelCase_							)

								return output


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =""""""

								for token in tokens:
												for _ in range(token.length							):
																output += output[-token.offset]
												output += token.indicator

								return output


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):

								if not text:
												raise ValueError("""We need some text to work with."""							)

								# Initialise result parameters to default values
								lowerCamelCase       ,     lowerCamelCase		       =0, 0

								if not search_buffer:
												return Token(UpperCAmelCase_      ,       UpperCAmelCase_      ,       text[length]							)

								for i, character in enumerate(UpperCAmelCase_							):
												lowerCamelCase		       =len(UpperCAmelCase_							) - i
												if character == text[0]:
																lowerCamelCase		       =self._match_length_from_index(UpperCAmelCase_      ,       UpperCAmelCase_      ,       0      ,       UpperCAmelCase_							)
																# if the found length is bigger than the current or if it's equal,
																# which means it's offset is smaller: update offset and length
																if found_length >= length:
																				lowerCamelCase       ,     lowerCamelCase		       =found_offset, found_length

								return Token(UpperCAmelCase_      ,       UpperCAmelCase_      ,       text[length]							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								if not text or text[text_index] != window[window_index]:
												return 0
								return 1 + self._match_length_from_index(
								    UpperCAmelCase_      ,       window + text[text_index]      ,       text_index + 1      ,       window_index + 1							)


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							# Initialize compressor class
							UpperCAmelCase__  :	Any     =LZaaCompressor(window_size=13, lookahead_buffer_size=6)

							# Example
							UpperCAmelCase__  :	int     ='''cabracadabrarrarrad'''
							UpperCAmelCase__  :	Dict     =lzaa_compressor.compress(TEXT)
							print(lzaa_compressor.compress('''ababcbababaa'''))
							UpperCAmelCase__  :	Dict     =lzaa_compressor.decompress(compressed_text)
							assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."












from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

_lowerCamelCase       =	{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							dict    ,      UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					{start}
		SCREAMING_SNAKE_CASE__								=					[start]
		SCREAMING_SNAKE_CASE__								=					Queue()
		queue.put(UpperCamelCase__			)
		while not queue.empty():
				SCREAMING_SNAKE_CASE__								=					queue.get()
				for w in graph[v]:
						if w not in explored:
								explored.add(UpperCamelCase__			)
								result.append(UpperCamelCase__			)
								queue.put(UpperCamelCase__			)
		return result






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							dict    ,      UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					{start}
		SCREAMING_SNAKE_CASE__								=					[start]
		SCREAMING_SNAKE_CASE__								=					deque([start]			)
		while queue:
				SCREAMING_SNAKE_CASE__								=					queue.popleft()
				for child in graph[v]:
						if child not in visited:
								visited.add(UpperCamelCase__			)
								result.append(UpperCamelCase__			)
								queue.append(UpperCamelCase__			)
		return result






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					f'''from __main__ import G, {name}'''
		SCREAMING_SNAKE_CASE__								=					10_000
		SCREAMING_SNAKE_CASE__								=					timeit(f'''{name}(G, \'A\')'''    ,      setup=UpperCamelCase__    ,      number=UpperCamelCase__			)
		print(f'''{name:<35} finished {number} runs in {res:.5f} seconds'''			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	benchmark_function('breadth_first_search')
	benchmark_function('breadth_first_search_with_deque')
	# breadth_first_search                finished 10000 runs in 0.20999 seconds
	# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds