def read_log_file(filename):
    result = []
    with open(filename) as f:
        for line in f:
            if "ERROR" in line:
                result.append(line)
    return result

# 使用方式
errors = read_log_file("huge.log")
for error in errors:
    process_error(error)

def read_log_file(filename):
    with open(filename) as f:
        for line in f:
            if "ERROR" in line:
                yield line

# 使用方式
for error in read_log_file("huge.log"):
    process_error(error)

def counter():
    print("Starting")
    i = 0
    while True:
        print(f"Generating {i}")
        yield i
        i += 1

# 創(chuàng)建生成器對(duì)象
c = counter()  # 此時(shí)不會(huì)打印任何內(nèi)容
print("Generator created")

# 獲取前三個(gè)值
print(next(c))  # 打印 "Starting" 和 "Generating 0"，返回 0
print(next(c))  # 打印 "Generating 1"，返回 1
print(next(c))  # 打印 "Generating 2"，返回 2

def stream_completion(prompt):
    # 模擬 API 調(diào)用
    response = ["生成", "AI", "回復(fù)", "需要", "時(shí)間"]
    for token in response:
        yield token

# 使用方式
for chunk in stream_completion("你好"):
    print(chunk, end="", flush=True)

# 列表推導(dǎo)式
squares = [x*x for x in range(1000000)]  # 立即生成所有結(jié)果

# Generator 表達(dá)式
squares = (x*x for x in range(1000000))  # 按需生成

def averager():
    total = 0
    count = 0
    average = None
    while True:
        # yield 在這里扮演雙重角色：
        # 1. 向外返回 average 值
        # 2. 接收外部發(fā)送的 value
        value = yield average
        if value is None:
            break
        total += value
        count += 1
        average = total / count

# 讓我們一步步看發(fā)生了什么
avg = averager()          # 創(chuàng)建生成器對(duì)象，但函數(shù)體還未開(kāi)始執(zhí)行
print("第一步：創(chuàng)建生成器")

next(avg)                 # 啟動(dòng)生成器，運(yùn)行到第一個(gè) yield，返回 None
print("第二步：生成器已啟動(dòng)，等待第一個(gè)值")

print(avg.send(10))      # 1. send(10) 將 10 傳給 value
                         # 2. 計(jì)算 average = 10/1 = 10.0
                         # 3. 到達(dá) yield，返回 10.0
                         # 4. 生成器暫停，等待下一個(gè)值

print(avg.send(20))      # 1. value 獲得值 20
                         # 2. 計(jì)算 average = 30/2 = 15.0
                         # 3. yield 返回 15.0

print(avg.send(30))      # 1. value 獲得值 30
                         # 2. 計(jì)算 average = 60/3 = 20.0
                         # 3. yield 返回 20.0

from typing import Generator, Iterator
from typing import TypeVar, Generic

T_co = TypeVar('T_co', covariant=True)      # 生成器產(chǎn)出的類型
T_contra = TypeVar('T_contra', contravariant=True)  # send 方法接收的類型
V_co = TypeVar('V_co', covariant=True)      # return 語(yǔ)句返回的類型

def number_processor() -> Generator[int, str, float]:
    # 這個(gè)生成器：
    # - yield 產(chǎn)出 int
    # - 接收 str 類型的輸入
    # - 最終 return float 類型的值
    count = 0
    total = 0.0
    while count < 3:
        text = yield count  # 產(chǎn)出 int，接收 str
        if text:
            total += len(text)
        count += 1
    return total  # 返回 float

# 使用示例
proc = number_processor()
next(proc)  # 啟動(dòng)生成器，返回 0
print(proc.send("hello"))  # 輸出 1
print(proc.send("world"))  # 輸出 2
try:
    proc.send("!")  # 生成器將結(jié)束，拋出 StopIteration
except StopIteration as e:
    print(f"最終結(jié)果：{e.value}")  # 打印 11.0 (len("hello") + len("world") + len("!"))

from typing import Generator, TypeVar

# 定義一些類來(lái)演示
class Animal:
    pass

class Dog(Animal):
    pass

class Chihuahua(Dog):
    pass

# 定義類型變量
T_co = TypeVar('T_co', covariant=True)
T_contra = TypeVar('T_contra', contravariant=True)

def dog_generator() -> Generator[Dog, Animal, None]:
    # 這個(gè)生成器:
    # - 產(chǎn)出 Dog (協(xié)變位置)
    # - 接收 Animal (逆變位置)
    dog = yield Dog()  # dog 的類型是 Animal
    
# 協(xié)變（T_co）示例：
# 如果一個(gè)函數(shù)返回 Dog，它也可以用在需要返回 Animal 的地方
generator1: Generator[Animal, Animal, None] = dog_generator()  # 沒(méi)問(wèn)題！
# 因?yàn)?Dog 是 Animal 的子類，所以可以用 Dog 替代 Animal

# 逆變（T_contra）示例：
# 如果一個(gè)函數(shù)接收 Animal，它也可以接收 Dog 或 Chihuahua
generator2 = dog_generator()
generator2.send(Chihuahua())  # 沒(méi)問(wèn)題！
# 因?yàn)楹瘮?shù)期望接收 Animal，那接收 Animal 的子類當(dāng)然也可以

from typing import Generator, Iterator
from itertools import chain

def read_chunks(file_path: str) -> Generator[str, None, None]:
    with open(file_path) as f:
        while chunk := f.read(1024):
            yield chunk

def process_chunk(chunk: str) -> Generator[str, None, None]:
    # 處理單個(gè) chunk 中的行
    # 注意：chunk 可能在行中間截?cái)?，需要處理這種情況
    lines = chunk.split('\n')
    for line in lines:
        if line.strip():
            yield line.upper()

def filter_keywords(lines: Iterator[str]) -> Generator[str, None, None]:
    keywords = {'ERROR', 'WARNING', 'CRITICAL'}
    for line in lines:
        if any(k in line for k in keywords):
            yield line

# 正確的流式處理版本
def process_log_file(file_path: str):
    # 當(dāng)前行的未完成部分
    partial_line = ''
    
    for chunk in read_chunks(file_path):
        # 處理可能被截?cái)嗟男?
        if partial_line:
            chunk = partial_line + chunk
            partial_line = ''
            
        # 分割成行，保留最后一個(gè)可能不完整的行
        lines = chunk.split('\n')
        if not chunk.endswith('\n'):
            partial_line = lines[-1]
            lines = lines[:-1]
            
        # 處理完整的行
        for line in lines:
            if line.strip():
                # 直接在這里處理，無(wú)需存儲(chǔ)所有行
                upper_line = line.upper()
                if any(k in upper_line for k in {'ERROR', 'WARNING', 'CRITICAL'}):
                    print(upper_line)
    
    # 處理最后一個(gè)不完整的行（如果有的話）
    if partial_line and partial_line.strip():
        upper_line = partial_line.upper()
        if any(k in upper_line for k in {'ERROR', 'WARNING', 'CRITICAL'}):
            print(upper_line)

# 或者，使用更函數(shù)式的寫(xiě)法
def process_log_file_functional(file_path: str):
    def handle_chunks() -> Generator[str, None, None]:
        partial_line = ''
        for chunk in read_chunks(file_path):
            if partial_line:
                chunk = partial_line + chunk
                partial_line = ''
            
            lines = chunk.split('\n')
            if not chunk.endswith('\n'):
                partial_line = lines[-1]
                lines = lines[:-1]
            
            yield from (line for line in lines if line.strip())
        
        if partial_line and partial_line.strip():
            yield partial_line

    # 現(xiàn)在我們真正實(shí)現(xiàn)了流式處理
    lines = handle_chunks()
    upper_lines = (line.upper() for line in lines)
    filtered_lines = filter_keywords(upper_lines)
    
    for line in filtered_lines:
        print(line)